The Application of Remote Sensing Data to GIS Studies of Land Use, Land Cover, and Vegetation Mapping in the State of Hawaii

NASA Technical Reports Server (NTRS)

Hogan, Christine A.

1996-01-01

A land cover-vegetation map with a base classification system for remote sensing use in a tropical island environment was produced of the island of Hawaii for the State of Hawaii to evaluate whether or not useful land cover information can be derived from Landsat TM data. In addition, an island-wide change detection mosaic combining a previously created 1977 MSS land classification with the TM-based classification was produced. In order to reach the goal of transferring remote sensing technology to State of Hawaii personnel, a pilot project was conducted while training State of Hawaii personnel in remote sensing technology and classification systems. Spectral characteristics of young island land cover types were compared to determine if there are differences in vegetation types on lava, vegetation types on soils, and barren lava from soils, and if they can be detected remotely, based on differences in pigments detecting plant physiognomic type, health, stress at senescence, heat, moisture level, and biomass. Geographic information systems (GIS) and global positioning systems (GPS) were used to assist in image rectification and classification. GIS was also used to produce large-format color output maps. An interactive GIS program was written to provide on-line access to scanned photos taken at field sites. The pilot project found Landsat TM to be a credible source of land cover information for geologically young islands, and TM data bands are effective in detecting spectral characteristics of different land cover types through remote sensing. Large agriculture field patterns were resolved and mapped successfully from wildland vegetation, but small agriculture field patterns were not. Additional processing was required to work with the four TM scenes from two separate orbits which span three years, including El Nino and drought dates. Results of the project emphasized the need for further land cover and land use processing and research. Change in vegetation composition was noted in the change detection image.

Sensing Urban Land-Use Patterns by Integrating Google Tensorflow and Scene-Classification Models

NASA Astrophysics Data System (ADS)

Yao, Y.; Liang, H.; Li, X.; Zhang, J.; He, J.

2017-09-01

With the rapid progress of China's urbanization, research on the automatic detection of land-use patterns in Chinese cities is of substantial importance. Deep learning is an effective method to extract image features. To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features. Using the Google Tensorflow framework, a powerful convolution neural network (CNN) library was created. First, the transferred model was previously trained on ImageNet, one of the largest object-image data sets, to fully develop the model's ability to generate feature vectors of standard remote-sensing land-cover data sets (UC Merced and WHU-SIRI). Then, a random-forest-based classifier was constructed and trained on these generated vectors to classify the actual urban land-use pattern on the scale of traffic analysis zones (TAZs). To avoid the multi-scale effect of remote-sensing imagery, a large random patch (LRP) method was used. The proposed method could efficiently obtain acceptable accuracy (OA = 0.794, Kappa = 0.737) for the study area. In addition, the results show that the proposed method can effectively overcome the multi-scale effect that occurs in urban land-use classification at the irregular land-parcel level. The proposed method can help planners monitor dynamic urban land use and evaluate the impact of urban-planning schemes.

Data needs and data bases for climate studies

NASA Technical Reports Server (NTRS)

Matthews, Elaine

1986-01-01

Two complementary global digital data bases of vegetation and land use, compiled at 1 deg resolution from published sources for use in climate studies, are discussed. The data bases were implemented, in several individually tailored formulations, in a series of climate related applications including: land-surface prescriptions in three-dimensional general circulation models, global biogeochemical cycles (CO2, methane), critical-area mapping for satellite monitoring of land-cover change, and large-scale remote sensing of surface reflectance. The climate applications are discussed with reference to data needs, and data availability from traditional and remote sensing sources.

A thermal-based remote sensing modeling system for estimating daily evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared (TIR) remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation d...

Land Use and Land Cover Change Modeling Using Remote Sensing and Soft Computing Approach to Assess Sugarcane Expansion Impacts in Tropical Agriculture

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Victoria, D.; Zullo, J., Jr.; Gomes, D.; Bayma-Silva, G.

2014-12-01

Agriculture is related with land-use/cover changes (LUCC) over large areas and, in recent years, increase in demand of ethanol fuel has been influence in expansion of areas occupied with corn and sugar cane, raw material for ethanol production. Nevertheless, there´s a concern regarding the impacts on food security, such as, decrease in areas planted with food crops. Considering that the LUCC is highly dynamic, the use of Remote Sensing is a tool for monitoring changes quickly and precisely in order to provide information for agricultural planning. In this work, Remote Sensing techniques were used to monitor the LUCC occurred in municipalities of São Paulo state- Brazil related with sugarcane crops expansion in order to (i) evaluate and quantify the previous land cover in areas of sugarcane crop expansion, and (ii) provide information to elaborate a future land cover scenario based on Self Organizing Map (SOM) approach. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey. The Landsat images were then segmented into homogeneous objects, with represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. The segmentation procedure resulted in polygons over the three time periods along twenty years (1990-2010). The land cover for each object was visually identified, based on its shape, texture and spectral characteristics. Land cover types considered were: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. SOM technique was used to estimate the values for the future land cover scenarios for the selected municipalities, using the information of land change provided by the remote sensing and data from official sources.

LAnd surface remote sensing Products VAlidation System (LAPVAS) and its preliminary application

NASA Astrophysics Data System (ADS)

Lin, Xingwen; Wen, Jianguang; Tang, Yong; Ma, Mingguo; Dou, Baocheng; Wu, Xiaodan; Meng, Lumin

2014-11-01

The long term record of remote sensing product shows the land surface parameters with spatial and temporal change to support regional and global scientific research widely. Remote sensing product with different sensors and different algorithms is necessary to be validated to ensure the high quality remote sensing product. Investigation about the remote sensing product validation shows that it is a complex processing both the quality of in-situ data requirement and method of precision assessment. A comprehensive validation should be needed with long time series and multiple land surface types. So a system named as land surface remote sensing product is designed in this paper to assess the uncertainty information of the remote sensing products based on a amount of in situ data and the validation techniques. The designed validation system platform consists of three parts: Validation databases Precision analysis subsystem, Inter-external interface of system. These three parts are built by some essential service modules, such as Data-Read service modules, Data-Insert service modules, Data-Associated service modules, Precision-Analysis service modules, Scale-Change service modules and so on. To run the validation system platform, users could order these service modules and choreograph them by the user interactive and then compete the validation tasks of remote sensing products (such as LAI ,ALBEDO ,VI etc.) . Taking SOA-based architecture as the framework of this system. The benefit of this architecture is the good service modules which could be independent of any development environment by standards such as the Web-Service Description Language(WSDL). The standard language: C++ and java will used as the primary programming language to create service modules. One of the key land surface parameter, albedo, is selected as an example of the system application. It is illustrated that the LAPVAS has a good performance to implement the land surface remote sensing product validation.

Change detection from remotely sensed images: From pixel-based to object-based approaches

NASA Astrophysics Data System (ADS)

Hussain, Masroor; Chen, Dongmei; Cheng, Angela; Wei, Hui; Stanley, David

2013-06-01

The appetite for up-to-date information about earth's surface is ever increasing, as such information provides a base for a large number of applications, including local, regional and global resources monitoring, land-cover and land-use change monitoring, and environmental studies. The data from remote sensing satellites provide opportunities to acquire information about land at varying resolutions and has been widely used for change detection studies. A large number of change detection methodologies and techniques, utilizing remotely sensed data, have been developed, and newer techniques are still emerging. This paper begins with a discussion of the traditionally pixel-based and (mostly) statistics-oriented change detection techniques which focus mainly on the spectral values and mostly ignore the spatial context. This is succeeded by a review of object-based change detection techniques. Finally there is a brief discussion of spatial data mining techniques in image processing and change detection from remote sensing data. The merits and issues of different techniques are compared. The importance of the exponential increase in the image data volume and multiple sensors and associated challenges on the development of change detection techniques are highlighted. With the wide use of very-high-resolution (VHR) remotely sensed images, object-based methods and data mining techniques may have more potential in change detection.

The Land-Use and Land-Cover Change Analysis in Beijing Huairou in Last Ten Years

NASA Astrophysics Data System (ADS)

Zhao, Q.; Liu, G.; Tu, J.; Wang, Z.

2018-04-01

With eCognition software, the sample-based object-oriented classification method is used. Remote sensing images in Huairou district of Beijing had been classified using remote sensing images of last ten years. According to the results of image processing, the land use types in Huairou district of Beijing were analyzed in the past ten years, and the changes of land use types in Huairou district were obtained, and the reasons for its occurrence were analyzed.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

Boylan, M.; Vlasin, R. D.

1976-01-01

Uses of remote sensing imagery were investigated based on exploring and evaluating the capability and reliability of all kinds of imagery for improving decision making on issues of land use at all scales of governmental administration. Emphasis was placed on applications to solving immediate problems confronting public agencies and private organizations. Resulting applications of remote sensing use by public agencies, public organizations, and related private corporations are described.

Performing and updating an inventory of Oregon's expanding irrigated agricultural lands utilizing remote sensing technology

NASA Technical Reports Server (NTRS)

Hall, M. J.

1981-01-01

An inventory technique based upon using remote sensing technology, interpreting both high altitude aerial photography and LANDSAT multispectral scanner imagery, is discussed. It is noted that once the final land use inventory maps of irrigated agricultural lands are available and approximately scaled they may be overlaid directly onto either multispectral scanner or return beam vidicon prints, thereby providing an inexpensive updating procedure.

Land use/cover classification in the Brazilian Amazon using satellite images.

PubMed

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant'anna, Sidnei João Siqueira

2012-09-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data.

Land use/cover classification in the Brazilian Amazon using satellite images

PubMed Central

Lu, Dengsheng; Batistella, Mateus; Li, Guiying; Moran, Emilio; Hetrick, Scott; Freitas, Corina da Costa; Dutra, Luciano Vieira; Sant’Anna, Sidnei João Siqueira

2013-01-01

Land use/cover classification is one of the most important applications in remote sensing. However, mapping accurate land use/cover spatial distribution is a challenge, particularly in moist tropical regions, due to the complex biophysical environment and limitations of remote sensing data per se. This paper reviews experiments related to land use/cover classification in the Brazilian Amazon for a decade. Through comprehensive analysis of the classification results, it is concluded that spatial information inherent in remote sensing data plays an essential role in improving land use/cover classification. Incorporation of suitable textural images into multispectral bands and use of segmentation-based method are valuable ways to improve land use/cover classification, especially for high spatial resolution images. Data fusion of multi-resolution images within optical sensor data is vital for visual interpretation, but may not improve classification performance. In contrast, integration of optical and radar data did improve classification performance when the proper data fusion method was used. Of the classification algorithms available, the maximum likelihood classifier is still an important method for providing reasonably good accuracy, but nonparametric algorithms, such as classification tree analysis, has the potential to provide better results. However, they often require more time to achieve parametric optimization. Proper use of hierarchical-based methods is fundamental for developing accurate land use/cover classification, mainly from historical remotely sensed data. PMID:24353353

Considerations and techniques for incorporating remotely sensed imagery into the land resource management process.

NASA Technical Reports Server (NTRS)

Brooner, W. G.; Nichols, D. A.

1972-01-01

Development of a scheme for utilizing remote sensing technology in an operational program for regional land use planning and land resource management program applications. The scheme utilizes remote sensing imagery as one of several potential inputs to derive desired and necessary data, and considers several alternative approaches to the expansion and/or reduction and analysis of data, using automated data handling techniques. Within this scheme is a five-stage program development which includes: (1) preliminary coordination, (2) interpretation and encoding, (3) creation of data base files, (4) data analysis and generation of desired products, and (5) applications.

The Analysis of Object-Based Change Detection in Mining Area: a Case Study with Pingshuo Coal Mine

NASA Astrophysics Data System (ADS)

Zhang, M.; Zhou, W.; Li, Y.

2017-09-01

Accurate information on mining land use and land cover change are crucial for monitoring and environmental change studies. In this paper, RapidEye Remote Sensing Image (Map 2012) and SPOT7 Remote Sensing Image (Map 2015) in Pingshuo Mining Area are selected to monitor changes combined with object-based classification and change vector analysis method, we also used R in highresolution remote sensing image for mining land classification, and found the feasibility and the flexibility of open source software. The results show that (1) the classification of reclaimed mining land has higher precision, the overall accuracy and kappa coefficient of the classification of the change region map were 86.67 % and 89.44 %. It's obvious that object-based classification and change vector analysis which has a great significance to improve the monitoring accuracy can be used to monitor mining land, especially reclaiming mining land; (2) the vegetation area changed from 46 % to 40 % accounted for the proportion of the total area from 2012 to 2015, and most of them were transformed into the arable land. The sum of arable land and vegetation area increased from 51 % to 70 %; meanwhile, build-up land has a certain degree of increase, part of the water area was transformed into arable land, but the extent of the two changes is not obvious. The result illustrated the transformation of reclaimed mining area, at the same time, there is still some land convert to mining land, and it shows the mine is still operating, mining land use and land cover are the dynamic procedure.

Monitoring land at regional and national scales and the role of remote sensing

NASA Astrophysics Data System (ADS)

Dymond, John R.; Bégue, Agnes; Loseen, Danny

There is a need world wide for monitoring land and its ecosystems to ensure their sustainable use. Despite the laudable intentions of Agenda 21 at the Rio Earth Summit, 1992, in which many countries agreed to monitor and report on the status of their land, systematic monitoring of land has yet to begin. The problem is truly difficult, as the earth's surface is vast and the funds available for monitoring are relatively small. This paper describes several methods for cost-effective monitoring of large land areas, including: strategic monitoring; statistical sampling; risk-based approaches; integration of land and water monitoring; and remote sensing. The role of remote sensing is given special attention, as it is the only method that can monitor land exhaustively and directly, at regional and national scales. It is concluded that strategic monitoring, whereby progress towards environmental goals is assessed, is a vital element in land monitoring as it provides a means for evaluating the utility of monitoring designs.

Magnitude and variability of land evaporation and its components at the global scale

USDA-ARS?s Scientific Manuscript database

A physics-based methodology is applied to estimate global land-surface evaporation from multi-satellite observations. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely sensed observations within a Priestley and Taylor-based framework. Daily actual e...

The Real-Time Monitoring Service Platform for Land Supervision Based on Cloud Integration

NASA Astrophysics Data System (ADS)

Sun, J.; Mao, M.; Xiang, H.; Wang, G.; Liang, Y.

2018-04-01

Remote sensing monitoring has become the important means for land and resources departments to strengthen supervision. Aiming at the problems of low monitoring frequency and poor data currency in current remote sensing monitoring, this paper researched and developed the cloud-integrated real-time monitoring service platform for land supervision which enhanced the monitoring frequency by acquiring the domestic satellite image data overall and accelerated the remote sensing image data processing efficiency by exploiting the intelligent dynamic processing technology of multi-source images. Through the pilot application in Jinan Bureau of State Land Supervision, it has been proved that the real-time monitoring technical method for land supervision is feasible. In addition, the functions of real-time monitoring and early warning are carried out on illegal land use, permanent basic farmland protection and boundary breakthrough in urban development. The application has achieved remarkable results.

History and use of remote sensing for conservation and management of federal lands in Alaska, USA

USGS Publications Warehouse

Markon, Carl

1995-01-01

Remote sensing has been used to aid land use planning efforts for federal public lands in Alaska since the 1940s. Four federal land management agencies-the U.S. Fish and Wildlife Service, US. Bureau of Land Management, US. National Park Service, and U.S. Forest Service-have used aerial photography and satellite imagery to document the extent, type, and condition of Alaska's natural resources. Aerial photographs have been used to collect detailed information over small to medium-sized areas. This standard management tool is obtainable using equipment ranging from hand-held 35-mm cameras to precision metric mapping cameras. Satellite data, equally important, provide synoptic views of landscapes, are digitally manipulatable, and are easily merged with other digital databases. To date, over 109.2 million ha (72%) of Alaska's land cover have been mapped via remote sensing. This information has provided a base for conservation, management, and planning on federal public lands in Alaska.

Sensing land pollution.

NASA Technical Reports Server (NTRS)

Bowden, L. W.

1971-01-01

Land pollution is described in numerous ways by various societies. Pollutants of land are material by-products of human activity and range from environmentally ineffective to positively toxic. The pollution of land by man is centuries old and correlates directly with economy, technology and population. In order to remotely sense land pollution, standards or thresholds must be established. Examples of the potential for sensing land pollution and quality are presented. The technological capabilities for remotely sensed land quality is far advanced over the judgment on how to use the sensed data. Until authoritative and directive decisions on land pollution policy are made, sensing of pollutants will be a random, local and academic affair.

Using NASA Remote Sensing Data to Reduce Uncertainty of Land-use Transitions in Global Carbon-Climate Models

NASA Astrophysics Data System (ADS)

Chini, L. P.; Hurtt, G. C.; Frolking, S. E.; Sahajpal, R.; Potapov, P.; Hansen, M.; Fisk, J.

2016-12-01

For the 5th IPCC Assessment almost all Earth System Models (ESMs) incorporated new gridded products of land-use and land-use change that were harmonized to ensure a continuous transition from historical to future data in a consistent format for all models. However, these Land-Use Harmonization (LUH) data products are estimates, constrained with data where available, and with modeling assumptions, and the remaining challenge is to quantify, and reduce, the uncertainty in these products. At the same time, satellite remote sensing of the terrestrial biosphere has also evolved. Global-scale land cover extent and change monitoring is now possible given systematically acquired earth observation data sets, advanced characterization algorithms and data intensive computing capabilities. Here we consider: how can satellite remote sensing products be used to generate (and reduce uncertainty in) new gridded maps of land-use transitions for use in coupled carbon-climate simulations? As part of the international effort to develop the next generation of land-use datasets (LUH2), new NASA remote-sensing-based maps of global forest extent and change (Hansen et al. 2013) were used as both an added constraint and diagnostic in the LUH process. Harmonizing this remote sensing data with the LUH data was a major computational challenge involving 143 billion 30m Landsat pixels, and the simulation of over 20 billion LUH unknowns. Our approach involved first harmonizing the definitions of forest loss between the observed and simulated data for the years 2000-2012. Next, new spatial patterns of historical wood harvest were calculated to match the observed forest loss transitions while simultaneously meeting all other constraints of the model, and ensuring consistency throughout the historical time-period. After reconciling definitions and developing new wood harvest patterns the LUH2 global forest loss for the period 2000-2012 was reduced from over 8.3 million km2 to 1.78 million km2 (compared with the remote-sensing-based forest loss of 2.03 million km2). Next steps are to evaluate the ability of these land-use transitions to improve the representation of land-use-related climate forcings in ESM experiments, and to then build upon the LUH framework to incorporate additional remote-sensing data constraints.

Desertification Assessment and Monitoring Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Z.; del Barrio, G.; Li, X.

2016-08-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired during the last two years could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) were estimated in Otindag sandy land by comparison of the pixel-invariant (Spectral Mixture Analysis, SMA) and pixel-variable (Multi-Endmember Spectral Mixture Analysis, MESMA, Automated Monte Carlo Unmixing Analysis, AutoMCU) methods, based on GF-1 data and field measured spectral library.(2) Based on GF-1 data, SMA was applied to solve vegetation cover and transitional sandy land detection in Zhenglan Banner, Inner Mongolia, China.(3) By defined a new indictor, Moisture-responded NPP(MNPP), a new method for identification of degraded lands was put forward, and the land degradation in Xinlin Gol league, Inner Mongolia Autonomous Region, China was assessed preliminarily. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed.

Assessment and prediction of land ecological environment quality change based on remote sensing-a case study of the Dongting lake area in China

NASA Astrophysics Data System (ADS)

Hu, Wenmin; Wang, Zhongcheng; Li, Chunhua; Zhao, Jin; Li, Yi

2018-02-01

Multi-source remote sensing data is rarely used for the comprehensive assessment of land ecologic environment quality. In this study, a digital environmental model was proposed with the inversion algorithm of land and environmental factors based on the multi-source remote sensing data, and a comprehensive index (Ecoindex) was applied to reconstruct and predict the land environment quality of the Dongting Lake Area to assess the effect of human activities on the environment. The main finding was that with the decrease of Grade I and Grade II quality had a decreasing tendency in the lake area, mostly in suburbs and wetlands. Atmospheric water vapour, land use intensity, surface temperature, vegetation coverage, and soil water content were the main driving factors. The cause of degradation was the interference of multi-factor combinations, which led to positive and negative environmental agglomeration effects. Positive agglomeration, such as increased rainfall and vegetation coverage and reduced land use intensity, could increase environmental quality, while negative agglomeration resulted in the opposite. Therefore, reasonable ecological restoration measures should be beneficial to limit the negative effects and decreasing tendency, improve the land ecological environment quality and provide references for macroscopic planning by the government.

Application of remote sensing to estimating soil erosion potential

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Kiefer, R. W.

1980-01-01

A variety of remote sensing data sources and interpretation techniques has been tested in a 6136 hectare watershed with agricultural, forest and urban land cover to determine the relative utility of alternative aerial photographic data sources for gathering the desired land use/land cover data. The principal photographic data sources are high altitude 9 x 9 inch color infrared photos at 1:120,000 and 1:60,000 and multi-date medium altitude color and color infrared photos at 1:60,000. Principal data for estimating soil erosion potential include precipitation, soil, slope, crop, crop practice, and land use/land cover data derived from topographic maps, soil maps, and remote sensing. A computer-based geographic information system organized on a one-hectare grid cell basis is used to store and quantify the information collected using different data sources and interpretation techniques. Research results are compared with traditional Universal Soil Loss Equation field survey methods.

Remote sensing, land use, and demography - A look at people through their effects on the land

NASA Technical Reports Server (NTRS)

Paul, C. K.; Landini, A. J.

1976-01-01

Relevant causes of failure by the remote sensing community in the urban scene are analyzed. The reasons for the insignificant role of remote sensing in urban land use data collection are called the law of realism, the incompatibility of remote sensing and urban management system data formats is termed the law of nominal/ordinal systems compatibility, and the land use/population correlation dilemma is referred to as the law of missing persons. The study summarizes the three laws of urban land use information for which violations, avoidance, or ignorance have caused the decline of present remote sensing research. Particular attention is given to the rationale for urban land use information and for remote sensing. It is shown that remote sensing of urban land uses compatible with the three laws can be effectively developed by realizing the 10 percent contribution of remote sensing to urban land use planning data collection.

Multi-Modal Active Perception for Autonomously Selecting Landing Sites on Icy Moons

NASA Technical Reports Server (NTRS)

Arora, A.; Furlong, P. M.; Wong, U.; Fong, T.; Sukkarieh, S.

2017-01-01

Selecting suitable landing sites is fundamental to achieving many mission objectives in planetary robotic lander missions. However, due to sensing limitations, landing sites which are both safe and scientifically valuable often cannot be determined reliably from orbit, particularly, in icy moon missions where orbital sensing data is noisy and incomplete. This paper presents an active perception approach to Entry Descent and Landing (EDL) which enables the lander to autonomously plan informative descent trajectories, acquire high quality sensing data during descent and exploit this additional information to select higher utility landing sites. Our approach consists of two components: probabilistic modeling of landing site features and approximate trajectory planning using a sampling based planner. The proposed framework allows the lander to plan long horizons paths and remain robust to noisy data. Results in simulated environments show large performance improvements over alternative approaches and show promise that our approach has strong potential to improve science return of not only icy moon missions but EDL systems in general.

Evaluation of impact of earthquake on agriculture in Nepal based on remote sensing

NASA Astrophysics Data System (ADS)

Sekiyama, Ayako; Shimada, Sawahiko; Okazawa, Hiromu; Mihara, Machito; Kuo, Kuang Ting

2016-07-01

The big earthquake happening on April, 2015 killed over than 8000 people in Nepal. The effect of earthquake not only affected safety of local people but also agricultural field. Agricultural economy dominates income of local people. Therefore, restoration of agricultural areas are required for improving life of local people. However, lack of information about agricultural areas is main problem for local government to assess and restore damaged agricultural areas. Remote sensing was applied for accessing damaged agricultural field due to its advantages in observing responds of environment without temporal and spatial restriction. Accordingly, the objective of the study is to evaluate impact of earthquake on agriculture in Nepal based on remote sensing. The experimental procedure includes conducting the impact of earthquake on changes of total agricultural area, and analysis of response of greenness affected by earthquake in agricultural land. For conducting agricultural land changes, land use map was first created and classified into four categories: road, city, forest, and agricultural land. Changes before and after earthquake in total area of agricultural land were analyzed by GIS. Moreover, vegetation index was used as indicator for evaluating greenness responds in agricultural land and computed based on high-resolution satellite imagery such as World view-3. Finally, the conclusion of the study and suggestions will be made and provided for helping local government in Nepal restore agricultural areas.

Research and Application of Remote Sensing Monitoring Method for Desertification Land Under Time and Space Constraints

NASA Astrophysics Data System (ADS)

Zhang, Nannnan; Wang, Rongbao; Zhang, Feng

2018-04-01

Serious land desertification and sandified threaten the urban ecological security and the sustainable economic and social development. In recent years, a large number of mobile sand dunes in Horqin sandy land flow into the northwest of Liaoning Province under the monsoon, make local agriculture suffer serious harm. According to the characteristics of desertification land in northwestern Liaoning, based on the First National Geographical Survey data, the Second National Land Survey data and the 1984-2014 Landsat satellite long time sequence data and other multi-source data, we constructed a remote sensing monitoring index system of desertification land in Northwest Liaoning. Through the analysis of space-time-spectral characteristics of desertification land, a method for multi-spectral remote sensing image recognition of desertification land under time-space constraints is proposed. This method was used to identify and extract the distribution and classification of desertification land of Chaoyang City (a typical citie of desertification in northwestern Liaoning) in 2008 and 2014, and monitored the changes and transfers of desertification land from 2008 to 2014. Sandification information was added to the analysis of traditional landscape changes, improved the analysis model of desertification land landscape index, and the characteristics and laws of landscape dynamics and landscape pattern change of desertification land from 2008 to 2014 were analyzed and revealed.

Digital spatial soil and land information for agriculture development

NASA Astrophysics Data System (ADS)

Sharma, R. K.; Laghathe, Pankaj; Meena, Ranglal; Barman, Alok Kumar; Das, Satyendra Nath

2006-12-01

Natural resource management calls for study of natural system prevailing in the country. In India floods and droughts visit regularly, causing extensive damages of natural wealth including agriculture that are crucial for sustenance of economic growth. The Indian Sub-continent drained by many major rivers and their tributaries where watershed, the hydrological unit forms a natural system that allows management and development of land resources following natural harmony. Acquisition of various kinds and levels of soil and land characteristics using both conventional and remote sensing techniques and subsequent development of digital spatial data base are essential to evolve strategy for planning watershed development programmes, their monitoring and impact evaluation. The multi-temporal capability of remote sensing sensors helps to update the existing data base which are of dynamic in nature. The paper outlines the concept of spatial data base development, generation using remote sensing techniques, designing of data structure, standardization and integration with watershed layers and various non spatial attribute data for various applications covering watershed development planning, alternate land use planning, soil and water conservation, diversified agriculture practices, generation of soil health card, soil and land reclamation, etc. The soil and land characteristics are vital to derive various interpretative groupings or master table that helps to generate the desired level of information of various clients using the GIS platform. The digital spatial data base on soils and watersheds generated by All India Soil and Land Use Survey will act as a sub-server of the main GIS based Web Server being hoisted by the planning commission for application of spatial data for planning purposes under G2G domain. It will facilitate e-governance for natural resource management using modern technology.

[A review on research of land surface water and heat fluxes].

PubMed

Sun, Rui; Liu, Changming

2003-03-01

Many field experiments were done, and soil-vegetation-atmosphere transfer(SVAT) models were stablished to estimate land surface heat fluxes. In this paper, the processes of experimental research on land surface water and heat fluxes are reviewed, and three kinds of SVAT model(single layer model, two layer model and multi-layer model) are analyzed. Remote sensing data are widely used to estimate land surface heat fluxes. Based on remote sensing and energy balance equation, different models such as simplified model, single layer model, extra resistance model, crop water stress index model and two source resistance model are developed to estimate land surface heat fluxes and evapotranspiration. These models are also analyzed in this paper.

A patch-based convolutional neural network for remote sensing image classification.

PubMed

Sharma, Atharva; Liu, Xiuwen; Yang, Xiaojun; Shi, Di

2017-11-01

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures. In this paper, considering the spatial relation of a pixel to its neighborhood, we propose a new deep patch-based CNN system tailored for medium-resolution remote sensing data. The system is designed by incorporating distinctive characteristics of medium-resolution data; in particular, the system computes patch-based samples from multidimensional top of atmosphere reflectance data. With a test site from the Florida Everglades area (with a size of 771 square kilometers), the proposed new system has outperformed pixel-based neural network, pixel-based CNN and patch-based neural network by 24.36%, 24.23% and 11.52%, respectively, in overall classification accuracy. By combining the proposed deep CNN and the huge collection of medium-resolution remote sensing data, we believe that much more accurate land cover datasets can be produced over large areas. Copyright © 2017 Elsevier Ltd. All rights reserved.

Abstracting of suspected illegal land use in urban areas using case-based classification of remote sensing images

NASA Astrophysics Data System (ADS)

Chen, Fulong; Wang, Chao; Yang, Chengyun; Zhang, Hong; Wu, Fan; Lin, Wenjuan; Zhang, Bo

2008-11-01

This paper proposed a method that uses a case-based classification of remote sensing images and applied this method to abstract the information of suspected illegal land use in urban areas. Because of the discrete cases for imagery classification, the proposed method dealt with the oscillation of spectrum or backscatter within the same land use category, and it not only overcame the deficiency of maximum likelihood classification (the prior probability of land use could not be obtained) but also inherited the advantages of the knowledge-based classification system, such as artificial intelligence and automatic characteristics. Consequently, the proposed method could do the classifying better. Then the researchers used the object-oriented technique for shadow removal in highly dense city zones. With multi-temporal SPOT 5 images whose resolution was 2.5×2.5 meters, the researchers found that the method can abstract suspected illegal land use information in urban areas using post-classification comparison technique.

LACO-Wiki: A land cover validation tool and a new, innovative teaching resource for remote sensing and the geosciences

NASA Astrophysics Data System (ADS)

See, Linda; Perger, Christoph; Dresel, Christopher; Hofer, Martin; Weichselbaum, Juergen; Mondel, Thomas; Steffen, Fritz

2016-04-01

The validation of land cover products is an important step in the workflow of generating a land cover map from remotely-sensed imagery. Many students of remote sensing will be given exercises on classifying a land cover map followed by the validation process. Many algorithms exist for classification, embedded within proprietary image processing software or increasingly as open source tools. However, there is little standardization for land cover validation, nor a set of open tools available for implementing this process. The LACO-Wiki tool was developed as a way of filling this gap, bringing together standardized land cover validation methods and workflows into a single portal. This includes the storage and management of land cover maps and validation data; step-by-step instructions to guide users through the validation process; sound sampling designs; an easy-to-use environment for validation sample interpretation; and the generation of accuracy reports based on the validation process. The tool was developed for a range of users including producers of land cover maps, researchers, teachers and students. The use of such a tool could be embedded within the curriculum of remote sensing courses at a university level but is simple enough for use by students aged 13-18. A beta version of the tool is available for testing at: http://www.laco-wiki.net.

How robust are burn severity indices when applied in a new region? Evaluation of alternate field-based and remote-sensing methods

Treesearch

C. Alina Cansler; Donald McKenzie

2012-01-01

Remotely sensed indices of burn severity are now commonly used by researchers and land managers to assess fire effects, but their relationship to field-based assessments of burn severity has been evaluated only in a few ecosystems. This analysis illustrates two cases in which methodological refinements to field-based and remotely sensed indices of burn severity...

The Land Use and Land Cover Dichotomy: A Comparison of Two Land Classification Systems in Support of Urban Earth Science Applications

NASA Technical Reports Server (NTRS)

McAllister, William K.

2003-01-01

One is likely to read the terms 'land use' and 'land cover' in the same sentence, yet these concepts have different origins and different applications. Land cover is typically analyzed by earth scientists working with remotely sensed images. Land use is typically studied by urban planners who must prescribe solutions that could prevent future problems. This apparent dichotomy has led to different classification systems for land-based data. The works of earth scientists and urban planning practitioners are beginning to come together in the field of spatial analysis and in their common use of new spatial analysis technology. In this context, the technology can stimulate a common 'language' that allows a broader sharing of ideas. The increasing amount of land use and land cover change challenges the various efforts to classify in ways that are efficient, effective, and agreeable to all groups of users. If land cover and land uses can be identified by remote methods using aerial photography and satellites, then these ways are more efficient than field surveys of the same area. New technology, such as high-resolution satellite sensors, and new methods, such as more refined algorithms for image interpretation, are providing refined data to better identify the actual cover and apparent use of land, thus effectiveness is improved. However, the closer together and the more vertical the land uses are, the more difficult the task of identification is, and the greater is the need to supplement remotely sensed data with field study (in situ). Thus, a number of land classification methods were developed in order to organize the greatly expanding volume of data on land characteristics in ways useful to different groups. This paper distinguishes two land based classification systems, one developed primarily for remotely sensed data, and the other, a more comprehensive system requiring in situ collection methods. The intent is to look at how the two systems developed and how they can work together so that land based information can be shared among different users and compared over time.

Natural resources evaluation by the use of remote sensing and GIS technology for agricultural development

NASA Astrophysics Data System (ADS)

Pham Viet, C.; Nguyen Phuong, M.

1993-11-01

A multilevel Geographic Information System based on Remote Sensing and GIS-Technology is established to assess erosion susceptibility and select suitable land for soil conservation and regional planning, management. A cross analysis between the thematic maps and field data is done to examine the relationship between natural condition and land suitability for agriculture. The Land resources evaluation models are affective for understanding the cultivation possibility and can be used as a regional project to be applied in various Vietnam regions for agricultural development.

A global dataset of crowdsourced land cover and land use reference data.

PubMed

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-06-13

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general.

A global dataset of crowdsourced land cover and land use reference data

PubMed Central

Fritz, Steffen; See, Linda; Perger, Christoph; McCallum, Ian; Schill, Christian; Schepaschenko, Dmitry; Duerauer, Martina; Karner, Mathias; Dresel, Christopher; Laso-Bayas, Juan-Carlos; Lesiv, Myroslava; Moorthy, Inian; Salk, Carl F.; Danylo, Olha; Sturn, Tobias; Albrecht, Franziska; You, Liangzhi; Kraxner, Florian; Obersteiner, Michael

2017-01-01

Global land cover is an essential climate variable and a key biophysical driver for earth system models. While remote sensing technology, particularly satellites, have played a key role in providing land cover datasets, large discrepancies have been noted among the available products. Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. Here we describe the global land cover and land use reference data derived from the Geo-Wiki crowdsourcing platform via four campaigns. These global datasets provide information on human impact, land cover disagreement, wilderness and land cover and land use. Hence, they are relevant for the scientific community that requires reference data for global satellite-derived products, as well as those interested in monitoring global terrestrial ecosystems in general. PMID:28608851

Forest inventory with LiDAR and stereo DSM on Washington department of natural resources lands

Treesearch

Jacob L. Strunk; Peter J. Gould

2015-01-01

DNR’s forest inventory group has completed its first version of a new remote-sensing based forest inventory system covering 1.4 million acres of DNR forest lands. We use a combination of field plots, lidar, NAIP, and a NAIP-derived canopy surface DSM. Given that height drives many key inventory variables (e.g. height, volume, biomass, carbon), remote-sensing derived...

Separating vegetation and soil temperature using airborne multiangular remote sensing image data

NASA Astrophysics Data System (ADS)

Liu, Qiang; Yan, Chunyan; Xiao, Qing; Yan, Guangjian; Fang, Li

2012-07-01

Land surface temperature (LST) is a key parameter in land process research. Many research efforts have been devoted to increase the accuracy of LST retrieval from remote sensing. However, because natural land surface is non-isothermal, component temperature is also required in applications such as evapo-transpiration (ET) modeling. This paper proposes a new algorithm to separately retrieve vegetation temperature and soil background temperature from multiangular thermal infrared (TIR) remote sensing data. The algorithm is based on the localized correlation between the visible/near-infrared (VNIR) bands and the TIR band. This method was tested on the airborne image data acquired during the Watershed Allied Telemetry Experimental Research (WATER) campaign. Preliminary validation indicates that the remote sensing-retrieved results can reflect the spatial and temporal trend of component temperatures. The accuracy is within three degrees while the difference between vegetation and soil temperature can be as large as twenty degrees.

Application research on land use remote sensing dynamic monitoring: A case study of Anning district, Lanzhou

NASA Astrophysics Data System (ADS)

Zhu, Yunqiang; Zhu, Huazhong; Lu, Heli; Ni, Jianguang; Zhu, Shaoxia

2005-10-01

Remote sensing dynamic monitoring of land use can detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper discusses the technological procedure of remote sensing dynamic monitoring of land use including the process of remote sensing images, the extraction of annual change information of land use, field survey, indoor post processing and accuracy assessment. Especially, we emphasize on comparative research on the choice of remote sensing rectifying models, image fusion algorithms and accuracy assessment methods. Taking Anning district in Lanzhou as an example, we extract the land use change information of the district during 2002-2003, access monitoring accuracy and analyze the reason of land use change.

Object-based land cover classification and change analysis in the Baltimore metropolitan area using multitemporal high resolution remote sensing data

Treesearch

Weiqi Zhou; Austin Troy; Morgan Grove

2008-01-01

Accurate and timely information about land cover pattern and change in urban areas is crucial for urban land management decision-making, ecosystem monitoring and urban planning. This paper presents the methods and results of an object-based classification and post-classification change detection of multitemporal high-spatial resolution Emerge aerial imagery in the...

A thermal-based remote sensing modeling system for estimating evapotranspiration from field to global scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a robust but relatively simple thermal-based energy balance model that parameterizes the key soil/s...

Studies and Application of Remote Sensing Retrieval Method of Soil Moisture Content in Land Parcel Units in Irrigation Area

NASA Astrophysics Data System (ADS)

Zhu, H.; Zhao, H. L.; Jiang, Y. Z.; Zang, W. B.

2018-05-01

Soil moisture is one of the important hydrological elements. Obtaining soil moisture accurately and effectively is of great significance for water resource management in irrigation area. During the process of soil moisture content retrieval with multiremote sensing data, multi- remote sensing data always brings multi-spatial scale problems which results in inconformity of soil moisture content retrieved by remote sensing in different spatial scale. In addition, agricultural water use management has suitable spatial scale of soil moisture information so as to satisfy the demands of dynamic management of water use and water demand in certain unit. We have proposed to use land parcel unit as the minimum unit to do soil moisture content research in agricultural water using area, according to soil characteristics, vegetation coverage characteristics in underlying layer, and hydrological characteristic into the basis of study unit division. We have proposed division method of land parcel units. Based on multi thermal infrared and near infrared remote sensing data, we calculate the ndvi and tvdi index and make a statistical model between the tvdi index and soil moisture of ground monitoring station. Then we move forward to study soil moisture remote sensing retrieval method on land parcel unit scale. And the method has been applied in Hetao irrigation area. Results show that compared with pixel scale the soil moisture content in land parcel unit scale has displayed stronger correlation with true value. Hence, remote sensing retrieval method of soil moisture content in land parcel unit scale has shown good applicability in Hetao irrigation area. We converted the research unit into the scale of land parcel unit. Using the land parcel units with unified crops and soil attributes as the research units more complies with the characteristics of agricultural water areas, avoids the problems such as decomposition of mixed pixels and excessive dependence on high-resolution data caused by the research units of pixels, and doesn't involve compromises in the spatial scale and simulating precision like the grid simulation. When the application needs are met, the production efficiency of products can also be improved at a certain degree.

An Updating System for the Gridded Population Database of China Based on Remote Sensing, GIS and Spatial Database Technologies.

PubMed

Yang, Xiaohuan; Huang, Yaohuan; Dong, Pinliang; Jiang, Dong; Liu, Honghui

2009-01-01

The spatial distribution of population is closely related to land use and land cover (LULC) patterns on both regional and global scales. Population can be redistributed onto geo-referenced square grids according to this relation. In the past decades, various approaches to monitoring LULC using remote sensing and Geographic Information Systems (GIS) have been developed, which makes it possible for efficient updating of geo-referenced population data. A Spatial Population Updating System (SPUS) is developed for updating the gridded population database of China based on remote sensing, GIS and spatial database technologies, with a spatial resolution of 1 km by 1 km. The SPUS can process standard Moderate Resolution Imaging Spectroradiometer (MODIS L1B) data integrated with a Pattern Decomposition Method (PDM) and an LULC-Conversion Model to obtain patterns of land use and land cover, and provide input parameters for a Population Spatialization Model (PSM). The PSM embedded in SPUS is used for generating 1 km by 1 km gridded population data in each population distribution region based on natural and socio-economic variables. Validation results from finer township-level census data of Yishui County suggest that the gridded population database produced by the SPUS is reliable.

Modeling tropical land-use and land-cover change related to sugarcane crops using remote sensing and soft computing techniques

NASA Astrophysics Data System (ADS)

Vicente, L. E.; Koga-Vicente, A.; Friedel, M. J.; Zullo, J.; Victoria, D.; Gomes, D.; Bayma, G.

2013-12-01

Agriculture is closely related to land-use/cover changes (LUCC). The increase in demand for ethanol necessitates the expansion of areas occupied by corn and sugar cane. In São Paulo state, the conversion of this land raises concern for impacts on food security, such as the decrease in traditional food crop production areas. We used remote sensing data to train and evaluate future land-cover scenarios using a machine-learning algorithm. The land cover classification procedure was based on Landsat 5 TM images, obtained from the Global Land Survey, covering three time periods over twenty years (1990 - 2010). Landsat images were segmented into homogeneous objects, which represent areas on the ground with similar spatial and spectral characteristics. These objects are related to the distinct land cover types that occur in each municipality. Based on the object shape, texture and spectral characteristics, land use/cover was visually identified, considering the following classes: sugarcane plantations, pasture lands, natural cover, forest plantation, permanent crop, short cycle crop, water bodies and urban areas. Results for the western regions of São Paulo state indicate that sugarcane crop area advanced mostly upon pasture areas with few areas of food crops being replaced by sugarcane.

Land Use and Land Cover (LULC) Change Detection in Islamabad and its Comparison with Capital Development Authority (CDA) 2006 Master Plan

NASA Astrophysics Data System (ADS)

Hasaan, Zahra

2016-07-01

Remote sensing is very useful for the production of land use and land cover statistics which can be beneficial to determine the distribution of land uses. Using remote sensing techniques to develop land use classification mapping is a convenient and detailed way to improve the selection of areas designed to agricultural, urban and/or industrial areas of a region. In Islamabad city and surrounding the land use has been changing, every day new developments (urban, industrial, commercial and agricultural) are emerging leading to decrease in vegetation cover. The purpose of this work was to develop the land use of Islamabad and its surrounding area that is an important natural resource. For this work the eCognition Developer 64 computer software was used to develop a land use classification using SPOT 5 image of year 2012. For image processing object-based classification technique was used and important land use features i.e. Vegetation cover, barren land, impervious surface, built up area and water bodies were extracted on the basis of object variation and compared the results with the CDA Master Plan. The great increase was found in built-up area and impervious surface area. On the other hand vegetation cover and barren area followed a declining trend. Accuracy assessment of classification yielded 92% accuracies of the final land cover land use maps. In addition these improved land cover/land use maps which are produced by remote sensing technique of class definition, meet the growing need of legend standardization.

Object-Based Random Forest Classification of Land Cover from Remotely Sensed Imagery for Industrial and Mining Reclamation

NASA Astrophysics Data System (ADS)

Chen, Y.; Luo, M.; Xu, L.; Zhou, X.; Ren, J.; Zhou, J.

2018-04-01

The RF method based on grid-search parameter optimization could achieve a classification accuracy of 88.16 % in the classification of images with multiple feature variables. This classification accuracy was higher than that of SVM and ANN under the same feature variables. In terms of efficiency, the RF classification method performs better than SVM and ANN, it is more capable of handling multidimensional feature variables. The RF method combined with object-based analysis approach could highlight the classification accuracy further. The multiresolution segmentation approach on the basis of ESP scale parameter optimization was used for obtaining six scales to execute image segmentation, when the segmentation scale was 49, the classification accuracy reached the highest value of 89.58 %. The classification accuracy of object-based RF classification was 1.42 % higher than that of pixel-based classification (88.16 %), and the classification accuracy was further improved. Therefore, the RF classification method combined with object-based analysis approach could achieve relatively high accuracy in the classification and extraction of land use information for industrial and mining reclamation areas. Moreover, the interpretation of remotely sensed imagery using the proposed method could provide technical support and theoretical reference for remotely sensed monitoring land reclamation.

A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. A thermal-based scheme, called the Two-Source Energy Balance (TSEB) model, solves for the soil/substrate and canopy temp...

Remote sensing and GIS for land use/cover mapping and integrated land management: case from the middle Ganga plain

NASA Astrophysics Data System (ADS)

Singh, R. B.; Kumar, Dilip

2012-06-01

In India, land resources have reached a critical stage due to the rapidly growing population. This challenge requires an integrated approach toward harnessing land resources, while taking into account the vulnerable environmental conditions. Remote sensing and Geographical Information System (GIS) based technologies may be applied to an area in order to generate a sustainable development plan that is optimally suited to the terrain and to the productive potential of the local resources. The present study area is a part of the middle Ganga plain, known as Son-Karamnasa interfluve, in India. Alternative land use systems and the integration of livestock enterprises with the agricultural system have been suggested for land resources management. The objective of this paper is to prepare a land resource development plan in order to increase the productivity of land for sustainable development. The present study will contribute necessary input for policy makers to improve the socio-economic and environmental conditions of the region.

NLCD 2011 database

EPA Pesticide Factsheets

National Land Cover Database 2011 (NLCD 2011) is the most recent national land cover product created by the Multi-Resolution Land Characteristics (MRLC) Consortium. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data. This dataset is associated with the following publication:Homer, C., J. Dewitz, L. Yang, S. Jin, P. Danielson, G. Xian, J. Coulston, N. Herold, J. Wickham , and K. Megown. Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information. PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING. American Society for Photogrammetry and Remote Sensing, Bethesda, MD, USA, 81(0): 345-354, (2015).

Optimal land use/land cover classification using remote sensing imagery for hydrological modeling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2009-10-01

Land use/land cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/land cover. This paper presents different approaches to attain an optimal land use/land cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/land cover map was not sufficient for the delineation of HRUs, since the agricultural land use/land cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Subsequently the digital classification on fused data (ASAR and ASTER) were attempted to map land use/land cover classes with emphasis to delineate the paddy and maize crops but the supervised classification over fused datasets did not provide the desired accuracy and proper delineation of paddy and maize crops. Eventually, we adopted a visual classification approach on fused data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modeling.

Land cover mapping after the tsunami event over Nanggroe Aceh Darussalam (NAD) province, Indonesia

NASA Astrophysics Data System (ADS)

Lim, H. S.; MatJafri, M. Z.; Abdullah, K.; Alias, A. N.; Mohd. Saleh, N.; Wong, C. J.; Surbakti, M. S.

2008-03-01

Remote sensing offers an important means of detecting and analyzing temporal changes occurring in our landscape. This research used remote sensing to quantify land use/land cover changes at the Nanggroe Aceh Darussalam (Nad) province, Indonesia on a regional scale. The objective of this paper is to assess the changed produced from the analysis of Landsat TM data. A Landsat TM image was used to develop land cover classification map for the 27 March 2005. Four supervised classifications techniques (Maximum Likelihood, Minimum Distance-to- Mean, Parallelepiped and Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier) were performed to the satellite image. Training sites and accuracy assessment were needed for supervised classification techniques. The training sites were established using polygons based on the colour image. High detection accuracy (>80%) and overall Kappa (>0.80) were achieved by the Parallelepiped with Maximum Likelihood Classifier Tiebreaker classifier in this study. This preliminary study has produced a promising result. This indicates that land cover mapping can be carried out using remote sensing classification method of the satellite digital imagery.

Evaluation of various LandFlux evapotranspiration algorithms using the LandFlux-EVAL synthesis benchmark products and observational data

NASA Astrophysics Data System (ADS)

Michel, Dominik; Hirschi, Martin; Jimenez, Carlos; McCabe, Mathew; Miralles, Diego; Wood, Eric; Seneviratne, Sonia

2014-05-01

Research on climate variations and the development of predictive capabilities largely rely on globally available reference data series of the different components of the energy and water cycles. Several efforts aimed at producing large-scale and long-term reference data sets of these components, e.g. based on in situ observations and remote sensing, in order to allow for diagnostic analyses of the drivers of temporal variations in the climate system. Evapotranspiration (ET) is an essential component of the energy and water cycle, which can not be monitored directly on a global scale by remote sensing techniques. In recent years, several global multi-year ET data sets have been derived from remote sensing-based estimates, observation-driven land surface model simulations or atmospheric reanalyses. The LandFlux-EVAL initiative presented an ensemble-evaluation of these data sets over the time periods 1989-1995 and 1989-2005 (Mueller et al. 2013). Currently, a multi-decadal global reference heat flux data set for ET at the land surface is being developed within the LandFlux initiative of the Global Energy and Water Cycle Experiment (GEWEX). This LandFlux v0 ET data set comprises four ET algorithms forced with a common radiation and surface meteorology. In order to estimate the agreement of this LandFlux v0 ET data with existing data sets, it is compared to the recently available LandFlux-EVAL synthesis benchmark product. Additional evaluation of the LandFlux v0 ET data set is based on a comparison to in situ observations of a weighing lysimeter from the hydrological research site Rietholzbach in Switzerland. These analyses serve as a test bed for similar evaluation procedures that are envisaged for ESA's WACMOS-ET initiative (http://wacmoset.estellus.eu). Reference: Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17(10): 3707-3720.

Optimal land use/cover classification using remote sensing imagery for hydrological modelling in a Himalayan watershed

NASA Astrophysics Data System (ADS)

Saran, Sameer; Sterk, Geert; Kumar, Suresh

2007-10-01

Land use/cover is an important watershed surface characteristic that affects surface runoff and erosion. Many of the available hydrological models divide the watershed into Hydrological Response Units (HRU), which are spatial units with expected similar hydrological behaviours. The division into HRU's requires good-quality spatial data on land use/cover. This paper presents different approaches to attain an optimal land use/cover map based on remote sensing imagery for a Himalayan watershed in northern India. First digital classifications using maximum likelihood classifier (MLC) and a decision tree classifier were applied. The results obtained from the decision tree were better and even improved after post classification sorting. But the obtained land use/cover map was not sufficient for the delineation of HRUs, since the agricultural land use/cover class did not discriminate between the two major crops in the area i.e. paddy and maize. Therefore we adopted a visual classification approach using optical data alone and also fused with ENVISAT ASAR data. This second step with detailed classification system resulted into better classification accuracy within the 'agricultural land' class which will be further combined with topography and soil type to derive HRU's for physically-based hydrological modelling.

A computer software system for integration and analysis of grid-based remote sensing data with other natural resource data. Remote Sensing Project

NASA Technical Reports Server (NTRS)

Tilmann, S. E.; Enslin, W. R.; Hill-Rowley, R.

1977-01-01

A computer-based information system is described designed to assist in the integration of commonly available spatial data for regional planning and resource analysis. The Resource Analysis Program (RAP) provides a variety of analytical and mapping phases for single factor or multi-factor analyses. The unique analytical and graphic capabilities of RAP are demonstrated with a study conducted in Windsor Township, Eaton County, Michigan. Soil, land cover/use, topographic and geological maps were used as a data base to develope an eleven map portfolio. The major themes of the portfolio are land cover/use, non-point water pollution, waste disposal, and ground water recharge.

Extraction of land cover change information from ENVISAT-ASAR data in Chengdu Plain

NASA Astrophysics Data System (ADS)

Xu, Wenbo; Fan, Jinlong; Huang, Jianxi; Tian, Yichen; Zhang, Yong

2006-10-01

Land cover data are essential to most global change research objectives, including the assessment of current environmental conditions and the simulation of future environmental scenarios that ultimately lead to public policy development. Chinese Academy of Sciences generated a nationwide land cover database in order to carry out the quantification and spatial characterization of land use/cover changes (LUCC) in 1990s. In order to improve the reliability of the database, we will update the database anytime. But it is difficult to obtain remote sensing data to extract land cover change information in large-scale. It is hard to acquire optical remote sensing data in Chengdu plain, so the objective of this research was to evaluate multitemporal ENVISAT advanced synthetic aperture radar (ASAR) data for extracting land cover change information. Based on the fieldwork and the nationwide 1:100000 land cover database, the paper assesses several land cover changes in Chengdu plain, for example: crop to buildings, forest to buildings, and forest to bare land. The results show that ENVISAT ASAR data have great potential for the applications of extracting land cover change information.

Tsunami exposure estimation with land-cover data: Oregon and the Cascadia subduction zone

USGS Publications Warehouse

Wood, N.

2009-01-01

A Cascadia subduction-zone earthquake has the potential to generate tsunami waves which would impact more than 1000 km of coastline on the west coast of the United States and Canada. Although the predictable extent of tsunami inundation is similar for low-lying land throughout the region, human use of tsunami-prone land varies, creating variations in community exposure and potential impacts. To better understand such variations, land-cover information derived from midresolution remotely-sensed imagery (e.g., 30-m-resolution Landsat Thematic Mapper imagery) was coupled with tsunami-hazard information to describe tsunami-prone land along the Oregon coast. Land-cover data suggest that 95% of the tsunami-prone land in Oregon is undeveloped and is primarily wetlands and unconsolidated shores. Based on Spearman rank correlation coefficients (rs), correlative relationships are strong and statistically significant (p < 0.05) between city-level estimates of the amount of land-cover pixels classified as developed (impervious cover greater than 20%) and the amount of various societal assets, including residential and employee populations, homes, businesses, and tax-parcel values. Community exposure to tsunami hazards, described here by the amount and relative percentage of developed land in tsunami-prone areas, varies considerably among the 26 communities of the study area, and these variations relate to city size. Correlative relationships are strong and significant (p < 0.05) for community exposure rankings based on land-cover data and those based on aggregated socioeconomic data. In the absence of socioeconomic data or community-based knowledge, the integration of hazards information and land-cover information derived from midresolution remotely-sensed imagery to estimate community exposure may be a useful first step in understanding variations in community vulnerability to regional hazards.

Remote sensing as a source of land cover information utilized in the universal soil loss equation

NASA Technical Reports Server (NTRS)

Morris-Jones, D. R.; Morgan, K. M.; Kiefer, R. W.; Scarpace, F. L.

1979-01-01

In this study, methods for gathering the land use/land cover information required by the USLE were investigated with medium altitude, multi-date color and color infrared 70-mm positive transparencies using human and computer-based interpretation techniques. Successful results, which compare favorably with traditional field study methods, were obtained within the test site watershed with airphoto data sources and human airphoto interpretation techniques. Computer-based interpretation techniques were not capable of identifying soil conservation practices but were successful to varying degrees in gathering other types of desired land use/land cover information.

Spatial heterogeneity of leaf area index across scales from simulation and remote sensing

NASA Astrophysics Data System (ADS)

Reichenau, Tim G.; Korres, Wolfgang; Montzka, Carsten; Schneider, Karl

2016-04-01

Leaf area index (LAI, single sided leaf area per ground area) influences mass and energy exchange of vegetated surfaces. Therefore LAI is an input variable for many land surface schemes of coupled large scale models, which do not simulate LAI. Since these models typically run on rather coarse resolution grids, LAI is often inferred from coarse resolution remote sensing. However, especially in agriculturally used areas, a grid cell of these products often covers more than a single land-use. In that case, the given LAI does not apply to any single land-use. Therefore, the overall spatial heterogeneity in these datasets differs from that on resolutions high enough to distinguish areas with differing land-use. Detailed process-based plant growth models simulate LAI for separate plant functional types or specific species. However, limited availability of observations causes reduced spatial heterogeneity of model input data (soil, weather, land-use). Since LAI is strongly heterogeneous in space and time and since processes depend on LAI in a nonlinear way, a correct representation of LAI spatial heterogeneity is also desirable on coarse resolutions. The current study assesses this issue by comparing the spatial heterogeneity of LAI from remote sensing (RapidEye) and process-based simulations (DANUBIA simulation system) across scales. Spatial heterogeneity is assessed by analyzing LAI frequency distributions (spatial variability) and semivariograms (spatial structure). Test case is the arable land in the fertile loess plain of the Rur catchment near the Germany-Netherlands border.

Evaluation of MuSyQ land surface albedo based on LAnd surface Parameters VAlidation System (LAPVAS)

NASA Astrophysics Data System (ADS)

Dou, B.; Wen, J.; Xinwen, L.; Zhiming, F.; Wu, S.; Zhang, Y.

2016-12-01

satellite derived Land surface albedo is an essential climate variable which controls the earth energy budget and it can be used in applications such as climate change, hydrology, and numerical weather prediction. However, the accuracy and uncertainty of surface albedo products should be evaluated with a reliable reference truth data prior to applications. A new comprehensive and systemic project of china, called the Remote Sensing Application Network (CRSAN), has been launched recent years. Two subjects of this project is developing a Multi-source data Synergized Quantitative Remote Sensin g Production System ( MuSyQ ) and a Web-based validation system named LAnd surface remote sensing Product VAlidation System (LAPVAS) , which aims to generate a quantitative remote sensing product for ecosystem and environmental monitoring and validate them with a reference validation data and a standard validation system, respectively. Land surface BRDF/albedo is one of product datasets of MuSyQ which has a pentad period with 1km spatial resolution and is derived by Multi-sensor Combined BRDF Inversion ( MCBI ) Model. In this MuSyQ albedo evaluation, a multi-validation strategy is implemented by LAPVAS, including directly and multi-scale validation with field measured albedo and cross validation with MODIS albedo product with different land cover. The results reveal that MuSyQ albedo data with a 5-day temporal resolution is in higher sensibility and accuracy during land cover change period, e.g. snowing. But results without regard to snow or changed land cover, MuSyQ albedo generally is in similar accuracy with MODIS albedo and meet the climate modeling requirement of an absolute accuracy of 0.05.

Exploring Remote Sensing Products Online with Giovanni for Studying Urbanization

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina; Kempler, Steve

2012-01-01

Recently, a Large amount of MODIS land products at multi-spatial resolutions have been integrated into the online system, Giovanni, to support studies on land cover and land use changes focused on Northern Eurasia and Monsoon Asia regions. Giovanni (Goddard Interactive Online Visualization ANd aNalysis Infrastructure) is a Web-based application developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES-DISC) providing a simple and intuitive way to visualize, analyze, and access Earth science remotely-sensed and modeled data. The customized Giovanni Web portals (Giovanni-NEESPI and Giovanni-MAIRS) are created to integrate land, atmospheric, cryospheric, and social products, that enable researchers to do quick exploration and basic analyses of land surface changes and their relationships to climate at global and regional scales. This presentation documents MODIS land surface products in Giovanni system. As examples, images and statistical analysis results on land surface and local climate changes associated with urbanization over Yangtze River Delta region, China, using data in Giovanni are shown.

Monitoring Rangeland Health by Remote Sensing

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote s...

Post-classification approaches to estimating change in forest area using remotely sense auxiliary data.

Treesearch

Ronald E. McRoberts

2014-01-01

Multiple remote sensing-based approaches to estimating gross afforestation, gross deforestation, and net deforestation are possible. However, many of these approaches have severe data requirements in the form of long time series of remotely sensed data and/or large numbers of observations of land cover change to train classifiers and assess the accuracy of...

Quantifying ecosystem carbon losses and gains following development in New England: A combined field, modeling, and remote sensing approach

NASA Astrophysics Data System (ADS)

Raciti, S. M.; Hutyra, L.; Briber, B. M.; Dunn, A. L.; Friedl, M. A.; Woodcock, C.; Zhu, Z.; Olofsson, P.

2013-12-01

If current trends continue, the world's urban population may double and urban land area may quadruple over the next 50 years. Despite the rapid expansion of urban areas, the trajectories of carbon losses and gains following development remain poorly quantified. We are using a combination of field measurements, modeling, and remote sensing to advance our ability to measure and monitor trajectories of ecosystem carbon over space and time. To characterize how carbon stocks change across urban-to-rural gradients, we previously established field plots to survey live and dead tree biomass, tree canopy, soil and foliar carbon and nitrogen concentrations, and a range of landscape characteristics (Raciti et al. 2012). In 2013, we extended our field sampling to focus specifically on places that experienced land use and land cover change over the past 35 years. This chronosequence approach was informed by Landsat time series (1982-present) and property records (before 1982). The Landsat time series approach differs from traditional remote-sensing-based land use change detection methods because it leverages the entire Landsat archive of imagery using a Fourier fitting approach (Zhu et al. 2012). The result is a temporally and spatially continuous map of land use and land cover change across the study region. We used these field and remote sensing data to inform a carbon bookkeeping model that estimates changes in past and potential future carbon stocks over time. Here we present preliminary results of this work for eastern Massachusetts.

A Physically-Based Drought Product Using Thermal Remote Sensing of Evapotranspiration

USDA-ARS?s Scientific Manuscript database

Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonst...

Land Remote Sensing Overview

NASA Technical Reports Server (NTRS)

Byrnes, Ray

2007-01-01

A general overview of the USGS land remote sensing program is presented. The contents include: 1) Brief overview of USGS land remote sensing program; 2) Highlights of JACIE work at USGS; 3) Update on NASA/USGS Landsat Data Continuity Mission; and 4) Notes on alternative data sources.

Use of remote sensing in agriculture

NASA Technical Reports Server (NTRS)

Pettry, D. E.; Powell, N. L.

1975-01-01

The remote sensing studies of (a) cultivated peanut areas in Southeastern Virginia; (b) studies at the Virginia Truck and Ornamentals Research Station near Painter, Virginia, the Eastern Virginia Research Station near Warsaw, Virginia, the Tidewater Research and Continuing Education Center near Suffolk, Virginia, and the Southern Piedmont Research and Continuing Education Center Blackstone, Virginia; and (c) land use classification studies at Virginia Beach, Virginia are presented. The practical feasibility of using false color infrared imagery to detect and determine the areal extent of peanut disease infestation of Cylindrocladium black rot and Sclerotinia blight is demonstrated. These diseases pose a severe hazard to this major agricultural food commodity. The value of remote sensing technology in terrain analyses and land use classification of diverse land areas is also investigated. Continued refinement of spectral signatures of major agronomic crops and documentation of pertinent environmental variables have provided a data base for the generation of an agricultural-environmental prediction model.

Multiple Scale Remote Sensing for Monitoring Rangelands

USDA-ARS?s Scientific Manuscript database

Based on a land-cover classification from NASA’s MODerate resolution Imaging Spectroradiometer (MODIS), rangelands cover 48% of the Earth’s land surface, not including Antarctica. Nearly all analyses imply the most economical means of monitoring large areas of rangelands worldwide is with remote se...

Analysis of spatiotemporal variability of C-factor derived from remote sensing data

NASA Astrophysics Data System (ADS)

Pechanec, Vilém; Mráz, Alexander; Benc, Antonín; Cudlín, Pavel

2018-01-01

Soil erosion is an important phenomenon that contributes to the degradation of agricultural land. Even though it is a natural process, human activities can significantly increase its impact on land degradation and present serious limitation on sustainable agricultural land use. Nowadays, the risk of soil erosion is assessed either qualitatively by expert assessment or quantitatively using model-based approach. One of the primary factors affecting the soil erosion assessment is a cover-management factor, C-factor. In the Czech Republic, several models are used to assess the C-factor on a long-term basis based on data collected using traditional tabular methods. This paper presents work to investigate the estimation of both long-term and short-term cover-management factors using remote sensing data. The results demonstrate a successful development of C-factor maps for each month of 2014, growing season average, and annual average for the Czech Republic. C-factor values calculated from remote sensing data confirmed expected trend in their temporal variability for selected crops. The results presented in this paper can be used for enhancing existing methods for estimating C-factor, planning future agricultural activities, and designing technical remediations and improvement activities of land use in the Czech Republic, which are also financially supported by the European Union funds.

Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany

NASA Astrophysics Data System (ADS)

Waldhoff, Guido; Lussem, Ulrike; Bareth, Georg

2017-09-01

Spatial land use information is one of the key input parameters for regional agro-ecosystem modeling. Furthermore, to assess the crop-specific management in a spatio-temporal context accurately, parcel-related crop rotation information is additionally needed. Such data is scarcely available for a regional scale, so that only modeled crop rotations can be incorporated instead. However, the spectrum of the occurring multiannual land use patterns on arable land remains unknown. Thus, this contribution focuses on the mapping of the actually practiced crop rotations in the Rur catchment, located in the western part of Germany. We addressed this by combining multitemporal multispectral remote sensing data, ancillary information and expert-knowledge on crop phenology in a GIS-based Multi-Data Approach (MDA). At first, a methodology for the enhanced differentiation of the major crop types on an annual basis was developed. Key aspects are (i) the usage of physical block data to separate arable land from other land use types, (ii) the classification of remote sensing scenes of specific time periods, which are most favorable for the differentiation of certain crop types, and (iii) the combination of the multitemporal classification results in a sequential analysis strategy. Annual crop maps of eight consecutive years (2008-2015) were combined to a crop sequence dataset to have a profound data basis for the mapping of crop rotations. In most years, the remote sensing data basis was highly fragmented. Nevertheless, our method enabled satisfying crop mapping results. As an example for the annual crop mapping workflow, the procedure and the result of 2015 are illustrated. For the generation of the crop sequence dataset, the eight annual crop maps were geometrically smoothened and integrated into a single vector data layer. The resulting dataset informs about the occurring crop sequence for individual areas on arable land, so that crop rotation schemes can be derived. The resulting dataset reveals that the spectrum of the practiced crop rotations is extremely heterogeneous and contains a large amount of crop sequences, which strongly diverge from model crop rotations. Consequently, the integration of remote sensing-based crop rotation data can considerably reduce uncertainties regarding the management in regional agro-ecosystem modeling. Finally, the developed methods and the results are discussed in detail.

Linking remote sensing, land cover and disease.

PubMed

Curran, P J; Atkinson, P M; Foody, G M; Milton, E J

2000-01-01

Land cover is a critical variable in epidemiology and can be characterized remotely. A framework is used to describe both the links between land cover and radiation recorded in a remotely sensed image, and the links between land cover and the disease carried by vectors. The framework is then used to explore the issues involved when moving from remotely sensed imagery to land cover and then to vector density/disease risk. This exploration highlights the role of land cover; the need to develop a sound knowledge of each link in the predictive sequence; the problematic mismatch between the spatial units of the remotely sensed and epidemiological data and the challenges and opportunities posed by adding a temporal mismatch between the remotely sensed and epidemiological data. The paper concludes with a call for both greater understanding of the physical components of the proposed framework and the utilization of optimized statistical tools as prerequisites to progress in this field.

Analysis On Land Cover In Municipality Of Malang With Landsat 8 Image Through Unsupervised Classification

NASA Astrophysics Data System (ADS)

Nahari, R. V.; Alfita, R.

2018-01-01

Remote sensing technology has been widely used in the geographic information system in order to obtain data more quickly, accurately and affordably. One of the advantages of using remote sensing imagery (satellite imagery) is to analyze land cover and land use. Satellite image data used in this study were images from the Landsat 8 satellite combined with the data from the Municipality of Malang government. The satellite image was taken in July 2016. Furthermore, the method used in this study was unsupervised classification. Based on the analysis towards the satellite images and field observations, 29% of the land in the Municipality of Malang was plantation, 22% of the area was rice field, 12% was residential area, 10% was land with shrubs, and the remaining 2% was water (lake/reservoir). The shortcoming of the methods was 25% of the land in the area was unidentified because it was covered by cloud. It is expected that future researchers involve cloud removal processing to minimize unidentified area.

National Satellite Land Remote Sensing Data Archive

USGS Publications Warehouse

Faundeen, John L.; Kelly, Francis P.; Holm, Thomas M.; Nolt, Jenna E.

2013-01-01

The National Satellite Land Remote Sensing Data Archive (NSLRSDA) resides at the U.S. Geological Survey's (USGS) Earth Resources Observation and Science (EROS) Center. Through the Land Remote Sensing Policy Act of 1992, the U.S. Congress directed the Department of the Interior (DOI) to establish a permanent Government archive containing satellite remote sensing data of the Earth's land surface and to make this data easily accessible and readily available. This unique DOI/USGS archive provides a comprehensive, permanent, and impartial observational record of the planet's land surface obtained throughout more than five decades of satellite remote sensing. Satellite-derived data and information products are primary sources used to detect and understand changes such as deforestation, desertification, agricultural crop vigor, water quality, invasive plant species, and certain natural hazards such as flood extent and wildfire scars.

Incorporating Land-Use Mapping Uncertainty in Remote Sensing Based Calibration of Land-Use Change Models

NASA Astrophysics Data System (ADS)

Cockx, K.; Van de Voorde, T.; Canters, F.; Poelmans, L.; Uljee, I.; Engelen, G.; de Jong, K.; Karssenberg, D.; van der Kwast, J.

2013-05-01

Building urban growth models typically involves a process of historic calibration based on historic time series of land-use maps, usually obtained from satellite imagery. Both the remote sensing data analysis to infer land use and the subsequent modelling of land-use change are subject to uncertainties, which may have an impact on the accuracy of future land-use predictions. Our research aims to quantify and reduce these uncertainties by means of a particle filter data assimilation approach that incorporates uncertainty in land-use mapping and land-use model parameter assessment into the calibration process. This paper focuses on part of this work, more in particular the modelling of uncertainties associated with the impervious surface cover estimation and urban land-use classification adopted in the land-use mapping approach. Both stages are submitted to a Monte Carlo simulation to assess their relative contribution to and their combined impact on the uncertainty in the derived land-use maps. The approach was applied on the central part of the Flanders region (Belgium), using a time-series of Landsat/SPOT-HRV data covering the years 1987, 1996, 2005 and 2012. Although the most likely land-use map obtained from the simulation is very similar to the original classification, it is shown that the errors related to the impervious surface sub-pixel fraction estimation have a strong impact on the land-use map's uncertainty. Hence, incorporating uncertainty in the land-use change model calibration through particle filter data assimilation is proposed to address the uncertainty observed in the derived land-use maps and to reduce uncertainty in future land-use predictions.

Estimation of Multiple Parameters over Vegetated Surfaces by Integrating Optical-Thermal Remote Sensing Observations

NASA Astrophysics Data System (ADS)

Ma, H.

2016-12-01

Land surface parameters from remote sensing observations are critical in monitoring and modeling of global climate change and biogeochemical cycles. Current methods for estimating land surface parameters are generally parameter-specific algorithms and are based on instantaneous physical models, which result in spatial, temporal and physical inconsistencies in current global products. Besides, optical and Thermal Infrared (TIR) remote sensing observations are usually separated to use based on different models , and the Middle InfraRed (MIR) observations have received little attention due to the complexity of the radiometric signal that mixes both reflected and emitted fluxes. In this paper, we proposed a unified algorithm for simultaneously retrieving a total of seven land surface parameters, including Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), land surface albedo, Land Surface Temperature (LST), surface emissivity, downward and upward longwave radiation, by exploiting remote sensing observations from visible to TIR domain based on a common physical Radiative Transfer (RT) model and a data assimilation framework. The coupled PROSPECT-VISIR and 4SAIL RT model were used for canopy reflectance modeling. At first, LAI was estimated using a data assimilation method that combines MODIS daily reflectance observation and a phenology model. The estimated LAI values were then input into the RT model to simulate surface spectral emissivity and surface albedo. Besides, the background albedo and the transmittance of solar radiation, and the canopy albedo were also calculated to produce FAPAR. Once the spectral emissivity of seven MODIS MIR to TIR bands were retrieved, LST can be estimated from the atmospheric corrected surface radiance by exploiting an optimization method. At last, the upward longwave radiation were estimated using the retrieved LST, broadband emissivity (converted from spectral emissivity) and the downward longwave radiation (modeled by MODTRAN). These seven parameters were validated over several representative sites with different biome type, and compared with MODIS and GLASS product. Results showed that this unified inversion algorithm can retrieve temporally complete and physical consistent land surface parameters with high accuracy.

San Juan National Forest Land Management Planning Support System (LMPSS) requirements definition

NASA Technical Reports Server (NTRS)

Werth, L. F. (Principal Investigator)

1981-01-01

The role of remote sensing data as it relates to a three-component land management planning system (geographic information, data base management, and planning model) can be understood only when user requirements are known. Personnel at the San Juan National Forest in southwestern Colorado were interviewed to determine data needs for managing and monitoring timber, rangelands, wildlife, fisheries, soils, water, geology and recreation facilities. While all the information required for land management planning cannot be obtained using remote sensing techniques, valuable information can be provided for the geographic information system. A wide range of sensors such as small and large format cameras, synthetic aperture radar, and LANDSAT data should be utilized. Because of the detail and accuracy required, high altitude color infrared photography should serve as the baseline data base and be supplemented and updated with data from the other sensors.

Quality Evaluation of Land-Cover Classification Using Convolutional Neural Network

NASA Astrophysics Data System (ADS)

Dang, Y.; Zhang, J.; Zhao, Y.; Luo, F.; Ma, W.; Yu, F.

2018-04-01

Land-cover classification is one of the most important products of earth observation, which focuses mainly on profiling the physical characters of the land surface with temporal and distribution attributes and contains the information of both natural and man-made coverage elements, such as vegetation, soil, glaciers, rivers, lakes, marsh wetlands and various man-made structures. In recent years, the amount of high-resolution remote sensing data has increased sharply. Accordingly, the volume of land-cover classification products increases, as well as the need to evaluate such frequently updated products that is a big challenge. Conventionally, the automatic quality evaluation of land-cover classification is made through pixel-based classifying algorithms, which lead to a much trickier task and consequently hard to keep peace with the required updating frequency. In this paper, we propose a novel quality evaluation approach for evaluating the land-cover classification by a scene classification method Convolutional Neural Network (CNN) model. By learning from remote sensing data, those randomly generated kernels that serve as filter matrixes evolved to some operators that has similar functions to man-crafted operators, like Sobel operator or Canny operator, and there are other kernels learned by the CNN model that are much more complex and can't be understood as existing filters. The method using CNN approach as the core algorithm serves quality-evaluation tasks well since it calculates a bunch of outputs which directly represent the image's membership grade to certain classes. An automatic quality evaluation approach for the land-cover DLG-DOM coupling data (DLG for Digital Line Graphic, DOM for Digital Orthophoto Map) will be introduced in this paper. The CNN model as an robustness method for image evaluation, then brought out the idea of an automatic quality evaluation approach for land-cover classification. Based on this experiment, new ideas of quality evaluation of DLG-DOM coupling land-cover classification or other kinds of labelled remote sensing data can be further studied.

Cloud tolerance of remote sensing technologies to measure land surface temperature

USDA-ARS?s Scientific Manuscript database

Conventional means to estimate land surface temperature (LST) from space relies on the thermal infrared (TIR) spectral window and is limited to cloud-free scenes. To also provide LST estimates during periods with clouds, a new method was developed to estimate LST based on passive microwave (MW) obse...

Soil moisture status estimation over Three Gorges area with Landsat TM data based on temperature vegetation dryness index

NASA Astrophysics Data System (ADS)

Xu, Lina; Niu, Ruiqing; Li, Jiong; Dong, Yanfang

2011-12-01

Soil moisture is the important indicator of climate, hydrology, ecology, agriculture and other parameters of the land surface and atmospheric interface. Soil moisture plays an important role on the water and energy exchange at the land surface/atmosphere interface. Remote sensing can provide information on large area quickly and easily, so it is significant to do research on how to monitor soil moisture by remote sensing. This paper presents a method to assess soil moisture status using Landsat TM data over Three Gorges area in China based on TVDI. The potential of Temperature- Vegetation Dryness Index (TVDI) from Landsat TM data in assessing soil moisture was investigated in this region. After retrieving land surface temperature and vegetation index a TVDI model based on the features of Ts-NDVI space is established. And finally, soil moisture status is estimated according to TVDI. It shows that TVDI has the advantages of stability and high accuracy to estimating the soil moisture status.

Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Bengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since2012could be summarized as follows: (1)Photosynthetic vegetation(PV)and non-photosynthetic vegetation(NPV)fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library. (2) The accuracy of sandy land classification was as high as81.52%when the object-oriented method and Support Vector Machine (SVM) classifiers were used. (3) A new Monthly net primary productivity (NPP)dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data. (4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China(PEDC) was assessed preliminarily.

Mid Term Progress Report: Desertification Assessment and Monitoring in China Based on Remote Sensing

NASA Astrophysics Data System (ADS)

Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Wengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong

2014-11-01

The objective of Dragon 3 Project 10367 is the development of techniques research for desertification assessment and monitoring in China using remote sensing data in combination with climate and environmental-related data. The main achievements acquired since 2012 could be summarized as follows:(1) Photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) fraction were retrieved separately through utilizing Auto Monte Carlo Unmixing technique (AutoMCU), based on BJ-1 data and field measured spectral library.(2) The accuracy of sandy land classification was as high as 81.52% when the object-oriented method and Support Vector Machine (SVM) classifiers were used.(3) A new Monthly net primary productivity (NPP) dataset from 2002 to 2010 for the whole China were established with Envisat-MERIS fraction of absorbed photosynthetically active radiation (FPAR) data.(4) The 2dRUE proved to be a good indicator for land degradation, based on which, land degradation status in the general potential extent of desertification in China (PEDC) was assessed preliminarily.

Monitoring land use/cover changes on the Romanian Black Sea Coast

NASA Astrophysics Data System (ADS)

Zoran, L. F. V.; Dida, A. I.; Zoran, M. A.

2014-10-01

Remotely sensed satellite data are critical to understanding the coastal zones' physical and social systems interaction, complementing ground based methods and providing accurate wide range, objective and comparable, at widely-varying scales, synoptically data. For some environmental agreements remote sensing may provide the only viable means of compliance verification because the phenomena are monitored occurs over large and inaccessible geographic areas. The main aim of this paper was the assessment of coastal zone land cover/use changes based on fusion technique of satellite remote sensing imagery. The evaluation of coastal zone landscapes was based upon different sub-functions which refer to landscape features such as water, soil, land-use, buildings, groundwater, biotope types. A newly proposed sub-pixel mapping algorithm was applied to a set of multispectral and multitemporal satellite data for Danube Delta, Constantza and Black Sea coastal zone areas in Romania. A land cover classification and subsequent environmental quality analysis for change detection was done based on Landsat TM , Landsat ETM, QuickBird satellite images over 1990 to 2013 period of time. Spectral signatures of different terrain features have been used to separate and classify surface units of coastal zone and sub-coastal zone area.The change in the position of the coastline in Constantza area was examined in relation with the urban expansion. A distinction was made between landfill/sedimentation processes on the one hand and dredging/erosion processes on the other. We considered the Romanian Black Sea coastal zone dynamics in connection with the spatio-temporal variation of physical and biogeochemical processes and their influences on the environmental state in the near-shore area.

A synthesis of remote sensing and local knowledge approaches in land degradation assessment in the Bawku East District, Ghana

NASA Astrophysics Data System (ADS)

Yiran, G. A. B.; Kusimi, J. M.; Kufogbe, S. K.

2012-02-01

A greater percentage of Northern Ghana is under threat of land degradation and is negatively impacting on the well-being of the people owing to deforestation, increasing incidence of drought, indiscriminate bush burning and desertification. The problem is becoming severe with serious implications on the livelihoods of the people as the land is the major resource from which they eke their living. Reversing land degradation requires sustainable land use planning which should be based on detailed up-to-date information on landscape attributes. This information can be generated through remote sensing analytical studies. Therefore, an attempt has been made in this study to collect data for planning by employing remote sensing techniques and ground truthing. The analysis included satellite image classification and change detection between Landsat images captured in 1989, 1999 and 2006. The images were classified into the following classes: water bodies, close savannah woodland, open savannah woodland, grassland/unharvested farmland, exposed soil, burnt scars, and settlement. Change detection performed between the 1989 and 1999 and 1989 and 2006 showed that the environment is deteriorating. Land covers such as close savannah woodland, open savannah woodland and exposed soil diminished over the period whereas settlement and water bodies increased. The grassland/unharvested farmland showed high increases because the images were captured at the time that some farms were still crops or crop residue. Urbanization, land clearing for farming, over grazing, firewood fetching and bush burning were identified as some of the underlying forces of vegetal cover degradation. The socio-cultural beliefs and practices of the people also influenced land cover change as sacred groves as well as medicinal plants are preserved. Local knowledge is recognized and used in the area but it is not properly integrated with scientific knowledge for effective planning for sustainable land management. This is due to lack of expertise in remote sensing and geographic information systems (GIS) in the area.

State resource management and role of remote sensing. [California

NASA Technical Reports Server (NTRS)

Johnson, H. D.

1981-01-01

Remote sensing by satellite can provide valuable information to state officials when making decisions regarding resources management. Portions of California's investment for Prosperity program which seem likely candidates for remote sensing include: (1) surveying vegetation type, age, and density in forests and wildlife habitats; (2) controlling fires through chaparal management; (3) monitoring wetlands and measuring ocean biomass; (4) eliminating ground water overdraught; (5) locating crops in overdraught areas, assessing soil erosion and the areas of poorly drained soils and those affected by salt; (6) monitoring coastal lands and resources; (7) changes in landscapes for recreational purposes; (8) inventorying irrigated lands; (9) classifying ground cover; (10) monitoring farmland conversion; and (11) supplying data for a statewide computerized farmlands data base.

The application of remote sensing to resource management and environmental quality programs in Kansas

NASA Technical Reports Server (NTRS)

Barr, B. G.; Martinko, E. A. (Principal Investigator)

1982-01-01

Capabilities to process data for state agencies in Kansas were upgraded through the vehicle of a low cost data processing system. Short term projects in which agencies identified areas of immediate needs, and longer terms projects, continued from previous years, are described including studies of Arkansas River irrigation; evaluation of rangeland in the Cimarron National Grassland; a model of the Walnut Creek watershed groundwater; selection of a pronghorn antelope release site; the establishment of a geographical data base for tax reassessment in Finney County; a land use/land cover inventory and hazards assessment; and applied R & D in agricultural remote sensing. The topics discussed at a short course in remote sensing and publications are listed.

L-band Microwave Remote Sensing and Land Data Assimilation Improve the Representation of Prestorm Soil Moisture Conditions for Hydrologic Forecasting

NASA Technical Reports Server (NTRS)

Crow, W. T.; Chen, F.; Reichle, R. H.; Liu, Q.

2017-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

Development of a ground hydrology model suitable for global climate modeling using soil morphology and vegetation cover, and an evaluation of remotely sensed information

NASA Technical Reports Server (NTRS)

Zobler, L.; Lewis, R.

1988-01-01

The long-term purpose was to contribute to scientific understanding of the role of the planet's land surfaces in modulating the flows of energy and matter which influence the climate, and to quantify and monitor human-induced changes to the land environment that may affect global climate. Highlights of the effort include the following: production of geo-coded, digitized World Soil Data file for use with the Goddard Institute for Space Studies (GISS) climate model; contribution to the development of a numerical physically-based model of ground hydrology; and assessment of the utility of remote sensing for providing data on hydrologically significant land surface variables.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting.

PubMed

Crow, W T; Chen, F; Reichle, R H; Liu, Q

2017-06-16

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events.

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

PubMed Central

Crow, W.T.; Chen, F.; Reichle, R.H.; Liu, Q.

2018-01-01

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i.e., total stream flow divided by total rainfall accumulation in depth units) and pre-storm surface soil moisture estimates from a range of surface soil moisture data products. Results demonstrate that both satellite-based, L-band microwave radiometry and the application of land data assimilation techniques have significantly improved the utility of surface soil moisture data sets for forecasting stream flow response to future rainfall events. PMID:29657342

Classification of forest land attributes using multi-source remotely sensed data

NASA Astrophysics Data System (ADS)

Pippuri, Inka; Suvanto, Aki; Maltamo, Matti; Korhonen, Kari T.; Pitkänen, Juho; Packalen, Petteri

2016-02-01

The aim of the study was to (1) examine the classification of forest land using airborne laser scanning (ALS) data, satellite images and sample plots of the Finnish National Forest Inventory (NFI) as training data and to (2) identify best performing metrics for classifying forest land attributes. Six different schemes of forest land classification were studied: land use/land cover (LU/LC) classification using both national classes and FAO (Food and Agricultural Organization of the United Nations) classes, main type, site type, peat land type and drainage status. Special interest was to test different ALS-based surface metrics in classification of forest land attributes. Field data consisted of 828 NFI plots collected in 2008-2012 in southern Finland and remotely sensed data was from summer 2010. Multinomial logistic regression was used as the classification method. Classification of LU/LC classes were highly accurate (kappa-values 0.90 and 0.91) but also the classification of site type, peat land type and drainage status succeeded moderately well (kappa-values 0.51, 0.69 and 0.52). ALS-based surface metrics were found to be the most important predictor variables in classification of LU/LC class, main type and drainage status. In best classification models of forest site types both spectral metrics from satellite data and point cloud metrics from ALS were used. In turn, in the classification of peat land types ALS point cloud metrics played the most important role. Results indicated that the prediction of site type and forest land category could be incorporated into stand level forest management inventory system in Finland.

Fuzzy Nonlinear Proximal Support Vector Machine for Land Extraction Based on Remote Sensing Image

PubMed Central

Zhong, Xiaomei; Li, Jianping; Dou, Huacheng; Deng, Shijun; Wang, Guofei; Jiang, Yu; Wang, Yongjie; Zhou, Zebing; Wang, Li; Yan, Fei

2013-01-01

Currently, remote sensing technologies were widely employed in the dynamic monitoring of the land. This paper presented an algorithm named fuzzy nonlinear proximal support vector machine (FNPSVM) by basing on ETM+ remote sensing image. This algorithm is applied to extract various types of lands of the city Da’an in northern China. Two multi-category strategies, namely “one-against-one” and “one-against-rest” for this algorithm were described in detail and then compared. A fuzzy membership function was presented to reduce the effects of noises or outliers on the data samples. The approaches of feature extraction, feature selection, and several key parameter settings were also given. Numerous experiments were carried out to evaluate its performances including various accuracies (overall accuracies and kappa coefficient), stability, training speed, and classification speed. The FNPSVM classifier was compared to the other three classifiers including the maximum likelihood classifier (MLC), back propagation neural network (BPN), and the proximal support vector machine (PSVM) under different training conditions. The impacts of the selection of training samples, testing samples and features on the four classifiers were also evaluated in these experiments. PMID:23936016

[Research progress on remote sensing of ecological and environmental changes in the Three Gorges Reservoir area, China].

PubMed

Teng, Ming-jun; Zeng, Li-xiong; Xiao, Wen-fa; Zhou, Zhi-xiang; Huang, Zhi-lin; Wang, Peng-cheng; Dian, Yuan-yong

2014-12-01

The Three Gorges Reservoir area (TGR area) , one of the most sensitive ecological zones in China, has dramatically changes in ecosystem configurations and services driven by the Three Gorges Engineering Project and its related human activities. Thus, understanding the dynamics of ecosystem configurations, ecological processes and ecosystem services is an attractive and critical issue to promote regional ecological security of the TGR area. The remote sensing of environment is a promising approach to the target and is thus increasingly applied to and ecosystem dynamics of the TGR area on mid- and macro-scales. However, current researches often showed controversial results in ecological and environmental changes in the TGR area due to the differences in remote sensing data, scale, and land-use/cover classification. Due to the complexity of ecological configurations and human activities, challenges still exist in the remote-sensing based research of ecological and environmental changes in the TGR area. The purpose of this review was to summarize the research advances in remote sensing of ecological and environmental changes in the TGR area. The status, challenges and trends of ecological and environmental remote-sensing in the TGR area were further discussed and concluded in the aspect of land-use/land-cover, vegetation dynamics, soil and water security, ecosystem services, ecosystem health and its management. The further researches on the remote sensing of ecological and environmental changes were proposed to improve the ecosystem management of the TGR area.

The U.S. Geological Survey Land Remote Sensing Program

USGS Publications Warehouse

,

2007-01-01

The fundamental goals of the U.S. Geological Survey's Land Remote Sens-ing (LRS) Program are to provide the Federal Government and the public with a primary source of remotely sensed data and applications and to be a leader in defining the future of land remote sensing, nationally and internationally. Remotely sensed data provide information that enhance the understand-ing of ecosystems and the capabilities for predicting ecosystem change. The data promote an understanding of the role of the environment and wildlife in human health issues, the requirements for disaster response, the effects of climate variability, and the availability of energy and mineral resources. Also, as land satellite systems acquire global coverage, the program coordinates a network of international receiving stations and users of the data. It is the responsibility of the program to assure that data from land imaging satellites, airborne photography, radar, and other technologies are available to the national and global science communities.

Cover estimations using object-based image analysis rule sets developed across multiple scales in pinyon-juniper woodlands

USDA-ARS?s Scientific Manuscript database

Numerous studies have been conducted that evaluate the utility of remote sensing for monitoring and assessing vegetation and ground cover to support land management decisions and complement ground-measurements. However, few land cover comparisons have been made using high-resolution imagery and obj...

Land use classification in Bolivia

NASA Technical Reports Server (NTRS)

Brockmann, C. E.; Brooner, W. G.

1975-01-01

The Bolivian LANDSAT Program is an integrated, multidisciplinary project designed to provide thematic analysis of LANDSAT, Skylab, and other remotely sensed data for natural resource management and development in Bolivia, is discussed. Among the first requirements in the program is the development of a legend, and appropriate methodologies, for the analysis and classification of present land use based on landscape cover. The land use legend for Bolivia consists of approximately 80 categories in a hierarchical organization which may be collapsed for generalization, or expanded for greater detail. The categories, and their definitions, provide for both a graphic and textual description of the complex and diverse landscapes found in Bolivia, and are designed for analysis from LANDSAT and other remotely sensed data at scales of 1:1,000,000 and 1:250,000. Procedures and example products developed are described and illustrated, for the systematic analysis and mapping of present land use for all of Bolivia.

Real-Time Hazard Detection and Avoidance Demonstration for a Planetary Lander

NASA Technical Reports Server (NTRS)

Epp, Chirold D.; Robertson, Edward A.; Carson, John M., III

2014-01-01

The Autonomous Landing Hazard Avoidance Technology (ALHAT) Project is chartered to develop and mature to a Technology Readiness Level (TRL) of six an autonomous system combining guidance, navigation and control with terrain sensing and recognition functions for crewed, cargo, and robotic planetary landing vehicles. In addition to precision landing close to a pre-mission defined landing location, the ALHAT System must be capable of autonomously identifying and avoiding surface hazards in real-time to enable a safe landing under any lighting conditions. This paper provides an overview of the recent results of the ALHAT closed loop hazard detection and avoidance flight demonstrations on the Morpheus Vertical Testbed (VTB) at the Kennedy Space Center, including results and lessons learned. This effort is also described in the context of a technology path in support of future crewed and robotic planetary exploration missions based upon the core sensing functions of the ALHAT system: Terrain Relative Navigation (TRN), Hazard Detection and Avoidance (HDA), and Hazard Relative Navigation (HRN).

Integrated Use of Remote Sensing, GIS and GPS Technology for Monitoring the Environmental Problem of Shyamnagar

NASA Astrophysics Data System (ADS)

Akbar, M. S.; Sarker, M. H.; Sattar, M. A.; Sarwar, G. M.; Rahman, S. M. M.; Rahman, M. M.; Khan, Z. U.

2017-05-01

Cultivation of shrimp mostly in unplanned way has been considered as one of the major environmental disasters of Shamnagar. Villagers surrounding the rivers are mainly involved with fish (shrimp) cultivation. So, fertile agriculture land has been converted to shrimp cultivation. Conversion of agriculture land to other usage is a common but acute problem for land resources of the country like Bangladesh. Conventional methods for collecting this information are relatively costly and time consuming. Contrarily, Remote Sensing satellite observation with its unique capability to provide cost-effective support in compiling the latest information about the natural resource. Remote sensing, in conjunction with GIS, has been widely applied and been recognized as a powerful and effective tool in detecting land use and land cover changes. RapidEye, Landsat8 images were used to identify land use and land cover of the area during the period 2008 and 2015. Google images were used to identify the micro-level land use features of the same period. Multi-spectral classifications using unsupervised and supervised classification were done and results have been compared based on the field investigation. The study reveals that during the period 2008 to 2015 agricultural practice has been reduced from 35 % to 21 % and shrimp cultivation area increased from 38 % to 50 %. Due to the impact of high salinity and salt water intrusion caused by natural disaster, agricultural activities is reduced and farmers have been converted to other practices, as a result shrimp farming is gaining popularity in the area.

Land cover change of watersheds in Southern Guam from 1973 to 2001.

PubMed

Wen, Yuming; Khosrowpanah, Shahram; Heitz, Leroy

2011-08-01

Land cover change can be caused by human-induced activities and natural forces. Land cover change in watershed level has been a main concern for a long time in the world since watersheds play an important role in our life and environment. This paper is focused on how to apply Landsat Multi-Spectral Scanner (MSS) satellite image of 1973 and Landsat Thematic Mapper (TM) satellite image of 2001 to determine the land cover changes of coastal watersheds from 1973 to 2001. GIS and remote sensing are integrated to derive land cover information from Landsat satellite images of 1973 and 2001. The land cover classification is based on supervised classification method in remote sensing software ERDAS IMAGINE. Historical GIS data is used to replace the areas covered by clouds or shadows in the image of 1973 to improve classification accuracy. Then, temporal land cover is utilized to determine land cover change of coastal watersheds in southern Guam. The overall classification accuracies for Landsat MSS image of 1973 and Landsat TM image of 2001 are 82.74% and 90.42%, respectively. The overall classification of Landsat MSS image is particularly satisfactory considering its coarse spatial resolution and relatively bad data quality because of lots of clouds and shadows in the image. Watershed land cover change in southern Guam is affected greatly by anthropogenic activities. However, natural forces also affect land cover in space and time. Land cover information and change in watersheds can be applied for watershed management and planning, and environmental modeling and assessment. Based on spatio-temporal land cover information, the interaction behavior between human and environment may be evaluated. The findings in this research will be useful to similar research in other tropical islands.

Biodiversity and agriculture in dynamic landscapes: Integrating ground and remotely-sensed baseline surveys.

PubMed

Gillison, Andrew N; Asner, Gregory P; Fernandes, Erick C M; Mafalacusser, Jacinto; Banze, Aurélio; Izidine, Samira; da Fonseca, Ambrósio R; Pacate, Hermenegildo

2016-07-15

Sustainable biodiversity and land management require a cost-effective means of forecasting landscape response to environmental change. Conventional species-based, regional biodiversity assessments are rarely adequate for policy planning and decision making. We show how new ground and remotely-sensed survey methods can be coordinated to help elucidate and predict relationships between biodiversity, land use and soil properties along complex biophysical gradients that typify many similar landscapes worldwide. In the lower Zambezi valley, Mozambique we used environmental, gradient-directed transects (gradsects) to sample vascular plant species, plant functional types, vegetation structure, soil properties and land-use characteristics. Soil fertility indices were derived using novel multidimensional scaling of soil properties. To facilitate spatial analysis, we applied a probabilistic remote sensing approach, analyzing Landsat 7 satellite imagery to map photosynthetically active and inactive vegetation and bare soil along each gradsect. Despite the relatively low sample number, we found highly significant correlations between single and combined sets of specific plant, soil and remotely sensed variables that permitted testable spatial projections of biodiversity and soil fertility across the regional land-use mosaic. This integrative and rapid approach provides a low-cost, high-return and readily transferable methodology that permits the ready identification of testable biodiversity indicators for adaptive management of biodiversity and potential agricultural productivity. Copyright © 2016 Elsevier Ltd. All rights reserved.

Object-based land cover classification based on fusion of multifrequency SAR data and THAICHOTE optical imagery

NASA Astrophysics Data System (ADS)

Sukawattanavijit, Chanika; Srestasathiern, Panu

2017-10-01

Land Use and Land Cover (LULC) information are significant to observe and evaluate environmental change. LULC classification applying remotely sensed data is a technique popularly employed on a global and local dimension particularly, in urban areas which have diverse land cover types. These are essential components of the urban terrain and ecosystem. In the present, object-based image analysis (OBIA) is becoming widely popular for land cover classification using the high-resolution image. COSMO-SkyMed SAR data was fused with THAICHOTE (namely, THEOS: Thailand Earth Observation Satellite) optical data for land cover classification using object-based. This paper indicates a comparison between object-based and pixel-based approaches in image fusion. The per-pixel method, support vector machines (SVM) was implemented to the fused image based on Principal Component Analysis (PCA). For the objectbased classification was applied to the fused images to separate land cover classes by using nearest neighbor (NN) classifier. Finally, the accuracy assessment was employed by comparing with the classification of land cover mapping generated from fused image dataset and THAICHOTE image. The object-based data fused COSMO-SkyMed with THAICHOTE images demonstrated the best classification accuracies, well over 85%. As the results, an object-based data fusion provides higher land cover classification accuracy than per-pixel data fusion.

Remote sensing and GIS based information system for sustainable resources planning at Panchayat level

NASA Astrophysics Data System (ADS)

Velmurugan, A.; Bhatt, S.; Dadhwal, V. K.

2006-12-01

Spatial databases of natural resources are very much essential to ensure enhanced productivity by conserving soil and water and to maintain ecological integrity of any region. Integration of various thematic layers prepared from high resolution data and detailed field survey would be preferred for grass root level planning (Panchayat) aimed to realize the potential of production system on a sustained basis. In this study, a detailed spatial data base was created for part of Kasaragod dist., Kerala, India. Detailed soil survey was carried out using cadastral map and registered over high resolution satellite data (IRS LISS-IV) which helped to identify problems and potentials of the area. Nearly 600 ha of land were found to be at higher erosion risk category out of ten soil series identified in the study area. Remote sensing data was used to prepare land use/land cover map and coconut (53%) followed by mixed vegetation type (16%) were found to be dominant. Soil site suitability assessment for major crops of the area was carried out and crossed with present land use to get the mismatch in land use/land utilization type. Alternate land use plan was prepared considering the potentials and problems of various available resources. Decision Support System (DSS) along with user interface is developed to support decision and extract relevant information. As organic carbon is one of the most important indicators of soil fertility C stock in the present and proposed land use was also estimated to understand the environmental significance.

Improved ground-based remote-sensing systems help monitor plant response to climate and other changes

USGS Publications Warehouse

Dye, Dennis G.; Bogle, Rian

2016-05-26

Scientists at the U.S. Geological Survey are improving and developing new ground-based remote-sensing instruments and techniques to study how Earth’s vegetation responds to changing climates. Do seasonal grasslands and forests “green up” early (or late) and grow more (or less) during unusually warm years? How do changes in temperature and precipitation affect these patterns? Innovations in ground-based remote-sensing instrumentation can help us understand, assess, and mitigate the effects of climate change on vegetation and related land resources.

Cluster Method Analysis of K. S. C. Image

NASA Technical Reports Server (NTRS)

Rodriguez, Joe, Jr.; Desai, M.

1997-01-01

Information obtained from satellite-based systems has moved to the forefront as a method in the identification of many land cover types. Identification of different land features through remote sensing is an effective tool for regional and global assessment of geometric characteristics. Classification data acquired from remote sensing images have a wide variety of applications. In particular, analysis of remote sensing images have special applications in the classification of various types of vegetation. Results obtained from classification studies of a particular area or region serve towards a greater understanding of what parameters (ecological, temporal, etc.) affect the region being analyzed. In this paper, we make a distinction between both types of classification approaches although, focus is given to the unsupervised classification method using 1987 Thematic Mapped (TM) images of Kennedy Space Center.

Applying remote sensing and GIS techniques in solving rural county information needs

NASA Technical Reports Server (NTRS)

Johannsen, Chris J.; Fernandez, R. Norberto; Lozano-Garcia, D. Fabian

1992-01-01

The project designed was to acquaint county government officials and their clientele with remote sensing and GIS products that contain information about land conditions and land use. Other users determined through the course of this project were federal agencies working at the county level, agricultural businesses and others in need of spatial information. The specific project objectives were: (1) to investigate the feasibility of using remotely sensed data to identify and quantify specific land cover categories and conditions for purposes of tax assessment, cropland area measurements and land use evaluation; (2) to investigate the use of satellite remote sensing data as an aid in assessing soil management practices; and (3) to evaluate the use of remotely sensed data to assess soil resources and conditions which affect productivity.

An assessment of the effectiveness of a random forest classifier for land-cover classification

NASA Astrophysics Data System (ADS)

Rodriguez-Galiano, V. F.; Ghimire, B.; Rogan, J.; Chica-Olmo, M.; Rigol-Sanchez, J. P.

2012-01-01

Land cover monitoring using remotely sensed data requires robust classification methods which allow for the accurate mapping of complex land cover and land use categories. Random forest (RF) is a powerful machine learning classifier that is relatively unknown in land remote sensing and has not been evaluated thoroughly by the remote sensing community compared to more conventional pattern recognition techniques. Key advantages of RF include: their non-parametric nature; high classification accuracy; and capability to determine variable importance. However, the split rules for classification are unknown, therefore RF can be considered to be black box type classifier. RF provides an algorithm for estimating missing values; and flexibility to perform several types of data analysis, including regression, classification, survival analysis, and unsupervised learning. In this paper, the performance of the RF classifier for land cover classification of a complex area is explored. Evaluation was based on several criteria: mapping accuracy, sensitivity to data set size and noise. Landsat-5 Thematic Mapper data captured in European spring and summer were used with auxiliary variables derived from a digital terrain model to classify 14 different land categories in the south of Spain. Results show that the RF algorithm yields accurate land cover classifications, with 92% overall accuracy and a Kappa index of 0.92. RF is robust to training data reduction and noise because significant differences in kappa values were only observed for data reduction and noise addition values greater than 50 and 20%, respectively. Additionally, variables that RF identified as most important for classifying land cover coincided with expectations. A McNemar test indicates an overall better performance of the random forest model over a single decision tree at the 0.00001 significance level.

Remote sensing impact on corridor selection and placement

NASA Technical Reports Server (NTRS)

Thomson, F. J.; Sellman, A. N.

1975-01-01

Computer-aided corridor selection techniques, utilizing digitized data bases of socio-economic, census, and cadastral data, and developed for highway corridor routing are considered. Land resource data generated from various remote sensing data sources were successfully merged with the ancillary data files of a corridor selection model and prototype highway corridors were designed using the combined data set. Remote sensing derived information considered useful for highway corridor location, special considerations in geometric correction of remote sensing data to facilitate merging it with ancillary data files, and special interface requirements are briefly discussed.

Estimation of Surface Air Temperature from MODIS 1km Resolution Land Surface Temperature Over Northern China

NASA Technical Reports Server (NTRS)

Shen, Suhung; Leptoukh, Gregory G.; Gerasimov, Irina

2010-01-01

Surface air temperature is a critical variable to describe the energy and water cycle of the Earth-atmosphere system and is a key input element for hydrology and land surface models. It is a very important variable in agricultural applications and climate change studies. This is a preliminary study to examine statistical relationships between ground meteorological station measured surface daily maximum/minimum air temperature and satellite remotely sensed land surface temperature from MODIS over the dry and semiarid regions of northern China. Studies were conducted for both MODIS-Terra and MODIS-Aqua by using year 2009 data. Results indicate that the relationships between surface air temperature and remotely sensed land surface temperature are statistically significant. The relationships between the maximum air temperature and daytime land surface temperature depends significantly on land surface types and vegetation index, but the minimum air temperature and nighttime land surface temperature has little dependence on the surface conditions. Based on linear regression relationship between surface air temperature and MODIS land surface temperature, surface maximum and minimum air temperatures are estimated from 1km MODIS land surface temperature under clear sky conditions. The statistical errors (sigma) of the estimated daily maximum (minimum) air temperature is about 3.8 C(3.7 C).

[Built-up land change and its impact on ecological quality in a fast-growing economic zone: Jinjiang County, Fujian Province, China].

PubMed

Shi, Ting Ting; Xu, Han Qiu; Tang, Fei

2017-04-18

Since China's reform and opening-up, the rapid growth of China's economy has greatly accelerated the expansion of built-up land, which has affected regional ecological environment to a great extent. Taking Jinjiang County of Fujian Province, one of the fastest economic-developing counties in the coastal areas of southeastern China, as a case study area, this paper focused on analyzing the rapid built-up land expansion process of the county and its impact on county's ecological quality using remote sensing techniques. Based on two Landsat images of 1996 and 2015 of Jinjiang, the built-up land of the county was extracted using the index-based built-up index (IBI) and its change was analyzed. In the meantime, the ecological status of Jinjiang was evaluated with a recently-proposed remote sensing based ecological index (RSEI) and the relationship between the built-up land dynamics and the ecological status changes of the county was quantitatively examined. The results showed that during the period from 1996 to 2015, the area of built-up land of Jinjiang had a net increase of 68.54 km 2 , a growth of 45%, and the expansion intensity was 0.55. The expansion of the built-up lands has caused overall degradation of the county's ecological quality. The mean value of RSEI of the county had declined from 0.532 in 1996 to 0.460 in 2015, a drop of13.5%. The area proportion of high ecological-quality grades also significantly fell from 39% in 1996 to 21% in 2015. The built-up land expansion intensity was negatively correlated with the ecological quality change.

Wageningen UR Unmanned Aerial Remote Sensing Facility - Overview of activities

NASA Astrophysics Data System (ADS)

Bartholomeus, Harm; Keesstra, Saskia; Kooistra, Lammert; Suomalainen, Juha; Mucher, Sander; Kramer, Henk; Franke, Jappe

2016-04-01

To support environmental management there is an increasing need for timely, accurate and detailed information on our land. Unmanned Aerial Systems (UAS) are increasingly used to monitor agricultural crop development, habitat quality or urban heat efficiency. An important reason is that UAS technology is maturing quickly while the flexible capabilities of UAS fill a gap between satellite based and ground based geo-sensing systems. In 2012, different groups within Wageningen University and Research Centre have established an Unmanned Airborne Remote Sensing Facility. The objective of this facility is threefold: a) To develop innovation in the field of remote sensing science by providing a platform for dedicated and high-quality experiments; b) To support high quality UAS services by providing calibration facilities and disseminating processing procedures to the UAS user community; and c) To promote and test the use of UAS in a broad range of application fields like habitat monitoring, precision agriculture and land degradation assessment. The facility is hosted by the Laboratory of Geo-Information Science and Remote Sensing (GRS) and the Department of Soil Physics and Land Management (SLM) of Wageningen University together with the team Earth Informatics (EI) of Alterra. The added value of the Unmanned Aerial Remote Sensing Facility is that compared to for example satellite based remote sensing more dedicated science experiments can be prepared. This includes for example higher frequent observations in time (e.g., diurnal observations), observations of an object under different observation angles for characterization of BRDF and flexibility in use of camera's and sensors types. In this way, laboratory type of set ups can be tested in a field situation and effects of up-scaling can be tested. In the last years we developed and implemented different camera systems (e.g. a hyperspectral pushbroom system, and multispectral frame cameras) which we operated in projects all around the world, while new camera systems are being planned such as LiDAR and a full frame hyperspectral camera. In the presentation we will give an overview of our activities, ranging from erosion studies, decision support for precision agriculture, determining leaf biochemistry and canopy structure in tropical forests to the mapping of coastal zones.

NASA applications project in Miami County, Indiana

NASA Technical Reports Server (NTRS)

Johannsen, Chris J.; Fernandez, R. Norberto; Lozano-Garcia, D. Fabian

1990-01-01

This project was designed to acquaint county government officials and their clientele with remote sensing and geographic information systems (GIS) products that contain information about land conditions and land use. The specific project objectives are: (1) to investigate the feasibility of using remotely sensed data to identify and quantify specific land cover categories and conditions for purposes of tax assessment, cropland area measurements, and land use evaluation; (2) to evaluate the use of remotely sensed data to assess soil resources and conditions which affect productivity; (3) to investigate the use of satellite remote sensing data as an aid in assessing soil management practices; and (4) to evaluate the market potential of products derived from the above projects.

Future of Land Remote Sensing: What is Needed

NASA Technical Reports Server (NTRS)

Goward, Samuel N.

2007-01-01

A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do. A viewgraph presentation describing the future of land remote sensing and the new technologies needed for clear views of the Earth is shown. The contents include: 1) Viewing the Earth; 2) Multi-Imagery; 3) May Missions and Sensors; 4) What is Needed; 5) Things to Think About; 6) Global Land Remote Sensing in Landsat 7 Era; 7) Seasonality; 8) Cloud Contamination; 9) NRC Decadal Study; 10) Atmospheric Attenuation; 11) Geo-Registration; 12) Orthorectification Required; 13) Band Registration with OLI; and 14) Things to Do.

Using indigenous knowledge to link hyper-temporal land cover mapping with land use in the Venezuelan Amazon: "The Forest Pulse".

PubMed

Olivero, Jesús; Ferri, Francisco; Acevedo, Pelayo; Lobo, Jorge M; Fa, John E; Farfán, Miguel Á; Romero, David; Real, Raimundo

2016-12-01

Remote sensing and traditional ecological knowledge (TEK) can be combined to advance conservation of remote tropical regions, e.g. Amazonia, where intensive in situ surveys are often not possible. Integrating TEK into monitoring and management of these areas allows for community participation, as well as for offering novel insights into sustainable resource use. In this study, we developed a 250 m resolution land-cover map of the Western Guyana Shield (Venezuela) based on remote sensing, and used TEK to validate its relevance for indigenous livelihoods and land uses. We first employed a hyper-temporal remotely sensed vegetation index to derive a land classification system. During a 1 300 km, eight day fluvial expedition in roadless areas in the Amazonas State (Venezuela), we visited six indigenous communities who provided geo-referenced data on hunting, fishing and farming activities. We overlaid these TEK data onto the land classification map, to link land classes with indigenous use. We characterized land classes using patterns of greenness temporal change and topo-hydrological information, and proposed 12 land-cover types, grouped into five main landscapes: 1) water bodies; 2) open lands/forest edges; 3) evergreen forests; 4) submontane semideciduous forests, and 5) cloud forests. Each land cover class was identified with a pulsating profile describing temporal changes in greenness, hence we labelled our map as "The Forest Pulse". These greenness profiles showed a slightly increasing trend, for the period 2000 to 2009, in the land classes representing grassland and scrubland, and a slightly decreasing trend in the classes representing forests. This finding is consistent with a gain in carbon in grassland as a consequence of climate warming, and also with some loss of vegetation in the forests. Thus, our classification shows potential to assess future effects of climate change on landscape. Several classes were significantly connected with agriculture, fishing, overall hunting, and more specifically the hunting of primates, Mazama americana, Dasyprocta fuliginosa, and Tayassu pecari. Our results showed that TEK-based approaches can serve as a basis for validating the livelihood relevance of landscapes in high-value conservation areas, which can form the basis for furthering the management of natural resources in these regions.

A methodology for small scale rural land use mapping in semi-arid developing countries using orbital imagery. Part 6: A low-cost method for land use mapping using simple visual techniques of interpretation. [Spain

NASA Technical Reports Server (NTRS)

Vangenderen, J. L. (Principal Investigator); Lock, B. F.

1976-01-01

The author has identified the following significant results. It was found that color composite transparencies and monocular magnification provided the best base for land use interpretation. New methods for determining optimum sample sizes and analyzing interpretation accuracy levels were developed. All stages of the methodology were assessed, in the operational sense, during the production of a 1:250,000 rural land use map of Murcia Province, Southeast Spain.

Assessing the effects of land use spatial structure on urban heat islands using HJ-1B remote sensing imagery in Wuhan, China

NASA Astrophysics Data System (ADS)

Wu, Hao; Ye, Lu-Ping; Shi, Wen-Zhong; Clarke, Keith C.

2014-10-01

Urban heat islands (UHIs) have attracted attention around the world because they profoundly affect biological diversity and human life. Assessing the effects of the spatial structure of land use on UHIs is essential to better understanding and improving the ecological consequences of urbanization. This paper presents the radius fractal dimension to quantify the spatial variation of different land use types around the hot centers. By integrating remote sensing images from the newly launched HJ-1B satellite system, vegetation indexes, landscape metrics and fractal dimension, the effects of land use patterns on the urban thermal environment in Wuhan were comprehensively explored. The vegetation indexes and landscape metrics of the HJ-1B and other remote sensing satellites were compared and analyzed to validate the performance of the HJ-1B. The results have showed that land surface temperature (LST) is negatively related to only positive normalized difference vegetation index (NDVI) but to Fv across the entire range of values, which indicates that fractional vegetation (Fv) is an appropriate predictor of LST more than NDVI in forest areas. Furthermore, the mean LST is highly correlated with four class-based metrics and three landscape-based metrics, which suggests that the landscape composition and the spatial configuration both influence UHIs. All of them demonstrate that the HJ-1B satellite has a comparable capacity for UHI studies as other commonly used remote sensing satellites. The results of the fractal analysis show that the density of built-up areas sharply decreases from the hot centers to the edges of these areas, while the densities of water, forest and cropland increase. These relationships reveal that water, like forest and cropland, has a significant effect in mitigating UHIs in Wuhan due to its large spatial extent and homogeneous spatial distribution. These findings not only confirm the applicability and effectiveness of the HJ-1B satellite system for studying UHIs but also reveal the impacts of the spatial structure of land use on UHIs, which is helpful for improving the planning and management of the urban environment.

Annotated bibliography of remote sensing methods for monitoring desertification

USGS Publications Warehouse

Walker, A.S.; Robinove, Charles J.

1981-01-01

Remote sensing techniques are valuable for locating, assessing, and monitoring desertification. Remotely sensed data provide a permanent record of the condition of the land in a format that allows changes in land features and condition to be measured. The annotated bibliography of 118 items discusses remote sensing methods that may be applied to desertification studies.

Planning applications of remote sensing in Arizona

NASA Technical Reports Server (NTRS)

Clark, R. B.; Mouat, D. A.

1976-01-01

Planners in Arizona have been experiencing the inevitable problems which occur when large areas of rural and remote lands are converted to urban-recreational uses over a relatively short period of time. Among the planning problems in the state are unplanned and illegal subdivisions, surburban sprawl, surface hydrologic problems related to ephemeral stream overflow, rapidly changing land use patterns, large size of administrative units, and lack of land use inventory data upon which to base planning decisions.

Evolving land cover classification algorithms for multispectral and multitemporal imagery

NASA Astrophysics Data System (ADS)

Brumby, Steven P.; Theiler, James P.; Bloch, Jeffrey J.; Harvey, Neal R.; Perkins, Simon J.; Szymanski, John J.; Young, Aaron C.

2002-01-01

The Cerro Grande/Los Alamos forest fire devastated over 43,000 acres (17,500 ha) of forested land, and destroyed over 200 structures in the town of Los Alamos and the adjoining Los Alamos National Laboratory. The need to measure the continuing impact of the fire on the local environment has led to the application of a number of remote sensing technologies. During and after the fire, remote-sensing data was acquired from a variety of aircraft- and satellite-based sensors, including Landsat 7 Enhanced Thematic Mapper (ETM+). We now report on the application of a machine learning technique to the automated classification of land cover using multi-spectral and multi-temporal imagery. We apply a hybrid genetic programming/supervised classification technique to evolve automatic feature extraction algorithms. We use a software package we have developed at Los Alamos National Laboratory, called GENIE, to carry out this evolution. We use multispectral imagery from the Landsat 7 ETM+ instrument from before, during, and after the wildfire. Using an existing land cover classification based on a 1992 Landsat 5 TM scene for our training data, we evolve algorithms that distinguish a range of land cover categories, and an algorithm to mask out clouds and cloud shadows. We report preliminary results of combining individual classification results using a K-means clustering approach. The details of our evolved classification are compared to the manually produced land-cover classification.

The study method of estimation tidal flat with remote sensing waterlines

NASA Astrophysics Data System (ADS)

Gao, Zhiqiang; Liu, Xiangyang; Zhang, Yuanyuan; Liu, Chaoshun; Sun, Zhibin

2016-09-01

A tidal flat, the important potential land resource, is the sensitive area of intersection between the sea and the land. With Chinese HJ-1A/B remote sensing images of 2014 as data sources, based on the definition of a tidal flat, using DSAS software and Jenks Natural Breaks classification method synthetically, a more reasonable and accurate method of extracting tidal flat was imposed. In addition, the Bohai Rim was taken as an example to carry out investigation on the current situation of tidal flat. This paper can provide basic date and scientific evidence for rational utilization and sustainable development of tidal flat.

Human and remote sensing data to investigate the frontiers of urbanization in the south of Mexico City.

PubMed

Rodriguez Lopez, Juan Miguel; Heider, Katharina; Scheffran, Jürgen

2017-04-01

The data presented here were originally collected for the article "Frontiers of Urbanization: Identifying and Explaining Urbanization Hot Spots in the South of Mexico City Using Human and Remote Sensing" (Rodriguez et al. 2017) [4]. They were divided into three databases (remote sensing, human sensing, and census information), using a multi-method approach with the goal of analyzing the impact of urbanization on protected areas in southern Mexico City. The remote sensing database was prepared as a result of a semi-automatic classification, dividing the land cover data into urban and non-urban classes. The second data set details an alternative view of the phenomena of urbanization by concentrating on illegal settlements in the conservation zone. It was based on voluntary complaints about environmental and land use offences filed at the Procuraduria Ambiental y del Ordenamiento Territorial del Distrito Federal (PAOT), which is a governmental entity responsible for reviewing and processing grievances on five basic topics: illegal land use, deterioration of green areas, waste, noise/vibrations, and animals. Anyone can file a PAOT complaint by phone, electronically, or in person. The complaint ends with a resolution, act of conciliation, or recommendation for action by other actors, such as the police or health office. The third data about unemployment was extracted from Mexico׳s National Census 2010 database available via public access.

Estimation of urban surface water at subpixel level from neighborhood pixels using multispectral remote sensing image (Conference Presentation)

NASA Astrophysics Data System (ADS)

Xie, Huan; Luo, Xin; Xu, Xiong; Wang, Chen; Pan, Haiyan; Tong, Xiaohua; Liu, Shijie

2016-10-01

Water body is a fundamental element in urban ecosystems and water mapping is critical for urban and landscape planning and management. As remote sensing has increasingly been used for water mapping in rural areas, this spatially explicit approach applied in urban area is also a challenging work due to the water bodies mainly distributed in a small size and the spectral confusion widely exists between water and complex features in the urban environment. Water index is the most common method for water extraction at pixel level, and spectral mixture analysis (SMA) has been widely employed in analyzing urban environment at subpixel level recently. In this paper, we introduce an automatic subpixel water mapping method in urban areas using multispectral remote sensing data. The objectives of this research consist of: (1) developing an automatic land-water mixed pixels extraction technique by water index; (2) deriving the most representative endmembers of water and land by utilizing neighboring water pixels and adaptive iterative optimal neighboring land pixel for respectively; (3) applying a linear unmixing model for subpixel water fraction estimation. Specifically, to automatically extract land-water pixels, the locally weighted scatter plot smoothing is firstly used to the original histogram curve of WI image . And then the Ostu threshold is derived as the start point to select land-water pixels based on histogram of the WI image with the land threshold and water threshold determination through the slopes of histogram curve . Based on the previous process at pixel level, the image is divided into three parts: water pixels, land pixels, and mixed land-water pixels. Then the spectral mixture analysis (SMA) is applied to land-water mixed pixels for water fraction estimation at subpixel level. With the assumption that the endmember signature of a target pixel should be more similar to adjacent pixels due to spatial dependence, the endmember of water and land are determined by neighboring pure land or pure water pixels within a distance. To obtaining the most representative endmembers in SMA, we designed an adaptive iterative endmember selection method based on the spatial similarity of adjacent pixels. According to the spectral similarity in a spatial adjacent region, the spectrum of land endmember is determined by selecting the most representative land pixel in a local window, and the spectrum of water endmember is determined by calculating an average of the water pixels in the local window. The proposed hierarchical processing method based on WI and SMA (WISMA) is applied to urban areas for reliability evaluation using the Landsat-8 Operational Land Imager (OLI) images. For comparison, four methods at pixel level and subpixel level were chosen respectively. Results indicate that the water maps generated by the proposed method correspond as closely with the truth water maps with subpixel precision. And the results showed that the WISMA achieved the best performance in water mapping with comprehensive analysis of different accuracy evaluation indexes (RMSE and SE).

Land Cover Changes between 1974 and 2008 in Ulaanbaatar, Mongolia

NASA Astrophysics Data System (ADS)

Bagan, H.; Kinoshita, T.; Yamagata, Y.

2009-12-01

In the past 35 years, a combination of human actions and natural causes has led to a significant decline in land quality in Ulaanbaatar, the capital city of Mongolia. Human causes include changes in conventional livestock husbandry, overgrazing, and exploitation for traditional uses. Natural causes include a harsh, dry climate, short growing seasons, and thin soils. Since 1995, many herders left the countryside to come to the city in search of new opportunities, the Ger areas (wooden houses and Ger) have expended, resulting in urban sprawl. Since urbanization usually advance in an uncontrolled or unorganized way in Mongolia, they have destructive effects on the environment, particularly on basic ecosystems, wildlife habitat, and pollution of natural resources (e.g. air and water). Land use and land cover changes occurred in the region are investigated using satellite images acquired in 1974 (Landsat MSS), 1990 (Landsat TM), 2000 (ASTER), 2006 (IKONOS), and 2008 (ALOS). Pre-processing of all data included orthorectification and registration to precisely geolocated imagery. In the detection of changes, classification approaches were employed using a self-organizing map (SOM) neural network classifier (Fig. 1a) and new developed subspace classification method (Fig. 1b). From the time-series classified remote sensing images, we extract the land cover and land cover temporal changes from 1974 to 2008. The results show some important findings regarding the size and nature of the change occurred in the study area. A significant amount of steppe and forest lands have been destroyed or replaced by residential areas; as a result, the total area of urban region doubled in the 35-year period with a higher urbanization rate between 2000 and 2008. Key words: Environment; Land Cover; Urban; Change detection; Classification. References Chinbat,B., Bayantur,M., & Amarsaikhan.D. (2006). Investigation of the internal structure changes of ulaanbaatar city using RS and GIS. ISPRS Commission VII Mid-term Symposium “Remote Sensing: From Pixels to Processes”, Enschede, the Netherlands, 8-11 May 2006. 511-516. Bagan, H., Wang, Q., Watanabe, M., Karneyarna, S., & Bao, Y. (2008). Land-cover classification using ASTER multi-band combinations based on wavelet fusion and SOM neural network. Photogrammetric Engineering and Remote Sensing, 74, 333-342. Bagan, H., Yasuoka, Y., Endo, T., Wang, X., & Feng, Z. (2008). Classification of airborne hyperspectral data based on the average learning subspace method. IEEE Geoscience and Remote Sensing Letters, 5, 368-372. Figure 1. The self-organizing map (SOM) neural network classifier (a) and the subspace classification method (b).

Monitoring Land Surface Soil Moisture from Space with in-Situ Sensors Validation: The Huntsville Example

NASA Technical Reports Server (NTRS)

Wu, Steve Shih-Tseng

1997-01-01

Based on recent advances in microwave remote sensing of soil moisture and in pursuit of research interests in areas of hydrology, soil climatology, and remote sensing, the Center for Hydrology, Soil Climatology, and Remote Sensing (HSCARS) conducted the Huntsville '96 field experiment in Huntsville, Alabama from July 1-14, 1996. We, researchers at the Global Hydrology and Climate Center's MSFC/ES41, are interested in using ground-based microwave sensors, to simulate land surface brightness signatures of those spaceborne sensors that were in operation or to be launched in the near future. The analyses of data collected by the Advanced Microwave Precipitation Radiometer (AMPR) and the C-band radiometer, which together contained five frequencies (6.925,10.7,19.35, 37.1, and 85.5 GHz), and with concurrent in-situ collection of surface cover conditions (surface temperature, surface roughness, vegetation, and surface topology) and soil moisture content, would result in a better understanding of the data acquired over land surfaces by the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR), because these spaceborne sensors contained these five frequencies. This paper described the approach taken and the specific objective to be accomplished in the Huntsville '97 field experiment.

A cloud mask methodology for high resolution remote sensing data combining information from high and medium resolution optical sensors

NASA Astrophysics Data System (ADS)

Sedano, Fernando; Kempeneers, Pieter; Strobl, Peter; Kucera, Jan; Vogt, Peter; Seebach, Lucia; San-Miguel-Ayanz, Jesús

2011-09-01

This study presents a novel cloud masking approach for high resolution remote sensing images in the context of land cover mapping. As an advantage to traditional methods, the approach does not rely on thermal bands and it is applicable to images from most high resolution earth observation remote sensing sensors. The methodology couples pixel-based seed identification and object-based region growing. The seed identification stage relies on pixel value comparison between high resolution images and cloud free composites at lower spatial resolution from almost simultaneously acquired dates. The methodology was tested taking SPOT4-HRVIR, SPOT5-HRG and IRS-LISS III as high resolution images and cloud free MODIS composites as reference images. The selected scenes included a wide range of cloud types and surface features. The resulting cloud masks were evaluated through visual comparison. They were also compared with ad-hoc independently generated cloud masks and with the automatic cloud cover assessment algorithm (ACCA). In general the results showed an agreement in detected clouds higher than 95% for clouds larger than 50 ha. The approach produced consistent results identifying and mapping clouds of different type and size over various land surfaces including natural vegetation, agriculture land, built-up areas, water bodies and snow.

Operational monitoring of land-cover change using multitemporal remote sensing data

NASA Astrophysics Data System (ADS)

Rogan, John

2005-11-01

Land-cover change, manifested as either land-cover modification and/or conversion, can occur at all spatial scales, and changes at local scales can have profound, cumulative impacts at broader scales. The implication of operational land-cover monitoring is that researchers have access to a continuous stream of remote sensing data, with the long term goal of providing for consistent and repetitive mapping. Effective large area monitoring of land-cover (i.e., >1000 km2) can only be accomplished by using remotely sensed images as an indirect data source in land-cover change mapping and as a source for land-cover change model projections. Large area monitoring programs face several challenges: (1) choice of appropriate classification scheme/map legend over large, topographically and phenologically diverse areas; (2) issues concerning data consistency and map accuracy (i.e., calibration and validation); (3) very large data volumes; (4) time consuming data processing and interpretation. Therefore, this dissertation research broadly addresses these challenges in the context of examining state-of-the-art image pre-processing, spectral enhancement, classification, and accuracy assessment techniques to assist the California Land-cover Mapping and Monitoring Program (LCMMP). The results of this dissertation revealed that spatially varying haze can be effectively corrected from Landsat data for the purposes of change detection. The Multitemporal Spectral Mixture Analysis (MSMA) spectral enhancement technique produced more accurate land-cover maps than those derived from the Multitemporal Kauth Thomas (MKT) transformation in northern and southern California study areas. A comparison of machine learning classifiers showed that Fuzzy ARTMAP outperformed two classification tree algorithms, based on map accuracy and algorithm robustness. Variation in spatial data error (positional and thematic) was explored in relation to environmental variables using geostatistical interpolation techniques. Finally, the land-cover modification maps generated for three time intervals (1985--1990--1996--2000), with nine change-classes revealed important variations in land-cover gain and loss between northern and southern California study areas.

Analysis of economic values of land use and land cover changes in crisis territories by satellite data: models of socio-economy and population dynamics in war

NASA Astrophysics Data System (ADS)

Kostyuchenko, Yuriy V.; Yuschenko, Maxim; Movchan, Dmytro; Kopachevsky, Ivan

2017-10-01

Problem of remote sensing data harnessing for decision making in conflict territories is considered. Approach for analysis of socio-economic and demographic parameters with a limited set of data and deep uncertainty is described. Number of interlinked techniques to estimate a population and economy in crisis territories are proposed. Stochastic method to assessment of population dynamics using multi-source data using remote sensing data is proposed. Adaptive Markov's chain based method to study of land-use changes using satellite data is proposed. Proposed approach is applied to analysis of socio-economic situation in Donbas (East Ukraine) territory of conflict in 2014-2015. Land-use and landcover patterns for different periods were analyzed using the Landsat and MODIS data . The land-use classification scheme includes the following categories: (1) urban or built-up land, (2) barren land, (3) cropland, (4) horticulture farms, (5) livestock farms, (6) forest, and (7) water. It was demonstrated, that during the period 2014-2015 was not detected drastic changes in land-use structure of study area. Heterogeneously distributed decreasing of horticulture farms (4-6%), livestock farms (5-6%), croplands (3-4%), and increasing of barren land (6-7%) have been observed. Way to analyze land-cover productivity variations using satellite data is proposed. Algorithm is based on analysis of time-series of NDVI and NDWI distributions. Drastic changes of crop area and its productivity were detected. Set of indirect indicators, such as night light intensity, is also considered. Using the approach proposed, using the data utilized, the local and regional GDP, local population, and its dynamics are estimated.

Monitoring and Evaluation of Cultivated Land Irrigation Guarantee Capability with Remote Sensing

NASA Astrophysics Data System (ADS)

Zhang, C., Sr.; Huang, J.; Li, L.; Wang, H.; Zhu, D.

2015-12-01

Abstract: Cultivated Land Quality Grade monitoring and evaluation is an important way to improve the land production capability and ensure the country food safety. Irrigation guarantee capability is one of important aspects in the cultivated land quality monitoring and evaluation. In the current cultivated land quality monitoring processing based on field survey, the irrigation rate need much human resources investment in long investigation process. This study choses Beijing-Tianjin-Hebei as study region, taking the 1 km × 1 km grid size of cultivated land unit with a winter wheat-summer maize double cropping system as study object. A new irrigation capacity evaluation index based on the ratio of the annual irrigation requirement retrieved from MODIS data and the actual quantity of irrigation was proposed. With the years of monitoring results the irrigation guarantee capability of study area was evaluated comprehensively. The change trend of the irrigation guarantee capability index (IGCI) with the agricultural drought disaster area in rural statistical yearbook of Beijing-Tianjin-Hebei area was generally consistent. The average of IGCI value, the probability of irrigation-guaranteed year and the weighted average which controlled by the irrigation demand index were used and compared in this paper. The experiment results indicate that the classification result from the present method was close to that from irrigation probability in the gradation on agriculture land quality in 2012, with overlap of 73% similar units. The method of monitoring and evaluation of cultivated land IGCI proposed in this paper has a potential in cultivated land quality level monitoring and evaluation in China. Key words: remote sensing, evapotranspiration, MODIS cultivated land quality, irrigation guarantee capability Authors: Chao Zhang, Jianxi Huang, Li Li, Hongshuo Wang, Dehai Zhu China Agricultural University zhangchaobj@gmail.com

Completing the Picture: Importance of Considering Participatory Mapping for REDD+ Measurement, Reporting and Verification (MRV)

PubMed Central

Rafanoharana, Serge; Boissière, Manuel; Wijaya, Arief; Wardhana, Wahyu

2016-01-01

Remote sensing has been widely used for mapping land cover and is considered key to monitoring changes in forest areas in the REDD+ Measurement, Reporting and Verification (MRV) system. But Remote Sensing as a desk study cannot capture the whole picture; it also requires ground checking. Therefore, complementing remote sensing analysis using participatory mapping can help provide information for an initial forest cover assessment, gain better understanding of how local land use might affect changes, and provide a way to engage local communities in REDD+. Our study looked at the potential of participatory mapping in providing complementary information for remotely sensed maps. The research sites were located in different ecological and socio-economic contexts in the provinces of Papua, West Kalimantan and Central Java, Indonesia. Twenty-one maps of land cover and land use were drawn with local community participation during focus group discussions in seven villages. These maps, covering a total of 270,000ha, were used to add information to maps developed using remote sensing, adding 39 land covers to the eight from our initial desk assessment. They also provided additional information on drivers of land use and land cover change, resource areas, territory claims and land status, which we were able to correlate to understand changes in forest cover. Incorporating participatory mapping in the REDD+ MRV protocol would help with initial remotely sensed land classifications, stratify an area for ground checks and measurement plots, and add other valuable social data not visible at the RS scale. Ultimately, it would provide a forum for local communities to discuss REDD+ activities and develop a better understanding of REDD+. PMID:27977685

Analysis of Land Use Change and Urbanization in the Kucukcekmece Water Basin (Istanbul, Turkey) with Temporal Satellite Data using Remote Sensing and GIS.

PubMed

Coskun, H Gonca; Alganci, Ugur; Usta, Gokce

2008-11-13

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds.

Analysis of Land Use Change and Urbanization in the Kucukcekmece Water Basin (Istanbul, Turkey) with Temporal Satellite Data using Remote Sensing and GIS

PubMed Central

Coskun, H. Gonca; Alganci, Ugur; Usta, Gokce

2008-01-01

Accurate and timely information about land use and land cover (LULC) and its changes in urban areas are crucial for urban land management decision-making, ecosystem monitoring and urban planning. Also, monitoring and representation of urban sprawl and its effects on the LULC patterns and hydrological processes of an urbanized watershed is an essential part of water resource planning and management. This paper presents an image analysis study using multi temporal digital satellite imagery of LULC and changes in the Kucukcekmece Watershed (Metropolitan Istanbul, Turkey) from 1992 to 2006. The Kucukcekmece Basin includes portions of the Kucukcekmece District within the municipality of Istanbul so it faces a dramatic urbanization. An urban monitoring analysis approach was first used to implement a land cover classification. A change detection method controlled with ground truth information was then used to determine changes in land cover. During the study period, the variability and magnitude of hydrological components based on land-use patterns were cumulatively influenced by urban sprawl in the watershed. The proposed approach, which uses a combination of Remote Sensing (RS) and Geographical Information System (GIS) techniques, is an effective tool that enhances land-use monitoring, planning, and management of urbanized watersheds. PMID:27873924

Remote-sensing applications as utilized in Florida's coastal zone management program

NASA Technical Reports Server (NTRS)

Worley, D. R.

1975-01-01

Land use maps were developed from photomaps obtained by remote sensing in order to develop a comprehensive state plan for the protection, development, and zoning of coastal regions. Only photographic remote sensors have been used in support of the coastal council's planning/management methodology. Standard photointerpretation and cartographic application procedures for map compilation were used in preparing base maps.

Predicting relative species composition within mixed conifer forest pixels using zero‐inflated models and Landsat imagery

Treesearch

Shannon L. Savage; Rick L. Lawrence; John R. Squires

2015-01-01

Ecological and land management applications would often benefit from maps of relative canopy cover of each species present within a pixel, instead of traditional remote-sensing based maps of either dominant species or percent canopy cover without regard to species composition. Widely used statistical models for remote sensing, such as randomForest (RF),...

Utility of remotely sensed imagery for assessing the impact of salvage logging after forest fires

Treesearch

Sarah A. Lewis; Peter R. Robichaud; Andrew T. Hudak; Brian Austin; Robert J. Liebermann

2012-01-01

Remotely sensed imagery provides a useful tool for land managers to assess the extent and severity of post-wildfire salvage logging disturbance. This investigation uses high resolution QuickBird and National Agricultural Imagery Program (NAIP) imagery to map soil exposure after ground-based salvage operations. Three wildfires with varying post-fire salvage activities...

[A review of atmospheric aerosol research by using polarization remote sensing].

PubMed

Guo, Hong; Gu, Xing-Fa; Xie, Dong-Hai; Yu, Tao; Meng, Qing-Yan

2014-07-01

In the present paper, aerosol research by using polarization remote sensing in last two decades (1993-2013) was reviewed, including aerosol researches based on POLDER/PARASOL, APS(Aerosol Polarimetry Sensor), Polarized Airborne camera and Ground-based measurements. We emphasize the following three aspects: (1) The retrieval algorithms developed for land and marine aerosol by using POLDER/PARASOL; The validation and application of POLDER/PARASOL AOD, and cross-comparison with AOD of other satellites, such as MODIS AOD. (2) The retrieval algorithms developed for land and marine aerosol by using MICROPOL and RSP/APS. We also introduce the new progress in aerosol research based on The Directional Polarimetric Camera (DPC), which was produced by Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (CAS). (3) The aerosol retrieval algorithms by using measurements from ground-based instruments, such as CE318-2 and CE318-DP. The retrieval results from spaceborne sensors, airborne camera and ground-based measurements include total AOD, fine-mode AOD, coarse-mode AOD, size distribution, particle shape, complex refractive indices, single scattering albedo, scattering phase function, polarization phase function and AOD above cloud. Finally, based on the research, the authors present the problems and prospects of atmospheric aerosol research by using polarization remote sensing, and provide a valuable reference for the future studies of atmospheric aerosol.

Thermal Infrared Remote Sensing for Analysis of Landscape Ecological Processes: Current Insights and Trends. Chapter 3

NASA Technical Reports Server (NTRS)

Quattrochi, Dale A.; Luvall, Jeffrey C.

2014-01-01

NASA or NOAA Earth-observing satellites are not the only space-based TIR platforms. The European Space Agency (ESA), the Chinese, and other countries have in orbit or plan to launch TIR remote sensing systems. Satellite remote sensing provides an excellent opportunity to study land-atmosphere energy exchanges at the regional scale. A predominant application of TIR data has been in inferring evaporation, evapotranspiration (ET), and soil moisture. In addition to using TIR data for ET and soil moisture analysis over vegetated surfaces, there is also a need for using these data for assessment of drought conditions. The concept of ecological thermodynamics provides a quantification of surface energy fluxes for landscape characterization in relation to the overall amount of energy input and output from specific land cover types.

Reduction of Topographic Effect for Curve Number Estimated from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Zhang, Wen-Yan; Lin, Chao-Yuan

2016-04-01

The Soil Conservation Service Curve Number (SCS-CN) method is commonly used in hydrology to estimate direct runoff volume. The CN is the empirical parameter which corresponding to land use/land cover, hydrologic soil group and antecedent soil moisture condition. In large watersheds with complex topography, satellite remote sensing is the appropriate approach to acquire the land use change information. However, the topographic effect have been usually found in the remotely sensed imageries and resulted in land use classification. This research selected summer and winter scenes of Landsat-5 TM during 2008 to classified land use in Chen-You-Lan Watershed, Taiwan. The b-correction, the empirical topographic correction method, was applied to Landsat-5 TM data. Land use were categorized using K-mean classification into 4 groups i.e. forest, grassland, agriculture and river. Accuracy assessment of image classification was performed with national land use map. The results showed that after topographic correction, the overall accuracy of classification was increased from 68.0% to 74.5%. The average CN estimated from remotely sensed imagery decreased from 48.69 to 45.35 where the average CN estimated from national LULC map was 44.11. Therefore, the topographic correction method was recommended to normalize the topographic effect from the satellite remote sensing data before estimating the CN.

Use of land surface remotely sensed satellite and airborne data for environmental exposure assessment in cancer research

USGS Publications Warehouse

Maxwell, S.K.; Meliker, J.R.; Goovaerts, P.

2010-01-01

In recent years, geographic information systems (GIS) have increasingly been used for reconstructing individual-level exposures to environmental contaminants in epidemiological research. Remotely sensed data can be useful in creating space-time models of environmental measures. The primary advantage of using remotely sensed data is that it allows for study at the local scale (e.g., residential level) without requiring expensive, time-consuming monitoring campaigns. The purpose of our study was to identify how land surface remotely sensed data are currently being used to study the relationship between cancer and environmental contaminants, focusing primarily on agricultural chemical exposure assessment applications. We present the results of a comprehensive literature review of epidemiological research where remotely sensed imagery or land cover maps derived from remotely sensed imagery were applied. We also discuss the strengths and limitations of the most commonly used imagery data (aerial photographs and Landsat satellite imagery) and land cover maps.

Towards a Remote Sensing Based Assessment of Land Susceptibility to Degradation: Examining Seasonal Variation in Land Use-Land Cover for Modelling Land Degradation in a Semi-Arid Context

NASA Astrophysics Data System (ADS)

Mashame, Gofamodimo; Akinyemi, Felicia

2016-06-01

Land degradation (LD) is among the major environmental and anthropogenic problems driven by land use-land cover (LULC) and climate change worldwide. For example, poor LULC practises such as deforestation, livestock overstocking, overgrazing and arable land use intensification on steep slopes disturbs the soil structure leaving the land susceptible to water erosion, a type of physical land degradation. Land degradation related problems exist in Sub-Saharan African countries such as Botswana which is semi-arid in nature. LULC and LD linkage information is still missing in many semi-arid regions worldwide.Mapping seasonal LULC is therefore very important in understanding LULC and LD linkages. This study assesses the impact of seasonal LULC variation on LD utilizing Remote Sensing (RS) techniques for Palapye region in Central District, Botswana. LULC classes for the dry and rainy seasons were classified using LANDSAT 8 images at Level I according to the Food and Agriculture Organization (FAO) International Organization of Standardization (ISO) code 19144. Level I consists of 10 LULC classes. The seasonal variations in LULC are further related to LD susceptibility in the semi-arid context. The results suggest that about 985 km² (22%) of the study area is susceptible to LD by water, major LULC types affected include: cropland, paved/rocky material, bare land, built-up area, mining area, and water body. Land degradation by water susceptibility due to seasonal land use-land cover variations is highest in the east of the study area where there is high cropland to bare land conversion.

Analyzing Land Use Changes in the Metropolitan Jilin City of Northeastern China Using Remote Sensing and GIS.

PubMed

Hu, Dan; Yang, Guodong; Wu, Qiong; Li, Hongqing; Liu, Xusheng; Niu, Xuefeng; Wang, Zhiheng; Wang, Qiong

2008-09-03

Remote sensing and GIS have been widely employed to study temporal and spatial urban land use changes in southern and southeastern China. However, few studies have been conducted in northeastern regions. This study analyzed land use change and spatial patterns of urban expansion in the metropolitan area of Jilin City, located on the extension of Changbai Mountain, based on aerial photos from 1989 and 2005 Spot images. The results indicated that urban land and transportation land increased dramatically (by 94.04% and 211.20%, respectively); isolated industrial and mining land decreased moderately (by 29.54%); rural residential land increased moderately (by 26.48%); dry land and paddy fields increased slightly (by 15.68% and 11.78%, respectively); forest and orchards decreased slightly (by 5.27% and 4.61%, respectively); grasslands and unused land decreased dramatically (by 99.12% and 86.04%, respectively). Sloped dry land (more than 4 degrees) was mainly distributed on the land below 10 degrees with an east, southeastern and south sunny direction aspect, and most sloped dry land transformed to forest was located on an east aspect lower than 12 degrees, while forest changed to dry land were mainly distributed on east and south aspects lower than 10 degrees. A spatial dependency analysis of land use change showed that the increased urban land was a logarithmic function of distance to the Songhua River. This study also provided some data with spatial details about the uneven land development in the upstream areas of Songhua River basin.

Remote sensing of effects of land-use practices on water quality. [environmental surveys using Landsat satellites

NASA Technical Reports Server (NTRS)

Graves, D. H.

1975-01-01

Research efforts are presented for the use of remote sensing in environmental surveys in Kentucky. Ground truth parameters were established that represent the vegetative cover of disturbed and undisturbed watersheds in the Cumberland Plateau of eastern Kentucky. Several water quality parameters were monitored of the watersheds utilized in the establishment of ground truth data. The capabilities of multistage-multispectral aerial photography and satellite imagery were evaluated in detecting various land use practices. The use of photographic signatures of known land use areas utilizing manually-operated spot densitometers was studied. The correlation of imagery signature data to water quality data was examined. Potential water quality predictions were developed from forested and nonforested watersheds based upon the above correlations. The cost effectiveness of predicting water quality values was evaluated using multistage and satellite imagery sampling techniques.

Satellite land remote sensing advancements for the eighties; Proceedings of the Eighth Pecora Symposium, Sioux Falls, SD, October 4-7, 1983

NASA Technical Reports Server (NTRS)

1984-01-01

Among the topics discussed are NASA's land remote sensing plans for the 1980s, the evolution of Landsat 4 and the performance of its sensors, the Landsat 4 thematic mapper image processing system radiometric and geometric characteristics, data quality, image data radiometric analysis and spectral/stratigraphic analysis, and thematic mapper agricultural, forest resource and geological applications. Also covered are geologic applications of side-looking airborne radar, digital image processing, the large format camera, the RADARSAT program, the SPOT 1 system's program status, distribution plans, and simulation program, Space Shuttle multispectral linear array studies of the optical and biological properties of terrestrial land cover, orbital surveys of solar-stimulated luminescence, the Space Shuttle imaging radar research facility, and Space Shuttle-based polar ice sounding altimetry.

Advances in U.S. Land Imaging Capabilities

NASA Astrophysics Data System (ADS)

Stryker, T. S.

2017-12-01

Advancements in Earth observations, cloud computing, and data science are improving everyday life. Information from land-imaging satellites, such as the U.S. Landsat system, helps us to better understand the changing landscapes where we live, work, and play. This understanding builds capacity for improved decision-making about our lands, waters, and resources, driving economic growth, protecting lives and property, and safeguarding the environment. The USGS is fostering the use of land remote sensing technology to meet local, national, and global challenges. A key dimension to meeting these challenges is the full, free, and open provision of land remote sensing observations for both public and private sector applications. To achieve maximum impact, these data must also be easily discoverable, accessible, and usable. The presenter will describe the USGS Land Remote Sensing Program's current capabilities and future plans to collect and deliver land remote sensing information for societal benefit. He will discuss these capabilities in the context of national plans and policies, domestic partnerships, and international collaboration. The presenter will conclude with examples of how Landsat data is being used on a daily basis to improve lives and livelihoods.

Combining forest inventory, satellite remote sensing, and geospatial data for mapping forest attributes of the conterminous United States

Treesearch

Mark Nelson; Greg Liknes; Charles H. Perry

2009-01-01

Analysis and display of forest composition, structure, and pattern provides information for a variety of assessments and management decision support. The objective of this study was to produce geospatial datasets and maps of conterminous United States forest land ownership, forest site productivity, timberland, and reserved forest land. Satellite image-based maps of...

Urban Spatial Ecological Performance Based on the Data of Remote Sensing of Guyuan

NASA Astrophysics Data System (ADS)

Ren, X.-J.; Chen, X.-J.; Ma, Q.

2018-04-01

The evolution analysis of urban landuse and spatial ecological performance are necessary and useful to recognizing the stage of urban development and revealing the regularity and connotation of urban spatial expansion. Moreover, it lies in the core that should be exmined in the urban sustainable development. In this paper, detailed information has been acquired from the high-resolution satellite imageries of Guyuan, China case study. With the support of GIS, the land-use mapping information and the land cover changes are analyzed, and the process of urban spatial ecological performance evolution by the hierarchical methodology is explored. Results demonstrate that in the past 11 years, the urban spatial ecological performance show an improved process with the dramatic landcover change in Guyuan. Firstly, the landuse structure of Guyuan changes significantly and shows an obvious stage characteristic. Secondly, the urban ecological performance of Guyuan continues to be optimized over the 11 years. Thirdly, the findings suggest that a dynamic monitoring mechanism of urban land use based on high-resolution remote sensing data should be established in urban development, and the rational development of urban land use should be guided by the spatial ecological performance as the basic value orientation.

Remote sensing monitoring the spatio-temporal changes of aridification in the Mongolian Plateau based on the general Ts-NDVI space, 1981-2012

NASA Astrophysics Data System (ADS)

Cao, Xiaoming; Feng, Yiming; Wang, Juanle

2017-06-01

This paper has developed a general Ts-NDVI triangle space with vegetation index time-series data from AVHRR and MODIS to monitor soil moisture in the Mongolian Plateau during 1981-2012, and studied the spatio-temporal variations of drought based on the temperature vegetation dryness index (TVDI). The results indicated that (1) the developed general Ts-NDVI space extracted from the AVHRR and MODIS remote sensing data would be an effective method to monitor regional drought, moreover, it would be more meaningful if the single time Ts-NDVI space showed an unstable condition; (2) the inverted TVDI was expected to reflect the water deficit in the study area. It was found to be in close negative agreement with precipitation and 10 cm soil moisture; (3) in the Mongolian Plateau, TVDI presented a zonal distribution with changes in land use/land cover types, vegetation cover and latitude. The soil moisture is low in bare land, construction land and grassland. During 1981-2012, drought was widely spread throughout the plateau, and aridification was obvious in the study period. Vegetation degradation, overgrazing, and climate warming could be considered as the main reasons.

Finding a Sense of Home across the Pacific in Old Age- Chinese American Senior's Report of Life Satisfaction in a Foreign Land.

PubMed

Zhan, Heying Jenny; Wang, Qi; Fawcett, Zoe; Li, Xiaoqing; Fan, Xiying

2017-03-01

Finding a sense of home for international migrants is challenging. It is even more so for older adults who migrate to a foreign country later in life to follow their adult children. This study examines Chinese immigrant elders' report of their sense of home and life-satisfaction. Based on 21 intensive interviews and107 surveys with elderly immigrants of Chinese descent, this research finds that a comfortable living condition in a natural and built environment contribute to Chinese elders' narrative of a sense of home. The lack of English language, however, makes immigrant Chinese elders feel very unsettled. Being together with children and having good social benefits are major pull factors that contribute to immigrant elders' decision to settle down in a foreign country. Those who report a stronger sense of home tend to report a higher level of life satisfaction. In conclusion, the authors argue that immigrant elders are defining their sense of home with a greater sense of independence from their adult children. Favorable social policies toward older adults, such as Medicare, Medicaid, low income housing, and social services, are important factors that make older immigrants feel a sense of home in a foreign land, although the inability to communicate is a barrier to a complete sense of home for transnational migrants in old age.

Comparison of two Classification methods (MLC and SVM) to extract land use and land cover in Johor Malaysia

NASA Astrophysics Data System (ADS)

Rokni Deilmai, B.; Ahmad, B. Bin; Zabihi, H.

2014-06-01

Mapping is essential for the analysis of the land use and land cover, which influence many environmental processes and properties. For the purpose of the creation of land cover maps, it is important to minimize error. These errors will propagate into later analyses based on these land cover maps. The reliability of land cover maps derived from remotely sensed data depends on an accurate classification. In this study, we have analyzed multispectral data using two different classifiers including Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). To pursue this aim, Landsat Thematic Mapper data and identical field-based training sample datasets in Johor Malaysia used for each classification method, which results indicate in five land cover classes forest, oil palm, urban area, water, rubber. Classification results indicate that SVM was more accurate than MLC. With demonstrated capability to produce reliable cover results, the SVM methods should be especially useful for land cover classification.

A hybrid Land Cover Dataset for Russia: a new methodology for merging statistics, remote sensing and in-situ information

NASA Astrophysics Data System (ADS)

Schepaschenko, D.; McCallum, I.; Shvidenko, A.; Kraxner, F.; Fritz, S.

2009-04-01

There is a critical need for accurate land cover information for resource assessment, biophysical modeling, greenhouse gas studies, and for estimating possible terrestrial responses and feedbacks to climate change. However, practically all existing land cover datasets have quite a high level of uncertainty and suffer from a lack of important details that does not allow for relevant parameterization, e.g., data derived from different forest inventories. The objective of this study is to develop a methodology in order to create a hybrid land cover dataset at the level which would satisfy requirements of the verified terrestrial biota full greenhouse gas account (Shvidenko et al., 2008) for large regions i.e. Russia. Such requirements necessitate a detailed quantification of land classes (e.g., for forests - dominant species, age, growing stock, net primary production, etc.) with additional information on uncertainties of the major biometric and ecological parameters in the range of 10-20% and a confidence interval of around 0.9. The approach taken here allows the integration of different datasets to explore synergies and in particular the merging and harmonization of land and forest inventories, ecological monitoring, remote sensing data and in-situ information. The following datasets have been integrated: Remote sensing: Global Land Cover 2000 (Fritz et al., 2003), Vegetation Continuous Fields (Hansen et al., 2002), Vegetation Fire (Sukhinin, 2007), Regional land cover (Schmullius et al., 2005); GIS: Soil 1:2.5 Mio (Dokuchaev Soil Science Institute, 1996), Administrative Regions 1:2.5 Mio, Vegetation 1:4 Mio, Bioclimatic Zones 1:4 Mio (Stolbovoi & McCallum, 2002), Forest Enterprises 1:2.5 Mio, Rivers/Lakes and Roads/Railways 1:1 Mio (IIASA's data base); Inventories and statistics: State Land Account (FARSC RF, 2006), State Forest Account - SFA (FFS RF, 2003), Disturbances in forests (FFS RF, 2006). The resulting hybrid land cover dataset at 1-km resolution comprises the following classes: Forest (each grid links to the SFA database, which contains 86,613 records); Agriculture (5 classes, parameterized by 89 administrative units); Wetlands (8 classes, parameterized by 83 zone/region units); Open Woodland, Burnt area; Shrub/grassland (50 classes, parameterized by 300 zone/region units); Water; Unproductive area. This study has demonstrated the ability to produce a highly detailed (both spatially and thematically) land cover dataset over Russia. Future efforts include further validation of the hybrid land cover dataset for Russia, and its use for assessment of the terrestrial biota full greenhouse gas budget across Russia. The methodology proposed in this study could be applied at the global level. Results of such an undertaking would however be highly dependent upon the quality of the available ground data. The implementation of the hybrid land cover dataset was undertaken in a way that it can be regularly updated based on new ground data and remote sensing products (ie. MODIS).

The role of remotely sensed and other spatial data for predictive modeling: the Umatilla, Oregon example

USGS Publications Warehouse

Loveland, Thomas R.; Johnson, Gary E.

1981-01-01

The U. S. Geological Survey's Earth Resources Observations Systems Data Center, in cooperation with the U.S. Army Corps of Engineers, Portland District, developed and tested techniques that used remotely sensed and other spatial data in predictive models to evaluate irrigation agriculture in the Umatilla River Basin of north-central Oregon. Landsat data and 1:24,000-scale aerial photographs were initially used to map he expansion of irrigate from 1973 to 1979 and to identify crops under irrigation in 1979. The crop data were then used with historical water requirement figures and digital topographic and hydrographic data to estimate water and power use for the 1979 irrigation season. The final project task involved production of a composite map of land suitability for irrigation development based on land cover (from Landsat), land-ownership, soil irrigability, slope gradient, and potential energy costs. The methods and data used in the study demonstrated the flexibility of remotely sensed and other spatial data as input for predictive models. When combined, they provided useful answers to complex questions facing resource managers.

76 FR 65529 - Agency Information Collection Activities: Comment Request

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-21

... National Land Remote Sensing Education, Outreach and Research Activity (NLRSEORA). As required by the... Drive MS 517, Reston, VA, 20192 (mail) . SUPPLEMENTARY INFORMATION: Title: National Land Remote Sensing... Remote Sensing Program, therefore it is more appropriate to refer to this effort as an activity rather...

Land cover's refined classification based on multi source of remote sensing information fusion: a case study of national geographic conditions census in China

NASA Astrophysics Data System (ADS)

Cheng, Tao; Zhang, Jialong; Zheng, Xinyan; Yuan, Rujin

2018-03-01

The project of The First National Geographic Conditions Census developed by Chinese government has designed the data acquisition content and indexes, and has built corresponding classification system mainly based on the natural property of material. However, the unified standard for land cover classification system has not been formed; the production always needs converting to meet the actual needs. Therefore, it proposed a refined classification method based on multi source of remote sensing information fusion. It takes the third-level classes of forest land and grassland for example, and has collected the thematic data of Vegetation Map of China (1:1,000,000), attempts to develop refined classification utilizing raster spatial analysis model. Study area is selected, and refined classification is achieved by using the proposed method. The results show that land cover within study area is divided principally among 20 classes, from subtropical broad-leaved forest (31131) to grass-forb community type of low coverage grassland (41192); what's more, after 30 years in the study area, climatic factors, developmental rhythm characteristics and vegetation ecological geographical characteristics have not changed fundamentally, only part of the original vegetation types have changed in spatial distribution range or land cover types. Research shows that refined classification for the third-level classes of forest land and grassland could make the results take on both the natural attributes of the original and plant community ecology characteristics, which could meet the needs of some industry application, and has certain practical significance for promoting the product of The First National Geographic Conditions Census.

Environmental application of remote sensing methods to coastal zone land use and marine resource management. Appendix F: User's guide for advection, convection prototype. [southeastern Virginia

NASA Technical Reports Server (NTRS)

1972-01-01

A user's manual is provided for the environmental computer model proposed for the Richmond-Cape Henry Environmental Laboratory (RICHEL) application project for coastal zone land use investigations and marine resources management. The model was developed around the hydrologic cycle and includes two data bases consisting of climate and land use variables. The main program is described, along with control parameters to be set and pertinent subroutines.

Rural Land Use Change during 1986–2002 in Lijiang, China, Based on Remote Sensing and GIS Data

PubMed Central

Peng, Jian; Wu, Jiansheng; Yin, He; Li, Zhengguo; Chang, Qing; Mu, Tianlong

2008-01-01

As a local environmental issue with global importance, land use/land cover change (LUCC) has always been one of the key issues in geography and environmental studies with the expansion of regional case studies. While most of LUCC studies in China have focused on urban land use change, meanwhile, compared with the rapid change of urban land use in the coastal areas of eastern China, slow but distinct rural land use changes have also occurred in the mountainous areas of western China since the late 1980s. In this case through a study in Lijiang County of Yunnan Province, with the application of remote sensing data and geographic information system techniques, the process of rural land use change in mountain areas of western China was monitored through extensive statistical analysis of detailed regional data. The results showed significant increases in construction land, paddy field and dry land, and a decrease in dense forest land and waste grassland between 1986 and 2002. The conversions between dense forest land and sparse forest land, grassland, waste grassland and dry land were the primary processes of rural land use change. Sparse forest land had the highest rate of land use change, with glacier or snow-capped land the lowest; while human settlement and rural economic development were found to be the main driving forces of regional difference in the integrated land use change rate among the 24 towns of Lijiang County. Quantified through landscape metrics, spatial patterns of rural land use change were represented as an increase in landscape diversity and landscape fragmentation, and the regularization of patch shapes, suggesting the intensification of human disturbances and degradation of ecological quality in the rural landscape. PMID:27873983

Rural Land Use Change during 1986-2002 in Lijiang, China, Based on Remote Sensing and GIS Data.

PubMed

Peng, Jian; Wu, Jiansheng; Yin, He; Li, Zhengguo; Chang, Qing; Mu, Tianlong

2008-12-11

As a local environmental issue with global importance, land use/land cover change (LUCC) has always been one of the key issues in geography and environmental studies with the expansion of regional case studies. While most of LUCC studies in China have focused on urban land use change, meanwhile, compared with the rapid change of urban land use in the coastal areas of eastern China, slow but distinct rural land use changes have also occurred in the mountainous areas of western China since the late 1980s. In this case through a study in Lijiang County of Yunnan Province, with the application of remote sensing data and geographic information system techniques, the process of rural land use change in mountain areas of western China was monitored through extensive statistical analysis of detailed regional data. The results showed significant increases in construction land, paddy field and dry land, and a decrease in dense forest land and waste grassland between 1986 and 2002. The conversions between dense forest land and sparse forest land, grassland, waste grassland and dry land were the primary processes of rural land use change. Sparse forest land had the highest rate of land use change, with glacier or snow-capped land the lowest; while human settlement and rural economic development were found to be the main driving forces of regional difference in the integrated land use change rate among the 24 towns of Lijiang County. Quantified through landscape metrics, spatial patterns of rural land use change were represented as an increase in landscape diversity and landscape fragmentation, and the regularization of patch shapes, suggesting the intensification of human disturbances and degradation of ecological quality in the rural landscape.

Remote sensing inputs to landscape models which predict future spatial land use patterns for hydrologic models

NASA Technical Reports Server (NTRS)

Miller, L. D.; Tom, C.; Nualchawee, K.

1977-01-01

A tropical forest area of Northern Thailand provided a test case of the application of the approach in more natural surroundings. Remote sensing imagery subjected to proper computer analysis has been shown to be a very useful means of collecting spatial data for the science of hydrology. Remote sensing products provide direct input to hydrologic models and practical data bases for planning large and small-scale hydrologic developments. Combining the available remote sensing imagery together with available map information in the landscape model provides a basis for substantial improvements in these applications.

Specifications for updating USGS land use and land cover maps

USGS Publications Warehouse

Milazzo, Valerie A.

1983-01-01

To meet the increasing demands for up-to-date land use and land cover information, a primary goal of the U.S. Geological Survey's (USGS) national land use and land cover mapping program is to provide for periodic updating of maps and data in a timely and uniform manner. The technical specifications for updating existing USGS land use and land cover maps that are presented here cover both the interpretive aspects of detecting and identifying land use and land cover changes and the cartographic aspects of mapping and presenting the change data in conventional map format. They provide the map compiler with the procedures and techniques necessary to then use these change data to update existing land use and land cover maps in a manner that is both standardized and repeatable. Included are specifications for the acquisition of remotely sensed source materials, selection of compilation map bases, handling of data base corrections, editing and quality control operations, generation of map update products for USGS open file, and the reproduction and distribution of open file materials. These specifications are planned to become part of the National Mapping Division's Technical Instructions.

15 CFR 960.9 - License term.

Code of Federal Regulations, 2010 CFR

2010-01-01

... SERVICE LICENSING OF PRIVATE REMOTE SENSING SYSTEMS Licenses § 960.9 License term. (a) Each license for... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the...

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics

NASA Technical Reports Server (NTRS)

Roundy, Joshua K.; Santanello, Joseph A.

2017-01-01

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products. NASA's AQUA satellite and retrievals of soil moisture and lower tropospheric temperature and humidity properties are used as input. Overall, the AQUA-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in-situ and reanalysis products. In addition, this work represents the first time that in-situ observations were utilized for the coupling classification and CDI. The combination of in-situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics.

Utility of Satellite Remote Sensing for Land-Atmosphere Coupling and Drought Metrics

PubMed Central

Roundy, Joshua K.; Santanello, Joseph A.

2018-01-01

Feedbacks between the land and the atmosphere can play an important role in the water cycle and a number of studies have quantified Land-Atmosphere (L-A) interactions and feedbacks through observations and prediction models. Due to the complex nature of L-A interactions, the observed variables are not always available at the needed temporal and spatial scales. This work derives the Coupling Drought Index (CDI) solely from satellite data and evaluates the input variables and the resultant CDI against in-situ data and reanalysis products. NASA’s AQUA satellite and retrievals of soil moisture and lower tropospheric temperature and humidity properties are used as input. Overall, the AQUA-based CDI and its inputs perform well at a point, spatially, and in time (trends) compared to in-situ and reanalysis products. In addition, this work represents the first time that in-situ observations were utilized for the coupling classification and CDI. The combination of in-situ and satellite remote sensing CDI is unique and provides an observational tool for evaluating models at local and large scales. Overall, results indicate that there is sufficient information in the signal from simultaneous measurements of the land and atmosphere from satellite remote sensing to provide useful information for applications of drought monitoring and coupling metrics. PMID:29645012

Remote sensing. [land use mapping

NASA Technical Reports Server (NTRS)

Jinich, A.

1979-01-01

Various imaging techniques are outlined for use in mapping, land use, and land management in Mexico. Among the techniques discussed are pattern recognition and photographic processing. The utilization of information from remote sensing devices on satellites are studied. Multispectral band scanners are examined and software, hardware, and other program requirements are surveyed.

Spatiotemporal Analysis for Wildlife-Vehicle Based on Accident Statistics of the County Straubing-Bogen in Lower Bavaria

NASA Astrophysics Data System (ADS)

Pagany, R.; Dorner, W.

2016-06-01

During the last years the numbers of wildlife-vehicle-collisions (WVC) in Bavaria increased considerably. Despite the statistical registration of WVC and preventive measures at areas of risk along the roads, the number of such accidents could not be contained. Using geospatial analysis on WVC data of the last five years for county Straubing-Bogen, Bavaria, a small-scale methodology was found to analyse the risk of WVC along the roads in the investigated area. Various indicators were examined, which may be related to WVC. The risk depends on the time of the day and year which shows correlations in turn to the traffic density and wildlife population. Additionally the location of the collision depends on the species and on different environmental parameters. Accidents seem to correlate with the land use left and right of the street. Land use data and current vegetation were derived from remote sensing data, providing information of the general land use, also considering the vegetation period. For this a number of hot spots was selected to identify potential dependencies between land use, vegetation and season. First results from these hotspots show, that WVCs do not only depend on land use, but may show a correlation with the vegetation period. With regard to agriculture and seasonal as well as annual changes this indicates that warnings will fail due to their static character in contrast to the dynamic situation of land use and resulting risk for WVCs. This shows that there is a demand for remote sensing data with a high spatial and temporal resolution as well as a methodology to derive WVC warnings considering land use and vegetation. With remote sensing data, it could become possible to classify land use and calculate risk levels for WVC. Additional parameters, derived from remote sensed data that could be considered are relief and crops as well as other parameters such as ponds, natural and infrastructural barriers that could be related to animal behaviour and should be considered by future research.

Hydrography synthesis using LANDSAT remote sensing and the SCS models

NASA Technical Reports Server (NTRS)

Ragan, R. M.; Jackson, T. J.

1976-01-01

The land cover requirements of the Soil Conservation Service (SCS) Model used for hydrograph synthesis in urban areas were modified to be LANDSAT compatible. The Curve Numbers obtained with these alternate land cover categories compare well with those obtained in published example problems using the conventional categories. Emergency spillway hydrographs and synthetic flood frequency flows computed for a 21.1 sq. mi. test area showed excellent agreement between the conventional aerial photo-based and the Landsat-based SCS approaches.

Land desertification monitoring and assessment in Yulin of Northwest China using remote sensing and geographic information systems (GIS).

PubMed

Zhang, Yuanzhi; Chen, Zhengyi; Zhu, Boqin; Luo, Xiuyue; Guan, Yanning; Guo, Shan; Nie, Yueping

2008-12-01

The objective of this study is to develop techniques for assessing and analysing land desertification in Yulin of Northwest China, as a typical monitoring region through the use of remotely sensed data and geographic information systems (GIS). The methodology included the use of Landsat TM data from 1987, 1996 and 2006, supplemented by aerial photos in 1960, topographic maps, field work and use of other existing data. From this, land cover, the Normalised Difference Vegetation Index (NDVI), farmland, woodland and grassland maps at 1:100,000 were prepared for land desertification monitoring in the area. In the study, all data was entered into a GIS using ILWIS software to perform land desertification monitoring. The results indicate that land desertification in the area has been developing rapidly during the past 40 years. Although land desertification has to some extent been controlled in the area by planting grasses and trees, the issue of land desertification is still serious. The study also demonstrates an example of why the integration of remote sensing with GIS is critical for the monitoring of environmental changes in arid and semi-arid regions, e.g. in land desertification monitoring in the Yulin pilot area. However, land desertification monitoring using remote sensing and GIS still needs to be continued and also refined for the purpose of long-term monitoring and the management of fragile ecosystems in the area.

Land degradation and Poverty in maize producing areas of Kenya - Development of an interdisciplinary analysis framework using GIS and remote sensing

NASA Astrophysics Data System (ADS)

Graw, Valerie; Nkonya, Ephraim; Menz, Gunter

2014-05-01

Land degradation causes poverty and vice versa. But both processes are highly complex, hard to predict and to mitigate, and need insights from different perspectives. Therefore an interdisciplinary framework for the understanding of land degradation processes by linking biophysical data with socio-economic trends is necessary. Agricultural systems in Kenya are affected by land degradation and especially recent developments such as agricultural innovations including the use of hybrid seeds and chemical fertilizer have an impact on the environment. Vegetation analysis, used as a proxy indicator for the status of land is carried out to monitor environmental changes in maize producing areas of western Kenya. One of the methods used in this study includes time series analysis of vegetation data from 2001 to 2010 based on MODIS NDVI data with 250m and 500m resolution. Occurring trends are linked to rainfall estimation data and annually classified land use cover data with 500m resolution based on MODIS within the same time period. Analysis of significant trends in combination with land cover information show recent land change dynamics. As these changes are not solely biophysically driven, socio-economic variables representing marginality - defined as the root cause of poverty- are also considered. The most poor are primarily facing the most vulnerable and thereby less fertile soils. Moreover they are lacking access to information to eventually use existing potential. This makes the analysis of changing environmental processes and household characteristics in the interplay important to understand in order to highlight the most influencing variables. Within the new interdisciplinary analysis framework the concept of marginality includes different dimensions referring to certain livelihood characteristics such as health and education which describe a more diverse picture of poverty than the known economic perspective. Household surveys and census data from different time periods allow the analysis of socio-economic trends and link this information to biophysical factors. If relationships between certain variables are understood, adapted land management strategies can be developed. This study aims at linking pixel-level information with established remote sensing methods to the socio-economic concept of marginality based on household surveys and census data on administrative levels. Besides remote sensing and statistical analysis of socio-economic data a GIS is used for geospatial analysis. As most studies on land degradation focus on biophysical aspects such as vegetation or soil degradation this study uses an innovative approach by integrating biophysical analysis without neglecting a human oriented approach which plays a key role in environmental systems nowadays. This interdisciplinary research helps to get closer to the right and adapted policies and land management strategies as land degradation processes do not stick to administrative boundaries but policy advice does.

Area-averaged evapotranspiration over a heterogeneous land surface: aggregation of multi-point EC flux measurements with a high-resolution land-cover map and footprint analysis

NASA Astrophysics Data System (ADS)

Xu, Feinan; Wang, Weizhen; Wang, Jiemin; Xu, Ziwei; Qi, Yuan; Wu, Yueru

2017-08-01

The determination of area-averaged evapotranspiration (ET) at the satellite pixel scale/model grid scale over a heterogeneous land surface plays a significant role in developing and improving the parameterization schemes of the remote sensing based ET estimation models and general hydro-meteorological models. The Heihe Watershed Allied Telemetry Experimental Research (HiWATER) flux matrix provided a unique opportunity to build an aggregation scheme for area-averaged fluxes. On the basis of the HiWATER flux matrix dataset and high-resolution land-cover map, this study focused on estimating the area-averaged ET over a heterogeneous landscape with footprint analysis and multivariate regression. The procedure is as follows. Firstly, quality control and uncertainty estimation for the data of the flux matrix, including 17 eddy-covariance (EC) sites and four groups of large-aperture scintillometers (LASs), were carefully done. Secondly, the representativeness of each EC site was quantitatively evaluated; footprint analysis was also performed for each LAS path. Thirdly, based on the high-resolution land-cover map derived from aircraft remote sensing, a flux aggregation method was established combining footprint analysis and multiple-linear regression. Then, the area-averaged sensible heat fluxes obtained from the EC flux matrix were validated by the LAS measurements. Finally, the area-averaged ET of the kernel experimental area of HiWATER was estimated. Compared with the formerly used and rather simple approaches, such as the arithmetic average and area-weighted methods, the present scheme is not only with a much better database, but also has a solid grounding in physics and mathematics in the integration of area-averaged fluxes over a heterogeneous surface. Results from this study, both instantaneous and daily ET at the satellite pixel scale, can be used for the validation of relevant remote sensing models and land surface process models. Furthermore, this work will be extended to the water balance study of the whole Heihe River basin.

Research on the application of satellite remote sensing to local, state, regional, and national programs involved with resource management and environmental quality

NASA Technical Reports Server (NTRS)

Walters, R. L.; Eastmond, R. J.; Barr, B. G.

1973-01-01

Project summaries and project reports are presented in the area of satellite remote sensing as applied to local, regional, and national environmental programs. Projects reports include: (1) Douglas County applications program; (2) vegetation damage and heavy metal concentration in new lead belt; (3) evaluating reclamation of strip-mined land; (4) remote sensing applied to land use planning at Clinton Reservoir; and (5) detailed land use mapping in Kansas City, Kansas.

Remote sensing for rural development planning in Africa

NASA Technical Reports Server (NTRS)

Dunford, C.; Mouat, D. A.; Norton-Griffiths, M.; Slaymaker, D. M.

1983-01-01

Multilevel remote-sensing techniques were combined to provide land resource and land-use information for rural development planning in Arusha Region, Tanzania. Enhanced Landsat imagery, supplemented by low-level aerial survey data, slope angle data from topographic sheets, and existing reports on vegetation and soil conditions, was used jointly by image analysts and district-level land-management officials to divide the region's six districts into land-planning units. District-planning officials selected a number of these land-planning units for priority planning and development activities. For the priority areas, natural color aerial photographs provided detailed information for land-use planning discussions between district officials and villagers. Consideration of the efficiency of this remote sensing approach leads to general recommendations for similar applications. The technology and timing of data collection and interpretation activities should allow maximum participation by intended users of the information.

Monitoring and Assessment of Military Installation Land Condition under Training Disturbance Using Remote Sensing

NASA Astrophysics Data System (ADS)

Rijal, Santosh

Various military training activities are conducted in more than 11.3 million hectares of land (> 5,500 training sites) in the United States (U.S.). These training activities directly and indirectly degrade the land. Land degradation can impede continuous military training. In order to sustain long term training missions and Army combat readiness, the environmental conditions of the military installations need to be carefully monitored and assessed. Furthermore, the National Environmental Policy Act of 1969 (NEPA) and the U.S. Army Regulation 200-2 require the DoD to minimize the environmental impacts of training and document the environmental consequences of their actions. To achieve these objectives, the Department of Army initiated an Integrated Training Area Management (ITAM) program to manage training lands through assessing their environmental requirements and establishing policies and procedures to achieve optimum, sustainable use of training lands. One of the programs under ITAM, Range and Training Land Assessment (RTLA) was established to collect field-based data for monitoring installation's environmental condition. Due to high cost and inefficiencies involved in the collection of field data, the RTLA program was stopped in several military installations. Therefore, there has been a strong need to develop an efficient and low cost remote sensing based methodology for assessing and monitoring land conditions of military installations. It is also important to make a long-term assessment of installation land condition for understanding cumulative impacts of continuous military training activities. Additionally, it is unclear that compared to non-military land condition, to what extent military training activities have led to the degradation of land condition for military installations. The first paper of this dissertation developed a soil erosion relevant and image derived cover factor (ICF) based on linear spectral mixture (LSM) analysis to assess and monitor the land condition of military land and compare it with non-military land. The results from this study can provide FR land managers with the information of the spatial variation and temporal trend of land condition in FR. Fort Riley land managers can also use this method for monitoring their land condition at a very low cost. This method can thus be applied to other military installations as well as non-military lands. Furthermore, one of the most significant environmental problems in military installations of the U.S. is the formation of gullies due to the intensive use of military vehicle. However, to our knowledge, no remote sensing based method has been developed and used to assess the detection of gullies in military installations. In the second paper of this dissertation, light detection and ranging (LiDAR) derived digital elevation model (DEM) of 2010 and WorldView-2 images of 2010 were used to quantify the gullies in FR. This method can be easily applied to assess gullies in non-military installations. On the other hand, modeling the land condition of military installation is critical to understand the spatial and temporal pattern of military training induced disturbance and land recovery. In the third paper, it was assumed that the military training induced disturbance was spatially auto-correlated and thus four regression models including i) linear stepwise regression (LSR) ii) logistic regression (LR), iii) geographically weighted linear regression (GWR), and iv) geographically weighted logistic regression (GWLR) were developed and compared using remote sensing image derived spectral variables for years 1990, 1997, 1998, 1999, and 2001. It was found that the spatial distribution of the military training disturbance was well demonstrated by all the regression models with higher intensities of military training disturbance in the northwest and central west parts of the installation. Compared to other regression models, GWR accurately estimated the land condition of FR. This result provided the applicability of using local variability based regression model to accurately predict land condition. Different plant communities of military installations respond differently to military training induced disturbance. The information of the spatial distribution of plant species in military installations is important to gain insight of the resilient capacity of the land following disturbances. For the purpose, in the fourth paper, hyperspectral in-situ data were collected from FR and KPBS in the summer of 2015 using a hyperspectral instrument. Principal component analysis (PCA) and band relative importance (BRI) were used to identify relative importance of each of the spectral bands. The results from this study provided useful information about the optimal wavelengths that help distinguish different plant species of FR and can be easily used with high resolution hyperspectral images for mapping the spatial distribution of the plant species. This information will be helpful for the sustainable management of the tallgrass prairie ecosystem. (Abstract shortened by ProQuest.).

Analytical evaluation of ILM sensors, volume 1

NASA Technical Reports Server (NTRS)

Kirk, R. J.

1975-01-01

The functional requirements and operating environment constraints are defined for an independent landing monitor ILM which provides the flight crew with an independent assessment of the operation of the primary automatic landing system. The capabilities of radars, TV, forward looking infrared radiometers, multilateration, microwave radiometers, interferometers, and nuclear sensing concepts to meet the ILM conditions are analyzed. The most critical need for the ILM appears in the landing sequence from 1000 to 2000 meters from threshold through rollout. Of the sensing concepts analyzed, the following show potential of becoming feasible ILM's: redundant microwave landings systems, precision approach radar, airborne triangulation radar, multilateration with radar altimetry, and nuclear sensing.

Application of remote sensing technology to land evaluation, planning utilization of land resources, and assessment of wildlife areas in eastern South Dakota

NASA Technical Reports Server (NTRS)

1975-01-01

A soils map for land evaluation in Potter County (Eastern South Dakota) was developed to demonstrate the use of remote sensing technology in the area of diverse parent materials and topography. General land use and soils maps have also been developed for land planning LANDSAT, RB-57 imagery, and USGS photographs are being evaluated for making soils and land use maps. LANDSAT fulfilled the requirements for general land use and a general soils map. RB-57 imagery supplemented by large scale black and white stereo coverage was required to provide the detail needed for the final soils map for land evaluation. Color infrared prints excelled black and white coverage for this soil mapping effort. An identification and classification key for wetland types in the Lake Dakota Plain was developed for June 1975 using color infrared imagery. Wetland types in the region are now being mapped via remote sensing techniques to provide a current inventory for development of mitigation measures.

Research on lunar and planet development and utilization

NASA Astrophysics Data System (ADS)

Iwata, Tsutomu; Etou, Takao; Imai, Ryouichi; Oota, Kazuo; Kaneko, Yutaka; Maeda, Toshihide; Takano, Yutaka

1992-08-01

Status of the study on unmanned and manned lunar missions, unmanned Mars missions, lunar resource development and utilization missions, remote sensing exploration missions, survey and review to elucidate the problems of research and development for lunar resource development and utilization, and the techniques and equipment for lunar and planet exploration are presented. Following items were studied respectively: (1) spacecraft systems for unmanned lunar missions, such as lunar observation satellites, lunar landing vehicles, lunar surface rovers, lunar surface hoppers, and lunar sample retrieval; (2) spacecraft systems for manned lunar missions, such as manned lunar bases, lunar surface operation robots, lunar surface experiment systems, manned lunar take-off and landing vehicles, and lunar freight transportation ships; (3) spacecraft systems for Mars missions, such as Mars satellites, Phobos and Deimos sample retrieval vehicles, Mars landing explorers, Mars rovers, Mars sample retrieval; (4) lunar resource development and utilization; and (5) remote sensing exploration technologies.

Remote sensing of Essential Biodiversity Variables: new measurements linking ecosystem structure, function and composition

NASA Astrophysics Data System (ADS)

Schimel, D.; Pavlick, R.; Stavros, E. N.; Townsend, P. A.; Ustin, S.; Thompson, D. R.

2017-12-01

Remote sensing can inform a wide variety of essential biodiversity variables, including measurements that define primary productivity, forest structure, biome distribution, plant communities, land use-land cover change and climate drivers of change. Emerging remote sensing technologies can add significantly to remote sensing of EBVs, providing new, large scale insights on plant and habitat diversity itself, as well as causes and consequences of biodiversity change. All current biodiversity assessments identify major data gaps, with insufficient coverage in critical regions, limited observations to monitor change over time, with very limited revisit of sample locations, as well as taxon-specific biased biases. Remote sensing cannot fill many of the gaps in global biodiversity observations, but spectroscopic measurements in terrestrial and marine environments can aid in assessing plant/phytoplankton functional diversity and efficiently reveal patterns in space, as well as changes over time, and, by making use of chlorophyll fluorescence, reveal associated patterns in photosynthesis. LIDAR and RADAR measurements quantify ecosystem structure, and can precisely define changes due to growth, disturbance and land use. Current satellite-based EBVs have taken advantage of the extraordinary time series from LANDSAT and MODIS, but new measurements more directly reveal ecosystem structure, function and composition. We will present results from pre-space airborne studies showing the synergistic ability of a suite of new remote observation techniques to quantify biodiversity and ecosystem function and show how it changes during major disturbance events.

Remote sensing and GIS techniques for assessment of the soil water content in order to improve agricultural practice and reduce the negative impact on groundwater: case study, agricultural area Ştefan cel Mare, Călăraşi County.

PubMed

Tevi, Giuliano; Tevi, Anca

2012-01-01

Traditional agricultural practices based on non-customized irrigation and soil fertilization are harmful for the environment, and may pose a risk for human health. By continuing the use of these practices, it is not possible to ensure effective land management, which might be acquired by using advanced satellite technology configured for modern agricultural development. The paper presents a methodology based on the correlation between remote sensing data and field observations, aiming to identify the key features and to establish an interpretation pattern for the inhomogeneity highlighted by the remote sensing data. Instead of using classical methods for the evaluation of land features (field analysis, measurements and mapping), the approach is to use high resolution multispectral and hyperspectral methods, in correlation with data processing and geographic information systems (GIS), in order to improve the agricultural practices and mitigate their environmental impact (soil and shallow aquifer).

Land use planning

NASA Technical Reports Server (NTRS)

1975-01-01

The organization, objectives, and accomplishments of the panel on Land Use Planning are reported. Technology developments, and projected developments are discussed along with anticipated information requirements. The issues for users, recommended remote sensing programs, and space systems are presented. It was found that remote sensing systems are useful in future land use planning. It is recommended that a change detection system for monitoring land use and critical environmental areas be developed by 1979.

Sustainability of meat-based and plant-based diets and the environment.

PubMed

Pimentel, David; Pimentel, Marcia

2003-09-01

Worldwide, an estimated 2 billion people live primarily on a meat-based diet, while an estimated 4 billion live primarily on a plant-based diet. The US food production system uses about 50% of the total US land area, 80% of the fresh water, and 17% of the fossil energy used in the country. The heavy dependence on fossil energy suggests that the US food system, whether meat-based or plant-based, is not sustainable. The use of land and energy resources devoted to an average meat-based diet compared with a lactoovovegetarian (plant-based) diet is analyzed in this report. In both diets, the daily quantity of calories consumed are kept constant at about 3533 kcal per person. The meat-based food system requires more energy, land, and water resources than the lactoovovegetarian diet. In this limited sense, the lactoovovegetarian diet is more sustainable than the average American meat-based diet.

Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands.

PubMed

Mushinzimana, Emmanuel; Munga, Stephen; Minakawa, Noboru; Li, Li; Feng, Chen-Chieng; Bian, Ling; Kitron, Uriel; Schmidt, Cindy; Beck, Louisa; Zhou, Guofa; Githeko, Andrew K; Yan, Guiyun

2006-02-16

In the past two decades the east African highlands have experienced several major malaria epidemics. Currently there is a renewed interest in exploring the possibility of anopheline larval control through environmental management or larvicide as an additional means of reducing malaria transmission in Africa. This study examined the landscape determinants of anopheline mosquito larval habitats and usefulness of remote sensing in identifying these habitats in western Kenya highlands. Panchromatic aerial photos, Ikonos and Landsat Thematic Mapper 7 satellite images were acquired for a study area in Kakamega, western Kenya. Supervised classification of land-use and land-cover and visual identification of aquatic habitats were conducted. Ground survey of all aquatic habitats was conducted in the dry and rainy seasons in 2003. All habitats positive for anopheline larvae were identified. The retrieved data from the remote sensors were compared to the ground results on aquatic habitats and land-use. The probability of finding aquatic habitats and habitats with Anopheles larvae were modelled based on the digital elevation model and land-use types. The misclassification rate of land-cover types was 10.8% based on Ikonos imagery, 22.6% for panchromatic aerial photos and 39.2% for Landsat TM 7 imagery. The Ikonos image identified 40.6% of aquatic habitats, aerial photos identified 10.6%, and Landsate TM 7 image identified 0%. Computer models based on topographic features and land-cover information obtained from the Ikonos image yielded a misclassification rate of 20.3-22.7% for aquatic habitats, and 18.1-25.1% for anopheline-positive larval habitats. One-metre spatial resolution Ikonos images combined with computer modelling based on topographic land-cover features are useful tools for identification of anopheline larval habitats, and they can be used to assist to malaria vector control in western Kenya highlands.

Remote sensing of forest dynamics and land use in Amazonia

NASA Astrophysics Data System (ADS)

Toomey, Michael Paul

The rich, vast Amazonian ecosystem is directly and indirectly threatened by human activities; remote sensing serves as an essential tool for monitoring, understanding and mitigating these threats. A multi-faceted body of work is described here, addressing three major issues that employ and advance remote sensing techniques for the study of Amazonia and other tropical rainforest regions. In Chapter 2, canopy reflectance modeling and satellite observations were used to quantify the effect of epiphylls on remote sensing of humid forests. Modeling simulations demonstrated sensitivity of canopy-level near infrared and green reflectance to epiphylls on leaves. Time series of Moderate Resolution Imaging Spectrometer (MODIS) data corroborated the modeling results, suggesting a degree of coupling between epiphyll cover and vegetation indices which must be accounted for when using optical remote sensing in humid forests. In Chapter 4, 11 years (2000--2010) of MODIS land surface temperature (LST) data covering the entire Amazon basin were used to ascertain the role of heat stress during droughts in 2005 and 2010. Preliminary accuracy assessments showed that LST data provided reasonably accurate estimates of daytime air temperatures (RMSE = 1.45°C; Chapter 3). There were moderate to strong correlations between LST-based air temperature estimates and tower measurements (mean r = 0.64), illustrating a sensitivity to temporal variability. During both droughts, MODIS LST data detected anomalously high daytime and nighttime canopy temperatures throughout drought-affected regions. Multivariate linear models of LST and precipitation anomalies explained 65.1% of the variability in forest biomass losses, as determined from a wide network of forest inventory plots. These results suggest that models should incorporate both heat and moisture to predict drought effects on tropical forests. In Chapter 5, I performed high spatial and temporal resolution modeling of carbon stocks and fluxes in the state of Rondonia, Brazil for the period 1985--2009. Based on this analysis, Rondonia contributed ˜4% of pan-tropical humid forest deforestation emissions while carbon uptake by secondary forest was negligible due to limited spatial extent and high turnover rates. Spatial analysis of land cover change demonstrated the necessity for fine resolution carbon monitoring in tropical regions dominated by non-mechanized, smallholder land uses.

SMERGE: A multi-decadal root-zone soil moisture product for CONUS

NASA Astrophysics Data System (ADS)

Crow, W. T.; Dong, J.; Tobin, K. J.; Torres, R.

2017-12-01

Multi-decadal root-zone soil moisture products are of value for a range of water resource and climate applications. The NASA-funded root-zone soil moisture merging project (SMERGE) seeks to develop such products through the optimal merging of land surface model predictions with surface soil moisture retrievals acquired from multi-sensor remote sensing products. This presentation will describe the creation and validation of a daily, multi-decadal (1979-2015), vertically-integrated (both surface to 40 cm and surface to 100 cm), 0.125-degree root-zone product over the contiguous United States (CONUS). The modeling backbone of the system is based on hourly root-zone soil moisture simulations generated by the Noah model (v3.2) operating within the North American Land Data Assimilation System (NLDAS-2). Remotely-sensed surface soil moisture retrievals are taken from the multi-sensor European Space Agency Climate Change Initiative soil moisture data set (ESA CCI SM). In particular, the talk will detail: 1) the exponential smoothing approach used to convert surface ESA CCI SM retrievals into root-zone soil moisture estimates, 2) the averaging technique applied to merge (temporally-sporadic) remotely-sensed with (continuous) NLDAS-2 land surface model estimates of root-zone soil moisture into the unified SMERGE product, and 3) the validation of the SMERGE product using long-term, ground-based soil moisture datasets available within CONUS.

Remotely-Sensed Geology from Lander-Based to Orbital Perspectives: Results for FIDO Rover Field Tests

NASA Technical Reports Server (NTRS)

Jolliff, B.; Moersch, J.; Knoll, A.; Morris, R.; Arvidson, R.; Gilmore, M.; Greeley, R.; Herkenhoff, K.; McSween, H.; Squyres, S.

2000-01-01

Tests of the FIDO (Field Integration Design and Operations) rover and Athena-like operational scenarios were conducted May 7-16, 2000. A group located at the Jet Propulsion Lab, Pasadena, CA, formed the Core Operations Team (COT) that designed experiments and command sequences while another team tracked, maintained, and secured the rover in the field. The COT had no knowledge of the specific field location, thus the tests were done "blind." In addition to FIDO rover instrumentation, the COT had access to LANDSAT 7, TIMS, and AVIRIS regional coverage and color descent images. Using data from the FIDO instruments, primarily a color microscopic imager (CMI), infrared point spectrometer (IPS; 1.5-2.4 microns), and a three-color stereo panoramic camera (Pancam), the COT correlated lithologic features (mineralogy, rock types) from the simulated landing site to a regional scale. The May test results provide an example of how to relate site geology from landed rover investigations to the regional geology using remote sensing. The capability to relate mineralogic signatures using the point IR spectrometer to remotely sensed, multispectral or hyperspectral data proved to be key to integration of the in-situ and remote data. This exercise demonstrated the potential synergy between lander-based and orbital data, and highlighted the need to investigate a landing site in detail and at multiple scales.

Incorporating GIS and remote sensing for census population disaggregation

NASA Astrophysics Data System (ADS)

Wu, Shuo-Sheng'derek'

Census data are the primary source of demographic data for a variety of researches and applications. For confidentiality issues and administrative purposes, census data are usually released to the public by aggregated areal units. In the United States, the smallest census unit is census blocks. Due to data aggregation, users of census data may have problems in visualizing population distribution within census blocks and estimating population counts for areas not coinciding with census block boundaries. The main purpose of this study is to develop methodology for estimating sub-block areal populations and assessing the estimation errors. The City of Austin, Texas was used as a case study area. Based on tax parcel boundaries and parcel attributes derived from ancillary GIS and remote sensing data, detailed urban land use classes were first classified using a per-field approach. After that, statistical models by land use classes were built to infer population density from other predictor variables, including four census demographic statistics (the Hispanic percentage, the married percentage, the unemployment rate, and per capita income) and three physical variables derived from remote sensing images and building footprints vector data (a landscape heterogeneity statistics, a building pattern statistics, and a building volume statistics). In addition to statistical models, deterministic models were proposed to directly infer populations from building volumes and three housing statistics, including the average space per housing unit, the housing unit occupancy rate, and the average household size. After population models were derived or proposed, how well the models predict populations for another set of sample blocks was assessed. The results show that deterministic models were more accurate than statistical models. Further, by simulating the base unit for modeling from aggregating blocks, I assessed how well the deterministic models estimate sub-unit-level populations. I also assessed the aggregation effects and the resealing effects on sub-unit estimates. Lastly, from another set of mixed-land-use sample blocks, a mixed-land-use model was derived and compared with a residential-land-use model. The results of per-field land use classification are satisfactory with a Kappa accuracy statistics of 0.747. Model Assessments by land use show that population estimates for multi-family land use areas have higher errors than those for single-family land use areas, and population estimates for mixed land use areas have higher errors than those for residential land use areas. The assessments of sub-unit estimates using a simulation approach indicate that smaller areas show higher estimation errors, estimation errors do not relate to the base unit size, and resealing improves all levels of sub-unit estimates.

Energy and remote sensing. [satellite exploration, monitoring, siting

NASA Technical Reports Server (NTRS)

Summers, R. A.; Smith, W. L.; Short, N. M.

1977-01-01

Exploration for uranium, thorium, oil, gas and geothermal activity through remote sensing techniques is considered; satellite monitoring of coal-derived CO2 in the atmosphere, and the remote assessment of strip mining and land restoration are also mentioned. Reference is made to color ratio composites based on Landsat data, which may aid in the detection of uranium deposits, and to computer-enhanced black and white airborne scanning imagery, which may locate geothermal anomalies. Other applications of remote sensing to energy resources management, including mapping of transportation networks and power plant siting, are discussed.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

Contents: Multi-sensors and systems in remote sensing ; Radar sensing systems over land; Remote sensing techniques in oceanography; Influence of...propagation media and background; Infrared techniques in remote sensing ; Photography in remote sensing ; Analytical studies in remote sensing .

Land cover change detection of Hatiya Island, Bangladesh, using remote sensing techniques

NASA Astrophysics Data System (ADS)

Kumar, Lalit; Ghosh, Manoj Kumer

2012-01-01

Land cover change is a significant issue for environmental managers for sustainable management. Remote sensing techniques have been shown to have a high probability of recognizing land cover patterns and change detection due to periodic coverage, data integrity, and provision of data in a broad range of the electromagnetic spectrum. We evaluate the applicability of remote sensing techniques for land cover pattern recognition, as well as land cover change detection of the Hatiya Island, Bangladesh, and quantify land cover changes from 1977 to 1999. A supervised classification approach was used to classify Landsat Enhanced Thematic Mapper (ETM), Thematic Mapper (TM), and Multispectral Scanner (MSS) images into eight major land cover categories. We detected major land cover changes over the 22-year study period. During this period, marshy land, mud, mud with small grass, and bare soil had decreased by 85%, 46%, 44%, and 24%, respectively, while agricultural land, medium forest, forest, and settlement had positive changes of 26%, 45%, 363%, and 59%, respectively. The primary drivers of such landscape change were erosion and accretion processes, human pressure, and the reforestation and land reclamation programs of the Bangladesh Government.

Application of remotely sensed land-use information to improve estimates of streamflow characteristics, volume 8. [Maryland, Virginia, and Delaware

NASA Technical Reports Server (NTRS)

Pluhowski, E. J. (Principal Investigator)

1977-01-01

The author has identified the following significant results. Land use data derived from high altitude photography and satellite imagery were studied for 49 basins in Delaware, and eastern Maryland and Virginia. Applying multiple regression techniques to a network of gaging stations monitoring runoff from 39 of the basins, demonstrated that land use data from high altitude photography provided an effective means of significantly improving estimates of stream flow. Forty stream flow characteristic equations for incorporating remotely sensed land use information, were compared with a control set of equations using map derived land cover. Significant improvement was detected in six equations where level 1 data was added and in five equations where level 2 information was utilized. Only four equations were improved significantly using land use data derived from LANDSAT imagery. Significant losses in accuracy due to the use of remotely sensed land use information were detected only in estimates of flood peaks. Losses in accuracy for flood peaks were probably due to land cover changes associated with temporal differences among the primary land use data sources.

Literature review of the remote sensing of natural resources. [bibliography

NASA Technical Reports Server (NTRS)

Fears, C. B. (Editor); Inglis, M. H. (Editor)

1977-01-01

Abstracts of 596 documents related to remote sensors or the remote sensing of natural resources by satellite, aircraft, or ground-based stations are presented. Topics covered include general theory, geology and hydrology, agriculture and forestry, marine sciences, urban land use, and instrumentation. Recent documents not yet cited in any of the seven information sources used for the compilation are summarized. An author/key word index is provided.

Monitoring landscape level processes using remote sensing of large plots

Treesearch

Raymond L. Czaplewski

1991-01-01

Global and regional assessaents require timely information on landscape level status (e.g., areal extent of different ecosystems) and processes (e.g., changes in land use and land cover). To measure and understand these processes at the regional level, and model their impacts, remote sensing is often necessary. However, processing massive volumes of remotely sensing...

Image Classification Workflow Using Machine Learning Methods

NASA Astrophysics Data System (ADS)

Christoffersen, M. S.; Roser, M.; Valadez-Vergara, R.; Fernández-Vega, J. A.; Pierce, S. A.; Arora, R.

2016-12-01

Recent increases in the availability and quality of remote sensing datasets have fueled an increasing number of scientifically significant discoveries based on land use classification and land use change analysis. However, much of the software made to work with remote sensing data products, specifically multispectral images, is commercial and often prohibitively expensive. The free to use solutions that are currently available come bundled up as small parts of much larger programs that are very susceptible to bugs and difficult to install and configure. What is needed is a compact, easy to use set of tools to perform land use analysis on multispectral images. To address this need, we have developed software using the Python programming language with the sole function of land use classification and land use change analysis. We chose Python to develop our software because it is relatively readable, has a large body of relevant third party libraries such as GDAL and Spectral Python, and is free to install and use on Windows, Linux, and Macintosh operating systems. In order to test our classification software, we performed a K-means unsupervised classification, Gaussian Maximum Likelihood supervised classification, and a Mahalanobis Distance based supervised classification. The images used for testing were three Landsat rasters of Austin, Texas with a spatial resolution of 60 meters for the years of 1984 and 1999, and 30 meters for the year 2015. The testing dataset was easily downloaded using the Earth Explorer application produced by the USGS. The software should be able to perform classification based on any set of multispectral rasters with little to no modification. Our software makes the ease of land use classification using commercial software available without an expensive license.

Use of remote sensing technology for inventorying and planning utilization of land resources in South Dakota

NASA Technical Reports Server (NTRS)

1975-01-01

A project was undertaken in Meade County, South Dakota to provide (1) a general county-wide resource survey of land use and soils and (2) a detailed survey of land use for the environmentally sensitive area adjacent to the Black Hills. Imagery from LANDSAT-1 was visually interpreted to provide land use information and a general soils map. A detailed land use map for the Black Hills area was interpreted from RB-57 photographs and interpretations of soil characteristics were input into a computer data base and mapped. The detailed land use data were then used in conjunction with soil maps to provide information for the development of zoning ordinance maps and other land use planning in the Black Hills area. The use of photographs as base maps was also demonstrated. In addition, the use of airborne thermography to locate spoilage areas in sugar beet piles and to determine the apparent temperature of rooftops was evaluated.

Remote sensing sensors and applications in environmental resources mapping and modeling

USGS Publications Warehouse

Melesse, Assefa M.; Weng, Qihao; Thenkabail, Prasad S.; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA).

PubMed

Reichenau, Tim G; Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI.

Spatial Heterogeneity of Leaf Area Index (LAI) and Its Temporal Course on Arable Land: Combining Field Measurements, Remote Sensing and Simulation in a Comprehensive Data Analysis Approach (CDAA)

PubMed Central

Korres, Wolfgang; Montzka, Carsten; Fiener, Peter; Wilken, Florian; Stadler, Anja; Waldhoff, Guido; Schneider, Karl

2016-01-01

The ratio of leaf area to ground area (leaf area index, LAI) is an important state variable in ecosystem studies since it influences fluxes of matter and energy between the land surface and the atmosphere. As a basis for generating temporally continuous and spatially distributed datasets of LAI, the current study contributes an analysis of its spatial variability and spatial structure. Soil-vegetation-atmosphere fluxes of water, carbon and energy are nonlinearly related to LAI. Therefore, its spatial heterogeneity, i.e., the combination of spatial variability and structure, has an effect on simulations of these fluxes. To assess LAI spatial heterogeneity, we apply a Comprehensive Data Analysis Approach that combines data from remote sensing (5 m resolution) and simulation (150 m resolution) with field measurements and a detailed land use map. Test area is the arable land in the fertile loess plain of the Rur catchment on the Germany-Belgium-Netherlands border. LAI from remote sensing and simulation compares well with field measurements. Based on the simulation results, we describe characteristic crop-specific temporal patterns of LAI spatial variability. By means of these patterns, we explain the complex multimodal frequency distributions of LAI in the remote sensing data. In the test area, variability between agricultural fields is higher than within fields. Therefore, spatial resolutions less than the 5 m of the remote sensing scenes are sufficient to infer LAI spatial variability. Frequency distributions from the simulation agree better with the multimodal distributions from remote sensing than normal distributions do. The spatial structure of LAI in the test area is dominated by a short distance referring to field sizes. Longer distances that refer to soil and weather can only be derived from remote sensing data. Therefore, simulations alone are not sufficient to characterize LAI spatial structure. It can be concluded that a comprehensive picture of LAI spatial heterogeneity and its temporal course can contribute to the development of an approach to create spatially distributed and temporally continuous datasets of LAI. PMID:27391858

Water impact studies. [impact of remote sensing techniques on management storage, flow, and delivery of California water

NASA Technical Reports Server (NTRS)

Colwell, R. N.

1973-01-01

An investigation has begun into the potential impact of using modern remote sensing techniques as an aid in managing, even on a day-to-day basis, the storage, flow, and delivery of water made available through the California Water Project. It is obvious that the amount of this impact depends upon the extent to which remote sensing is proven to be useful in improving predictions of both the amount of water that will be available and the amount that will be needed. It is also proposed to investigate the potential impact of remote sensing techniques as an aid in monitoring, and perhaps even in directing, changes in land use and life style being brought about through the increased availability of water in central and southern California as a result of the California Water Project. The impact of remote sensing can be of appreciable significance only if: (1) the induced changes are very substantial ones; (2) remote sensing is found, in this context, to be very useful and potentially very cost effective; and (3) resource managers adopt this new technology. Analyses will be conducted of the changing economic bases and the new land use demands resulting from increased water availability in central and southern California.

[Evaluation of eco-environmental quality based on artificial neural network and remote sensing techniques].

PubMed

Li, Hongyi; Shi, Zhou; Sha, Jinming; Cheng, Jieliang

2006-08-01

In the present study, vegetation, soil brightness, and moisture indices were extracted from Landsat ETM remote sensing image, heat indices were extracted from MODIS land surface temperature product, and climate index and other auxiliary geographical information were selected as the input of neural network. The remote sensing eco-environmental background value of standard interest region evaluated in situ was selected as the output of neural network, and the back propagation (BP) neural network prediction model containing three layers was designed. The network was trained, and the remote sensing eco-environmental background value of Fuzhou in China was predicted by using software MATLAB. The class mapping of remote sensing eco-environmental background values based on evaluation standard showed that the total classification accuracy was 87. 8%. The method with a scheme of prediction first and classification then could provide acceptable results in accord with the regional eco-environment types.

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study

PubMed Central

2014-01-01

Background Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Methods Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. Results During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. Conclusions In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season. PMID:24927747

Remotely-sensed, nocturnal, dew point correlates with malaria transmission in Southern Province, Zambia: a time-series study.

PubMed

Nygren, David; Stoyanov, Cristina; Lewold, Clemens; Månsson, Fredrik; Miller, John; Kamanga, Aniset; Shiff, Clive J

2014-06-13

Plasmodium falciparum transmission has decreased significantly in Zambia in the last decade. The malaria transmission is influenced by environmental variables. Incorporation of environmental variables in models of malaria transmission likely improves model fit and predicts probable trends in malaria disease. This work is based on the hypothesis that remotely-sensed environmental factors, including nocturnal dew point, are associated with malaria transmission and sustain foci of transmission during the low transmission season in the Southern Province of Zambia. Thirty-eight rural health centres in Southern Province, Zambia were divided into three zones based on transmission patterns. Correlations between weekly malaria cases and remotely-sensed nocturnal dew point, nocturnal land surface temperature as well as vegetation indices and rainfall were evaluated in time-series analyses from 2012 week 19 to 2013 week 36. Zonal as well as clinic-based, multivariate, autoregressive, integrated, moving average (ARIMAX) models implementing environmental variables were developed to model transmission in 2011 week 19 to 2012 week 18 and forecast transmission in 2013 week 37 to week 41. During the dry, low transmission season significantly higher vegetation indices, nocturnal land surface temperature and nocturnal dew point were associated with the areas of higher transmission. Environmental variables improved ARIMAX models. Dew point and normalized differentiated vegetation index were significant predictors and improved all zonal transmission models. In the high-transmission zone, this was also seen for land surface temperature. Clinic models were improved by adding dew point and land surface temperature as well as normalized differentiated vegetation index. The mean average error of prediction for ARIMAX models ranged from 0.7 to 33.5%. Forecasts of malaria incidence were valid for three out of five rural health centres; however, with poor results at the zonal level. In this study, the fit of ARIMAX models improves when environmental variables are included. There is a significant association of remotely-sensed nocturnal dew point with malaria transmission. Interestingly, dew point might be one of the factors sustaining malaria transmission in areas of general aridity during the dry season.

Landsat: A Global Land-Observing Program

USGS Publications Warehouse

,

2003-01-01

Landsat represents the world's longest continuously acquired collection of space-based land remote sensing data. The Landsat Project is a joint initiative of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA) designed to gather Earth resource data from space. NASA developed and launched the spacecrafts, while the USGS handles the operations, maintenance, and management of all ground data reception, processing, archiving, product generation, and distribution.

Factors influencing spatial pattern in tropical forest clearance and stand age: Implications for carbon storage and species diversity.

Treesearch

E. H. Helmer; Thomas J. Brandeis; Ariel E. Lugo; Todd Kennaway

2008-01-01

Little is known about the tropical forests that undergo clearing as urban/built-up and other developed lands spread. This study uses remote sensing-based maps of Puerto Rico, multinomial logit models and forest inventory data to explain patterns of forest age and the age of forests cleared for land development and assess their implications for forest carbon storage and...

Parameters and structure of lunar regolith in Chang'E-3 landing area from lunar penetrating radar (LPR) data

NASA Astrophysics Data System (ADS)

Dong, Zehua; Fang, Guangyou; Ji, Yicai; Gao, Yunze; Wu, Chao; Zhang, Xiaojuan

2017-01-01

Chang'E-3 (CE-3) landed in the northwest Mare Imbrium, a region that has not been explored before. Yutu rover that released by CE-3 lander carried the first lunar surface penetrating radar (LPR) for exploring lunar regolith thickness and subsurface shallow geological structures. In this paper, based on the LPR data and the Panoramic Camera (PC) data, we first calculate the lunar surface regolith parameters in CE-3 landing area including its permittivity, density, conductivity and FeO + TiO2 content. LPR data provides a higher spatial resolution and more accuracy for the lunar regolith parameters comparing to other remote sensing techniques, such as orbit radar sounder and microwave sensing or earth-based powerful radar. We also derived the regolith thickness and its weathered rate with much better accuracy in the landing area. The results indicate that the regolith growth rate is much faster than previous estimation, the regolith parameters are not uniform even in such a small study area and the thickness and growth rate of lunar regolith here are different from other areas in Mare Imbrium. We infer that the main reason should be geological deformation that caused by multiple impacts of meteorites in different sizes.

Remote sensing of land surface phenology

USGS Publications Warehouse

Meier, G.A.; Brown, Jesslyn F.

2014-01-01

Remote sensing of land-surface phenology is an important method for studying the patterns of plant and animal growth cycles. Phenological events are sensitive to climate variation; therefore phenology data provide important baseline information documenting trends in ecology and detecting the impacts of climate change on multiple scales. The USGS Remote sensing of land surface phenology program produces annually, nine phenology indicator variables at 250 m and 1,000 m resolution for the contiguous U.S. The 12 year archive is available at http://phenology.cr.usgs.gov/index.php.

Land cover mapping and change detection in urban watersheds using QuickBird high spatial resolution satellite imagery

NASA Astrophysics Data System (ADS)

Hester, David Barry

The objective of this research was to develop methods for urban land cover analysis using QuickBird high spatial resolution satellite imagery. Such imagery has emerged as a rich commercially available remote sensing data source and has enjoyed high-profile broadcast news media and Internet applications, but methods of quantitative analysis have not been thoroughly explored. The research described here consists of three studies focused on the use of pan-sharpened 61-cm spatial resolution QuickBird imagery, the spatial resolution of which is the highest of any commercial satellite. In the first study, a per-pixel land cover classification method is developed for use with this imagery. This method utilizes a per-pixel classification approach to generate an accurate six-category high spatial resolution land cover map of a developing suburban area. The primary objective of the second study was to develop an accurate land cover change detection method for use with QuickBird land cover products. This work presents an efficient fuzzy framework for transforming map uncertainty into accurate and meaningful high spatial resolution land cover change analysis. The third study described here is an urban planning application of the high spatial resolution QuickBird-based land cover product developed in the first study. This work both meaningfully connects this exciting new data source to urban watershed management and makes an important empirical contribution to the study of suburban watersheds. Its analysis of residential roads and driveways as well as retail parking lots sheds valuable light on the impact of transportation-related land use on the suburban landscape. Broadly, these studies provide new methods for using state-of-the-art remote sensing data to inform land cover analysis and urban planning. These methods are widely adaptable and produce land cover products that are both meaningful and accurate. As additional high spatial resolution satellites are launched and the cost of high resolution imagery continues to decline, this research makes an important contribution to this exciting era in the science of remote sensing.

Mekong Land Cover Dasboard: Regional Land Cover Mointoring Systems

NASA Astrophysics Data System (ADS)

Saah, D. S.; Towashiraporn, P.; Aekakkararungroj, A.; Phongsapan, K.; Triepke, J.; Maus, P.; Tenneson, K.; Cutter, P. G.; Ganz, D.; Anderson, E.

2016-12-01

SERVIR-Mekong, a USAID-NASA partnership, helps decision makers in the Lower Mekong Region utilize GIS and Remote Sensing information to inform climate related activities. In 2015, SERVIR-Mekong conducted a geospatial needs assessment for the Lower Mekong countries which included individual country consultations. The team found that many countries were dependent on land cover and land use maps for land resource planning, quantifying ecosystem services, including resilience to climate change, biodiversity conservation, and other critical social issues. Many of the Lower Mekong countries have developed national scale land cover maps derived in part from remote sensing products and geospatial technologies. However, updates are infrequent and classification systems do not always meet the needs of key user groups. In addition, data products stop at political boundaries and are often not accessible making the data unusable across country boundaries and with resource management partners. Many of these countries rely on global land cover products to fill the gaps of their national efforts, compromising consistency between data and policies. These gaps in national efforts can be filled by a flexible regional land cover monitoring system that is co-developed by regional partners with the specific intention of meeting national transboundary needs, for example including consistent forest definitions in transboundary watersheds. Based on these facts, key regional stakeholders identified a need for a land cover monitoring system that will produce frequent, high quality land cover maps using a consistent regional classification scheme that is compatible with national country needs. SERVIR-Mekong is currently developing a solution that leverages recent developments in remote sensing science and technology, such as Google Earth Engine (GEE), and working together with production partners to develop a system that will use a common set of input data sources to generate high-quality regional land cover maps on a regular basis that are consistent and continuous across the landscape. The system is being designed to facilitate improved policy, planning, and decision making by a wide range of users (such as government agencies, local community groups, non-profit organizations, and the private sector).

"Land-Cover Conversion in Amazonia, The Role of ENV" Ironment and Substrate composition in Modifying SOI

NASA Technical Reports Server (NTRS)

Roberts, Dar A.; Chadwick, Oliver A.; Batista, Getulio T.

2003-01-01

LBA research from the first phase of LBA focused on three broad categories: 1) mapping land cover and quantifying rates of change, persistence of pasture, and area of recovering forest; 2) evaluating the role of environmental factors and land-use history on soil biogeochemistry; and 3) quantifying the natural and human controls on stream nutrient concentrations. The focus of the research was regional, concentrating primarily in the state of RondBnia, but also included land-cover mapping in the vicinity of Maraba, Para, and Manaus, Amazonas. Remote sensing analysis utilized Landsat Thematic Mapper (TM) and Multispectral Scanner (MS S) data to map historical patterns of land-cover change. Specific questions addressed by the remote sensing component of the research included: 1) what is the areal extent of dominant land-cover classes? 2) what are the rates of change of dominant land cover through processes of deforestation, disturbance and regeneration? and 3) what are the dynamic properties of each class that characterize temporal variability, duration, and frequency of repeat disturbance? Biogeochemical analysis focused on natural variability and impacts of land-use/land-cover changes on soil and stream biogeochemical properties at the regional scale. An emphasis was given to specific soil properties considered to be primary limiting factors regionally, including phosphorus, nitrogen, base cations and cation-exchange properties. Stream sampling emphasized the relative effects of the rates and timing of land-cover change on stream nutrients, demonstrating that vegetation conversion alone does not impact nutrients as much as subsequent land use and urbanization.

National Satellite Land Remote Sensing Data Archive

USGS Publications Warehouse

Faundeen, John L.; Longhenry, Ryan

2018-06-13

The National Satellite Land Remote Sensing Data Archive is managed on behalf of the Secretary of the Interior by the U.S. Geological Survey’s Earth Resources Observation and Science Center. The Land Remote Sensing Policy Act of 1992 (51 U.S.C. §601) directed the U.S. Department of the Interior to establish a permanent global archive consisting of imagery over land areas obtained from satellites orbiting the Earth. The law also directed the U.S. Department of the Interior, delegated to the U.S. Geological Survey, to ensure proper storage and preservation of imagery, and timely access for all parties. Since 2008, these images have been available at no cost to the user.

Monitoring wetland of Poyang Lake National Nature Reserve zone by remote sensing

NASA Astrophysics Data System (ADS)

Le, Xinghua; Fan, Zhewen; Fang, Yu; Yu, Yuping; Zhang, Yun

2008-10-01

In order to monitor the wetland of the Poyang Lake national nature reserve zone, we selected three different seasons TM image data which were achieved individually in April 23th in 1988, Nov 2nd in 1994, and Jan 1st in 2000. Based on the band 5, band 4 and band 3of TM image, we divided the land coverage of Poyang Lake national nature reserve zone into three classes--water field, meadow field and the other land use by rule of maximum likelihood. Using the outcome data to make the statistical analysis, combining with the GIS overlay function operation, the land coverage changes of the Poyang Lake national nature reserve zone can be achieved. Clipped by the Poyang Lake national nature reserve zone boundary, the land coverage changes of Poyang Lake national nature reserve zone in three different years can be attained. Compared with the different wetland coverage data in year of 1988, 1994, 2000, the Poyang Lake national nature reserve zone eco-environment can be inferred from it. After analyzing the land coverage changes data, we draw the conclusion that the effort of Poyang Lake national nature reserve administration bureaucracy has worked well in certain sense.

Integrating remotely sensed land cover observations and a biogeochemical model for estimating forest ecosystem carbon dynamics

USGS Publications Warehouse

Liu, J.; Liu, S.; Loveland, Thomas R.; Tieszen, L.L.

2008-01-01

Land cover change is one of the key driving forces for ecosystem carbon (C) dynamics. We present an approach for using sequential remotely sensed land cover observations and a biogeochemical model to estimate contemporary and future ecosystem carbon trends. We applied the General Ensemble Biogeochemical Modelling System (GEMS) for the Laurentian Plains and Hills ecoregion in the northeastern United States for the period of 1975-2025. The land cover changes, especially forest stand-replacing events, were detected on 30 randomly located 10-km by 10-km sample blocks, and were assimilated by GEMS for biogeochemical simulations. In GEMS, each unique combination of major controlling variables (including land cover change history) forms a geo-referenced simulation unit. For a forest simulation unit, a Monte Carlo process is used to determine forest type, forest age, forest biomass, and soil C, based on the Forest Inventory and Analysis (FIA) data and the U.S. General Soil Map (STATSGO) data. Ensemble simulations are performed for each simulation unit to incorporate input data uncertainty. Results show that on average forests of the Laurentian Plains and Hills ecoregion have been sequestrating 4.2 Tg C (1 teragram = 1012 gram) per year, including 1.9 Tg C removed from the ecosystem as the consequences of land cover change. ?? 2008 Elsevier B.V.

Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery

NASA Technical Reports Server (NTRS)

Estes, John E.; Gebelein, Jennifer

1999-01-01

This report is produced in accordance with the requirements outlined in the NASA Research Grant NAG9-1032 titled "Validation of Land Cover Maps Utilizing Astronaut Acquired Imagery". This grant funds the Remote Sensing Research Unit of the University of California, Santa Barbara. This document summarizes the research progress and accomplishments to date and describes current on-going research activities. Even though this grant has technically expired, in a contractual sense, work continues on this project. Therefore, this summary will include all work done through and 5 May 1999. The principal goal of this effort is to test the accuracy of a sub-regional portion of an AVHRR-based land cover product. Land cover mapped to three different classification systems, in the southwestern United States, have been subjected to two specific accuracy assessments. One assessment utilizing astronaut acquired photography, and a second assessment employing Landsat Thematic Mapper imagery, augmented in some cases, high aerial photography. Validation of these three land cover products has proceeded using a stratified sampling methodology. We believe this research will provide an important initial test of the potential use of imagery acquired from Shuttle and ultimately the International Space Station (ISS) for the operational validation of the Moderate Resolution Imaging Spectrometer (MODIS) land cover products.

Commercialization of the land remote sensing system: An examination of mechanisms and issues

NASA Technical Reports Server (NTRS)

Cauley, J. K.; Gaelick, C.; Greenberg, J. S.; Logsdon, J.; Monk, T.

1983-01-01

In September 1982 the Secretary of Commerce was authorized (by Title II of H.R. 5890 of the 97th Congress) to plan and provide for the management and operation of the civil land remote sensing satellite systems, to provide for user fees, and to plan for the transfer of the ownership and operation of future civil operational land remote sensing satellite systems to the private sector. As part of the planning for transfer, a number of approaches were to be compared including wholly private ownership and operation of the system by an entity competitively selected, mixed government/private ownership and operation, and a legislatively-chartered privately-owned corporation. The results of an analysis and comparison of a limited number of financial and organizational approaches for either transfer of the ownership and operation of the civil operational land remote sensing program to the private sector or government retention are presented.

Applications of remote sensing in resource management in Nebraska

NASA Technical Reports Server (NTRS)

Drew, J. V.

1974-01-01

The project is reported for studying the application of remote sensing in land use classification and delineation of major tectonic lineaments in Nebraska. Other research reported include the use of aircraft and ERTS-1 satellite imagery in detecting and estimating the acreage of irrigated land, and the application of remote sensing in estimating evapotranspiration in the Platte River Basin.

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

NASA Astrophysics Data System (ADS)

Rai, Praveen Kumar; Chandel, Rajeev Singh; Mishra, Varun Narayan; Singh, Prafull

2018-03-01

Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.

Determining urban land uses through building-associated element attributes derived from lidar and aerial photographs

NASA Astrophysics Data System (ADS)

Meng, Xuelian

Urban land-use research is a key component in analyzing the interactions between human activities and environmental change. Researchers have conducted many experiments to classify urban or built-up land, forest, water, agriculture, and other land-use and land-cover types. Separating residential land uses from other land uses within urban areas, however, has proven to be surprisingly troublesome. Although high-resolution images have recently become more available for land-use classification, an increase in spatial resolution does not guarantee improved classification accuracy by traditional classifiers due to the increase of class complexity. This research presents an approach to detect and separate residential land uses on a building scale directly from remotely sensed imagery to enhance urban land-use analysis. Specifically, the proposed methodology applies a multi-directional ground filter to generate a bare ground surface from lidar data, then utilizes a morphology-based building detection algorithm to identify buildings from lidar and aerial photographs, and finally separates residential buildings using a supervised C4.5 decision tree analysis based on the seven selected building land-use indicators. Successful execution of this study produces three independent methods, each corresponding to the steps of the methodology: lidar ground filtering, building detection, and building-based object-oriented land-use classification. Furthermore, this research provides a prototype as one of the few early explorations of building-based land-use analysis and successful separation of more than 85% of residential buildings based on an experiment on an 8.25-km2 study site located in Austin, Texas.

Land remote sensing in the 1980's

NASA Technical Reports Server (NTRS)

Thome, P. G.

1982-01-01

A discussion is presented concerning U.S. governmental funding policy for the Land Remote Sensing programs, in which the Landsat spacecraft and the research and development activities associated with them are essential elements. Even if present program management practices were to be changed in the next 1-2 years, the investment of significant amounts of private capital in land remote sensing may be 3-5 years away, due to the immaturity of the prospective markets for the services rendered and the present state of technological development. It is judged that even if NASA is successful in bringing significant private investment into remote sensing activities by the mid-1980s, government must continue to support basic research and expensive technology development in long term and high risk, but potentially high payoff, areas which the still-developing remote sensing industry cannot afford.

Remote sensing of land-based voids using computer enhanced infrared thermography

NASA Astrophysics Data System (ADS)

Weil, Gary J.

1989-10-01

Experiments are described in which computer-enhanced infrared thermography techniques are used to detect and describe subsurface land-based voids, such as voids surrounding buried utility pipes, voids in concrete structures such as airport taxiways, abandoned buried utility storage tanks, and caves and underground shelters. Infrared thermography also helps to evaluate bridge deck systems, highway pavements, and garage concrete. The IR thermography techniques make it possible to survey large areas quickly and efficiently. The paper also surveys the advantages and limitations of thermographic testing in comparison with other forms of NDT.

Understanding global changes on the land - A potential focus for NASA earth sciences and land remote sensing

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Tuyahov, A. J.; Hogg, H. C.

1983-01-01

Planned NASA contributions to the study of the interaction of living organisms with their physical and chemical environments are discussed. Five major land-related research objectives are stated and the role of remote sensing in achieving them is addressed. The importance of improved sensors and cooperation with domestic and international organizations is stressed.

Environmental application of remote sensing methods to coastal zone land use and marine resource management, Appendices A to E. [in southeastern Virginia

NASA Technical Reports Server (NTRS)

1972-01-01

Important data were compiled for use with the Richmond-Cape Henry Environmental Laboratory (RICHEL) remote sensing project in coastal zone land use and marine resources management, and include RICHEL climatological data and sources, a land use inventory, topographic and soil maps, and gaging records for RICHEL surface waters.

15 CFR 6.4 - Adjustments to penalties.

Code of Federal Regulations, 2014 CFR

2014-01-01

... $130,000. (f) National Oceanic and Atmospheric Administration. (1) 51 U.S.C. 60123(a)(3), Land Remote Sensing Policy Act of 2010; new penalty $10,000. (2) 51 U.S.C. 60148(c), Land Remote Sensing Policy Act of...

15 CFR 6.4 - Adjustments to penalties.

Code of Federal Regulations, 2013 CFR

2013-01-01

... $130,000. (f) National Oceanic and Atmospheric Administration. (1) 51 U.S.C. 60123(a)(3), Land Remote Sensing Policy Act of 2010; new penalty $10,000. (2) 51 U.S.C. 60148(c), Land Remote Sensing Policy Act of...

Difet: Distributed Feature Extraction Tool for High Spatial Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Eken, S.; Aydın, E.; Sayar, A.

2017-11-01

In this paper, we propose distributed feature extraction tool from high spatial resolution remote sensing images. Tool is based on Apache Hadoop framework and Hadoop Image Processing Interface. Two corner detection (Harris and Shi-Tomasi) algorithms and five feature descriptors (SIFT, SURF, FAST, BRIEF, and ORB) are considered. Robustness of the tool in the task of feature extraction from LandSat-8 imageries are evaluated in terms of horizontal scalability.

Estimation of the Relationship Between Remotely Sensed Anthropogenic Heat Discharge and Building Energy Use

NASA Technical Reports Server (NTRS)

Zhou, Yuyu; Weng, Qihao; Gurney, Kevin R.; Shuai, Yanmin; Hu, Xuefei

2012-01-01

This paper examined the relationship between remotely sensed anthropogenic heat discharge and energy use from residential and commercial buildings across multiple scales in the city of Indianapolis, Indiana, USA. The anthropogenic heat discharge was estimated with a remote sensing-based surface energy balance model, which was parameterized using land cover, land surface temperature, albedo, and meteorological data. The building energy use was estimated using a GIS-based building energy simulation model in conjunction with Department of Energy/Energy Information Administration survey data, the Assessor's parcel data, GIS floor areas data, and remote sensing-derived building height data. The spatial patterns of anthropogenic heat discharge and energy use from residential and commercial buildings were analyzed and compared. Quantitative relationships were evaluated across multiple scales from pixel aggregation to census block. The results indicate that anthropogenic heat discharge is consistent with building energy use in terms of the spatial pattern, and that building energy use accounts for a significant fraction of anthropogenic heat discharge. The research also implies that the relationship between anthropogenic heat discharge and building energy use is scale-dependent. The simultaneous estimation of anthropogenic heat discharge and building energy use via two independent methods improves the understanding of the surface energy balance in an urban landscape. The anthropogenic heat discharge derived from remote sensing and meteorological data may be able to serve as a spatial distribution proxy for spatially-resolved building energy use, and even for fossil-fuel CO2 emissions if additional factors are considered.

Combining optical remote sensing, agricultural statistics and field observations for culture recognition over a peri-urban region

NASA Astrophysics Data System (ADS)

Delbart, Nicolas; Emmanuelle, Vaudour; Fabienne, Maignan; Catherine, Ottlé; Jean-Marc, Gilliot

2017-04-01

This study explores the potential of multi-temporal optical remote sensing, with high revisit frequency, to derive missing information on agricultural calendar and crop types over the agricultural lands in the Versailles plain in the western Paris suburbs. This study comes besides past and ongoing studies on the use of radar and high spatial resolution optical remote sensing to monitor agricultural practices in this study area (e.g. Vaudour et al. 2014). Agricultural statistics, such as the Land Parcel Identification System (LPIS) for France, permit to know the nature of annual crops for each digitized declared field of this land parcel registry. However, within each declared field several cropped plots and a diversity of practices may exist, being marked by agricultural rotations which vary both spatially and temporally within it and differ from one year to the other. Even though the new LPIS to be released in 2016 is expected to describe individual plots within declared fields, its attributes may not enable to discriminate between winter and spring crops. Here we evaluate the potential of high observation frequency remote sensing to differentiate seasonal crops based essentially on the seasonality of the spectral properties. In particular, we use the Landsat data to spatially disaggregate the LPIS statistical data, on the basis of the analysis of the remote sensing spectral seasonality measured on a number of selected ground-observed fields. This work is carried out in the framework of the CNES TOSCA-PLEIADES-CO of the French Space Agency.

Monitoring terrestrial dissolved organic carbon export at land-water interfaces using remote sensing

NASA Astrophysics Data System (ADS)

Yu, Q.; Li, J.; Tian, Y. Q.

2017-12-01

Carbon flux from land to oceans and lakes is a crucial component of carbon cycling. However, this lateral carbon flow at land-water interface is often neglected in the terrestrial carbon cycle budget, mainly because observations of the carbon dynamics are very limited. Monitoring CDOM/DOC dynamics using remote sensing and assessing DOC export from land to water remains a challenge. Current CDOM retrieval algorithms in the field of ocean color are not simply applicable to inland aquatic ecosystems since they were developed for coarse resolution ocean-viewing imagery and less complex water types in open-sea. We developed a new semi-analytical algorithm, called SBOP (Shallow water Bio-Optical Properties algorithm) to adapt to shallow inland waters. SBOP was first developed and calibrated based on in situ hyperspectral radiometer data. Then we applied it to the Landsat-8 OLI images and evaluated the effectiveness of the multispectral images on inversion of CDOM absorption based on our field sampling at the Saginaw Bay in the Lake Huron. The algorithm performances (RMSE = 0.17 and R2 = 0.87 in the Saginaw Bay; R2 = 0.80 in the northeastern US lakes) is promising and we conclude the CDOM absorption can be derived from Landsat-8 OLI image in both optically deep and optically shallow waters with high accuracy. Our method addressed challenges on employing appropriate atmospheric correction, determining bottom reflectance influence for shallow waters, and improving for bio-optical properties retrieval, as well as adapting to both hyperspectral and the multispectral remote sensing imagery. Over 100 Landsat-8 images in Lake Huron, northeastern US lakes, and the Arctic major rivers were processed to understand the CDOM spatio-temporal dynamics and its associated driving factors.

How do large-scale agricultural investments affect land use and the environment on the western slopes of Mount Kenya? Empirical evidence based on small-scale farmers' perceptions and remote sensing.

PubMed

Zaehringer, Julie G; Wambugu, Grace; Kiteme, Boniface; Eckert, Sandra

2018-05-01

Africa has been heavily targeted by large-scale agricultural investments (LAIs) throughout the last decade, with scarcely known impacts on local social-ecological systems. In Kenya, a large number of LAIs were made in the region northwest of Mount Kenya. These large-scale farms produce vegetables and flowers mainly for European markets. However, land use in the region remains dominated by small-scale crop and livestock farms with less than 1 ha of land each, who produce both for their own subsistence and for the local markets. We interviewed 100 small-scale farmers living near five different LAIs to elicit their perceptions of the impacts that these LAIs have on their land use and the overall environment. Furthermore, we analyzed remotely sensed land cover and land use data to assess land use change in the vicinity of the five LAIs. While land use change did not follow a clear trend, a number of small-scale farmers did adapt their crop management to environmental changes such as a reduced river water flows and increased pests, which they attributed to the presence of LAIs. Despite the high number of open conflicts between small-scale land users and LAIs around the issue of river water abstraction, the main environmental impact, felt by almost half of the interviewed land users, was air pollution with agrochemicals sprayed on the LAIs' land. Even though only a low percentage of local land users and their household members were directly involved with LAIs, a large majority of respondents favored the presence of LAIs nearby, as they are believed to contribute to the region's overall economic development. Copyright © 2018 Elsevier Ltd. All rights reserved.

Information mining in remote sensing imagery

NASA Astrophysics Data System (ADS)

Li, Jiang

The volume of remotely sensed imagery continues to grow at an enormous rate due to the advances in sensor technology, and our capability for collecting and storing images has greatly outpaced our ability to analyze and retrieve information from the images. This motivates us to develop image information mining techniques, which is very much an interdisciplinary endeavor drawing upon expertise in image processing, databases, information retrieval, machine learning, and software design. This dissertation proposes and implements an extensive remote sensing image information mining (ReSIM) system prototype for mining useful information implicitly stored in remote sensing imagery. The system consists of three modules: image processing subsystem, database subsystem, and visualization and graphical user interface (GUI) subsystem. Land cover and land use (LCLU) information corresponding to spectral characteristics is identified by supervised classification based on support vector machines (SVM) with automatic model selection, while textural features that characterize spatial information are extracted using Gabor wavelet coefficients. Within LCLU categories, textural features are clustered using an optimized k-means clustering approach to acquire search efficient space. The clusters are stored in an object-oriented database (OODB) with associated images indexed in an image database (IDB). A k-nearest neighbor search is performed using a query-by-example (QBE) approach. Furthermore, an automatic parametric contour tracing algorithm and an O(n) time piecewise linear polygonal approximation (PLPA) algorithm are developed for shape information mining of interesting objects within the image. A fuzzy object-oriented database based on the fuzzy object-oriented data (FOOD) model is developed to handle the fuzziness and uncertainty. Three specific applications are presented: integrated land cover and texture pattern mining, shape information mining for change detection of lakes, and fuzzy normalized difference vegetation index (NDVI) pattern mining. The study results show the effectiveness of the proposed system prototype and the potentials for other applications in remote sensing.

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 6: Land use. Part 1: Introduction and overview

NASA Technical Reports Server (NTRS)

Lietzke, K. R.; Stevenson, P. A.

1974-01-01

The utility of an ERS system as an effective tool in land use management is analyzed. The potential new capabilities of a space based ERS system are qualitatively examined. A variety of resource management functions are postulated within which ERTS activities might occur and the present ERS investigations in these areas are outlined.

15 CFR 6.4 - Adjustments to penalties.

Code of Federal Regulations, 2012 CFR

2012-01-01

... $130,000. (e) National Oceanic and Atmospheric Administration. (1) 15 U.S.C. 5623(a)(3), Land Remote Sensing Policy Act of 1992, from $11,000 to $11,000. (2) 15 U.S.C. 5658(c), Land Remote Sensing Policy Act...

15 CFR 6.4 - Adjustments to penalties.

Code of Federal Regulations, 2011 CFR

2011-01-01

... $130,000. (e) National Oceanic and Atmospheric Administration. (1) 15 U.S.C. 5623(a)(3), Land Remote Sensing Policy Act of 1992, from $11,000 to $11,000. (2) 15 U.S.C. 5658(c), Land Remote Sensing Policy Act...

Citizen science land cover classification based on ground and satellite imagery: Case study Day River in Vietnam

NASA Astrophysics Data System (ADS)

Nguyen, Son Tung; Minkman, Ellen; Rutten, Martine

2016-04-01

Citizen science is being increasingly used in the context of environmental research, thus there are needs to evaluate cognitive ability of humans in classifying environmental features. With the focus on land cover, this study explores the extent to which citizen science can be applied in sensing and measuring the environment that contribute to the creation and validation of land cover data. The Day Basin in Vietnam was selected to be the study area. Different methods to examine humans' ability to classify land cover were implemented using different information sources: ground based photos - satellite images - field observation and investigation. Most of the participants were solicited from local people and/or volunteers. Results show that across methods and sources of information, there are similar patterns of agreement and disagreement on land cover classes among participants. Understanding these patterns is critical to create a solid basis for implementing human sensors in earth observation. Keywords: Land cover, classification, citizen science, Landsat 8

Classification of high resolution remote sensing image based on geo-ontology and conditional random fields

NASA Astrophysics Data System (ADS)

Hong, Liang

2013-10-01

The availability of high spatial resolution remote sensing data provides new opportunities for urban land-cover classification. More geometric details can be observed in the high resolution remote sensing image, Also Ground objects in the high resolution remote sensing image have displayed rich texture, structure, shape and hierarchical semantic characters. More landscape elements are represented by a small group of pixels. Recently years, the an object-based remote sensing analysis methodology is widely accepted and applied in high resolution remote sensing image processing. The classification method based on Geo-ontology and conditional random fields is presented in this paper. The proposed method is made up of four blocks: (1) the hierarchical ground objects semantic framework is constructed based on geoontology; (2) segmentation by mean-shift algorithm, which image objects are generated. And the mean-shift method is to get boundary preserved and spectrally homogeneous over-segmentation regions ;(3) the relations between the hierarchical ground objects semantic and over-segmentation regions are defined based on conditional random fields framework ;(4) the hierarchical classification results are obtained based on geo-ontology and conditional random fields. Finally, high-resolution remote sensed image data -GeoEye, is used to testify the performance of the presented method. And the experimental results have shown the superiority of this method to the eCognition method both on the effectively and accuracy, which implies it is suitable for the classification of high resolution remote sensing image.

[Changes of wetland landscape pattern in Dayang River Estuary based on high-resolution remote sensing image].

PubMed

Wu, Tao; Zhao, Dong-zhi; Zhang, Feng-shou; Wei, Bao-quan

2011-07-01

Based on the comprehensive consideration of the high resolution characteristics of remote sensing data and the current situation of land cover and land use in Dayang River Estuary wetland, a classification system with different resolutions of wetland landscape in the Estuary was established. The landscape pattern indices and landscape transition matrix were calculated by using the high resolution remote sensing data, and the dynamic changes of the landscape pattern from 1984 to 2008 were analyzed. In the study period, the wetland landscape components changed drastically. Wetland landscape transferred from natural wetland into artificial wetland, and wetland core regional area decreased. Natural wetland's largest patch area index descended, and the fragmentation degree ascended; while artificial wetland area expanded, its patch number decreased, polymerization degree increased, and the maximum patch area index had an obvious increasing trend. Increasing human activities, embankment construction, and reclamation for aquaculture were the main causes for the decrease of wetland area and the degradation of the ecological functions of Dayang River Estuary. To constitute long-term scientific and reasonable development plan, establish wetland nature reserves, protect riverway, draft strict inspective regimes for aquaculture reclamation, and energetically develop resource-based tourism industry would be the main strategies for the protection of the estuarine wetland.

A proposal for continuation of support for the application of remotely sensed data to state and regional problems. Part 1: Technical proposal

NASA Technical Reports Server (NTRS)

1981-01-01

The objectives, procedures, accomplishments, plans, and ultimate uses of information from current projects at the Mississippi Remote Sensing Center are discussed for the following applications: (1) land use planning; (2) strip mine inventory and reclamation; (3) biological management for white tailed deer; (4) forest habitats in potential lignite areas; (5) change discrimination in gravel operations; (6) discrimination of freshwater wetlands for inventory and monitoring; and (7) remote sensing data analysis support systems. The initiation of a conceptual design for a LANDSAT based, state wide information system is proposed.

Remote Sensing Sensors and Applications in Environmental Resources Mapping and Modelling

PubMed Central

Melesse, Assefa M.; Weng, Qihao; S.Thenkabail, Prasad; Senay, Gabriel B.

2007-01-01

The history of remote sensing and development of different sensors for environmental and natural resources mapping and data acquisition is reviewed and reported. Application examples in urban studies, hydrological modeling such as land-cover and floodplain mapping, fractional vegetation cover and impervious surface area mapping, surface energy flux and micro-topography correlation studies is discussed. The review also discusses the use of remotely sensed-based rainfall and potential evapotranspiration for estimating crop water requirement satisfaction index and hence provides early warning information for growers. The review is not an exhaustive application of the remote sensing techniques rather a summary of some important applications in environmental studies and modeling. PMID:28903290

[Application of optical flow dynamic texture in land use/cover change detection].

PubMed

Yan, Li; Gong, Yi-Long; Zhang, Yi; Duan, Wei

2014-11-01

In the present study, a novel change detection approach for high resolution remote sensing images is proposed based on the optical flow dynamic texture (OFDT), which could achieve the land use & land cover change information automatically with a dynamic description of ground-object changes. This paper describes the ground-object gradual change process from the principle using optical flow theory, which breaks the ground-object sudden change hypothesis in remote sensing change detection methods in the past. As the steps of this method are simple, it could be integrated in the systems and software such as Land Resource Management and Urban Planning software that needs to find ground-object changes. This method takes into account the temporal dimension feature between remote sensing images, which provides a richer set of information for remote sensing change detection, thereby improving the status that most of the change detection methods are mainly dependent on the spatial dimension information. In this article, optical flow dynamic texture is the basic reflection of changes, and it is used in high resolution remote sensing image support vector machine post-classification change detection, combined with spectral information. The texture in the temporal dimension which is considered in this article has a smaller amount of data than most of the textures in the spatial dimensions. The highly automated texture computing has only one parameter to set, which could relax the onerous manual evaluation present status. The effectiveness of the proposed approach is evaluated with the 2011 and 2012 QuickBird datasets covering Duerbert Mongolian Autonomous County of Daqing City, China. Then, the effects of different optical flow smooth coefficient and the impact on the description of the ground-object changes in the method are deeply analyzed: The experiment result is satisfactory, with an 87.29% overall accuracy and an 0.850 7 Kappa index, and the method achieves better performance than the post-classification change detection methods using spectral information only.

Geology of Lunar Landing Sites and Origin of Basin Ejecta from a Clementine Perspective

NASA Technical Reports Server (NTRS)

Jolliff, Bradley L.; Haskin, Larry A.

1998-01-01

The goals of this research were to examine Clementine multispectral data covering the Apollo landing sites in order to: (1) provide ground truth for the remotely sensed observations, (2) extend our understanding of the Apollo landing sites to the surrounding regions using the empirically calibrated Clementine data, and (3) investigate the composition and distribution of impact-basin ejecta using constraints based upon the remotely sensed data and the Apollo samples. Our initial efforts (in collaboration with P. Lucey and coworkers) to use the Apollo soil compositions to "calibrate" information derived from the remotely sensed data resulted in two extremely useful algorithms for computing estimates of the concentrations of FeO and TiO2 from the UV-VIS 5-band data. In this effort, we used the average surface soil compositions from 37 individual Apollo and 3 Luna sample stations that could be resolved using the Clementine data. We followed this work with a detailed investigation of the Apollo 17 landing site, where the sampling traverses were extensive and the spectral and compositional contrast between different soils covers a wide range. We have begun to investigate the nature and composition of basin ejecta by comparing the thick deposits on the rim of Imbrium in the vicinity of the Apollo 15 site and those occurring southeast of the Serenitatis basin, in the Apollo 17 region. We continue this work under NAG5-6784, "Composition, Lithology, and Heterogeneity of the lunar crust using remote sensing of impact-basin uplift structures and ejecta as probes. The main results of our work are given in the following brief summaries of major tasks. Detailed accounts of these results are given in the attached papers, manuscripts, and extended abstracts.

Spatial assessment of Geo-environmental data by the integration of Remote Sensing and GIS techniques for Sitakund Region, Eastern foldbelt, Bangladesh.

NASA Astrophysics Data System (ADS)

Gazi, M. Y.; Rahman, M.; Islam, M. A.; Kabir, S. M. M.

2016-12-01

Techniques of remote sensing and geographic information systems (GIS) have been applied for the analysis and interpretation of the Geo-environmental assessment to Sitakund area, located within the administrative boundaries of the Chittagong district, Bangladesh. Landsat ETM+ image with a ground resolution of 30-meter and Digital Elevation Model (DEM) has been adopted in this study in order to produce a set of thematic maps. The diversity of the terrain characteristics had a major role in the diversity of recipes and types of soils that are based on the geological structure, also helped to diversity in land cover and use in the region. The geological situation has affected on the general landscape of the study area. The problem of research lies in the possibility of the estimating the techniques of remote sensing and geographic information systems in the evaluation of the natural data for the study area spatially as well as determine the appropriate in grades for the appearance of the ground and in line with the reality of the region. Software for remote sensing and geographic information systems were adopted in the analysis, classification and interpretation of the prepared thematic maps in order to get to the building of the Geo-environmental assessment map of the study area. Low risk geo-environmental land mostly covered area of Quaternary deposits especially with area of slope wash deposits carried by streams. Medium and high risk geo-environmental land distributed with area of other formation with the study area, mostly the high risk shows area of folds and faults. The study has assessed the suitability of lands for agricultural purpose and settlements in less vulnerable areas within this region.

a Spiral-Based Downscaling Method for Generating 30 M Time Series Image Data

NASA Astrophysics Data System (ADS)

Liu, B.; Chen, J.; Xing, H.; Wu, H.; Zhang, J.

2017-09-01

The spatial detail and updating frequency of land cover data are important factors influencing land surface dynamic monitoring applications in high spatial resolution scale. However, the fragmentized patches and seasonal variable of some land cover types (e. g. small crop field, wetland) make it labor-intensive and difficult in the generation of land cover data. Utilizing the high spatial resolution multi-temporal image data is a possible solution. Unfortunately, the spatial and temporal resolution of available remote sensing data like Landsat or MODIS datasets can hardly satisfy the minimum mapping unit and frequency of current land cover mapping / updating at the same time. The generation of high resolution time series may be a compromise to cover the shortage in land cover updating process. One of popular way is to downscale multi-temporal MODIS data with other high spatial resolution auxiliary data like Landsat. But the usual manner of downscaling pixel based on a window may lead to the underdetermined problem in heterogeneous area, result in the uncertainty of some high spatial resolution pixels. Therefore, the downscaled multi-temporal data can hardly reach high spatial resolution as Landsat data. A spiral based method was introduced to downscale low spatial and high temporal resolution image data to high spatial and high temporal resolution image data. By the way of searching the similar pixels around the adjacent region based on the spiral, the pixel set was made up in the adjacent region pixel by pixel. The underdetermined problem is prevented to a large extent from solving the linear system when adopting the pixel set constructed. With the help of ordinary least squares, the method inverted the endmember values of linear system. The high spatial resolution image was reconstructed on the basis of high spatial resolution class map and the endmember values band by band. Then, the high spatial resolution time series was formed with these high spatial resolution images image by image. Simulated experiment and remote sensing image downscaling experiment were conducted. In simulated experiment, the 30 meters class map dataset Globeland30 was adopted to investigate the effect on avoid the underdetermined problem in downscaling procedure and a comparison between spiral and window was conducted. Further, the MODIS NDVI and Landsat image data was adopted to generate the 30m time series NDVI in remote sensing image downscaling experiment. Simulated experiment results showed that the proposed method had a robust performance in downscaling pixel in heterogeneous region and indicated that it was superior to the traditional window-based methods. The high resolution time series generated may be a benefit to the mapping and updating of land cover data.

Combining observations in the reflective solar and thermal domains for improved carbon and energy flux estimation

USDA-ARS?s Scientific Manuscript database

This study investigates the utility of integrating remotely sensed estimates of leaf chlorophyll (Cab) into a therma-based Two-Source Energy Balance (TSEB) model that estimates land-surface CO2 and energy fluxes using an analytical, light-use-efficiency (LUE) based model of canopy resistance. The LU...

The characteristics and interpretability of land surface change and implications for project design

USGS Publications Warehouse

Sohl, Terry L.; Gallant, Alisa L.; Loveland, Thomas R.

2004-01-01

The need for comprehensive, accurate information on land-cover change has never been greater. While remotely sensed imagery affords the opportunity to provide information on land-cover change over large geographic expanses at a relatively low cost, the characteristics of land-surface change bring into question the suitability of many commonly used methodologies. Algorithm-based methodologies to detect change generally cannot provide the same level of accuracy as the analyses done by human interpreters. Results from the Land Cover Trends project, a cooperative venture that includes the U.S. Geological Survey, Environmental Protection Agency, and National Aeronautics and Space Administration, have shown that land-cover conversion is a relatively rare event, occurs locally in small patches, varies geographically and temporally, and is spectrally ambiguous. Based on these characteristics of change and the type of information required, manual interpretation was selected as the primary means of detecting change in the Land Cover Trends project. Mixtures of algorithm-based detection and manual interpretation may often prove to be the most feasible and appropriate design for change-detection applications. Serious examination of the expected characteristics and measurability of change must be considered during the design and implementation phase of any change analysis project.

Remote Sensing Application to Land Use Classification in a Rapidly Changing Agricultural/Urban Area: City of Virginia Beach, Virginia. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Odenyo, V. A. O.

1975-01-01

Remote sensing data on computer-compatible tapes of LANDSAT 1 multispectral scanner imager were analyzed to generate a land use map of the City of Virginia Beach. All four bands were used in both the supervised and unsupervised approaches with the LAYSYS software system. Color IR imagery of a U-2 flight of the same area was also digitized and two sample areas were analyzed via the unsupervised approach. The relationships between the mapped land use and the soils of the area were investigated. A land use land cover map at a scale of 1:24,000 was obtained from the supervised analysis of LANDSAT 1 data. It was concluded that machine analysis of remote sensing data to produce land use maps was feasible; that the LAYSYS software system was usable for this purpose; and that the machine analysis was capable of extracting detailed information from the relatively small scale LANDSAT data in a much shorter time without compromising accuracy.

Application of remote sensing to land and water resource planning: The Pocomoke River Basin, Maryland

NASA Technical Reports Server (NTRS)

Wildesen, S. E.; Phillips, E. P.

1981-01-01

Because of the size of the Pocomoke River Basin, the inaccessibility of certain areas, and study time constraints, several remote sensing techniques were used to collect base information on the river corridor, (a 23.2 km channel) and on a 1.2 km wooded floodplain. This information provided an adequate understanding of the environment and its resources, thus enabling effective management options to be designed. The remote sensing techniques used for assessment included manual analysis of high altitude color-infrared photography, computer-assisted analysis of LANDSAT-2 imagery, and the application of airborne oceanographic Lidar for topographic mapping. Results show that each techniques was valuable in providing the needed base data necessary for resource planning.

An integrated system for land resources supervision based on the IoT and cloud computing

NASA Astrophysics Data System (ADS)

Fang, Shifeng; Zhu, Yunqiang; Xu, Lida; Zhang, Jinqu; Zhou, Peiji; Luo, Kan; Yang, Jie

2017-01-01

Integrated information systems are important safeguards for the utilisation and development of land resources. Information technologies, including the Internet of Things (IoT) and cloud computing, are inevitable requirements for the quality and efficiency of land resources supervision tasks. In this study, an economical and highly efficient supervision system for land resources has been established based on IoT and cloud computing technologies; a novel online and offline integrated system with synchronised internal and field data that includes the entire process of 'discovering breaches, analysing problems, verifying fieldwork and investigating cases' was constructed. The system integrates key technologies, such as the automatic extraction of high-precision information based on remote sensing, semantic ontology-based technology to excavate and discriminate public sentiment on the Internet that is related to illegal incidents, high-performance parallel computing based on MapReduce, uniform storing and compressing (bitwise) technology, global positioning system data communication and data synchronisation mode, intelligent recognition and four-level ('device, transfer, system and data') safety control technology. The integrated system based on a 'One Map' platform has been officially implemented by the Department of Land and Resources of Guizhou Province, China, and was found to significantly increase the efficiency and level of land resources supervision. The system promoted the overall development of informatisation in fields related to land resource management.

Analysis of polarization characteristics of plant canopies using land-based remote sensing measurements for development of ground truth methods

NASA Astrophysics Data System (ADS)

Sidko, Aleksandr; Pisman, Tamara; Botvich, Irina; Shevyrnogov, Anatoly

In order to develop satellite technology for monitoring of terrestrial plant canopies and land-based optical remote sensing techniques, one should employ new approaches to identifying farmlands and determining the plant species composition. The results present a study on polarized characteristics of spectral reflection factor of plant canopies (forests and farm crop canopies) under field conditions, using optical remote sensing techniques. The polarized components of the reflectance factor and the degree of polarization were calculated. Measurements were performed using a spectrophotometer with a polarized light filter attachment. Measurements were done within the spectral range from 400 to 840 nm. The viewing angle was no greater than 200 with respect to the nadir. Measurements of the polarization characteristics of the vegetation on the test ranges were conducted during June-July month when the height of the sun was at its zenith. Different wavelength dependences of the spectral reflection factor polarized component (Rq) and degree of polarization (P) were found both for the coniferous and broadleaf forests (pine and birch) and for farm crops (wheat and corn) and grass canopies. These differences can be used to determine species composition of plant canopies.

Regional surface soil heat flux estimate from multiple remote sensing data in a temperate and semiarid basin

NASA Astrophysics Data System (ADS)

Li, Nana; Jia, Li; Lu, Jing; Menenti, Massimo; Zhou, Jie

2017-01-01

The regional surface soil heat flux (G0) estimation is very important for the large-scale land surface process modeling. However, most of the regional G0 estimation methods are based on the empirical relationship between G0 and the net radiation flux. A physical model based on harmonic analysis was improved (referred to as "HM model") and applied over the Heihe River Basin northwest China with multiple remote sensing data, e.g., FY-2C, AMSR-E, and MODIS, and soil map data. The sensitivity analysis of the model was studied as well. The results show that the improved model describes the variation of G0 well. Land surface temperature (LST) and thermal inertia (Γ) are the two key input variables to the HM model. Compared with in situ G0, there are some differences, mainly due to the differences between remote-sensed LST and the in situ LST. The sensitivity analysis shows that the errors from -7 to -0.5 K in LST amplitude and from -300 to 300 J m-2 K-1 s-0.5 in Γ will cause about 20% errors, which are acceptable for G0 estimation.

Utilisation of Indian Remote Sensing Satellite (IRS) data for assessment of soil erosion process of a watershed in Chhotanagpur plateau region, India

NASA Astrophysics Data System (ADS)

Pramod Krishna, Akhouri

A watershed in Chhotanagpur plateau region was investigated utilizing space data from Indian Remote Sensing (IRS) Satellite towards spatial and temporal soil erosion process study. Geomorphologically, this plateau region is an undulating pediplain. The watershed namely Potpoto river watershed covering an area of 8160 hectares is situated in the vicinity of Ranchi, capital city of newly created Jharkahnd state. As per the national watershed atlas, Potpoto river is a tributary of Subarnarekha river system within the Upper Subarnarekha river basin under watershed no. 4H3C8. This rural to semi-urban watershed is important towards various services to Ranchi city as well as experiencing direct or indirect pressures of development. Drivers of land use changes at ground level are responsible for change in soil erosion rates in any watershed in coupled human-environment systems. This may adversely affect the soil cover of such watersheds depicted through changed rates of erosion. In a rural to semi-urban watershed like this, there are general tendencies of land use and thereby land cover changes from forests to agricultural lands, within agricultural land in terms of cropping pattern changes to cash-crops, orchards, commercial plantations and conversions to other land use categories as well towards infrastructure expansions. Universal Soil Loss Equation (USLE) was used as a basis to observe the intensity of erosion using remote sensing, rainfall data, soil data and land use/land cover map. IRS1C LISSIII and IRSP6 LISSIII data were used to identify land use status for the years 1996 and 2004 respectively. LISSIII sensor provides data in the visible to near infrared (Bands 2, 3, 4) as well as short wave infrared (Band 5) range of electromagnetic spectrum. In this study, bands 2 (0.52-0.59 microns), 3 (0.62-0.68 microns) and 4 (0.77-0.86 microns) were used with spatial resolution of 23.5 meters at nadir. Digital image processing was carried out using ERDAS Imagine software. Based on maximum likelihood classifier, the study area was classified into suitable land use/land cover classes. Digital elevation model (DEM) was created through contour heights from topographic maps. Watershed based erosion estimation was carried out including assessment of soil erosion due to land use land cover changes. This provides predictive assessment capability in soil erosion studies particularly with methods such as USLE. Soil erosion problem varies largely depending upon climate, topography, soil and land use etc. Multi-factor computations on rainfall erosivity, soil erodibility, topographic, cover and management, and conservation practice were carried out. Quantified details on soil erosion rates were generated in terms of land use land cover classes of the watershed for the years 1996 and 2004. Annual average soil loss for the watershed was calculated and erosion intensity maps were generated. Thus, space data utilized from the satellites IRS1C LISSIII and IRSP6 LISSIII greatly helped in important research assessment of an important land surface process like soil erosion spatially as well as temporally for a watershed under pressures of development, land use changes and land cover fragmentations.

The Identification of Land Utilization in Coastal Reclamation Areas in Tianjin Using High Resolution Remote Sensing Images

NASA Astrophysics Data System (ADS)

Meng, Y.; Cao, Y.; Tian, H.; Han, Z.

2018-04-01

In recent decades, land reclamation activities have been developed rapidly in Chinese coastal regions, especially in Bohai Bay. The land reclamation areas can effectively alleviate the contradiction between land resources shortage and human needs, but some idle lands that left unused after the government making approval the usage of sea areas are also supposed to pay attention to. Due to the particular features of land coverage identification in large regions, traditional monitoring approaches are unable to perfectly meet the needs of effectively and quickly land use classification. In this paper, Gaofen-1 remotely sensed satellite imagery data together with sea area usage ownership data were used to identify the land use classifications and find out the idle land resources. It can be seen from the result that most of the land use types and idle land resources can be identified precisely.

The economic value of remote sensing of earth resources from space: An ERTS overview and the value of continuity of service. Volume 6: Land use. Part 2: The role of ERTS in the establishment and updating of a nationwide land cover information system

NASA Technical Reports Server (NTRS)

Christie, R. J.

1974-01-01

The utility of an ERS system as an effective tool in land use management is analyzed. The cost effectiveness of satellites as a component of an ERS system is presented based on various projected levels of demand. It is indicated that a cost savings potential of $7.9 to $37.1 million annually is attributable to the inclusion of ERS-like satellites in the ERTS system.

Remote sensing of land use and water quality relationships - Wisconsin shore, Lake Michigan

NASA Technical Reports Server (NTRS)

Haugen, R. K.; Marlar, T. L.

1976-01-01

This investigation assessed the utility of remote sensing techniques in the study of land use-water quality relationships in an east central Wisconsin test area. The following types of aerial imagery were evaluated: high altitude (60,000 ft) color, color infrared, multispectral black and white, and thermal; low altitude (less than 5000 ft) color infrared, multispectral black and white, thermal, and passive microwave. A non-imaging hand-held four-band radiometer was evaluated for utility in providing data on suspended sediment concentrations. Land use analysis includes the development of mapping and quantification methods to obtain baseline data for comparison to water quality variables. Suspended sediment loads in streams, determined from water samples, were related to land use differences and soil types in three major watersheds. A multiple correlation coefficient R of 0.85 was obtained for the relationship between the 0.6-0.7 micrometer incident and reflected radiation data from the hand-held radiometer and concurrent ground measurements of suspended solids in streams. Applications of the methods and baseline data developed in this investigation include: mapping and quantification of land use; input to watershed runoff models; estimation of effects of land use changes on stream sedimentation; and remote sensing of suspended sediment content of streams. High altitude color infrared imagery was found to be the most acceptable remote sensing technique for the mapping and measurement of land use types.

Enhancement of Tropical Land Cover Mapping with Wavelet-Based Fusion and Unsupervised Clustering of SAR and Landsat Image Data

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Laporte, Nadine; Netanyahuy, Nathan S.; Zukor, Dorothy (Technical Monitor)

2001-01-01

The characterization and the mapping of land cover/land use of forest areas, such as the Central African rainforest, is a very complex task. This complexity is mainly due to the extent of such areas and, as a consequence, to the lack of full and continuous cloud-free coverage of those large regions by one single remote sensing instrument, In order to provide improved vegetation maps of Central Africa and to develop forest monitoring techniques for applications at the local and regional scales, we propose to utilize multi-sensor remote sensing observations coupled with in-situ data. Fusion and clustering of multi-sensor data are the first steps towards the development of such a forest monitoring system. In this paper, we will describe some preliminary experiments involving the fusion of SAR and Landsat image data of the Lope Reserve in Gabon. Similarly to previous fusion studies, our fusion method is wavelet-based. The fusion provides a new image data set which contains more detailed texture features and preserves the large homogeneous regions that are observed by the Thematic Mapper sensor. The fusion step is followed by unsupervised clustering and provides a vegetation map of the area.

Assessment and mapping of water pollution indices in zone-III of municipal corporation of hyderabad using remote sensing and geographic information system.

PubMed

Asadi, S S; Vuppala, Padmaja; Reddy, M Anji

2005-01-01

A preliminary survey of area under Zone-III of MCH was undertaken to assess the ground water quality, demonstrate its spatial distribution and correlate with the land use patterns using advance techniques of remote sensing and geographical information system (GIS). Twenty-seven ground water samples were collected and their chemical analysis was done to form the attribute database. Water quality index was calculated from the measured parameters, based on which the study area was classified into five groups with respect to suitability of water for drinking purpose. Thematic maps viz., base map, road network, drainage and land use/land cover were prepared from IRS ID PAN + LISS III merged satellite imagery forming the spatial database. Attribute database was integrated with spatial sampling locations map in Arc/Info and maps showing spatial distribution of water quality parameters were prepared in Arc View. Results indicated that high concentrations of total dissolved solids (TDS), nitrates, fluorides and total hardness were observed in few industrial and densely populated areas indicating deteriorated water quality while the other areas exhibited moderate to good water quality.

Long-term records of global radiation, carbon and water fluxes derived from multi-satellite data and a process-based model

NASA Astrophysics Data System (ADS)

Ryu, Youngryel; Jiang, Chongya

2016-04-01

To gain insights about the underlying impacts of global climate change on terrestrial ecosystem fluxes, we present a long-term (1982-2015) global radiation, carbon and water fluxes products by integrating multi-satellite data with a process-based model, the Breathing Earth System Simulator (BESS). BESS is a coupled processed model that integrates radiative transfer in the atmosphere and canopy, photosynthesis (GPP), and evapotranspiration (ET). BESS was designed most sensitive to the variables that can be quantified reliably, fully taking advantages of remote sensing atmospheric and land products. Originally, BESS entirely relied on MODIS as input variables to produce global GPP and ET during the MODIS era. This study extends the work to provide a series of long-term products from 1982 to 2015 by incorporating AVHRR data. In addition to GPP and ET, more land surface processes related datasets are mapped to facilitate the discovery of the ecological variations and changes. The CLARA-A1 cloud property datasets, the TOMS aerosol datasets, along with the GLASS land surface albedo datasets, were input to a look-up table derived from an atmospheric radiative transfer model to produce direct and diffuse components of visible and near infrared radiation datasets. Theses radiation components together with the LAI3g datasets and the GLASS land surface albedo datasets, were used to calculate absorbed radiation through a clumping corrected two-stream canopy radiative transfer model. ECMWF ERA interim air temperature data were downscaled by using ALP-II land surface temperature dataset and a region-dependent regression model. The spatial and seasonal variations of CO2 concentration were accounted by OCO-2 datasets, whereas NOAA's global CO2 growth rates data were used to describe interannual variations. All these remote sensing based datasets are used to run the BESS. Daily fluxes in 1/12 degree were computed and then aggregated to half-month interval to match with the spatial-temporal resolution of LAI3g dataset. The BESS GPP and ET products were compared to other independent datasets including MPI-BGC and CLM. Overall, the BESS products show good agreement with the other two datasets, indicating a compelling potential for bridging remote sensing and land surface models.

Remotely Sensed Based Lake/Reservoir Routing in Congo River Basin

NASA Astrophysics Data System (ADS)

Raoufi, R.; Beighley, E.; Lee, H.

2017-12-01

Lake and reservoir dynamics can influence local to regional water cycles but are often not well represented in hydrologic models. One challenge that limits their inclusion in models is the need for detailed storage-discharge behavior that can be further complicated in reservoirs where specific operation rules are employed. Here, the Hillslope River Routing (HRR) model is combined with a remotely sensed based Reservoir Routing (RR) method and applied to the Congo River Basin. Given that topographic data are often continuous over the entire terrestrial surface (i.e., does not differentiate between land and open water), the HRR-RR model integrates topographic derived river networks and catchment boundaries (e.g., HydroSHEDs) with water boundary extents (e.g., Global Lakes and Wetlands Database) to develop the computational framework. The catchments bordering lakes and reservoirs are partitioned into water and land portions, where representative flowpath characteristics are determined and vertical water balance and lateral routings is performed separately on each partition based on applicable process models (e.g., open water evaporation vs. evapotranspiration). To enable reservoir routing, remotely sensed water surface elevations and extents are combined to determine the storage change time series. Based on the available time series, representative storage change patterns are determined. Lake/reservoir routing is performed by combining inflows from the HRR-RR model and the representative storage change patterns to determine outflows. In this study, a suite of storage change patterns derived from remotely sensed measurements are determined representative patterns for wet, dry and average conditions. The HRR-RR model dynamically selects and uses the optimal storage change pattern for the routing process based on these hydrologic conditions. The HRR-RR model results are presented to highlight the importance of lake attenuation/routing in the Congo Basin.

Physical properties (particle size, rock abundance) from thermal infrared remote observations: Implications for Mars landing sites

NASA Technical Reports Server (NTRS)

Christensen, P. R.; Edgett, Kenneth S.

1994-01-01

Critical to the assessment of potential sites for the 1997 Pathfinder landing is estimation of general physical properties of the martian surface. Surface properties have been studied using a variety of spacecraft and earth-based remote sensing observations, plus in situ studies at the Viking lander sites. Because of their value in identifying landing hazards and defining scientific objectives, we focus this discussion on thermal inertia and rock abundance derived from middle-infrared (6 to 30 microns) observations. Used in conjunction with other datasets, particularly albedo and Viking orbiter images, thermal inertia and rock abundance provide clues about the properties of potential Mars landing sites.

Implementation of Sentinel-2 Data in the M4Land System for the Generation of Continuous Information Products in Agriculture

NASA Astrophysics Data System (ADS)

Klug, P.; Schlenz, F.; Hank, T.; Migdall, S.; Weiß, I.; Danner, M.; Bach, H.; Mauser, W.

2016-08-01

The analysis system developed in the frame of the M4Land project (Model based, Multi-temporal, Multi scale and Multi sensorial retrieval of continuous land management information) has proven its capabilities of classifying crop type and creating products on the intensity of agricultural production using optical remote sensing data from Landsat and RapidEye. In this study, Sentinel-2 data is used for the first time together with Landsat 7 ETM+ and 8 OLI data within the M4Land analysis system to derive continuously crop type and the agricultural intensity of fields in an area north of Munich, Germany and the year 2015.

Multicriteria analysis for sources of renewable energy using data from remote sensing

NASA Astrophysics Data System (ADS)

Matejicek, L.

2015-04-01

Renewable energy sources are major components of the strategy to reduce harmful emissions and to replace depleting fossil energy resources. Data from remote sensing can provide information for multicriteria analysis for sources of renewable energy. Advanced land cover quantification makes it possible to search for suitable sites. Multicriteria analysis, together with other data, is used to determine the energy potential and socially acceptability of suggested locations. The described case study is focused on an area of surface coal mines in the northwestern region of the Czech Republic, where the impacts of surface mining and reclamation constitute a dominant force in land cover changes. High resolution satellite images represent the main input datasets for identification of suitable sites. Solar mapping, wind predictions, the location of weirs in watersheds, road maps and demographic information complement the data from remote sensing for multicriteria analysis, which is implemented in a geographic information system (GIS). The input spatial datasets for multicriteria analysis in GIS are reclassified to a common scale and processed with raster algebra tools to identify suitable sites for sources of renewable energy. The selection of suitable sites is limited by the CORINE land cover database to mining and agricultural areas. The case study is focused on long term land cover changes in the 1985-2015 period. Multicriteria analysis based on CORINE data shows moderate changes in mapping of suitable sites for utilization of selected sources of renewable energy in 1990, 2000, 2006 and 2012. The results represent map layers showing the energy potential on a scale of a few preference classes (1-7), where the first class is linked to minimum preference and the last class to maximum preference. The attached histograms show the moderate variability of preference classes due to land cover changes caused by mining activities. The results also show a slight increase in the more preferred classes for utilization of sources of renewable energy due to an increase area of reclaimed sites. Using data from remote sensing, such as the multispectral images and the CORINE land cover datasets, can reduce the financial resources currently required for finding and assessing suitable areas.

Remote sensing on Indian and public lands

NASA Technical Reports Server (NTRS)

Torbert, G. B.; Woll, A. M.

1972-01-01

The use of remote sensing techniques by the Bureaus of Indian Affairs and Land Management in planning resource problems, making decisions, writing environmental impact statements, and monitoring their respective programs is investigated. For Indian affairs, data cover the Papago, Fort Apache, San Carlos, and South Dakota Reservations. For the Land Management Office, data cover cadastral surveys, California desert study, range watersheds, and efforts to establish a natural resources information system.

15 CFR 960.9 - License term.

Code of Federal Regulations, 2012 CFR

2012-01-01

... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA...

15 CFR 960.9 - License term.

Code of Federal Regulations, 2013 CFR

2013-01-01

... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA...

15 CFR 960.9 - License term.

Code of Federal Regulations, 2011 CFR

2011-01-01

... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA...

15 CFR 960.9 - License term.

Code of Federal Regulations, 2014 CFR

2014-01-01

... licensee to: (1) Provide data to the National Satellite Land Remote Sensing Data Archive for the basic data set; (2) Make data available to the National Satellite Land Remote Sensing Data Archive that the... AND ATMOSPHERIC ADMINISTRATION, DEPARTMENT OF COMMERCE GENERAL REGULATIONS OF THE ENVIRONMENTAL DATA...

Remote sensing in Michigan for land resource management: Highway impact assessment

NASA Technical Reports Server (NTRS)

1972-01-01

An existing section of M-14 freeway constructed in 1964 and a potential extension from Ann Arbor to Plymouth, Michigan provided an opportunity for investigating the potential uses of remote sensing techniques in providing projective information needed for assessing the impact of highway construction. Remote sensing data included multispectral scanner imagery and aerial photography. Only minor effects on vegetation, soils, and land use were found to have occurred in the existing corridor. Adverse changes expected to take place in the corridor proposed for extension of the freeway can be minimized by proper design of drainage ditches and attention to good construction practices. Remote sensing can be used to collect and present many types of data useful for highway impact assessment on land use, vegetation categories and species, soil properties and hydrologic characteristics.

Research on image evidence in land supervision and GIS management

NASA Astrophysics Data System (ADS)

Li, Qiu; Wu, Lixin

2006-10-01

Land resource development and utilization brings many problems. The numbers, the scale and volume of illegal land use cases are on the increasing. Since the territory is vast, and the land violations are concealment, it is difficulty for an effective land supervision and management. In this paper, the concepts of evidence, and preservation of evidence were described first. The concepts of image evidence (IE), natural evidence (NE), natural preservation of evidence (NPE), general preservation of evidence (GPE) were proposed based on the characteristics of remote sensing image (RSI) which has a characteristic of objectiveness, truthfulness, high spatial resolution, more information included. Using MapObjects and Visual Basic 6.0, under the Access management to implement the conjunction of spatial vector database and attribute data table; taking RSI as the data sources and background layer; combining the powerful management of geographic information system (GIS) for spatial data, and visual analysis, a land supervision and GIS management system was design and implemented based on NPE. The practical use in Beijing shows that the system is running well, and solved some problems in land supervision and management.

Automatic Extraction of Urban Built-Up Area Based on Object-Oriented Method and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Li, L.; Zhou, H.; Wen, Q.; Chen, T.; Guan, F.; Ren, B.; Yu, H.; Wang, Z.

2018-04-01

Built-up area marks the use of city construction land in the different periods of the development, the accurate extraction is the key to the studies of the changes of urban expansion. This paper studies the technology of automatic extraction of urban built-up area based on object-oriented method and remote sensing data, and realizes the automatic extraction of the main built-up area of the city, which saves the manpower cost greatly. First, the extraction of construction land based on object-oriented method, the main technical steps include: (1) Multi-resolution segmentation; (2) Feature Construction and Selection; (3) Information Extraction of Construction Land Based on Rule Set, The characteristic parameters used in the rule set mainly include the mean of the red band (Mean R), Normalized Difference Vegetation Index (NDVI), Ratio of residential index (RRI), Blue band mean (Mean B), Through the combination of the above characteristic parameters, the construction site information can be extracted. Based on the degree of adaptability, distance and area of the object domain, the urban built-up area can be quickly and accurately defined from the construction land information without depending on other data and expert knowledge to achieve the automatic extraction of the urban built-up area. In this paper, Beijing city as an experimental area for the technical methods of the experiment, the results show that: the city built-up area to achieve automatic extraction, boundary accuracy of 2359.65 m to meet the requirements. The automatic extraction of urban built-up area has strong practicality and can be applied to the monitoring of the change of the main built-up area of city.

A review on remotely sensed land surface temperature anomaly as an earthquake precursor

NASA Astrophysics Data System (ADS)

Bhardwaj, Anshuman; Singh, Shaktiman; Sam, Lydia; Joshi, P. K.; Bhardwaj, Akanksha; Martín-Torres, F. Javier; Kumar, Rajesh

2017-12-01

The low predictability of earthquakes and the high uncertainty associated with their forecasts make earthquakes one of the worst natural calamities, capable of causing instant loss of life and property. Here, we discuss the studies reporting the observed anomalies in the satellite-derived Land Surface Temperature (LST) before an earthquake. We compile the conclusions of these studies and evaluate the use of remotely sensed LST anomalies as precursors of earthquakes. The arrival times and the amplitudes of the anomalies vary widely, thus making it difficult to consider them as universal markers to issue earthquake warnings. Based on the randomness in the observations of these precursors, we support employing a global-scale monitoring system to detect statistically robust anomalous geophysical signals prior to earthquakes before considering them as definite precursors.

Northern Everglades, Florida, satellite image map

USGS Publications Warehouse

Thomas, Jean-Claude; Jones, John W.

2002-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program with support from the Everglades National Park. The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Assessment of Mars Exploration Rover Landing Site Predictions

NASA Technical Reports Server (NTRS)

Golombek, M. P.; Arvidson, R. E.; Bell, J. F., III; Christensen, P. R.; Crisp, J. A.; Ehlmann, B. L.; Fergason, R. L.; Grant, J. A.; Haldemann, A. F. C.; Parker, T. J.;

Scaling up functional traits for ecosystem services with remote sensing: concepts and methods.

PubMed

Abelleira Martínez, Oscar J; Fremier, Alexander K; Günter, Sven; Ramos Bendaña, Zayra; Vierling, Lee; Galbraith, Sara M; Bosque-Pérez, Nilsa A; Ordoñez, Jenny C

2016-07-01

Ecosystem service-based management requires an accurate understanding of how human modification influences ecosystem processes and these relationships are most accurate when based on functional traits. Although trait variation is typically sampled at local scales, remote sensing methods can facilitate scaling up trait variation to regional scales needed for ecosystem service management. We review concepts and methods for scaling up plant and animal functional traits from local to regional spatial scales with the goal of assessing impacts of human modification on ecosystem processes and services. We focus our objectives on considerations and approaches for (1) conducting local plot-level sampling of trait variation and (2) scaling up trait variation to regional spatial scales using remotely sensed data. We show that sampling methods for scaling up traits need to account for the modification of trait variation due to land cover change and species introductions. Sampling intraspecific variation, stratification by land cover type or landscape context, or inference of traits from published sources may be necessary depending on the traits of interest. Passive and active remote sensing are useful for mapping plant phenological, chemical, and structural traits. Combining these methods can significantly improve their capacity for mapping plant trait variation. These methods can also be used to map landscape and vegetation structure in order to infer animal trait variation. Due to high context dependency, relationships between trait variation and remotely sensed data are not directly transferable across regions. We end our review with a brief synthesis of issues to consider and outlook for the development of these approaches. Research that relates typical functional trait metrics, such as the community-weighted mean, with remote sensing data and that relates variation in traits that cannot be remotely sensed to other proxies is needed. Our review narrows the gap between functional trait and remote sensing methods for ecosystem service management.

The pan-sharpening of satellite and UAV imagery for agricultural applications

NASA Astrophysics Data System (ADS)

Jenerowicz, Agnieszka; Woroszkiewicz, Malgorzata

2016-10-01

Remote sensing techniques are widely used in many different areas of interest, i.e. urban studies, environmental studies, agriculture, etc., due to fact that they provide rapid, accurate and information over large areas with optimal time, spatial and spectral resolutions. Agricultural management is one of the most common application of remote sensing methods nowadays. Monitoring of agricultural sites and creating information regarding spatial distribution and characteristics of crops are important tasks to provide data for precision agriculture, crop management and registries of agricultural lands. For monitoring of cultivated areas many different types of remote sensing data can be used- most popular are multispectral satellites imagery. Such data allow for generating land use and land cover maps, based on various methods of image processing and remote sensing methods. This paper presents fusion of satellite and unnamed aerial vehicle (UAV) imagery for agricultural applications, especially for distinguishing crop types. Authors in their article presented chosen data fusion methods for satellite images and data obtained from low altitudes. Moreover the authors described pan- sharpening approaches and applied chosen pan- sharpening methods for multiresolution image fusion of satellite and UAV imagery. For such purpose, satellite images from Landsat- 8 OLI sensor and data collected within various UAV flights (with mounted RGB camera) were used. In this article, the authors not only had shown the potential of fusion of satellite and UAV images, but also presented the application of pan- sharpening in crop identification and management.

Tower-scale performance of four observation-based evapotranspiration algorithms within the WACMOS-ET project

NASA Astrophysics Data System (ADS)

Michel, Dominik; Miralles, Diego; Jimenez, Carlos; Ershadi, Ali; McCabe, Matthew F.; Hirschi, Martin; Seneviratne, Sonia I.; Jung, Martin; Wood, Eric F.; (Bob) Su, Z.; Timmermans, Joris; Chen, Xuelong; Fisher, Joshua B.; Mu, Quiaozen; Fernandez, Diego

2015-04-01

Research on climate variations and the development of predictive capabilities largely rely on globally available reference data series of the different components of the energy and water cycles. Several efforts have recently aimed at producing large-scale and long-term reference data sets of these components, e.g. based on in situ observations and remote sensing, in order to allow for diagnostic analyses of the drivers of temporal variations in the climate system. Evapotranspiration (ET) is an essential component of the energy and water cycle, which cannot be monitored directly on a global scale by remote sensing techniques. In recent years, several global multi-year ET data sets have been derived from remote sensing-based estimates, observation-driven land surface model simulations or atmospheric reanalyses. The LandFlux-EVAL initiative presented an ensemble-evaluation of these data sets over the time periods 1989-1995 and 1989-2005 (Mueller et al. 2013). The WACMOS-ET project (http://wacmoset.estellus.eu) started in the year 2012 and constitutes an ESA contribution to the GEWEX initiative LandFlux. It focuses on advancing the development of ET estimates at global, regional and tower scales. WACMOS-ET aims at developing a Reference Input Data Set exploiting European Earth Observations assets and deriving ET estimates produced by a set of four ET algorithms covering the period 2005-2007. The algorithms used are the SEBS (Su et al., 2002), Penman-Monteith from MODIS (Mu et al., 2011), the Priestley and Taylor JPL model (Fisher et al., 2008) and GLEAM (Miralles et al., 2011). The algorithms are run with Fluxnet tower observations, reanalysis data (ERA-Interim), and satellite forcings. They are cross-compared and validated against in-situ data. In this presentation the performance of the different ET algorithms with respect to different temporal resolutions, hydrological regimes, land cover types (including grassland, cropland, shrubland, vegetation mosaic, savanna, woody savanna, needleleaf forest, deciduous forest and mixed forest) are evaluated at the tower-scale in 24 pre-selected study regions on three continents (Europe, North America, and Australia). References: Fisher, J. B., Tu, K.P., and Baldocchi, D.D. Global estimates of the land-atmosphere water flux based on monthly AVHRR and ISLSCP-II data, validated at 16 FLUXNET sites, Remote Sens. Environ. 112, 901-919, 2008. Jiménez, C. et al. Global intercomparison of 12 land surface heat flux estimates. J. Geophys. Res. 116, D02102, 2011. 
 Miralles, D.G. et al. Global land-surface evaporation estimated from satellite-based observations. Hydrol. Earth Syst. Sci. 15, 453-469, 2011. 
 Mu, Q., Zhao, M. & Running, S.W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781-1800, 2011. 
 Mueller, B., Hirschi, M., Jimenez, C., Ciais, P., Dirmeyer, P. A., Dolman, A. J., Fisher, J. B., Jung, M., Ludwig, F., Maignan, F., Miralles, D. G., McCabe, M. F., Reichstein, M., Sheffield, J., Wang, K., Wood, E. F., Zhang, Y., and Seneviratne, S. I. (2013). Benchmark products for land evapotranspiration: LandFlux-EVAL multi-data set synthesis. Hydrology and Earth System Sciences, 17, 3707-3720. Mueller, B. et al. Benchmark products for land evapotranspiration: LandFlux-EVAL multi-dataset synthesis. Hydrol. Earth Syst. Sci. 17, 3707-3720, 2013. Su, Z. The Surface Energy Balance System (SEBS) for estimation of turbulent heat fluxes. Hydrol. Earth Syst. Sci. 6, 85-99, 2002.

Influence of crop type specification and spatial resolution on empirical modeling of field-scale Maize and Soybean carbon fluxes in the US Great Plains

NASA Astrophysics Data System (ADS)

McCombs, A. G.; Hiscox, A.; Wang, C.; Desai, A. R.

2016-12-01

A challenge in satellite land surface remote-sensing models of ecosystem carbon dynamics in agricultural systems is the lack of differentiation by crop type and management. This generalization can lead to large discrepancies between model predictions and eddy covariance flux tower observations of net ecosystem exchange of CO2 (NEE). Literature confirms that NEE varies remarkably among different crop types making the generalization of agriculture in remote sensing based models inaccurate. Here, we address this inaccuracy by identifying and mapping net ecosystem exchange (NEE) in agricultural fields by comparing bulk modeling and modeling by crop type, and using this information to develop empirical models for future use. We focus on mapping NEE in maize and soybean fields in the US Great Plains at higher spatial resolution using the fusion of MODIS and LandSAT surface reflectance. MODIS observed reflectance was downscaled using the ESTARFM downscaling methodology to match spatial scales to those found in LandSAT and that are more appropriate for carbon dynamics in agriculture fields. A multiple regression model was developed from surface reflectance of the downscaled MODIS and LandSAT remote sensing values calibrated against five FLUXNET/AMERIFLUX flux towers located on soybean and/or maize agricultural fields in the US Great Plains with multi-year NEE observations. Our new methodology improves upon bulk approximates to map and model carbon dynamics in maize and soybean fields, which have significantly different photosynthetic capacities.

Using Remote Sensing and Radar MET Data to Support Watershed Assessments Comprising IEM

EPA Science Inventory

Meteorological (MET) data required by watershed assessments that comprise Integrated Environmental Modeling (IEM) have traditionally been provided by land-based weather (gauge) stations; although these data may not be most appropriate for describing adequate spatial and temporal...

Disaster Emergency Rapid Assessment Based on Remote Sensing and Background Data

NASA Astrophysics Data System (ADS)

Han, X.; Wu, J.

2018-04-01

The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.

Flood Extent Delineation by Thresholding Sentinel-1 SAR Imagery Based on Ancillary Land Cover Information

NASA Astrophysics Data System (ADS)

Liang, J.; Liu, D.

2017-12-01

Emergency responses to floods require timely information on water extents that can be produced by satellite-based remote sensing. As SAR image can be acquired in adverse illumination and weather conditions, it is particularly suitable for delineating water extent during a flood event. Thresholding SAR imagery is one of the most widely used approaches to delineate water extent. However, most studies apply only one threshold to separate water and dry land without considering the complexity and variability of different dry land surface types in an image. This paper proposes a new thresholding method for SAR image to delineate water from other different land cover types. A probability distribution of SAR backscatter intensity is fitted for each land cover type including water before a flood event and the intersection between two distributions is regarded as a threshold to classify the two. To extract water, a set of thresholds are applied to several pairs of land cover types—water and urban or water and forest. The subsets are merged to form the water distribution for the SAR image during or after the flooding. Experiments show that this land cover based thresholding approach outperformed the traditional single thresholding by about 5% to 15%. This method has great application potential with the broadly acceptance of the thresholding based methods and availability of land cover data, especially for heterogeneous regions.

Watershed-scale land-use mapping with satellite imagery

USDA-ARS?s Scientific Manuscript database

Satellite remote sensing data has many advantages compared with other data sources, such as field methods and aerial photography, for land cover classification. In particular,it is useful in evaluating temporal and spatial effects. In addition, remote sensing can offer a cost-effective means of prov...

Estimating impacts of plantation forestry on plant biodiversity in southern Chile-a spatially explicit modelling approach.

PubMed

Braun, Andreas Christian; Koch, Barbara

2016-10-01

Monitoring the impacts of land-use practices is of particular importance with regard to biodiversity hotspots in developing countries. Here, conserving the high level of unique biodiversity is challenged by limited possibilities for data collection on site. Especially for such scenarios, assisting biodiversity assessments by remote sensing has proven useful. Remote sensing techniques can be applied to interpolate between biodiversity assessments taken in situ. Through this approach, estimates of biodiversity for entire landscapes can be produced, relating land-use intensity to biodiversity conditions. Such maps are a valuable basis for developing biodiversity conservation plans. Several approaches have been published so far to interpolate local biodiversity assessments in remote sensing data. In the following, a new approach is proposed. Instead of inferring biodiversity using environmental variables or the variability of spectral values, a hypothesis-based approach is applied. Empirical knowledge about biodiversity in relation to land-use is formalized and applied as ascription rules for image data. The method is exemplified for a large study site (over 67,000 km(2)) in central Chile, where forest industry heavily impacts plant diversity. The proposed approach yields a coefficient of correlation of 0.73 and produces a convincing estimate of regional biodiversity. The framework is broad enough to be applied to other study sites.

Detecting trends in regional ecosystem functioning: the importance of field data for calibrating and validating NEON airborne remote sensing instruments and science data products

NASA Astrophysics Data System (ADS)

McCorkel, J.; Kuester, M. A.; Johnson, B. R.; Krause, K.; Kampe, T. U.; Moore, D. J.

2011-12-01

The National Ecological Observatory Network (NEON) is a research facility under development by the National Science Foundation to improve our understanding of and ability to forecast the impacts of climate change, land-use change, and invasive species on ecology. The infrastructure, designed to operate over 30 years or more, includes site-based flux tower and field measurements, coordinated with airborne remote sensing observations to observe key ecological processes over a broad range of temporal and spatial scales. NEON airborne data on vegetation biochemical, biophysical, and structural properties and on land use and land cover will be captured at 1 to 2 meter resolution by an imaging spectrometer, a small-footprint waveform-LiDAR and a high-resolution digital camera. Annual coverage of the 60 NEON sites and capacity to support directed research flights or respond to unexpected events will require three airborne observation platforms (AOP). The integration of field and airborne data with satellite observations and other national geospatial data for analysis, monitoring and input to ecosystem models will extend NEON observations to regions across the United States not directly sampled by the observatory. The different spatial scales and measurement methods make quantitative comparisons between remote sensing and field data, typically collected over small sample plots (e.g. < 0.2 ha), difficult. New approaches to developing temporal and spatial scaling relationships between these data are necessary to enable validation of airborne and satellite remote sensing data and for incorporation of these data into continental or global scale ecological models. In addition to consideration of the methods used to collect ground-based measurements, careful calibration of the remote sensing instrumentation and an assessment of the accuracy of algorithms used to derive higher-level science data products are needed. Furthermore, long-term consistency of the data collected by all three airborne instrument packages over the NEON sites requires traceability of the calibration to national standards, field-based verification of instrument calibration and stability in the aircraft environment, and an independent assessment of the quality of derived data products. This work describes the development of the calibration laboratory, early evaluation of field-based vicarious calibration, development of scaling relationships, and test flights. Complementary laboratory- and field-based calibration of the AOP in addition to consistency with on-board calibration methods provide confidence that low-level data such as radiance and surface reflectance measurements are accurate and comparable among different sensors. Algorithms that calculate higher-level data products including essential climate variables will be validated against equivalent ground- and satellite-based results. Such a validated data set across multiple spatial and temporal scales is key to enabling ecosystem models to forecast the effects of climate change, land-use change and invasive species on the continental scale.

Remote sensing in Michigan for land resource management

NASA Technical Reports Server (NTRS)

Lowe, D. S.; Istvan, L. B.; Roller, N. E.; Sattinger, I. J.; Sellman, A. N.; Wagner, T. W.

1974-01-01

The application of NASA earth resource survey technology to resource management and environmental protection in Michigan was investigated. Remote sensing techniques to aid Michigan government agencies were applied in the following activities: (1) land use inventory and management, (2) great lakes shorelands protection and management, (3) wetlands protection and management, and (4) soil survey. In addition, information was disseminated on remote sensing technology, and advice and assistance was provided to a number of users.

Improved Lower Mekong River Basin Hydrological Decision Making Using NASA Satellite-based Earth Observation Systems

NASA Astrophysics Data System (ADS)

Bolten, J. D.; Mohammed, I. N.; Srinivasan, R.; Lakshmi, V.

2017-12-01

Better understanding of the hydrological cycle of the Lower Mekong River Basin (LMRB) and addressing the value-added information of using remote sensing data on the spatial variability of soil moisture over the Mekong Basin is the objective of this work. In this work, we present the development and assessment of the LMRB (drainage area of 495,000 km2) Soil and Water Assessment Tool (SWAT). The coupled model framework presented is part of SERVIR, a joint capacity building venture between NASA and the U.S. Agency for International Development, providing state-of-the-art, satellite-based earth monitoring, imaging and mapping data, geospatial information, predictive models, and science applications to improve environmental decision-making among multiple developing nations. The developed LMRB SWAT model enables the integration of satellite-based daily gridded precipitation, air temperature, digital elevation model, soil texture, and land cover and land use data to drive SWAT model simulations over the Lower Mekong River Basin. The LMRB SWAT model driven by remote sensing climate data was calibrated and verified with observed runoff data at the watershed outlet as well as at multiple sites along the main river course. Another LMRB SWAT model set driven by in-situ climate observations was also calibrated and verified to streamflow data. Simulated soil moisture estimates from the two models were then examined and compared to a downscaled Soil Moisture Active Passive Sensor (SMAP) 36 km radiometer products. Results from this work present a framework for improving SWAT performance by utilizing a downscaled SMAP soil moisture products used for model calibration and validation. Index Terms: 1622: Earth system modeling; 1631: Land/atmosphere interactions; 1800: Hydrology; 1836 Hydrological cycles and budgets; 1840 Hydrometeorology; 1855: Remote sensing; 1866: Soil moisture; 6334: Regional Planning

Using hyperspectral remote sensing for land cover classification

NASA Astrophysics Data System (ADS)

Zhang, Wendy W.; Sriharan, Shobha

2005-01-01

This project used hyperspectral data set to classify land cover using remote sensing techniques. Many different earth-sensing satellites, with diverse sensors mounted on sophisticated platforms, are currently in earth orbit. These sensors are designed to cover a wide range of the electromagnetic spectrum and are generating enormous amounts of data that must be processed, stored, and made available to the user community. The Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) collects data in 224 bands that are approximately 9.6 nm wide in contiguous bands between 0.40 and 2.45 mm. Hyperspectral sensors acquire images in many, very narrow, contiguous spectral bands throughout the visible, near-IR, and thermal IR portions of the spectrum. The unsupervised image classification procedure automatically categorizes the pixels in an image into land cover classes or themes. Experiments on using hyperspectral remote sensing for land cover classification were conducted during the 2003 and 2004 NASA Summer Faculty Fellowship Program at Stennis Space Center. Research Systems Inc.'s (RSI) ENVI software package was used in this application framework. In this application, emphasis was placed on: (1) Spectrally oriented classification procedures for land cover mapping, particularly, the supervised surface classification using AVIRIS data; and (2) Identifying data endmembers.

The effects of war on land-use/land-cover change: An analysis of Landsat imagery for northeast Bosnia

NASA Astrophysics Data System (ADS)

Witmer, Frank D. W.

The use of satellite technology by military planners has a relatively long history as a tool of warfare, but little research has used satellite technology to study the effects of war. This research addresses this gap by applying satellite remote sensing imagery to study the effects of war on land-use/land-cover change in northeast Bosnia. The war in Bosnia, 1992-1995, resulted in over 100,000 deaths, many more wounded, and the mass displacement of nearly half the population of 4.2 million. When combined with the destruction of much of the transportation infrastructure and housing stock, widespread mine placement, and loss of agricultural machinery, the impacts to both the people and land were dramatic. Though the most severe war impacts are visible at local scales (e.g. destroyed buildings), this study focuses on impacts to agricultural land, a larger scale visible to satellite sensors. Multispectral Landsat Thematic Mapper (TM) data (30m pixels) from before and during the war in addition to recent imagery from 2004/05 were used to detect abandoned agricultural land. The satellite images were co-registered to enable a perpixel analysis of changes based on the statistical properties of the pixels using multiple change detection methods. Ground reference data were collected in May of 2006 at survey sites selected using a stratified random sampling approach based on the derived map of abandoned agricultural land. Fine resolution (60cm) Quickbird imagery was also used to verify the accuracy of the classification. The remote sensing analysis results were then used to test two hypotheses related to war outcomes: (a) land abandonment is due to wartime minefields and (b) land abandonment is greater in pre-war Croat areas and areas where ethnic cleansing was heaviest. The effects of minefields on land abandonment was first tested in a geographic information system (GIS), and then by using multiple regression models that account for spatial autocorrelation among observations. The spatial regression analysis was conducted at the opstina (county) areal unit and used minefield locations, refugee returns and population change data as predictors of abandoned agricultural land. Results from these analyses show that a supervised classification of the Landsat TM data identified abandoned agricultural land with an overall accuracy of 82.5%. The GIS and spatial regression analysis of how war affects agricultural land showed that the presence of minefields and population declines are both associated with abandoned agricultural land. This research holds significance for both the remote sensing and civil war research communities. The use of freely available Quickbird imagery both as training data for the supervised classifier and as supplementary ground reference data suggest these methods are applicable to other civil wars (e.g. Darfur region of Sudan and the Horn of Africa) that may still be too dangerous for researchers to conduct field work in. By extending these methods to other war zones, comparisons of similarities and differences between such studies can then be made to draw broader conclusions of war impacts to land use and land cover.

Retrieval of an available water-based soil moisture proxy from thermal infrared remote sensing. Part I: Methodology and validation

USDA-ARS?s Scientific Manuscript database

A retrieval of soil moisture is proposed using surface flux estimates from satellite-based thermal infrared (TIR) imagery and the Atmosphere-Land-Exchange-Inversion (ALEXI) model. The ability of ALEXI to provide valuable information about the partitioning of the surface energy budget, which can be l...

Object-based image analysis for cadastral mapping using satellite images

NASA Astrophysics Data System (ADS)

Kohli, D.; Crommelinck, S.; Bennett, R.; Koeva, M.; Lemmen, C.

2017-10-01

Cadasters together with land registry form a core ingredient of any land administration system. Cadastral maps comprise of the extent, ownership and value of land which are essential for recording and updating land records. Traditional methods for cadastral surveying and mapping often prove to be labor, cost and time intensive: alternative approaches are thus being researched for creating such maps. With the advent of very high resolution (VHR) imagery, satellite remote sensing offers a tremendous opportunity for (semi)-automation of cadastral boundaries detection. In this paper, we explore the potential of object-based image analysis (OBIA) approach for this purpose by applying two segmentation methods, i.e. MRS (multi-resolution segmentation) and ESP (estimation of scale parameter) to identify visible cadastral boundaries. Results show that a balance between high percentage of completeness and correctness is hard to achieve: a low error of commission often comes with a high error of omission. However, we conclude that the resulting segments/land use polygons can potentially be used as a base for further aggregation into tenure polygons using participatory mapping.

Review of Land Use and Land Cover Change research progress

NASA Astrophysics Data System (ADS)

Chang, Yue; Hou, Kang; Li, Xuxiang; Zhang, Yunwei; Chen, Pei

2018-02-01

Land Use and Land Cover Change (LUCC) can reflect the pattern of human land use in a region, and plays an important role in space soil and water conservation. The study on the change of land use patterns in the world is of great significance to cope with global climate change and sustainable development. This paper reviews the main research progress of LUCC at home and abroad, and suggests that land use change has been shifted from land use planning and management to land use change impact and driving factors. The development of remote sensing technology provides the basis and data for LUCC with dynamic monitoring and quantitative analysis. However, there is no uniform standard for land use classification at present, which brings a lot of inconvenience to the collection and analysis of land cover data. Globeland30 is an important milestone contribution to the study of international LUCC system. More attention should be paid to the accuracy and results contrasting test of land use classification obtained by remote sensing technology.

Remote sensing and GIS-based site suitability analysis for tourism development in Gili Indah, East Lombok

NASA Astrophysics Data System (ADS)

Agnes, Debrina; Nandatama, Akbar; Isdyantoko, Bagus Andi; Aditya Nugraha, Fajri; Ghivarry, Giusti; Putra Aghni, Perwira; ChandraWijaya, Renaldi; Widayani, Prima

2016-11-01

Gili Indah area, located in Jerowaru, East Lombok Regency is a region that classified as farm area in spatial layout planning map of West Nusa Tenggara province. Gili Indah area has a potential as a new tourism attraction within its gilis (local term for ‘small island’). Assessment should be done to prevent ecological disturbance and infringement towards spatial layout planning map caused by incorrect landuse. Land suitability assessment will be done using remote sensing approach whilst satellite imagery being used to get information about ocean ecology and land physical spatial distribution that will be the parameter of tourism land suitability, such as water clarity, ocean current, type of beaches’ substrate, and beach typology. Field observation then will evaluate the accuracy of data extraction also as a material to do reinterpretation. The actual physical condition will be pictured after the spatial model built with GIS by tiered qualitative analysis approach. The result of assessment and mapping of tourism land suitability is that parts of Gili Indah Area (GiliMaringkik, Greater GiliBembeq, and Small GiliBembeq) are suitable for archipelago tourism while the others is not.

Method of interpretation of remotely sensed data and applications to land use

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Dossantos, A. P.; Foresti, C.; Demoraesnovo, E. M. L.; Niero, M.; Lombardo, M. A.

1981-01-01

Instructional material describing a methodology of remote sensing data interpretation and examples of applicatons to land use survey are presented. The image interpretation elements are discussed for different types of sensor systems: aerial photographs, radar, and MSS/LANDSAT. Visual and automatic LANDSAT image interpretation is emphasized.

Integration of Remote Sensing Products with Ground-Based Measurements to Understand the Dynamics of Nepal's Forests and Plantation Sites

NASA Astrophysics Data System (ADS)

Gilani, H.; Jain, A. K.

2016-12-01

This study assembles information from three sources - remote sensing, terrestrial photography and ground-based inventory data, to understand the dynamics of Nepal's tropical and sub-tropical forests and plantation sites for the period 1990-2015. Our study focuses on following three specific district areas, which have conserved forests through social and agroforestry management practices: 1. Dolakha district: This site has been selected to study the impact of community-based forest management on land cover change using repeat photography and satellite imagery, in combination with interviews with community members. The study time period is during the period 1990-2010. We determined that satellite data with ground photographs can provide transparency for long term monitoring. The initial results also suggests that community-based forest management program in the mid-hills of Nepal was successful. 2. Chitwan district: Here we use high resolution remote sensing data and optimized community field inventories to evaluate potential application and operational feasibility of community level REDD+ measuring, reporting and verification (MRV) systems. The study uses temporal dynamics of land cover transitions, tree canopy size classes and biomass over a Kayar khola watershed REDD+ study area with community forest to evaluate satellite Image segmentation for land cover, linear regression model for above ground biomass (AGB), and estimation and monitoring field data for tree crowns and AGB. We study three specific years 2002, 2009, 2012. Using integration of WorldView-2 and airborne LiDAR data for tree species level. 3. Nuwakot district: This district was selected to study the impact of establishment of tree plantation on total barren/fallow. Over the last 40 year, this area has went through a drastic changes, from barren land to forest area with tree species consisting of Dalbergia sissoo, Leucaena leucocephala, Michelia champaca, etc. In 1994, this district area was registered and established to grow and process high quality trees shaded of Arabica coffee beans. Here we use temporal satellite images and repeat terrestrial and aerial photographs, along with plot level biomass to show impact of this positive transformation of the landscape on above and below ground carbon masses. The study time period is 1990-2015.

Evaluation of Resources Carrying Capacity in China Based on Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Liu, K.; Gan, Y. H.; Zhang, T.; Luo, Z. Y.; Wang, J. J.; Lin, F. N.

2018-04-01

This paper accurately extracted the information of arable land, grassland (wetland), forest land, water area and construction land, based on 1 : 250000 basic geographic information data. It made model modification of comprehensive CCRR to achieve carrying capacity calculation taking resource quality into consideration. Ultimately it achieved a comprehensive assessment of CCRR status in China. The top ten cities where the status of carrying capacity of resources was overloaded were Wenzhou, Shanghai, Chengdu, Baoding, Shantou, Jieyang, Dongguan, Fuyang, Zhoukou and Handan. The cities were basically distributed in the central and southern areas with convenient transportation and more economically developed areas. Among the cities in surplus status, resources carrying capacity in Hulun Buir was the most abundant, followed by Heihe, Bayingolin Mongol Autonomous Prefecture, Qiqihar, Chifeng and Jiamusi, all of which were located in northeastern China with a small population and plentiful cultivated land.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

PubMed Central

Midekisa, Alemayehu; Senay, Gabriel B; Wimberly, Michael C

2014-01-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region. Key Points Remote sensing produced an accurate wetland map for the Ethiopian highlands Wetlands were associated with spatial variability in malaria risk Mapping and monitoring wetlands can improve malaria spatial decision support PMID:25653462

Downscaling essential climate variable soil moisture using multisource data from 2003 to 2010 in China

NASA Astrophysics Data System (ADS)

Wang, Hui-Lin; An, Ru; You, Jia-jun; Wang, Ying; Chen, Yuehong; Shen, Xiao-ji; Gao, Wei; Wang, Yi-nan; Zhang, Yu; Wang, Zhe; Quaye-Ballard, Jonathan Arthur

2017-10-01

Soil moisture plays an important role in the water cycle within the surface ecosystem, and it is the basic condition for the growth of plants. Currently, the spatial resolutions of most soil moisture data from remote sensing range from ten to several tens of km, while those observed in-situ and simulated for watershed hydrology, ecology, agriculture, weather, and drought research are generally <1 km. Therefore, the existing coarse-resolution remotely sensed soil moisture data need to be downscaled. This paper proposes a universal and multitemporal soil moisture downscaling method suitable for large areas. The datasets comprise land surface, brightness temperature, precipitation, and soil and topographic parameters from high-resolution data and active/passive microwave remotely sensed essential climate variable soil moisture (ECV_SM) data with a spatial resolution of 25 km. Using this method, a total of 288 soil moisture maps of 1-km resolution from the first 10-day period of January 2003 to the last 10-day period of December 2010 were derived. The in-situ observations were used to validate the downscaled ECV_SM. In general, the downscaled soil moisture values for different land cover and land use types are consistent with the in-situ observations. Mean square root error is reduced from 0.070 to 0.061 using 1970 in-situ time series observation data from 28 sites distributed over different land uses and land cover types. The performance was also assessed using the GDOWN metric, a measure of the overall performance of the downscaling methods based on the same dataset. It was positive in 71.429% of cases, indicating that the suggested method in the paper generally improves the representation of soil moisture at 1-km resolution.

Quantifying the Terrestrial Surface Energy Fluxes Using Remotely-Sensed Satellite Data

NASA Astrophysics Data System (ADS)

Siemann, Amanda Lynn

The dynamics of the energy fluxes between the land surface and the atmosphere drive local and regional climate and are paramount to understand the past, present, and future changes in climate. Although global reanalysis datasets, land surface models (LSMs), and climate models estimate these fluxes by simulating the physical processes involved, they merely simulate our current understanding of these processes. Global estimates of the terrestrial, surface energy fluxes based on observations allow us to capture the dynamics of the full climate system. Remotely-sensed satellite data is the source of observations of the land surface which provide the widest spatial coverage. Although net radiation and latent heat flux global, terrestrial, surface estimates based on remotely-sensed satellite data have progressed, comparable sensible heat data products and ground heat flux products have not progressed at this scale. Our primary objective is quantifying and understanding the terrestrial energy fluxes at the Earth's surface using remotely-sensed satellite data with consistent development among all energy budget components [through the land surface temperature (LST) and input meteorology], including validation of these products against in-situ data, uncertainty assessments, and long-term trend analysis. The turbulent fluxes are constrained by the available energy using the Bowen ratio of the un-constrained products to ensure energy budget closure. All final products are within uncertainty ranges of literature values, globally. When validated against the in-situ estimates, the sensible heat flux estimates using the CFSR air temperature and constrained with the products using the MODIS albedo produce estimates closest to the FLUXNET in-situ observations. Poor performance over South America is consistent with the largest uncertainties in the energy budget. From 1984-2007, the longwave upward flux increase due to the LST increase drives the net radiation decrease, and the decrease in the available energy balances the decrease in the sensible heat flux. These datasets are useful for benchmarking climate models and LSM output at the global annual scale and the regional scale subject to the regional uncertainties and performance. Future work should improve the input data, particularly the temperature gradient and Zilitinkevich empirical constant, to reduce uncertainties.

A Multiscale Random Field Model for Bayesian Image Segmentation

DTIC Science & Technology

1994-06-01

ATrN: Natural Resources Branch ATTN G ieCN-C3 D-E Aberden Povig Ground . MD 21005 At Aii-DI (2)AWN IS-TEOMAMr: ATZHI-DtE (2) ATTN: ISH-BECOM Fort...based remotely-sensed data and ground -level data for natural resource inventory and evaluation. Coupling remotely sensed digital data with traditional...ecological ground data could help Army land managers inventory and monitor natural resources. This study used LCTA data sets to D T IC test image

Remote Sensing of Complex Flows by Doppler Wind Lidar: Issues and Preliminary Recommendations

DOE Office of Scientific and Technical Information (OSTI.GOV)

Clifton, Andrew; Boquet, Matthieu; Burin Des Roziers, Edward

Remote sensing of winds using lidar has become popular and useful in the wind energy industry. Extensive experience has been gained with using lidar for applications including land-based and offshore resource assessment, plant operations, and turbine control. Prepared by members of International Energy Agency Task 32, this report describes the state of the art in the use of Doppler wind lidar for resource assessment in complex flows. The report will be used as input for future recommended practices on this topic.

Viking Landers and remote sensing

NASA Technical Reports Server (NTRS)

Moore, H. J.; Jakosky, B. M.; Christensen, P. R.

1987-01-01

Thermal and radar remote sensing signatures of the materials in the lander sample fields can be crudely estimated from evaluations of their physical-mechanical properties, laboratory data on thermal conductivities and dielectric constants, and theory. The estimated thermal inertias and dielectric constants of some of the materials in the sample field are close to modal values estimated from orbital and earth-based observations. This suggests that the mechanical properties of the surface materials of much of Mars will not be significantly different that those of the landing sites.

A dataset mapping the potential biophysical effects of vegetation cover change

NASA Astrophysics Data System (ADS)

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-02-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes.

A dataset mapping the potential biophysical effects of vegetation cover change

PubMed Central

Duveiller, Gregory; Hooker, Josh; Cescatti, Alessandro

2018-01-01

Changing the vegetation cover of the Earth has impacts on the biophysical properties of the surface and ultimately on the local climate. Depending on the specific type of vegetation change and on the background climate, the resulting competing biophysical processes can have a net warming or cooling effect, which can further vary both spatially and seasonally. Due to uncertain climate impacts and the lack of robust observations, biophysical effects are not yet considered in land-based climate policies. Here we present a dataset based on satellite remote sensing observations that provides the potential changes i) of the full surface energy balance, ii) at global scale, and iii) for multiple vegetation transitions, as would now be required for the comprehensive evaluation of land based mitigation plans. We anticipate that this dataset will provide valuable information to benchmark Earth system models, to assess future scenarios of land cover change and to develop the monitoring, reporting and verification guidelines required for the implementation of mitigation plans that account for biophysical land processes. PMID:29461538

Applications experiments in the Houston region

NASA Technical Reports Server (NTRS)

Erb, R. B.

1972-01-01

Organization, staffing, and planning for experimental applications of remote sensing data to gain land use and water management information are described. Work was started on an experimental data base, aerial multispectral photography, and forest and wild life surveys as part of a regional inventory and monitory program.

Unmanned airborne thermal and mutilspectral imagery for estimating evapotranspiration in irrigated vineyards

USDA-ARS?s Scientific Manuscript database

Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying rootzone water availability, evapotranspiration (ET) and crop condition. This paper describes the most recent modifications applied to the robust but relatively simple LST-based energy bal...

Using Remote Sensing and Radar Meteorological Data to Support Watershed Assessments Comprising Integrated Environmental Modeling

EPA Science Inventory

Meteorological (MET) data required by watershed assessments comprising Integrated Environmental Modeling (IEM) traditionally have been provided by land-based weather (gauge) stations, although these data may not be the most appropriate for adequate spatial and temporal resolution...

Rule-based land use/land cover classification in coastal areas using seasonal remote sensing imagery: a case study from Lianyungang City, China.

PubMed

Yang, Xiaoyan; Chen, Longgao; Li, Yingkui; Xi, Wenjia; Chen, Longqian

2015-07-01

Land use/land cover (LULC) inventory provides an important dataset in regional planning and environmental assessment. To efficiently obtain the LULC inventory, we compared the LULC classifications based on single satellite imagery with a rule-based classification based on multi-seasonal imagery in Lianyungang City, a coastal city in China, using CBERS-02 (the 2nd China-Brazil Environmental Resource Satellites) images. The overall accuracies of the classification based on single imagery are 78.9, 82.8, and 82.0% in winter, early summer, and autumn, respectively. The rule-based classification improves the accuracy to 87.9% (kappa 0.85), suggesting that combining multi-seasonal images can considerably improve the classification accuracy over any single image-based classification. This method could also be used to analyze seasonal changes of LULC types, especially for those associated with tidal changes in coastal areas. The distribution and inventory of LULC types with an overall accuracy of 87.9% and a spatial resolution of 19.5 m can assist regional planning and environmental assessment efficiently in Lianyungang City. This rule-based classification provides a guidance to improve accuracy for coastal areas with distinct LULC temporal spectral features.

Effectiveness of Vegetation Index Transformation for Land Use Identifying and Mapping in the Area of Oil palm Plantation based on SPOT-6 Imagery (Case Study: PT.Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu)

NASA Astrophysics Data System (ADS)

Setyowati, H. A.; S, S. H. Murti B.; Sukentyas, E. S.

2016-11-01

The reflection of land surface, atmosphere and vegetation conditions affect the reflectance value of the object is recorded on remote sensing image so that it can affect the outcome of information extraction from remote sensing imagery one multispectral classification. This study aims to assess the ability of the transformation of generic vegetation index (Wide Dynamic Range Vegetation Index), the vegetation index transformation that is capable reducing the influence of the atmosphere (Atmospherically Resistant Vegetation Index), and the transformation of vegetation index that is capable of reducing the influence of the background soil (Second Modified Soil Adjusted Vegetation Index) for the identification and mapping of land use in the oil palm plantation area based on SPOT-6 archived on June 13, 2013 from LAPAN. The study area selected oil palm plantations PT. Tunggal Perkasa Plantations, Air Molek, Indragiri Hulu, Riau Province. The method is using the transformation of the vegetation index ARVI, MSAVI2, and WDRVI. Sample selection method used was stratified random sampling. The test method used mapping accuracy of the confusion matrix. The results showed that the best transformation of the vegetation index for the identification and mapping of land use in the plantation area is ARVI transformation with a total of accuracy is 96%. Accuracy of mapping land use settlements 100%, replanting 82.35%, 81.25% young oil palm, old oil palm 99.46%, 100% bush, body of water 100%, and 100% bare-soil.

Definitive Mineralogy from the Mars Science Laboratory Chemin Instrument

NASA Technical Reports Server (NTRS)

Yen, A. S.; Bish, D. L.; Blake, D. F.; Vaniman, D. T.; Treiman, A. H.; Ming, D. W.; Morris, Richard V.; Farmer, J. D.; Downs, R. T.; Chipera, S. J.;

Interactive Computing and Processing of NASA Land Surface Observations Using Google Earth Engine

NASA Technical Reports Server (NTRS)

Molthan, Andrew; Burks, Jason; Bell, Jordan

2016-01-01

Google's Earth Engine offers a "big data" approach to processing large volumes of NASA and other remote sensing products. h\\ps://earthengine.google.com/ Interfaces include a Javascript or Python-based API, useful for accessing and processing over large periods of record for Landsat and MODIS observations. Other data sets are frequently added, including weather and climate model data sets, etc. Demonstrations here focus on exploratory efforts to perform land surface change detection related to severe weather, and other disaster events.

Commercial use of remote sensing in agriculture: a case study

NASA Astrophysics Data System (ADS)

Gnauck, Gary E.

1999-12-01

Over 25 years of research have clearly shown that an analysis of remote sensing imagery can provide information on agricultural crops. Most of this research has been funded by and directed toward the needs of government agencies. Commercial use of agricultural remote sensing has been limited to very small-scale operations supplying remote sensing services to a few selected customers. Datron/Transco Inc. undertook an internally funded remote sensing program directed toward the California cash crop industry (strawberries, lettuce, tomatoes, other fresh vegetables and cotton). The objectives of this program were twofold: (1) to assess the need and readiness of agricultural land managers to adopt remote sensing as a management tool, and (2) determine what technical barriers exist to large-scale implementation of this technology on a commercial basis. The program was divided into three phases: Planning, Engineering Test and Evaluation, and Commercial Operations. Findings: Remote sensing technology can deliver high resolution multispectral imagery with rapid turnaround, that can provide information on crop stress insects, disease and various soil parameters. The limiting factors to the use of remote sensing in agriculture are a lack of familiarization by the land managers, difficulty in translating 'information' into increased revenue or reduced cost for the land manager, and the large economies of scale needed to make the venture commercially viable.

Analyses and simulation to spatial pattern of land utilization in Guangzhu City

NASA Astrophysics Data System (ADS)

Zhang, Xin-chang; Zhang, Wen-jiang; Ma, Kun

2006-10-01

Based on Landsat TM remote sensing images in 1990 and 2000, we analyses the temporal and spatial pattern Characters of land use in the 1990s in Guangzhou city. We also simulate the scenarios of land-use pattern in 2010 by integrating the Markov process into cellular automata model. The results show that the area of constructions was rapid increasing during the last ten years of the 20th century, at the same time the arable land, woodland and unused land areas were decreasing, the orchard and water areas were rarely changed; In the first ten years of 21st century, land use pattern keep the change trend in the 1990s, land of constructions continue rapid increasing; arable land and unused land areas continue rapid decreasing; woodland, orchard and water areas keep steadily. Research shows that the extent of urban area has increased exponentially in Guangzhou city, no evidences show that the arable land decreasing rate will slow down in the near future. So, it is necessary to enhance the control functions of land use planning and take actives measures to protect arable land.

Linkages between Land Surface Phenology Metrics and Natural and Anthropogenic Events in Drylands (Invited)

NASA Astrophysics Data System (ADS)

de Beurs, K.; Brown, M. E.; Ahram, A.; Walker, J.; Henebry, G. M.

2013-12-01

Tracking vegetation dynamics across landscapes using remote sensing, or 'land surface phenology,' is a key mechanism that allows us to understand ecosystem changes. Land surface phenology models rely on vegetation information from remote sensing, such as the datasets derived from the Advanced Very High Resolution Radiometer (AVHRR), the newer MODIS sensors on Aqua and Terra, and sometimes the higher spatial resolution Landsat data. Vegetation index data can aid in the assessment of variables such as the start of season, growing season length and overall growing season productivity. In this talk we use Landsat, MODIS and AVHRR data and derive growing season metrics based on land surface phenology models that couple vegetation indices with satellite derived accumulated growing degreeday and evapotranspiration estimates. We calculate the timing and the height of the peak of the growing season and discuss the linkage of these land surface phenology metrics with natural and anthropogenic changes on the ground in dryland ecosystems. First we will discuss how the land surface phenology metrics link with annual and interannual price fluctuations in 229 markets distributed over Africa. Our results show that there is a significant correlation between the peak height of the growing season and price increases for markets in countries such as Nigeria, Somalia and Niger. We then demonstrate how land surface phenology metrics can improve models of post-conflict resolution in global drylands. We link the Uppsala Conflict Data Program's dataset of political, economic and social factors involved in civil war termination with an NDVI derived phenology metric and the Palmer Drought Severity Index (PDSI). An analysis of 89 individual conflicts in 42 dryland countries (totaling 892 individual country-years of data between 1982 and 2005) revealed that, even accounting for economic and political factors, countries that have higher NDVI growth following conflict have a lower risk of reverting to civil war. Finally, the patchy and heterogeneous arrangement of vegetation in dryland areas sometimes complicates the extraction of phenological signals using existing remote sensing data. We conclude by demonstrating how the phenological analysis of a range of dryland land cover classes benefits from the availability of synthetic images at Landsat spatial resolution and MODIS time intervals.

The application of remote sensing techniques to inter and intra urban analysis

NASA Technical Reports Server (NTRS)

Horton, F. E.

1972-01-01

This is an effort to assess the applicability of air and spaceborne photography toward providing data inputs to urban and regional planning, management, and research. Through evaluation of remote sensing inputs to urban change detection systems, analyzing an effort to replicate an existing urban land use data file using remotely sensed data, estimating population and dwelling units from imagery, and by identifying and evaluating a system of urban places ultilizing space photography, it was determined that remote sensing can provide data concerning land use, changes in commercial structure, data for transportation planning, housing quality, residential dynamics, and population density.

Remote-sensing-based analysis of landscape change in the desiccated seabed of the Aral Sea--a potential tool for assessing the hazard degree of dust and salt storms.

PubMed

Löw, F; Navratil, P; Kotte, K; Schöler, H F; Bubenzer, O

2013-10-01

With the recession of the Aral Sea in Central Asia, once the world's fourth largest lake, a huge new saline desert emerged which is nowadays called the Aralkum. Saline soils in the Aralkum are a major source for dust and salt storms in the region. The aim of this study was to analyze the spatio-temporal land cover change dynamics in the Aralkum and discuss potential implications for the recent and future dust and salt storm activity in the region. MODIS satellite time series were classified from 2000-2008 and change of land cover was quantified. The Aral Sea desiccation accelerated between 2004 and 2008. The area of sandy surfaces and salt soils, which bear the greatest dust and salt storm generation potential increased by more than 36 %. In parts of the Aralkum desalinization of soils was found to take place within 4-8 years. The implication of the ongoing regression of the Aral Sea is that the expansion of saline surfaces will continue. Knowing the spatio-temporal dynamics of both the location and the surface characteristics of the source areas for dust and salt storms allows drawing conclusions about the potential hazard degree of the dust load. The remote-sensing-based land cover assessment presented in this study could be coupled with existing knowledge on the location of source areas for an early estimation of trends in shifting dust composition. Opportunities, limits, and requirements of satellite-based land cover classification and change detection in the Aralkum are discussed.

Effects of land use/cover change and harvests on forest carbon dynamics in northern states of the United States from remote sensing and inventory data: 1992-2001

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey; James E. Smith

2011-01-01

We examined spatial patterns of changes in forest area and nonsoil carbon (C) dynamics affected by land use/cover change (LUC) and harvests in 24 northern states of the United States using an integrated methodology combining remote sensing and ground inventory data between 1992 and 2001. We used the Retrofit Change Product from the Multi-Resolution Land Characteristics...

Urban surface energy fluxes based on remotely-sensed data and micrometeorological measurements over the Kansai area, Japan

NASA Astrophysics Data System (ADS)

Sukeyasu, T.; Ueyama, M.; Ando, T.; Kosugi, Y.; Kominami, Y.

2017-12-01

The urban heat island is associated with land cover changes and increases in anthropogenic heat fluxes. Clear understanding of the surface energy budget at urban area is the most important for evaluating the urban heat island. In this study, we develop a model based on remotely-sensed data for the Kansai area in Japan and clarify temporal transitions and spatial distributions of the surface energy flux from 2000 to 2016. The model calculated the surface energy fluxes based on various satellite and GIS products. The model used land surface temperature, surface emissivity, air temperature, albedo, downward shortwave radiation and land cover/use type from the moderate resolution imaging spectroradiometer (MODIS) under cloud free skies from 2000 to 2016 over the Kansai area in Japan (34 to 35 ° N, 135 to 136 ° E). Net radiation was estimated by a radiation budget of upward/downward shortwave and longwave radiation. Sensible heat flux was estimated by a bulk aerodynamic method. Anthropogenic heat flux was estimated by the inventory data. Latent heat flux was examined with residues of the energy budget and parameterization of bulk transfer coefficients. We validated the model using observed fluxes from five eddy-covariance measurement sites: three urban sites and two forested sites. The estimated net radiation roughly agreed with the observations, but the sensible heat flux were underestimated. Based on the modeled spatial distributions of the fluxes, the daytime net radiation in the forested area was larger than those in the urban area, owing to higher albedo and land surface temperatures in the urban area than the forested area. The estimated anthropogenic heat flux was high in the summer and winter periods due to increases in energy-requirements.

Analysing spatio-temporal land degradation dynamics in dry rangelands using landscape metrics and satellite time series data

NASA Astrophysics Data System (ADS)

von Keyserlingk, Jennifer; Paton, Eva Nora; Förster, Saskia; Bronstert, Axel

2017-04-01

Many of the dry rangelands of Southern Europe are threatened by land degradation. This process not only reduces the land's ecological functioning, but also its capacity to provide ecosystem goods and services for local land users. In rangelands, one important aspect is vegetation degradation, which reduces the land's capacity to support livestock. Thus, there is an urgent need to understand the complex dynamics and drivers of land degradation. In the past, both have been difficult to study due to the extensive spatial and temporal scales involved. In the last decade, a large number of remotely sensed imageries has become available for free, which enables a new approach to this topic. The aim of this research is to study land degradation as a multidimensional process incorporating its spatial and temporal components. We developed a methodological approach that makes use of long-term satellite Landsat data. Here, we use imagery of a typical degraded Mediterranean rangeland in Southern Cyprus (Randi Forest) for the years 1998-2015. We have chosen the NDVI as a proxy for vegetation greenness and applied different spatial landscape metrics to calculate changes in vegetation patterns over time. Further, we applied a time-series based approach (BFAST) on selected pixels, to look for sudden changes and trends in the vegetation dynamics. The results promoted our knowledge on how land degradation dynamics in Mediterranean rangelands can be captured through spatio-temporal vegetation dynamics and allowed us to select the most suitable metrics for further analysis. In the long-term, we aim at using Landsat satellite data covering 30 years. To gain a functional understanding of land degradation, we want to overlay our results from the remotely sensed data with results of an eco-hydrological model (SWAT).

Natural Resource Information System. Volume 1: Overall description

NASA Technical Reports Server (NTRS)

1972-01-01

A prototype computer-based Natural Resource Information System was designed which could store, process, and display data of maximum usefulness to land management decision making. The system includes graphic input and display, the use of remote sensing as a data source, and it is useful at multiple management levels. A survey established current decision making processes and functions, information requirements, and data collection and processing procedures. The applications of remote sensing data and processing requirements were established. Processing software was constructed and a data base established using high-altitude imagery and map coverage of selected areas of SE Arizona. Finally a demonstration of system processing functions was conducted utilizing material from the data base.

A Study on Integrated Community Based Flood Mitigation with Remote Sensing Technique in Kota Bharu, Kelantan

NASA Astrophysics Data System (ADS)

'Ainullotfi, A. A.; Ibrahim, A. L.; Masron, T.

2014-02-01

This study is conducted to establish a community based flood management system that is integrated with remote sensing technique. To understand local knowledge, the demographic of the local society is obtained by using the survey approach. The local authorities are approached first to obtain information regarding the society in the study areas such as the population, the gender and the tabulation of settlement. The information about age, religion, ethnic, occupation, years of experience facing flood in the area, are recorded to understand more on how the local knowledge emerges. Then geographic data is obtained such as rainfall data, land use, land elevation, river discharge data. This information is used to establish a hydrological model of flood in the study area. Analysis were made from the survey approach to understand the pattern of society and how they react to floods while the analysis of geographic data is used to analyse the water extent and damage done by the flood. The final result of this research is to produce a flood mitigation method with a community based framework in the state of Kelantan. With the flood mitigation that involves the community's understanding towards flood also the techniques to forecast heavy rainfall and flood occurrence using remote sensing, it is hope that it could reduce the casualties and damage that might cause to the society and infrastructures in the study area.

The study on the spatial-temporal changes of land use pattern in eastern Sichuan basin based on RS/GIS

NASA Astrophysics Data System (ADS)

Sun, Siqi; Xiao, Yi; Guo, Luo

2018-02-01

Eastern Sichuan Basin is one of the areas sensitive to global climate change. Due to impacts from human disturbance, the farmland in the study area has been degrading, and the desertification of land has been expanding rapidly. Based on the data of Landsat TM/ETM image in 1990, 1995, 2000, 2005 and 2010, this thesis analysed the spatial characteristics and dynamic trends of land use pattern in eastern Sichuan basin using software for remote sense and geographical information system. The driving factors of land-use change in study area were also discussed. The results indicated that: (i) the area of farmland has significantly decreased because of degradation and conversion from grassland into building land; (ii) farmland patches have changed into fragmented and isolated ones; (iii) the main landscapes in study area, are farmland and forests; (iv) land-use change is significantly associated with the human activities. This study provides a strong theoretical and technical basis for the policy-making of environmental protection and management in Eastern Sichuan Basin of Sichuan Province in china.

Landsat: A Global Land-Imaging Project

USGS Publications Warehouse

Headley, Rachel

2010-01-01

Across nearly four decades since 1972, Landsat satellites continuously have acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space; consequently, NASA develops remote-sensing instruments and spacecraft, then launches and validates the satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Landsat: a global land imaging program

USGS Publications Warehouse

Byrnes, Raymond A.

2012-01-01

Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs across four decades. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. In practice, NASA develops remote-sensing instruments and spacecraft, launches satellites, and validates their performance. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground-data reception, archiving, product generation, and distribution. The result of this program is a visible, long-term record of natural and human-induced changes on the global landscape.

Geomorphometry of Drainage Basin for Natural Resources Management Using High Resolution Satellite Data an Indian Example

NASA Astrophysics Data System (ADS)

Venkateswarlu, P.; Reddy, M. A.; Prasad, A. T.

2003-12-01

Application of Remote Sensing and Geographic Information System for the development of land and water resources action plan at micro level for appropriate management of land/water resources of a watershed in rain fed region of Prakasam District in Andhra Pradesh, India forms the focal theme of this paper. The quantitative description of drainage basin geometry can be effectively determined using Remote Sensing and GIS techniques. Each of the sixty-two sub-watersheds of the study area have been studied in terms of the Morphometric parameters - Stream length, Bifurcation ratio, Length ratio, Drainage density, Stream frequency, Texture ratio, Form factor, Area Perimeters, Circularity ratio and Elongation ratio and prioritized all the sub-watersheds under study. The prioritization of sub sheds based on morphometry is compared with sediment yield prioritization and found nearly same for the study area. The information obtained from the thematic maps are integrated and action plans are suggested for land and water resources development on a sustainable basis. Landuse/Landcover, Hydrogeomorphology and Soil thematic maps were generated. In addition slope and Drainage maps were prepared from Survey of India toposheets. Based on the computerized database created using ARC/INFO software, information derived in terms of natural resources and their spatial distribution was then integrated with the socio economic data to formulate an action plan, which includes suggestion of alternative Landuse/Landcover practices. Such a plan is useful for natural resources management and for improving the socio-economic status of rural population on a sustainable basis. Keywords: Natural Resources, Remote Sensing, Morphometry sustainable development.

An integrated modeling framework of socio-economic, biophysical, and hydrological processes in Midwest landscapes: Remote sensing data, agro-hydrological model, and agent-based model

NASA Astrophysics Data System (ADS)

Ding, Deng

Intensive human-environment interactions are taking place in Midwestern agricultural systems. An integrated modeling framework is suitable for predicting dynamics of key variables of the socio-economic, biophysical, hydrological processes as well as exploring the potential transitions of system states in response to changes of the driving factors. The purpose of this dissertation is to address issues concerning the interacting processes and consequent changes in land use, water balance, and water quality using an integrated modeling framework. This dissertation is composed of three studies in the same agricultural watershed, the Clear Creek watershed in East-Central Iowa. In the first study, a parsimonious hydrologic model, the Threshold-Exceedance-Lagrangian Model (TELM), is further developed into RS-TELM (Remote Sensing TELM) to integrate remote sensing vegetation data for estimating evapotranspiration. The goodness of fit of RS-TELM is comparable to a well-calibrated SWAT (Soil and Water Assessment Tool) and even slightly superior in capturing intra-seasonal variability of stream flow. The integration of RS LAI (Leaf Area Index) data improves the model's performance especially over the agriculture dominated landscapes. The input of rainfall datasets with spatially explicit information plays a critical role in increasing the model's goodness of fit. In the second study, an agent-based model is developed to simulate farmers' decisions on crop type and fertilizer application in response to commodity and biofuel crop prices. The comparison between simulated crop land percentage and crop rotations with satellite-based land cover data suggest that farmers may be underestimating the effects that continuous corn production has on yields (yield drag). The simulation results given alternative market scenarios based on a survey of agricultural land owners and operators in the Clear Creek Watershed show that, farmers see cellulosic biofuel feedstock production in the form of perennial grasses or corn stover as a more risky enterprise than their current crop production systems, likely because of market and production risks and lock in effects. As a result farmers do not follow a simple farm-profit maximization rule. In the third study, the consequent water quantity and quality change of the potential land use transitions given alternative biofuel crop market scenarios is explored in a case study in the Clear Creek watershed. A computer program is developed to implement the loose-coupling strategy to couple an agent-based land use model with SWAT. The simulation results show that watershed-scale water quantity (water yield and runoff) and quality variables (sediment and nutrient loads) decrease in values as switchgrass price increases. However, negligence of farmers risk aversions towards biofuel crop adoption would cause overestimation of the impacts of switchgrass price on water quantity and quality.

South Florida Everglades: satellite image map

USGS Publications Warehouse

Jones, John W.; Thomas, Jean-Claude; Desmond, G.B.

2001-01-01

These satellite image maps are one product of the USGS Land Characteristics from Remote Sensing project, funded through the USGS Place-Based Studies Program (http://access.usgs.gov/) with support from the Everglades National Park (http://www.nps.gov/ever/). The objective of this project is to develop and apply innovative remote sensing and geographic information system techniques to map the distribution of vegetation, vegetation characteristics, and related hydrologic variables through space and over time. The mapping and description of vegetation characteristics and their variations are necessary to accurately simulate surface hydrology and other surface processes in South Florida and to monitor land surface changes. As part of this research, data from many airborne and satellite imaging systems have been georeferenced and processed to facilitate data fusion and analysis. These image maps were created using image fusion techniques developed as part of this project.

Classification of High Spatial Resolution, Hyperspectral Remote Sensing Imagery of the Little Miami River Watershed in Southwest Ohio, USA (Final)

EPA Science Inventory

EPA announced the availability of the final report,. This report and associated land use/land cover (LULC) coverage is the result o...

L-band microwave remote sensing and land data assimilation improve the representation of pre-storm soil moisture conditions for hydrologic forecasting

USDA-ARS?s Scientific Manuscript database

Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating the strength of the relationship between storm-scale runoff ratio (i...

Comparison of prognostic and diagnostic surface flux modeling approaches over the Nile River Basin

USDA-ARS?s Scientific Manuscript database

Regional evapotranspiration (ET) can be estimated using diagnostic remote sensing models, generally based on principles of energy balance, or with spatially distributed prognostic models that simultaneously balance both the energy and water budgets over landscapes using predictive equations for land...

Using Remote Sensing and Radar MET Data to Support Watershed Assessments Comprising IEM

USDA-ARS?s Scientific Manuscript database

Meteorological (MET) data required by watershed assessments that comprise Integrated Environmental Modeling (IEM) have traditionally been provided by land-based weather (gauge) stations; although these data may not be most appropriate for describing adequate spatial and temporal resolution if the ME...

GIS/RS-based Rapid Reassessment for Slope Land Capability Classification

NASA Astrophysics Data System (ADS)

Chang, T. Y.; Chompuchan, C.

2014-12-01

Farmland resources in Taiwan are limited because about 73% is mountainous and slope land. Moreover, the rapid urbanization and dense population resulted in the highly developed flat area. Therefore, the utilization of slope land for agriculture is more needed. In 1976, "Slope Land Conservation and Utilization Act" was promulgated to regulate the slope land utilization. Consequently, slope land capability was categorized into Class I-IV according to 4 criteria, i.e., average land slope, effective soil depth, degree of soil erosion, and parent rock. The slope land capability Class I-VI are suitable for cultivation and pasture. Whereas, Class V should be used for forestry purpose and Class VI should be the conservation land which requires intensive conservation practices. The field survey was conducted to categorize each land unit as the classification scheme. The landowners may not allow to overuse land capability limitation. In the last decade, typhoons and landslides frequently devastated in Taiwan. The rapid post-disaster reassessment of the slope land capability classification is necessary. However, the large-scale disaster on slope land is the constraint of field investigation. This study focused on using satellite remote sensing and GIS as the rapid re-evaluation method. Chenyulan watershed in Nantou County, Taiwan was selected to be a case study area. Grid-based slope derivation, topographic wetness index (TWI) and USLE soil loss calculation were used to classify slope land capability. The results showed that GIS-based classification give an overall accuracy of 68.32%. In addition, the post-disaster areas of Typhoon Morakot in 2009, which interpreted by SPOT satellite imageries, were suggested to classify as the conservation lands. These tools perform better in the large coverage post-disaster update for slope land capability classification and reduce time-consuming, manpower and material resources to the field investigation.

Open Land-Use Map: A Regional Land-Use Mapping Strategy for Incorporating OpenStreetMap with Earth Observations

NASA Astrophysics Data System (ADS)

Yang, D.; Fu, C. S.; Binford, M. W.

2017-12-01

The southeastern United States has high landscape heterogeneity, withheavily managed forestlands, highly developed agriculture lands, and multiple metropolitan areas. Human activities are transforming and altering land patterns and structures in both negative and positive manners. A land-use map for at the greater scale is a heavy computation task but is critical to most landowners, researchers, and decision makers, enabling them to make informed decisions for varying objectives. There are two major difficulties in generating the classification maps at the regional scale: the necessity of large training point sets and the expensive computation cost-in terms of both money and time-in classifier modeling. Volunteered Geographic Information (VGI) opens a new era in mapping and visualizing our world, where the platform is open for collecting valuable georeferenced information by volunteer citizens, and the data is freely available to the public. As one of the most well-known VGI initiatives, OpenStreetMap (OSM) contributes not only road network distribution, but also the potential for using this data to justify land cover and land use classifications. Google Earth Engine (GEE) is a platform designed for cloud-based mapping with a robust and fast computing power. Most large scale and national mapping approaches confuse "land cover" and "land-use", or build up the land-use database based on modeled land cover datasets. Unlike most other large-scale approaches, we distinguish and differentiate land-use from land cover. By focusing our prime objective of mapping land-use and management practices, a robust regional land-use mapping approach is developed by incorporating the OpenstreepMap dataset into Earth observation remote sensing imageries instead of the often-used land cover base maps.

Geomatics for Mapping of Groundwater Potential Zones in Northern Part of the United Arab Emiratis - Sharjah City

NASA Astrophysics Data System (ADS)

Al-Ruzouq, R.; Shanableh, A.; Merabtene, T.

2015-04-01

In United Arab Emirates (UAE) domestic water consumption has increased rapidly over the last decade. The increased demand for high-quality water, create an urgent need to evaluate the groundwater production of aquifers. The development of a reasonable model for groundwater potential is therefore crucial for future systematic developments, efficient management, and sustainable use of groundwater resources. The objective of this study is to map the groundwater potential zones in northern part of UAE and assess the contributing factors for exploration of potential groundwater resources. Remote sensing data and geographic information system will be used to locate potential zones for groundwater. Various maps (i.e., base, soil, geological, Hydro-geological, Geomorphologic Map, structural, drainage, slope, land use/land cover and average annual rainfall map) will be prepared based on geospatial techniques. The groundwater availability of the basin will qualitatively classified into different classes based on its hydro-geo-morphological conditions. The land use/land cover map will be also prepared for the different seasons using a digital classification technique with a ground truth based on field investigation.

Land use mapping and change detection using ERTS imagery in Montgomery County, Alabama

NASA Technical Reports Server (NTRS)

Wilms, R. P.

1973-01-01

The feasibility of using remotely sensed data from ERTS-1 for mapping land use and detecting land use change was investigated. Land use information was gathered from 1964 air photo mosaics and from 1972 ERTS data. The 1964 data provided the basis for comparison with ERTS-1 imagery. From this comparison, urban sprawl was quite evident for the city of Montgomery. A significant trend from forestland to agricultural was also discovered. The development of main traffic arteries between 1964 and 1972 was a vital factor in the development of some of the urban centers. Even though certain problems in interpreting and correlating land use data from ERTS imagery were encountered, it has been demonstrated that remotely sensed data from ERTS is useful for inventorying land use and detecting land use change.

Urban heat island impacts on plant phenology: intra-urban variability and response to land cover

NASA Astrophysics Data System (ADS)

Zipper, Samuel C.; Schatz, Jason; Singh, Aditya; Kucharik, Christopher J.; Townsend, Philip A.; Loheide, Steven P., II

2016-05-01

Despite documented intra-urban heterogeneity in the urban heat island (UHI) effect, little is known about spatial or temporal variability in plant response to the UHI. Using an automated temperature sensor network in conjunction with Landsat-derived remotely sensed estimates of start/end of the growing season, we investigate the impacts of the UHI on plant phenology in the city of Madison WI (USA) for the 2012-2014 growing seasons. Median urban growing season length (GSL) estimated from temperature sensors is ˜5 d longer than surrounding rural areas, and UHI impacts on GSL are relatively consistent from year-to-year. Parks within urban areas experience a subdued expression of GSL lengthening resulting from interactions between the UHI and a park cool island effect. Across all growing seasons, impervious cover in the area surrounding each temperature sensor explains >50% of observed variability in phenology. Comparisons between long-term estimates of annual mean phenological timing, derived from remote sensing, and temperature-based estimates of individual growing seasons show no relationship at the individual sensor level. The magnitude of disagreement between temperature-based and remotely sensed phenology is a function of impervious and grass cover surrounding the sensor, suggesting that realized GSL is controlled by both local land cover and micrometeorological conditions.

A hybrid approach to estimating national scale spatiotemporal variability of PM2.5 in the contiguous United States.

PubMed

Beckerman, Bernardo S; Jerrett, Michael; Serre, Marc; Martin, Randall V; Lee, Seung-Jae; van Donkelaar, Aaron; Ross, Zev; Su, Jason; Burnett, Richard T

2013-07-02

Airborne fine particulate matter exhibits spatiotemporal variability at multiple scales, which presents challenges to estimating exposures for health effects assessment. Here we created a model to predict ambient particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) across the contiguous United States to be applied to health effects modeling. We developed a hybrid approach combining a land use regression model (LUR) selected with a machine learning method, and Bayesian Maximum Entropy (BME) interpolation of the LUR space-time residuals. The PM2.5 data set included 104,172 monthly observations at 1464 monitoring locations with approximately 10% of locations reserved for cross-validation. LUR models were based on remote sensing estimates of PM2.5, land use and traffic indicators. Normalized cross-validated R(2) values for LUR were 0.63 and 0.11 with and without remote sensing, respectively, suggesting remote sensing is a strong predictor of ground-level concentrations. In the models including the BME interpolation of the residuals, cross-validated R(2) were 0.79 for both configurations; the model without remotely sensed data described more fine-scale variation than the model including remote sensing. Our results suggest that our modeling framework can predict ground-level concentrations of PM2.5 at multiple scales over the contiguous U.S.

Identification of cultivated land using remote sensing images based on object-oriented artificial bee colony algorithm

NASA Astrophysics Data System (ADS)

Li, Nan; Zhu, Xiufang

2017-04-01

Cultivated land resources is the key to ensure food security. Timely and accurate access to cultivated land information is conducive to a scientific planning of food production and management policies. The GaoFen 1 (GF-1) images have high spatial resolution and abundant texture information and thus can be used to identify fragmentized cultivated land. In this paper, an object-oriented artificial bee colony algorithm was proposed for extracting cultivated land from GF-1 images. Firstly, the GF-1 image was segmented by eCognition software and some samples from the segments were manually identified into 2 types (cultivated land and non-cultivated land). Secondly, the artificial bee colony (ABC) algorithm was used to search for classification rules based on the spectral and texture information extracted from the image objects. Finally, the extracted classification rules were used to identify the cultivated land area on the image. The experiment was carried out in Hongze area, Jiangsu Province using wide field-of-view sensor on the GF-1 satellite image. The total precision of classification result was 94.95%, and the precision of cultivated land was 92.85%. The results show that the object-oriented ABC algorithm can overcome the defect of insufficient spectral information in GF-1 images and obtain high precision in cultivated identification.

Are the Viking Lander sites representative of the surface of Mars?

NASA Technical Reports Server (NTRS)

Jakosky, B. M.; Christensen, P. R.

1986-01-01

Global remote sensing data of the Martian surface, collected by earth- and satellite-based instruments, are compared with data from the two Viking Landers to determine if the Lander data are representative of the Martian surface. The landing sites are boulder-strewn and feature abundant fine material and evidence of strong eolian forces. One site (VL-1) is in a plains-covered basin which is associated with volcanic activity; the VL-2 site is in the northern plains. Thermal IR, broadband albedo, color imaging and radar remote sensing has been carried out of the global Martian surface. The VL-1 data do not fit a general correlation observed between increases in 70-cm radar cross-sections and thermal inertia. A better fit is found with 12.5-cm cross sections, implying the presence of a thinner or discontinuous duricrust at the VL-1 site, compared to other higher-inertia regions. A thin dust layer is also present at the VL-2 site, based on the Lander reflectance data. The Lander sites are concluded to be among the three observed regions of anomalous reflectivity, which can be expected in low regions selected for the landings. Recommendations are furnished for landing sites of future surface probes in order to choose sites more typical of the global Martian surface.

Meta Data Mining in Earth Remote Sensing Data Archives

NASA Astrophysics Data System (ADS)

Davis, B.; Steinwand, D.

2014-12-01

Modern search and discovery tools for satellite based remote sensing data are often catalog based and rely on query systems which use scene- (or granule-) based meta data for those queries. While these traditional catalog systems are often robust, very little has been done in the way of meta data mining to aid in the search and discovery process. The recently coined term "Big Data" can be applied in the remote sensing world's efforts to derive information from the vast data holdings of satellite based land remote sensing data. Large catalog-based search and discovery systems such as the United States Geological Survey's Earth Explorer system and the NASA Earth Observing System Data and Information System's Reverb-ECHO system provide comprehensive access to these data holdings, but do little to expose the underlying scene-based meta data. These catalog-based systems are extremely flexible, but are manually intensive and often require a high level of user expertise. Exposing scene-based meta data to external, web-based services can enable machine-driven queries to aid in the search and discovery process. Furthermore, services which expose additional scene-based content data (such as product quality information) are now available and can provide a "deeper look" into remote sensing data archives too large for efficient manual search methods. This presentation shows examples of the mining of Landsat and Aster scene-based meta data, and an experimental service using OPeNDAP to extract information from quality band from multiple granules in the MODIS archive.

Earth Observation and Geospatial techniques for Soil Salinity and Land Capability Assessment over Sundarban Bay of Bengal Coast, India

NASA Astrophysics Data System (ADS)

Das, Sumanta; Choudhury, Malini Roy; Das, Subhasish; Nagarajan, M.

2016-12-01

To guarantee food security and job creation of small scale farmers to commercial farmers, unproductive farms in the South 24 PGS, West Bengal need land reform program to be restructured and evaluated for agricultural productivity. This study established a potential role of remote sensing and GIS for identification and mapping of salinity zone and spatial planning of agricultural land over the Basanti and Gosaba Islands(808.314sq. km) of South 24 PGS. District of West Bengal. The primary data i.e. soil pH, Electrical Conductivity (EC) and Sodium Absorption ratio (SAR) were obtained from soil samples of various GCP (Ground Control Points) locations collected at 50 mts. intervals by handheld GPS from 0-100 cm depths. The secondary information is acquired from the remotely sensed satellite data (LANDSAT ETM+) in different time scale and digital elevation model. The collected field samples were tested in the laboratory and were validated with Remote Sensing based digital indices analysisover the temporal satellite data to assess the potential changes due to over salinization. Soil physical properties such as texture, structure, depth and drainage condition is stored as attributes in a geographical soil database and linked with the soil map units. The thematic maps are integrated with climatic and terrain conditions of the area to produce land capability maps for paddy. Finally, The weighted overlay analysis was performed to assign theweights according to the importance of parameters taken into account for salineareaidentification and mapping to segregate higher, moderate, lower salinity zonesover the study area.

Ecological perspective: Linking ecology, GIS, and remote sensing to ecosystem management

USGS Publications Warehouse

Allen, Craig D.; Sample, V. Alaric

1994-01-01

Awareness of significant human impacts on the ecology of Earth's landscapes is not new (Thomas 1956). Over the past decade (Forman and Godron 1986, Urban et a1. 1987) applications of geographic information systems (GIS) and remote sensing technologies have supported a rapid rise in landscape.stale research. The heightened recognition within the research community of the ecological linkages between local sites and larger spatial scales has spawned increasing calls for more holistic management of landscapes (Noss 1983, Harris 1984, Risser 1985, Norse et al. 1986, Agee and Johnson 1988, Franklin 1989, Brooks and Grant 1992, Endangered Species Update-Special Issue 1993, Crow 1994, Grumbine 1994). As a result agencies such as the U.S. Forest Service, U.S. Fish and Wildlife Service, and National Park Service are now converging on "ecosystem management" as a new paradigm to sustainably manage wildlands and maintain biodiversity. However, as this transition occurs, several impediments to implementation of this new paradigm persist, including(1) significant uncenainty among many land managers about the definition and goals of ecosystem management,(2) inadequate ecological information on the past and present processes and structural conditions of target ecosystems,(3) insufficient experience on the part of land managers with the rapidly diversifying array of GIS and remote sensing tools to effectively use them to support ecology-based land management, and(4) a paucity of intimate, long-term relationships between people (including land managers) and the particular landscape communities to which they belong.This chapter provides an ecological perspective on these issues as applied to ecosystem management in a southwestern U.S. landscape.

Monitoring, analyzing and simulating of spatial-temporal changes of landscape pattern over mining area

NASA Astrophysics Data System (ADS)

Liu, Pei; Han, Ruimei; Wang, Shuangting

2014-11-01

According to the merits of remotely sensed data in depicting regional land cover and Land changes, multi- objective information processing is employed to remote sensing images to analyze and simulate land cover in mining areas. In this paper, multi-temporal remotely sensed data were selected to monitor the pattern, distri- bution and trend of LUCC and predict its impacts on ecological environment and human settlement in mining area. The monitor, analysis and simulation of LUCC in this coal mining areas are divided into five steps. The are information integration of optical and SAR data, LULC types extraction with SVM classifier, LULC trends simulation with CA Markov model, landscape temporal changes monitoring and analysis with confusion matrixes and landscape indices. The results demonstrate that the improved data fusion algorithm could make full use of information extracted from optical and SAR data; SVM classifier has an efficient and stable ability to obtain land cover maps, which could provide a good basis for both land cover change analysis and trend simulation; CA Markov model is able to predict LULC trends with good performance, and it is an effective way to integrate remotely sensed data with spatial-temporal model for analysis of land use / cover change and corresponding environmental impacts in mining area. Confusion matrixes are combined with landscape indices to evaluation and analysis show that, there was a sustained downward trend in agricultural land and bare land, but a continues growth trend tendency in water body, forest and other lands, and building area showing a wave like change, first increased and then decreased; mining landscape has undergone a from small to large and large to small process of fragmentation, agricultural land is the strongest influenced landscape type in this area, and human activities are the primary cause, so the problem should be pay more attentions by government and other organizations.

Remote sensing based approach for monitoring urban growth in Mexico city, Mexico: A case study

NASA Astrophysics Data System (ADS)

Obade, Vincent

The world is experiencing a rapid rate of urban expansion, largely contributed by the population growth. Other factors supporting urban growth include the improved efficiency in the transportation sector and increasing dependence on cars as a means of transport. The problems attributed to the urban growth include: depletion of energy resources, water and air pollution; loss of landscapes and wildlife, loss of agricultural land, inadequate social security and lack of employment or underemployment. Aerial photography is one of the popular techniques for analyzing, planning and minimizing urbanization related problems. However, with the advances in space technology, satellite remote sensing is increasingly being utilized in the analysis and planning of the urban environment. This article outlines the strengths and limitations of potential remote sensing techniques for monitoring urban growth. The selected methods include: Principal component analysis, Maximum likelihood classification and "decision tree". The results indicate that the "classification tree" approach is the most promising for monitoring urban change, given the improved accuracy and smooth transition between the various land cover classes

Web-GIS visualisation of permafrost-related Remote Sensing products for ESA GlobPermafrost

NASA Astrophysics Data System (ADS)

Haas, A.; Heim, B.; Schaefer-Neth, C.; Laboor, S.; Nitze, I.; Grosse, G.; Bartsch, A.; Kaab, A.; Strozzi, T.; Wiesmann, A.; Seifert, F. M.

2016-12-01

The ESA GlobPermafrost (www.globpermafrost.info) provides a remote sensing service for permafrost research and applications. The service comprises of data product generation for various sites and regions as well as specific infrastructure allowing overview and access to datasets. Based on an online user survey conducted within the project, the user community extensively applies GIS software to handle remote sensing-derived datasets and requires preview functionalities before accessing them. In response, we develop the Permafrost Information System PerSys which is conceptualized as an open access geospatial data dissemination and visualization portal. PerSys will allow visualisation of GlobPermafrost raster and vector products such as land cover classifications, Landsat multispectral index trend datasets, lake and wetland extents, InSAR-based land surface deformation maps, rock glacier velocity fields, spatially distributed permafrost model outputs, and land surface temperature datasets. The datasets will be published as WebGIS services relying on OGC-standardized Web Mapping Service (WMS) and Web Feature Service (WFS) technologies for data display and visualization. The WebGIS environment will be hosted at the AWI computing centre where a geodata infrastructure has been implemented comprising of ArcGIS for Server 10.4, PostgreSQL 9.2 and a browser-driven data viewer based on Leaflet (http://leafletjs.com). Independently, we will provide an `Access - Restricted Data Dissemination Service', which will be available to registered users for testing frequently updated versions of project datasets. PerSys will become a core project of the Arctic Permafrost Geospatial Centre (APGC) within the ERC-funded PETA-CARB project (www.awi.de/petacarb). The APGC Data Catalogue will contain all final products of GlobPermafrost, allow in-depth dataset search via keywords, spatial and temporal coverage, data type, etc., and will provide DOI-based links to the datasets archived in the long-term, open access PANGAEA data repository.

LAND COVER ASSESSMENT OF INDIGENOUS COMMUNITIES IN THE BOSAWAS REGION OF NICARAGUA

EPA Science Inventory

Data derived from remotely sensed images were utilized to conduct land cover assessments of three indigenous communities in northern Nicaragua. Historical land use, present land cover and land cover change processes were all identified through the use of a geographic informat...

Classifying land cover from an object-oriented approach - applied to LANDSAT 8 at the regional scale of the Lake Tana Basin (Ethiopia)

NASA Astrophysics Data System (ADS)

Lemma, Hanibal; Frankl, Amaury; Poesen, Jean; Adgo, Enyew; Nyssen, Jan

2017-04-01

Object-oriented image classification has been gaining prominence in the field of remote sensing and provides a valid alternative to the 'traditional' pixel based methods. Recent studies have proven the superiority of the object-based approach. So far, object-oriented land cover classifications have been applied either at limited spatial coverages (ranging 2 to 1091 km2) or by using very high resolution (0.5-16 m) imageries. The main aim of this study is to drive land cover information for large area from Landsat 8 OLI surface reflectance using the Estimation of Scale Parameter (ESP) tool and the object oriented software eCognition. The available land cover map of Lake Tana Basin (Ethiopia) is about 20 years old with a courser spatial scale (1:250,000) and has limited use for environmental modelling and monitoring studies. Up-to-date and basin wide land cover maps are essential to overcome haphazard natural resources management, land degradation and reduced agricultural production. Indeed, object-oriented approach involves image segmentation prior to classification, i.e. adjacent similar pixels are aggregated into segments as long as the heterogeneity in the spectral and spatial domains is minimized. For each segmented object, different attributes (spectral, textural and shape) were calculated and used for in subsequent classification analysis. Moreover, the commonly used error matrix is employed to determine the quality of the land cover map. As a result, the multiresolution segmentation (with parameters of scale=30, shape=0.3 and Compactness=0.7) produces highly homogeneous image objects as it is observed in different sample locations in google earth. Out of the 15,089 km2 area of the basin, cultivated land is dominant (69%) followed by water bodies (21%), grassland (4.8%), forest (3.7%) and shrubs (1.1%). Wetlands, artificial surfaces and bare land cover only about 1% of the basin. The overall classification accuracy is 80% with a Kappa coefficient of 0.75. With regard to individual classes, the classification show higher Producer's and User's accuracy (above 84%) for cultivated land, water bodies and forest, but lower (less than 70%) for shrubs, bare land and grassland. Key words: accuracy assessment, eCognition, Estimation of Scale Parameter, land cover, Landsat 8, remote sensing

[Spatiotemporal differentiation of construction land expansion in a typical town of south Jiangsu Province].

PubMed

Zhou, Rui; Li, Yue-hui; Hu, Yuan-man; Su, Hai-long; Wang, Jin-nian

2011-03-01

Choosing Xinzhuang Town in south Jiangsu Province as study area, and by using 1980, 1991, 2001, and 2009 high-resolution remote sensing images and GIS spatial analysis technology, an integrated expansion degree index model was established based on the existing indicators of construction land expansion, and the general and spatiotemporal differentiation characteristics of construction land expansion in the Town in three time periods of 1980-2009 were quantitatively analyzed. In 1980-2009, with the acceleration of rural urbanization and industrialization, the area of construction land in the Town increased significantly by 19.24 km2, and especially in 2001-2009, the expanded area, expanded contribution rate, and expansion intensity reached the maximum. The construction land expansion had an obvious spatial differentiation characteristic. In 1980-1991, the newly increased construction land mainly concentrated in town area. After 1991, the focus of construction land gradually spread to the villages with developed industries. Most of the increased construction lands were converted from paddy field and dry land, accounting for 88.1% of the total increased area, while the contribution from other land types was relatively small.

Role of remote sensing in the evaluation of anthropogenic activity and its effect on environment and human econonomic development: an Indian example

NASA Astrophysics Data System (ADS)

Perni, Venkateswarlu

In the scenario of a exponential growth of world population, changes in land use and evaluating the domestication and rearing of aquatic animals and plants; Remote Sensing has the role of an emerging discipline and provides essential tools of trade to the environmental scientist. Ecologically sustainable development of the aqua resources requires that management and use as compatible with the attributes of exploited resources. Aquaculture plays a crucial role in the development of fishing industry and contributes in rural development, increased foreign reserves besides replenishment of important aquatic species. The synoptivity, repititivity and multi spectral vision are the significant edges of Remote Sensing over conventional practices in the application domain. The present study aimed at mapping and monitoring of damages to the ecologically sensitive land farms like mangroves, sand deserts, wetlands, marshy areas etc., due to the development of aquaculture ponds and to analyze and understand the impact of pond aquaculture on water course, ground water quality, drinking water source etc. Indian Remote Sensing satellite:1D - Liss-III + PAN sensors merged data of two seasons is used to carry out change detection studies of mangroves, lakes/lagoons and coastal wetlands. To generate microscopic information of Machilipatnam and to monitor the water circulation in creeks the very high resolution IKONOS Panchromatic data is used. Geometrically rectified digital base map covering the study area is prepared on 1:63,630 scale. Satellite data of Land Sat TM, IRS Liss - II, Liss - III and PAN were used. Satellite data geometrically rectified with reference to base map using standard image-to-image tie up procedure besides necessary enhancement techniques for better interpretation. The economic impact of aquaculture is critically analyzed considering certain statistics and the resultant affects are presented. Tropical brackish water and saltwater aquaculture have contributed to the destruction of Mangrove Forests, Wetlands and Salt Marshes, because they have been cleared for use as ponds. It is observed that around 60

Unmanned aerial systems for forest reclamation monitoring: throwing balloons in the air

NASA Astrophysics Data System (ADS)

Andrade, Rita; Vaz, Eric; Panagopoulos, Thomas; Guerrero, Carlos

2014-05-01

Wildfires are a recurrent phenomenon in Mediterranean landscapes, deteriorating environment and ecosystems, calling out for adequate land management. Monitoring burned areas enhances our abilities to reclaim them. Remote sensing has become an increasingly important tool for environmental assessment and land management. It is fast, non-intrusive, and provides continuous spatial coverage. This paper reviews remote sensing methods, based on space-borne, airborne or ground-based multispectral imagery, for monitoring the biophysical properties of forest areas for site specific management. The usage of satellite imagery for land use management has been frequent in the last decades, it is of great use to determine plants health and crop conditions, allowing a synergy between the complexity of environment, anthropogenic landscapes and multi-temporal understanding of spatial dynamics. Aerial photography increments on spatial resolution, nevertheless it is heavily dependent on airborne availability as well as cost. Both these methods are required for wide areas management and policy planning. Comprising an active and high resolution imagery source, that can be brought at a specific instance, reducing cost while maintaining locational flexibility is of utmost importance for local management. In this sense, unmanned aerial vehicles provide maximum flexibility with image collection, they can incorporate thermal and multispectral sensors, however payload and engine operation time limit flight time. Balloon remote sensing is becoming increasingly sought after for site specific management, catering rapid digital analysis, permitting greater control of the spatial resolution as well as of datasets collection in a given time. Different wavelength sensors may be used to map spectral variations in plant growth, monitor water and nutrient stress, assess yield and plant vitality during different stages of development. Proximity could be an asset when monitoring forest plants vitality. Early predictions of re-vegetation success facilitate precise and timely diagnosis of stress, thus remedial actions can be taken at localized detail.

Irrigation scheduling by ET and soil water sensing

USDA-ARS?s Scientific Manuscript database

Irrigation scheduling is the process of deciding when, where and how much to irrigate, usually with the goal of optimizing economic return on investment in land, equipment, inputs and personnel. This hour-long seminar presents methods of irrigation scheduling based, on the one hand on estimates of t...

Mapping and monitoring potato cropping systems in Maine: geospatial methods and land use assessments

USDA-ARS?s Scientific Manuscript database

Geospatial frameworks and GIS-based approaches were used to assess current cropping practices in potato production systems in Maine. Results from the geospatial integration of remotely-sensed cropland layers (2008-2011) and soil datasets for Maine revealed a four-year potato systems footprint estima...

EVALUATING HYDROLOGICAL RESPONSE TO FORECASTED LAND-USE CHANGE: SCENARIO TESTING WITH THE AUTOMATED GEOSPATIAL WATERSHED ASSESSMENT TOOL

EPA Science Inventory

It is currently possible to measure landscape change over large areas and determine trends in environmental condition using advanced space-based technologies accompanied by geospatial analyses of the remotely sensed data. There are numerous earth-observing satellite platforms fo...

Monitoring and validating spatio-temporal continuously daily evapotranspiration and its components at river basin scale

NASA Astrophysics Data System (ADS)

Song, L.; Liu, S.; Kustas, W. P.; Nieto, H.

2017-12-01

Operational estimation of spatio-temporal continuously daily evapotranspiration (ET), and the components evaporation (E) and transpiration (T), at watershed scale is very useful for developing a sustainable water resource strategy in semi-arid and arid areas. In this study, multi-year all-weather daily ET, E and T were estimated using MODIS-based (Dual Temperature Difference) DTD model under different land covers in Heihe watershed, China. The remotely sensed ET was validated using ground measurements from large aperture scintillometer systems, with a source area of several kilometers, under grassland, cropland and riparian shrub-forest. The results showed that the remotely sensed ET produced mean absolute percent deviation (MAPD) errors of about 30% during the growing season for all-weather conditions, but the model performed better under clear sky conditions. However, uncertainty in interpolated MODIS land surface temperature input data under cloudy conditions to the DTD model, and the representativeness of LAS measurements for the heterogeneous land surfaces contribute to the discrepancies between the modeled and ground measured surface heat fluxes, especially for the more humid grassland and heterogeneous shrub-forest sites.

A two-step framework for reconstructing remotely sensed land surface temperatures contaminated by cloud

NASA Astrophysics Data System (ADS)

Zeng, Chao; Long, Di; Shen, Huanfeng; Wu, Penghai; Cui, Yaokui; Hong, Yang

2018-07-01

Land surface temperature (LST) is one of the most important parameters in land surface processes. Although satellite-derived LST can provide valuable information, the value is often limited by cloud contamination. In this paper, a two-step satellite-derived LST reconstruction framework is proposed. First, a multi-temporal reconstruction algorithm is introduced to recover invalid LST values using multiple LST images with reference to corresponding remotely sensed vegetation index. Then, all cloud-contaminated areas are temporally filled with hypothetical clear-sky LST values. Second, a surface energy balance equation-based procedure is used to correct for the filled values. With shortwave irradiation data, the clear-sky LST is corrected to the real LST under cloudy conditions. A series of experiments have been performed to demonstrate the effectiveness of the developed approach. Quantitative evaluation results indicate that the proposed method can recover LST in different surface types with mean average errors in 3-6 K. The experiments also indicate that the time interval between the multi-temporal LST images has a greater impact on the results than the size of the contaminated area.

[Application of Landsat ETM+ in monitoring of desertification in agro-pastoral ecotone of northern China].

PubMed

Mi, Jia; Wang, Kun; Wang, Hong-mei

2011-03-01

Agro-pastoral ecotone of northern China is a transitional and interlaced zone of agricultural cultivation region and grazing region The ecotone is a complex containing several ecosystems. Soil desertification has become a serious problem that endangered sustainable development in the ecotone. The area of desertification land has been increasing year after year in agro-pastoral ecotone of northern China. This problem concerns the ecological environment, economic development and living quality of people in northern and central eastern of China. For these reasons, ecotone has recently become a focus of research of restoration ecology and global climate change. Remote sensing monitoring of desertification land is a key technique to collect the status and development of sandy land, providing scientific bases for the national desertification control. Landsat ETM+ is an advanced multispectral remote sensing system for the research of regional scale and has been widely used in many fields, such as geologic surveys, mapping, vegetation monitoring, etc. In the present, the authors introduce that spectral characteristics, desertification information extraction, desertification classification and development analyses in detail, and summarizes the study progresses discusses the problems and trends.

Remote sensing techniques for conservation and management of natural vegetation ecosystems

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator); Verdesio, J. J.; Dossantos, J. R.

1981-01-01

The importance of using remote sensing techniques, in the visible and near-infrared ranges, for mapping, inventory, conservation and management of natural ecosystems is discussed. Some examples realized in Brazil or other countries are given to evaluate the products from orbital platform (MSS and RBV imagery of LANDSAT) and aerial level (photography) for ecosystems study. The maximum quantitative and qualitative information which can be obtained from each sensor, at different level, are discussed. Based on the developed experiments it is concluded that the remote sensing technique is a useful tool in mapping vegetation units, estimating biomass, forecasting and evaluation of fire damage, disease detection, deforestation mapping and change detection in land-use. In addition, remote sensing techniques can be used in controling implantation and planning natural/artificial regeneration.

Intelligent Detection of Structure from Remote Sensing Images Based on Deep Learning Method

NASA Astrophysics Data System (ADS)

Xin, L.

2018-04-01

Utilizing high-resolution remote sensing images for earth observation has become the common method of land use monitoring. It requires great human participation when dealing with traditional image interpretation, which is inefficient and difficult to guarantee the accuracy. At present, the artificial intelligent method such as deep learning has a large number of advantages in the aspect of image recognition. By means of a large amount of remote sensing image samples and deep neural network models, we can rapidly decipher the objects of interest such as buildings, etc. Whether in terms of efficiency or accuracy, deep learning method is more preponderant. This paper explains the research of deep learning method by a great mount of remote sensing image samples and verifies the feasibility of building extraction via experiments.

Ground zero and up; Nebraska's resources and land use. [using LANDSAT and Skylab data

NASA Technical Reports Server (NTRS)

Edwards, D. M.; Macklem, R.

1975-01-01

A one-semester high school course was developed about the use of remote sensing techniques for land earth resources planning and management. The slide-tape-workbook program was field tested with high school students to show a substantial increase in gain of knowledge and an attitude change in application of remote sensing techniques.

Satellite detection of land-use change and effects on regional forest aboveground biomass estimates

Treesearch

Daolan Zheng; Linda S. Heath; Mark J. Ducey

2008-01-01

We used remote-sensing-driven models to detect land-cover change effects on forest aboveground biomass (AGB) density (Mg·ha−1, dry weight) and total AGB (Tg) in Minnesota, Wisconsin, and Michigan USA, between the years 1992-2001, and conducted an evaluation of the approach. Inputs included remotely-sensed 1992 reflectance data...

Land Cover Change and Remote Sensing in the Classroom: An Exercise to Study Urban Growth

ERIC Educational Resources Information Center

Delahunty, Tina; Lewis-Gonzales, Sarah; Phelps, Jack; Sawicki, Ben; Roberts, Charles; Carpenter, Penny

2012-01-01

The processes and implications of urban growth are studied in a variety of disciplines as urban growth affects both the physical and human landscape. Remote sensing methods provide ways to visualize and mathematically represent urban growth; and resultant land cover change data enable both quantitative and qualitative analysis. This article helps…

Surveillance of Arthropod Vector-Borne Infectious Diseases Using Remote Sensing Techniques: A Review

PubMed Central

Kalluri, Satya; Gilruth, Peter; Rogers, David; Szczur, Martha

2007-01-01

Epidemiologists are adopting new remote sensing techniques to study a variety of vector-borne diseases. Associations between satellite-derived environmental variables such as temperature, humidity, and land cover type and vector density are used to identify and characterize vector habitats. The convergence of factors such as the availability of multi-temporal satellite data and georeferenced epidemiological data, collaboration between remote sensing scientists and biologists, and the availability of sophisticated, statistical geographic information system and image processing algorithms in a desktop environment creates a fertile research environment. The use of remote sensing techniques to map vector-borne diseases has evolved significantly over the past 25 years. In this paper, we review the status of remote sensing studies of arthropod vector-borne diseases due to mosquitoes, ticks, blackflies, tsetse flies, and sandflies, which are responsible for the majority of vector-borne diseases in the world. Examples of simple image classification techniques that associate land use and land cover types with vector habitats, as well as complex statistical models that link satellite-derived multi-temporal meteorological observations with vector biology and abundance, are discussed here. Future improvements in remote sensing applications in epidemiology are also discussed. PMID:17967056

Operational Remote Sensing Services in North Eastern Region of India for Natural Resources Management, Early Warning for Disaster Risk Reduction and Dissemination of Information and Services

NASA Astrophysics Data System (ADS)

Raju, P. L. N.; Sarma, K. K.; Barman, D.; Handique, B. K.; Chutia, D.; Kundu, S. S.; Das, R. Kr.; Chakraborty, K.; Das, R.; Goswami, J.; Das, P.; Devi, H. S.; Nongkynrih, J. M.; Bhusan, K.; Singh, M. S.; Singh, P. S.; Saikhom, V.; Goswami, C.; Pebam, R.; Borgohain, A.; Gogoi, R. B.; Singh, N. R.; Bharali, A.; Sarma, D.; Lyngdoh, R. B.; Mandal, P. P.; Chabukdhara, M.

2016-06-01

North Eastern Region (NER) of India comprising of eight states considered to be most unique and one of the most challenging regions to govern due to its unique physiographic condition, rich biodiversity, disaster prone and diverse socio-economic characteristics. Operational Remote Sensing services increased manifolds in the region with the establishment of North Eastern Space Applications Centre (NESAC) in the year 2000. Since inception, NESAC has been providing remote sensing services in generating inventory, planning and developmental activities, and management of natural resources, disasters and dissemination of information and services through geo-web services for NER. The operational remote sensing services provided by NESAC can be broadly divided into three categories viz. natural resource planning and developmental services, disaster risk reduction and early warning services and information dissemination through geo-portal services. As a apart of natural resources planning and developmental services NESAC supports the state forest departments in preparing the forest working plans by providing geospatial inputs covering entire NER, identifying the suitable culturable wastelands for cultivation of silkworm food plants, mapping of natural resources such as land use/land cover, wastelands, land degradation etc. on temporal basis. In the area of disaster risk reduction, NESAC has initiated operational services for early warning and post disaster assessment inputs for flood early warning system (FLEWS) using satellite remote sensing, numerical weather prediction, hydrological modeling etc.; forest fire alert system with actionable attribute information; Japanese Encephalitis Early Warning System (JEWS) based on mosquito vector abundance, pig population and historical disease intensity and agriculture drought monitoring for the region. The large volumes of geo-spatial databases generated as part of operational services are made available to the administrators and local government bodies for better management, preparing prospective planning, and sustainable use of available resources. The knowledge dissemination is being done through online web portals wherever the internet access is available and as well as offline space based information kiosks, where the internet access is not available or having limited bandwidth availability. This paper presents a systematic and comprehensive study on the remote sensing services operational in NER of India for natural resources management, disaster risk reduction and dissemination of information and services, in addition to outlining future areas and direction of space applications for the region.

Datasets related to in-land water for limnology and remote sensing applications: distance-to-land, distance-to-water, water-body identifier and lake-centre co-ordinates.

PubMed

Carrea, Laura; Embury, Owen; Merchant, Christopher J

2015-11-01

Datasets containing information to locate and identify water bodies have been generated from data locating static-water-bodies with resolution of about 300 m (1/360 ∘ ) recently released by the Land Cover Climate Change Initiative (LC CCI) of the European Space Agency. The LC CCI water-bodies dataset has been obtained from multi-temporal metrics based on time series of the backscattered intensity recorded by ASAR on Envisat between 2005 and 2010. The new derived datasets provide coherently: distance to land, distance to water, water-body identifiers and lake-centre locations. The water-body identifier dataset locates the water bodies assigning the identifiers of the Global Lakes and Wetlands Database (GLWD), and lake centres are defined for in-land waters for which GLWD IDs were determined. The new datasets therefore link recent lake/reservoir/wetlands extent to the GLWD, together with a set of coordinates which locates unambiguously the water bodies in the database. Information on distance-to-land for each water cell and the distance-to-water for each land cell has many potential applications in remote sensing, where the applicability of geophysical retrieval algorithms may be affected by the presence of water or land within a satellite field of view (image pixel). During the generation and validation of the datasets some limitations of the GLWD database and of the LC CCI water-bodies mask have been found. Some examples of the inaccuracies/limitations are presented and discussed. Temporal change in water-body extent is common. Future versions of the LC CCI dataset are planned to represent temporal variation, and this will permit these derived datasets to be updated.

Simultaneous comparison and assessment of eight remotely sensed maps of Philippine forests

NASA Astrophysics Data System (ADS)

Estoque, Ronald C.; Pontius, Robert G.; Murayama, Yuji; Hou, Hao; Thapa, Rajesh B.; Lasco, Rodel D.; Villar, Merlito A.

2018-05-01

This article compares and assesses eight remotely sensed maps of Philippine forest cover in the year 2010. We examined eight Forest versus Non-Forest maps reclassified from eight land cover products: the Philippine Land Cover, the Climate Change Initiative (CCI) Land Cover, the Landsat Vegetation Continuous Fields (VCF), the MODIS VCF, the MODIS Land Cover Type product (MCD12Q1), the Global Tree Canopy Cover, the ALOS-PALSAR Forest/Non-Forest Map, and the GlobeLand30. The reference data consisted of 9852 randomly distributed sample points interpreted from Google Earth. We created methods to assess the maps and their combinations. Results show that the percentage of the Philippines covered by forest ranges among the maps from a low of 23% for the Philippine Land Cover to a high of 67% for GlobeLand30. Landsat VCF estimates 36% forest cover, which is closest to the 37% estimate based on the reference data. The eight maps plus the reference data agree unanimously on 30% of the sample points, of which 11% are attributable to forest and 19% to non-forest. The overall disagreement between the reference data and Philippine Land Cover is 21%, which is the least among the eight Forest versus Non-Forest maps. About half of the 9852 points have a nested structure such that the forest in a given dataset is a subset of the forest in the datasets that have more forest than the given dataset. The variation among the maps regarding forest quantity and allocation relates to the combined effects of the various definitions of forest and classification errors. Scientists and policy makers must consider these insights when producing future forest cover maps and when establishing benchmarks for forest cover monitoring.

Development of Ground Reference GIS for Assessing Land Cover Maps of Northeast Yellowstone National Park

NASA Technical Reports Server (NTRS)

Spruce, Joe; Warner, Amanda; Terrie, Greg; Davis, Bruce

2001-01-01

GIS technology and ground reference data often play vital roles in assessing land cover maps derived from remotely sensed data. This poster illustrates these roles, using results from a study done in Northeast Yellowstone National Park. This area holds many forest, range, and wetland cover types of interest to park managers. Several recent studies have focused on this locale, including the NASA Earth Observations Commercial Applications Program (EOCAP) hyperspectral project performed by Yellowstone Ecosystems Studies (YES) on riparian and in-stream habitat mapping. This poster regards a spin-off to the EOCAP project in which YES and NASA's Earth Science Applications Directorate explored the potential for synergistic use of hyperspecral, synthetic aperture radar, and multiband thermal imagery in mapping land cover types. The project included development of a ground reference GIS for site-specific data needed to evaluate maps from remotely sensed imagery. Field survey data included reflectance of plant communities, native and exotic plant species, and forest health conditions. Researchers also collected GPS points, annotated aerial photographs, and took hand held photographs of reference sites. The use of ESRI, ERDAS, and ENVI software enabled reference data entry into a GIS for comparision to georeferenced imagery and thematic maps. The GIS-based ground reference data layers supported development and assessment of multiple maps from remotely sensed data sets acquired over the study area.

Land use changes and its driving forces in hilly ecological restoration area based on gis and rs of northern china

PubMed Central

Gao, Peng; Niu, Xiang; Wang, Bing; Zheng, Yunlong

2015-01-01

Land use change is one of the important aspects of the regional ecological restoration research. With remote sensing (RS) image in 2003, 2007 and 2012, using geographic information system (GIS) technologies, the land use pattern changes in Yimeng Mountain ecological restoration area in China and its driving force factors were studied. Results showed that: (1) Cultivated land constituted the largest area during 10 years, and followed by forest land and grass land; cultivated land and unused land were reduced by 28.43% and 44.32%, whereas forest land, water area and land for water facilities and others were increased. (2) During 2003–2007, forest land change showed the largest, followed by unused land and grass land; however, during 2008–2012, water area and land for water facilities change showed the largest, followed by grass land and unused land. (3) Land use degree was above the average level, it was in the developing period during 2003–2007 and in the degenerating period during 2008–2012. (4) Ecological Restoration Projects can greatly change the micro topography, increase vegetation coverage, and then induce significant changes in the land use distribution, which were the main driving force factors of the land use pattern change in the ecological restoration area. PMID:26047160

Proximal sensing for soil carbon accounting

NASA Astrophysics Data System (ADS)

England, Jacqueline R.; Viscarra Rossel, Raphael A.

2018-05-01

Maintaining or increasing soil organic carbon (C) is vital for securing food production and for mitigating greenhouse gas (GHG) emissions, climate change, and land degradation. Some land management practices in cropping, grazing, horticultural, and mixed farming systems can be used to increase organic C in soil, but to assess their effectiveness, we need accurate and cost-efficient methods for measuring and monitoring the change. To determine the stock of organic C in soil, one requires measurements of soil organic C concentration, bulk density, and gravel content, but using conventional laboratory-based analytical methods is expensive. Our aim here is to review the current state of proximal sensing for the development of new soil C accounting methods for emissions reporting and in emissions reduction schemes. We evaluated sensing techniques in terms of their rapidity, cost, accuracy, safety, readiness, and their state of development. The most suitable method for measuring soil organic C concentrations appears to be visible-near-infrared (vis-NIR) spectroscopy and, for bulk density, active gamma-ray attenuation. Sensors for measuring gravel have not been developed, but an interim solution with rapid wet sieving and automated measurement appears useful. Field-deployable, multi-sensor systems are needed for cost-efficient soil C accounting. Proximal sensing can be used for soil organic C accounting, but the methods need to be standardized and procedural guidelines need to be developed to ensure proficient measurement and accurate reporting and verification. These are particularly important if the schemes use financial incentives for landholders to adopt management practices to sequester soil organic C. We list and discuss requirements for developing new soil C accounting methods based on proximal sensing, including requirements for recording, verification, and auditing.

Hyperspectral remote sensing application for monitoring and preservation of plant ecosystems

NASA Astrophysics Data System (ADS)

Krezhova, Dora; Maneva, Svetla; Zdravev, Tomas; Petrov, Nikolay; Stoev, Antoniy

Remote sensing technologies have advanced significantly at last decade and have improved the capability to gather information about Earth’s resources and environment. They have many applications in Earth observation, such as mapping and updating land-use and cover, weather forecasting, biodiversity determination, etc. Hyperspectral remote sensing offers unique opportunities in the environmental monitoring and sustainable use of natural resources. Remote sensing sensors on space-based platforms, aircrafts, or on ground, are capable of providing detailed spectral, spatial and temporal information on terrestrial ecosystems. Ground-based sensors are used to record detailed information about the land surface and to create a data base for better characterizing the objects which are being imaged by the other sensors. In this paper some applications of two hyperspectral remote sensing techniques, leaf reflectance and chlorophyll fluorescence, for monitoring and assessment of the effects of adverse environmental conditions on plant ecosystems are presented. The effect of stress factors such as enhanced UV-radiation, acid rain, salinity, viral infections applied to some young plants (potato, pea, tobacco) and trees (plums, apples, paulownia) as well as of some growth regulators were investigated. Hyperspectral reflectance and fluorescence data were collected by means of a portable fiber-optics spectrometer in the visible and near infrared spectral ranges (450-850 nm and 600-900 nm), respectively. The differences between the reflectance data of healthy (control) and injured (stressed) plants were assessed by means of statistical (Student’s t-criterion), first derivative, and cluster analysis and calculation of some vegetation indices in four most informative for the investigated species regions: green (520-580 nm), red (640-680 nm), red edge (690-720 nm) and near infrared (720-780 nm). Fluorescence spectra were analyzed at five characteristic wavelengths located at the maximums of the emitted radiation and at the forefronts and rear slopes. The strong relationship, which was found between the results from the two remote sensing techniques and some biochemical and serological analyses (stress markers, DAS-ELISA test), indicates the importance of hyperspectral reflectance and fluorescence techniques for conducting, easily and without damage, rapid health condition assessments of vegetation. This study fills in the existed spectral data base and exemplifies the benefits of integrating remote sensing, Earth observation, plant physiology, ecology, and conducting of interdisciplinary investigations of terrestrial ecosystems.

Study on Big Database Construction and its Application of Sample Data Collected in CHINA'S First National Geographic Conditions Census Based on Remote Sensing Images

NASA Astrophysics Data System (ADS)

Cheng, T.; Zhou, X.; Jia, Y.; Yang, G.; Bai, J.

2018-04-01

In the project of China's First National Geographic Conditions Census, millions of sample data have been collected all over the country for interpreting land cover based on remote sensing images, the quantity of data files reaches more than 12,000,000 and has grown in the following project of National Geographic Conditions Monitoring. By now, using database such as Oracle for storing the big data is the most effective method. However, applicable method is more significant for sample data's management and application. This paper studies a database construction method which is based on relational database with distributed file system. The vector data and file data are saved in different physical location. The key issues and solution method are discussed. Based on this, it studies the application method of sample data and analyzes some kinds of using cases, which could lay the foundation for sample data's application. Particularly, sample data locating in Shaanxi province are selected for verifying the method. At the same time, it takes 10 first-level classes which defined in the land cover classification system for example, and analyzes the spatial distribution and density characteristics of all kinds of sample data. The results verify that the method of database construction which is based on relational database with distributed file system is very useful and applicative for sample data's searching, analyzing and promoted application. Furthermore, sample data collected in the project of China's First National Geographic Conditions Census could be useful in the earth observation and land cover's quality assessment.

The South Dakota cooperative land use effort: A state level remote sensing demonstration project

NASA Technical Reports Server (NTRS)

Tessar, P. A.; Hood, D. R.; Todd, W. J.

1975-01-01

Remote sensing technology can satisfy or make significant contributions toward satisfying many of the information needs of governmental natural resource planners and policy makers. Recognizing this potential, the South Dakota State Planning Bureau and the EROS Data Center together formulated the framework for an ongoing Land Use and Natural Resource Inventory and Information System Program. Statewide land use/land cover information is generated from LANDSAT digital data and high altitude photography. Many applications of the system are anticipated as it evolves and data are added from more conventional sources. The conceptualization, design, and implementation of the program are discussed.

Improving winter leaf area index estimation in coniferous forests and its significance in estimating the land surface albedo

NASA Astrophysics Data System (ADS)

Wang, Rong; Chen, Jing M.; Pavlic, Goran; Arain, Altaf

2016-09-01

Winter leaf area index (LAI) of evergreen coniferous forests exerts strong control on the interception of snow, snowmelt and energy balance. Simulation of winter LAI and associated winter processes in land surface models is challenging. Retrieving winter LAI from remote sensing data is difficult due to cloud contamination, poor illumination, lower solar elevation and higher radiation reflection by snow background. Underestimated winter LAI in evergreen coniferous forests is one of the major issues limiting the application of current remote sensing LAI products. It has not been fully addressed in past studies in the literature. In this study, we used needle lifespan to correct winter LAI in a remote sensing product developed by the University of Toronto. For the validation purpose, the corrected winter LAI was then used to calculate land surface albedo at five FLUXNET coniferous forests in Canada. The RMSE and bias values for estimated albedo were 0.05 and 0.011, respectively, for all sites. The albedo map over coniferous forests across Canada produced with corrected winter LAI showed much better agreement with the GLASS (Global LAnd Surface Satellites) albedo product than the one produced with uncorrected winter LAI. The results revealed that the corrected winter LAI yielded much greater accuracy in simulating land surface albedo, making the new LAI product an improvement over the original one. Our study will help to increase the usability of remote sensing LAI products in land surface energy budget modeling.

Hydrologic Remote Sensing and Land Surface Data Assimilation.

PubMed

Moradkhani, Hamid

2008-05-06

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface-atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

An investigation of vegetation and other Earth resource/feature parameters using LANDSAT and other remote sensing data. 1: LANDSAT. 2: Remote sensing of volcanic emissions. [New England forest and emissions from Mt. St. Helens and Central American volcanoes

NASA Technical Reports Server (NTRS)

Birnie, R. W.; Stoiber, R. E. (Principal Investigator)

1981-01-01

A fanning technique based on a simplistic physical model provided a classification algorithm for mixture landscapes. Results of applications to LANDSAT inventory of 1.5 million acres of forest land in Northern Maine are presented. Signatures for potential deer year habitat in New Hampshire were developed. Volcanic activity was monitored in Nicaragua, El Salvador, and Guatemala along with the Mt. St. Helens eruption. Emphasis in the monitoring was placed on the remote sensing of SO2 concentrations in the plumes of the volcanoes.

Evapotranspiration from combined reflected solar and emitted terrestrial radiation - Preliminary FIFE results from AVHRR data

NASA Technical Reports Server (NTRS)

Goward, S. N.; Hope, A. S.

1989-01-01

The relation between remotely sensed spectral vegetation indices and thermal IR measurements is studied. Land surface evapotranspiration is evaluated based on this relationship. Analysis of the AVHRR data, obtained in Kansas in 1987, reveal a strong correlation between the spectral vegetation indices and surface temperature and this relation covaries with surface moisture conditions. It is noted that the relation between remotely sensed measurements of canopy green foliage and surface temperature is useful for examining variations in the interface thermal inertia and energy balance Bowen ratio.

Development of user applications for earth resources survey data in urban and regional planning in the Puget Sound area

NASA Technical Reports Server (NTRS)

Westerlund, F. V.

1975-01-01

User applications of remote sensing in Washington State are described. The first project created a multi-temporal land use/land cover data base for the environs of the Seattle-Tacoma International Airport, to serve planning and management operations of the Port of Seattle. The second is an on-going effort to develop a capability within the Puget Sound Governmental Conference, a council of governments (COG), to inventory and monitor land use within its four county jurisdiction. Developmental work has focused on refinement of land use/cover classification systems applicable at this regional scale and various levels of detail in relation to program requirements of the agency. Related research, refinement of manual methods, user training and approaches to technology transfer are discussed.

Impacts of Myanmar's Democratic Transition on its Land Cover Dynamics.

NASA Astrophysics Data System (ADS)

Biswas, S.

2016-12-01

Recently Myanmar transitioned from a closed economy, military government to market based economy and democracy. The impacts of the political and economic transition on its land cover can be described by characterizing the land cover dynamics during the transition period. Preliminary stratified sampling of forest conversions revealed that most changes from forest to non-forest are due to establishment of rubber plantations. Agricultural concessions are granted by the government to develop the agriculture sector and rubber is the most common plantation crop in Southern Myanmar. This study establishes a method to map and quantify the extent and age of rubber plantations in Thaton district of Myanmar using satellite remote sensing, GIS and ground data. The resultant rubber maps can be used to inform policy on land use planning, agriculture, forest and sustainable development.

Integration of Remote Sensing Data In Operational Flood Forecast In Southwest Germany

NASA Astrophysics Data System (ADS)

Bach, H.; Appel, F.; Schulz, W.; Merkel, U.; Ludwig, R.; Mauser, W.

Methods to accurately assess and forecast flood discharge are mandatory to minimise the impact of hydrological hazards. However, existing rainfall-runoff models rarely accurately consider the spatial characteristics of the watershed, which is essential for a suitable and physics-based description of processes relevant for runoff formation. Spatial information with low temporal variability like elevation, slopes and land use can be mapped or extracted from remote sensing data. However, land surface param- eters of high temporal variability, like soil moisture and snow properties are hardly available and used in operational forecasts. Remote sensing methods can improve flood forecast by providing information on the actual water retention capacities in the watershed and facilitate the regionalisation of hydrological models. To prove and demonstrate this, the project 'InFerno' (Integration of remote sensing data in opera- tional water balance and flood forecast modelling) has been set up, funded by DLR (50EE0053). Within InFerno remote sensing data (optical and microwave) are thor- oughly processed to deliver spatially distributed parameters of snow properties and soil moisture. Especially during the onset of a flood this information is essential to estimate the initial conditions of the model. At the flood forecast centres of 'Baden- Württemberg' and 'Rheinland-Pfalz' (Southwest Germany) the remote sensing based maps on soil moisture and snow properties will be integrated in the continuously op- erated water balance and flood forecast model LARSIM. The concept is to transfer the developed methodology from the Neckar to the Mosel basin. The major challenges lie on the one hand in the implementation of algorithms developed for a multisensoral synergy and the creation of robust, operationally applicable remote sensing products. On the other hand, the operational flood forecast must be adapted to make full use of the new data sources. In the operational phase of the project ESA's ENVISAT satellite, which will be launched in 2002, will serve as remote sensing data source. Until EN- VISAT data is available, algorithm retrieval, software development and product gener- ation is performed using existing sensors with ENVISAT-like specifications. Based on these data sets test cases and demonstration runs are conducted and will be presented to prove the advantages of the approach.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application

PubMed Central

Maxwell, Susan K.

2010-01-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. PMID:21135917

Comparison of Effects of a Proprioceptive Exercise Program in Water and on Land the Balance of Chronic Stroke Patients

PubMed Central

Han, Seul Ki; Kim, Myung Chul; An, Chang Sik

2013-01-01

[Purpose] The purpose of this study was to compare changes in balance ability of land exercise and underwater exercise on chronic stroke patients. [Subjects] A total of 60 patients received exercise for 40 minutes, three times a week, for 6 weeks. [Methods] Subjects from both groups performed general conventional treatment during the experimental period. In addition, all subjects engaged in extra treatment sessions. This extra treatment consisted of unstable surface exercise. The underwater exercise group used wonder boards in a pool (depth 1.1m, water temperature 33.5 °C, air temperature 27 °C) dedicated to underwater exercise, and the land exercise group used balance mats. [Result] The joint position sense, sway area, Berg Balance Scale showed significant improvements in both groups. However, the joint position sense test, sway area, and Berg Balance Scale showed there was more improvement in the underwater exercise group than in the land exercise group. [Conclusion] The results suggest that underwater exercise is more effective than land exercise at improving the joint position sense and balance of stroke patients. PMID:24259761

Comparison of effects of a proprioceptive exercise program in water and on land the balance of chronic stroke patients.

PubMed

Han, Seul Ki; Kim, Myung Chul; An, Chang Sik

2013-10-01

[Purpose] The purpose of this study was to compare changes in balance ability of land exercise and underwater exercise on chronic stroke patients. [Subjects] A total of 60 patients received exercise for 40 minutes, three times a week, for 6 weeks. [Methods] Subjects from both groups performed general conventional treatment during the experimental period. In addition, all subjects engaged in extra treatment sessions. This extra treatment consisted of unstable surface exercise. The underwater exercise group used wonder boards in a pool (depth 1.1m, water temperature 33.5 °C, air temperature 27 °C) dedicated to underwater exercise, and the land exercise group used balance mats. [Result] The joint position sense, sway area, Berg Balance Scale showed significant improvements in both groups. However, the joint position sense test, sway area, and Berg Balance Scale showed there was more improvement in the underwater exercise group than in the land exercise group. [Conclusion] The results suggest that underwater exercise is more effective than land exercise at improving the joint position sense and balance of stroke patients.

Historical and projected coastal Louisiana land changes: 1978-2050

USGS Publications Warehouse

Barras, John; Beville, Shelly; Britsch, Del; Hartley, Stephen; Hawes, Suzanne; Johnston, James; Kemp, Paul; Kinler, Quin; Martucci, Antonio; Porthouse, Jon; Reed, Denise; Roy, Kevin; Sapkota, Sijan; Suhayda, Joseph

2003-01-01

An important component of the Louisiana Coastal Area (LCA) Comprehensive Coastwide Ecosystem Restoration Study is the projection of a “future condition” for the Louisiana coast if no further restoration measures were adopted. Such a projection gives an idea of what the future might hold without implementation of the LCA plan and provides a reference against which various ecosystem restoration proposals can be assessed as part of the planning process. One of the most fundamental measures of ecosystem degradation in coastal Louisiana has been the conversion of land (mostly emergent vegetated habitat) to open water. Thus, the projection of the future condition of the ecosystem must be based upon the determination of future patterns of land and water. To conduct these projections, a multidisciplinary LCA Land Change Study Group was formed that included individuals from agencies and academia with expertise in remote sensing, geographic information systems (GIS), ecosystem processes, and coastal land loss. Methods were based upon those used in prior studies for Coast 2050 (Louisiana Coastal Wetlands Conservation and Restoration Task Force [LCWCRTF] and the Wetlands Conservation and Restoration Authority 1998, 1999) and modified as described here to incorporate an improved understanding of coastal land loss and land gain processes with more advanced technical capabilities. The basic approach is to use historical data to assess recent trends in land loss and land gain and to project those changes into the future, taking into account spatial variations in the patterns and rates of land loss and land gain. This approach is accomplished by developing a base map, assessing and delineating areas of similar land change (polygons), and projecting changes into the future. This report describes the methodology and compares the current land change projection to previous projections.

Fifty years dynamics of Russian forests: Impacts on the earth system

NASA Astrophysics Data System (ADS)

Shvidenko, Anatoly; Schepaschenko, Dmitry; Kraxner, Florian

2015-04-01

The paper presents a succinct history of Russian forests during the time period of 1960-2010 and reanalysis of their impacts on global carbon and nitrogen cycles. We present dynamics of land cover change (including major categories of forest land) and biometric characteristics of forests (species composition, age structure, growing stock volume etc.) based on reconciling all relevant information (data of forest and land inventories, official forest management statistics, multi-sensor remote sensing products, data of forest pathological monitoring etc.). Completeness and reliability of background information was different during the period of the study. Forest inventory data and official statistics were partially modified based on relevant auxiliary information and used for 1960-2000. The analysis for 2001-2010 was provided with a crucial use of multi-sensor remote sensing data. For this last period a hybrid forest mask was developed at resolution of 230m by integration of 8 remote sensing products and using geographical weighted regression and data of crowdsourcing. During the considered 50 years forested areas of Russia substantially increased by middle of 1990s and slightly declined (at about 5%) after. Indicators needed for assessment of carbon and nitrogen cycles of forest ecosystems were defined for the entire period (aggregated estimates by decades for 1960-2000 and yearly for 2001-2010) based on unified methodology with some peculiarities following from availability of information. Major results were obtained by landscape-ecosystem method that uses as comprehensive as possible empirical and semi-empirical information on ecosystems and landscapes in form of an Integrated Land Information System and complimentary combines pool- and flux-based methods. We discuss and quantify major drivers of forest cover change (socio-economic, environmental and climatic) including forest management (harvest, reforestation and afforestation), impacts of seasonal weather on carbon fluxes (Net Primary Production, Heterotrophic Respiration), disturbances (fire, outbreaks of insects and diseases), and industrial pressure (land change, air pollution, water and soil contamination). During the entire period Russian forests provided the net carbon sink in range from 350-700 Tg C yr-1 with inter-annual variability in limits of 10-15% for the entire country. The overall sink is a result of superposition of trends of major carbon fluxes (caused by removal of harvested wood and use of forest products; land cover change; impact of climatic trends; change of disturbance regimes) and inter-annual variation of seasonal weather. Major indicators of the nitrogen cycle are assessed and discussed in connection with the carbon cycle. We provide comparative analysis of other results published for the considered period taken into account successive improvements of information and methodology used for studying the major biogeochemical cycles.

NASA Land Cover and Land Use Change (LCLUC): an interdisciplinary research program.

PubMed

Justice, Chris; Gutman, Garik; Vadrevu, Krishna Prasad

2015-01-15

Understanding Land Cover/Land Use Change (LCLUC) in diverse regions of the world and at varied spatial scales is one of the important challenges in global change research. In this article, we provide a brief overview of the NASA LCLUC program, its focus areas, and the importance of satellite remote sensing observations in LCLUC research including future directions. The LCLUC Program was designed to be a cross-cutting theme within NASA's Earth Science program. The program aims to develop and use remote sensing technologies to improve understanding of human interactions with the environment. Since 1997, the NASA LCLUC program has supported nearly 280 research projects on diverse topics such as forest loss and carbon, urban expansion, land abandonment, wetland loss, agricultural land use change and land use change in mountain systems. The NASA LCLUC program emphasizes studies where land-use changes are rapid or where there are significant regional or global LCLUC implications. Over a period of years, the LCLUC program has contributed to large regional science programs such as Land Biosphere-Atmosphere (LBA), the Northern Eurasia Earth Science Partnership Initiative (NEESPI), and the Monsoon Area Integrated Regional Study (MAIRS). The primary emphasis of the program will remain on using remote sensing datasets for LCLUC research. The program will continue to emphasize integration of physical and social sciences to address regional to global scale issues of LCLUC for the benefit of society. Copyright © 2014. Published by Elsevier Ltd.

Environmental Public Health Applications Using Remotely Sensed Data.

PubMed

Al-Hamdan, Mohammad Z; Crosson, William L; Economou, Sigrid A; Estes, Maurice G; Estes, Sue M; Hemmings, Sarah N; Kent, Shia T; Puckett, Mark; Quattrochi, Dale A; Rickman, Douglas L; Wade, Gina M; McClure, Leslie A

2014-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM 2.5 ), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making.

Environmental Public Health Applications Using Remotely Sensed Data

PubMed Central

Al-Hamdan, Mohammad Z.; Crosson, William L.; Economou, Sigrid A.; Estes, Maurice G.; Estes, Sue M.; Hemmings, Sarah N.; Kent, Shia T.; Puckett, Mark; Quattrochi, Dale A.; Rickman, Douglas L.; Wade, Gina M.; McClure, Leslie A.

2012-01-01

We describe a remote sensing and GIS-based study that has three objectives: (1) characterize fine particulate matter (PM2.5), insolation and land surface temperature using NASA satellite observations, EPA ground-level monitor data and North American Land Data Assimilation System (NLDAS) data products on a national scale; (2) link these data with public health data from the REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether these environmental risk factors are related to cognitive decline, stroke and other health outcomes; and (3) disseminate the environmental datasets and public health linkage analyses to end users for decision-making through the Centers for Disease Control and Prevention (CDC) Wide-ranging Online Data for Epidemiologic Research (WONDER) system. This study directly addresses a public health focus of the NASA Applied Sciences Program, utilization of Earth Sciences products, by addressing issues of environmental health to enhance public health decision-making. PMID:24910505

Preliminary determination of geothermal working area based on Thermal Infrared and Synthetic Aperture Radar (SAR) remote sensing

NASA Astrophysics Data System (ADS)

Agoes Nugroho, Indra; Kurniawahidayati, Beta; Syahputra Mulyana, Reza; Saepuloh, Asep

2017-12-01

Remote sensing is one of the methods for geothermal exploration. This method can be used to map the geological structures, manifestations, and predict the geothermal potential area. The results from remote sensing were used as guidance for the next step exploration. Analysis of target in remote sensing is an efficient method to delineate geothermal surface manifestation without direct contact to the object. The study took a place in District Merangin, Jambi Province, Indonesia. The area was selected due to existing of Merangin volcanic complex composed by Mounts Sumbing and Hulunilo with surface geothermal manifestations presented by hot springs and hot pools. The location of surface manifestations could be related with local and regional structures of Great Sumatra Fault. The methods used in this study were included identification of volcanic products, lineament extraction, and lineament density quantification. The objective of this study is to delineate the potential zones for sitting the geothermal working site based on Thermal Infrared and Synthetic Aperture Radar (SAR) sensors. The lineament-related to geological structures, was aimed for high lineament density, is using ALOS - PALSAR (Advanced Land Observing Satellite - The Phased Array type L-band Synthetic Aperture Radar) level 1.1. The Normalized Difference Vegetation Index (NDVI) analysis was used to predict the vegetation condition using Landsat 8 OLI-TIRS (The Operational Land Imager - Thermal Infrared Sensor). The brightness temperature was extracted from TIR band to estimate the surface temperature. Geothermal working area identified based on index overlay method from extracted parameter of remote sensing data was located at the western part of study area (Graho Nyabu area). This location was identified because of the existence of high surface temperature about 30°C, high lineament density about 4 - 4.5 km/km2 and low NDVI values less than 0.3.

Landsat's Enduring Legacy: Pioneering Global Land Observations from Space

NASA Technical Reports Server (NTRS)

Goward, Samuel N.; Williams, Darerl L.; Arvidson, Terry; Rocchio, Laura E. P.; Irons, James R.; Russell, Carol A.; Johnston, Shaida S.

2017-01-01

It is our hope that the "Landsat Legacy" story will appeal to a broader audience than just those who use Landsat data on a regular basis. In an era when ready access to images and data from Earth-observing satellites is routine, it is hard to believe that only a few decades ago this was not the case. As the world's first digital land-observing satellite program, Landsat missions laid the foundation for modern space-based Earth observation and blazed the trail in the new field of quantitative remote sensing.

Landsat: A global land-imaging mission

USGS Publications Warehouse

,

2012-01-01

Across four decades since 1972, Landsat satellites have continuously acquired space-based images of the Earth's land surface, coastal shallows, and coral reefs. The Landsat Program, a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA), was established to routinely gather land imagery from space. NASA develops remote-sensing instruments and spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and distribution. The result of this program is a long-term record of natural and human induced changes on the global landscape.

A regional land use survey based on remote sensing and other data: A report on a LANDSAT and computer mapping project, volume 2

NASA Technical Reports Server (NTRS)

Nez, G. (Principal Investigator); Mutter, D.

1977-01-01

The author has identified the following significant results. The project mapped land use/cover classifications from LANDSAT computer compatible tape data and combined those results with other multisource data via computer mapping/compositing techniques to analyze various land use planning/natural resource management problems. Data were analyzed on 1:24,000 scale maps at 1.1 acre resolution. LANDSAT analysis software and linkages with other computer mapping software were developed. Significant results were also achieved in training, communication, and identification of needs for developing the LANDSAT/computer mapping technologies into operational tools for use by decision makers.

Quantifying Arctic Terrestrial Environment Behaviors Using Geophysical, Point-Scale and Remote Sensing Data

NASA Astrophysics Data System (ADS)

Dafflon, B.; Hubbard, S. S.; Ulrich, C.; Peterson, J. E.; Wu, Y.; Wainwright, H. M.; Gangodagamage, C.; Kholodov, A. L.; Kneafsey, T. J.

2013-12-01

Improvement in parameterizing Arctic process-rich terrestrial models to simulate feedbacks to a changing climate requires advances in estimating the spatiotemporal variations in active layer and permafrost properties - in sufficiently high resolution yet over modeling-relevant scales. As part of the DOE Next-Generation Ecosystem Experiments (NGEE-Arctic), we are developing advanced strategies for imaging the subsurface and for investigating land and subsurface co-variability and dynamics. Our studies include acquisition and integration of various measurements, including point-based, surface-based geophysical, and remote sensing datasets These data have been collected during a series of campaigns at the NGEE Barrow, AK site along transects that traverse a range of hydrological and geomorphological conditions, including low- to high- centered polygons and drained thaw lake basins. In this study, we describe the use of galvanic-coupled electrical resistance tomography (ERT), capacitively-coupled resistivity (CCR) , permafrost cores, above-ground orthophotography, and digital elevation model (DEM) to (1) explore complementary nature and trade-offs between characterization resolution, spatial extent and accuracy of different datasets; (2) develop inversion approaches to quantify permafrost characteristics (such as ice content, ice wedge frequency, and presence of unfrozen deep layer) and (3) identify correspondences between permafrost and land surface properties (such as water inundation, topography, and vegetation). In terms of methods, we developed a 1D-based direct search approach to estimate electrical conductivity distribution while allowing exploration of multiple solutions and prior information in a flexible way. Application of the method to the Barrow datasets reveals the relative information content of each dataset for characterizing permafrost properties, which shows features variability from below one meter length scales to large trends over more than a kilometer. Further, we used Pole- and Kite-based low-altitude aerial photography with inferred DEM, as well as DEM from LiDAR dataset, to quantify land-surface properties and their co-variability with the subsurface properties. Comparison of the above- and below-ground characterization information indicate that while some permafrost characteristics correspond with changes in hydrogeomorphological expressions, others features show more complex linkages with landscape properties. Overall, our results indicate that remote sensing data, point-scale measurements and surface geophysical measurements enable the identification of regional zones having similar relations between subsurface and land surface properties. Identification of such zonation and associated permafrost-land surface properties can be used to guide investigations of carbon cycling processes and for model parameterization.

Stories from dynamic Earth: developing your sense of place through Landsat-based citizen science

NASA Astrophysics Data System (ADS)

Nelson, P.; Kennedy, R. E.; Nolin, A. W.; Hughes, J.; Bianchetti, R. A.; O'Connell, K.; Morrell, P.

2016-12-01

Many citizen science activities provide opportunities to understand a specific location on Earth at human scale and to collect local ecological knowledge that can improve the scientific endeavor of monitoring Earth. However, it can be challenging to comprehend ecological changes occurring at larger spatial and temporal scales. Based on the results of two professional development workshops designed for Oregon middle school science teachers in 2011-2013 and 2013-2016, we describe how working with multi-decade Landsat imagery transformed participants and students. Collaborating with scientists, the teachers used 30 years of time-series Landsat imagery with LandTrendr and IceTrendr algorithms to distill several study sites in Oregon, Washington, and Alaska (U.S) into periods of consistent long or short-duration landscape dynamics (e.g. stable areas, forestry activities, flooding, urbanization, tree growth). Using the spatial, tabular, and graphic outputs from this process, the teachers created climate change curriculum aligned to state and national standards. Web-enabled visualization tools, such as Google Earth, provided a platform that engaged students in understanding the drivers of their local landscape changes. Students and teachers reported increased interest in and understanding of their landscape. In addition to fulfilling classroom needs, the activities contributed data used in regional carbon modeling and land cover monitoring throughout California, Oregon, and Washington (U.S). We will discuss strategies and challenges to translating expert-level scientific data, models, methods, vocabulary, and conclusions into citizen science materials that support place-based climate change education across age ranges and educational disciplines. Finally, we share ways you can deepen your own sense of place while participating in citizen science activities that improve land cover and land use monitoring at local, regional, and global scales.

Remote Sensing and the Earth

NASA Technical Reports Server (NTRS)

Brosius, C. A.; Gervin, J. C.; Ragusa, J. M.

1977-01-01

A text book on remote sensing, as part of the earth resources Skylab programs, is presented. The fundamentals of remote sensing and its application to agriculture, land use, geology, water and marine resources, and environmental monitoring are summarized.

U. S. GEOLOGICAL SURVEY LAND REMOTE SENSING ACTIVITIES.

USGS Publications Warehouse

Frederick, Doyle G.

1983-01-01

USGS uses all types of remotely sensed data, in combination with other sources of data, to support geologic analyses, hydrologic assessments, land cover mapping, image mapping, and applications research. Survey scientists use all types of remotely sensed data with ground verifications and digital topographic and cartographic data. A considerable amount of research is being done by Survey scientists on developing automated geographic information systems that can handle a wide variety of digital data. The Survey is also investigating the use of microprocessor computer systems for accessing, displaying, and analyzing digital data.

Use of remote sensing for land use policy formulation. [in Michigan

NASA Technical Reports Server (NTRS)

Boylan, M.

1977-01-01

The use of remotely sensed data for eliminating abuses and mismanagement of land and water resources in Michigan is discussed. Applications discussed include inventory of mosquito breeding sites; analysis of biomass in old field ecosystems used for wastewater recycling; areas for agricultural use; and preservation of the Grand Mere Dune environment. Services to users are described and contact activities reported.

An Examination of Intertidal Temperatures Through Remotely Sensed Satellite Observations

NASA Astrophysics Data System (ADS)

Lakshmi, V.

2010-12-01

MODIS Aqua and Terra satellites produce both land surface temperatures and sea surface temperatures using calibrated algorithms. In this study, the land surface temperatures were retrieved during clear-sky (non-cloudy) conditions at a 1 km2 resolution (overpass time at 10:30 am) whereas the sea surface temperatures are also retrieved during clear-sky conditions at approximately 4 km resolution (overpass time at 1:30 pm). The purpose of this research was to examine remotely sensed sea surface (SST), intertidal (IST), and land surface temperatures (LST), in conjunction with observed in situ mussel body temperatures, as well as associated weather and tidal data. In Strawberry Hill, Oregon, it was determined that intertidal surface temperatures are similar to but distinctly different from land surface temperatures although influenced by sea surface temperatures. The air temperature and differential heating throughout the day, as well as location in relation to the shore, can greatly influence the remotely sensed surface temperatures. Therefore, remotely sensed satellite data is a very useful tool in examining intertidal temperatures for regional climatic changes over long time periods and may eventually help researchers forecast expected climate changes and help determine associated biological implications.

Constraints and Approach for Selecting the Mars Surveyor '01 Landing Site

NASA Technical Reports Server (NTRS)

Golombek, M.; Bridges, N.; Gilmore, M.; Haldemann, A.; Parker, T.; Saunders, R.; Spencer, D.; Smith, J.; Weitz, C.

1999-01-01

There are many similarities between the Mars Surveyor '01 (MS '01) landing site selection process and that of Mars Pathfinder. The selection process includes two parallel activities in which engineers define and refine the capabilities of the spacecraft through design, testing and modeling and scientists define a set of landing site constraints based on the spacecraft design and landing scenario. As for Pathfinder, the safety of the site is without question the single most important factor, for the simple reason that failure to land safely yields no science and exposes the mission and program to considerable risk. The selection process must be thorough and defensible and capable of surviving multiple withering reviews similar to the Pathfinder decision. On Pathfinder, this was accomplished by attempting to understand the surface properties of sites using available remote sensing data sets and models based on them. Science objectives are factored into the selection process only after the safety of the site is validated. Finally, as for Pathfinder, the selection process is being done in an open environment with multiple opportunities for community involvement including open workshops, with education and outreach opportunities.

Constraints, Approach and Present Status for Selecting the Mars Surveyor 2001 Landing Site

NASA Technical Reports Server (NTRS)

Golombek, M.; Anderson, F.; Bridges, N.; Briggs, G.; Gilmore, M.; Gulick, V.; Haldemann, A.; Parker, T.; Saunders, R.; Spencer, D.;

Open-Loop Flight Testing of COBALT Navigation and Sensor Technologies for Precise Soft Landing

NASA Technical Reports Server (NTRS)

Carson, John M., III; Restrepo, Caroline I.; Seubert, Carl R.; Amzajerdian, Farzin; Pierrottet, Diego F.; Collins, Steven M.; O'Neal, Travis V.; Stelling, Richard

2017-01-01

An open-loop flight test campaign of the NASA COBALT (CoOperative Blending of Autonomous Landing Technologies) payload was conducted onboard the Masten Xodiac suborbital rocket testbed. The payload integrates two complementary sensor technologies that together provide a spacecraft with knowledge during planetary descent and landing to precisely navigate and softly touchdown in close proximity to targeted surface locations. The two technologies are the Navigation Doppler Lidar (NDL), for high-precision velocity and range measurements, and the Lander Vision System (LVS) for map-relative state esti- mates. A specialized navigation filter running onboard COBALT fuses the NDL and LVS data in real time to produce a very precise Terrain Relative Navigation (TRN) solution that is suitable for future, autonomous planetary landing systems that require precise and soft landing capabilities. During the open-loop flight campaign, the COBALT payload acquired measurements and generated a precise navigation solution, but the Xodiac vehicle planned and executed its maneuvers based on an independent, GPS-based navigation solution. This minimized the risk to the vehicle during the integration and testing of the new navigation sensing technologies within the COBALT payload.

California's forest resources. Preliminary assessment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Not Available

1979-01-01

This Preliminary Assessment was prepared in response to the California Forest Resources Assessment and Policy Act of 1977 (FRAPA). This Act was passed to improve the information base upon which State resource administrators formulate forest policy. The Act provides for this report and a full assessment by 1987 and at five year intervals thereafter. Information is presented under the following chapter titles: introduction to the forest resources assessment program; the forest area: a general description; classifications of the forest lands; the watersheds; forest lands and the air resource; fish and wildlife resources; the forested rangelands; the wilderness; forest lands asmore » a recreation resource; the timber resource; wood energy; forest lands and the mineral, fossil fuels, and geothermal energy resources; mathematically modeling California's forest lands; vegetation mapping using remote sensing technology; important forest resources legislation; and, State and cooperative State/Federal forestry programs. Twelve indexes, a bibliography, and glossary are included. (JGB)« less

The Urbis Project: Identification and Characterization of Potential Urban Development Areas as a Web-Based Service

NASA Astrophysics Data System (ADS)

Manzke, Nina; Kada, Martin; Kastler, Thomas; Xu, Shaojuan; de Lange, Norbert; Ehlers, Manfred

2016-06-01

Urban sprawl and the related landscape fragmentation is a Europe-wide challenge in the context of sustainable urban planning. The URBan land recycling Information services for Sustainable cities (URBIS) project aims for the development, implementation, and validation of web-based information services for urban vacant land in European functional urban areas in order to provide end-users with site specific characteristics and to facilitate the identification and evaluation of potential development areas. The URBIS services are developed based on open geospatial data. In particular, the Copernicus Urban Atlas thematic layers serve as the main data source for an initial inventory of sites. In combination with remotely sensed data like SPOT5 images and ancillary datasets like OpenStreetMap, detailed site specific information is extracted. Services are defined for three main categories: i) baseline services, which comprise an initial inventory and typology of urban land, ii) update services, which provide a regular inventory update as well as an analysis of urban land use dynamics and changes, and iii) thematic services, which deliver specific information tailored to end-users' needs.

Microwave Remote Sensing and the Cold Land Processes Field Experiment

NASA Technical Reports Server (NTRS)

Kim, Edward J.; Cline, Don; Davis, Bert; Hildebrand, Peter H. (Technical Monitor)

2001-01-01

The Cold Land Processes Field Experiment (CLPX) has been designed to advance our understanding of the terrestrial cryosphere. Developing a more complete understanding of fluxes, storage, and transformations of water and energy in cold land areas is a critical focus of the NASA Earth Science Enterprise Research Strategy, the NASA Global Water and Energy Cycle (GWEC) Initiative, the Global Energy and Water Cycle Experiment (GEWEX), and the GEWEX Americas Prediction Project (GAPP). The movement of water and energy through cold regions in turn plays a large role in ecological activity and biogeochemical cycles. Quantitative understanding of cold land processes over large areas will require synergistic advancements in 1) understanding how cold land processes, most comprehensively understood at local or hillslope scales, extend to larger scales, 2) improved representation of cold land processes in coupled and uncoupled land-surface models, and 3) a breakthrough in large-scale observation of hydrologic properties, including snow characteristics, soil moisture, the extent of frozen soils, and the transition between frozen and thawed soil conditions. The CLPX Plan has been developed through the efforts of over 60 interested scientists that have participated in the NASA Cold Land Processes Working Group (CLPWG). This group is charged with the task of assessing, planning and implementing the required background science, technology, and application infrastructure to support successful land surface hydrology remote sensing space missions. A major product of the experiment will be a comprehensive, legacy data set that will energize many aspects of cold land processes research. The CLPX will focus on developing the quantitative understanding, models, and measurements necessary to extend our local-scale understanding of water fluxes, storage, and transformations to regional and global scales. The experiment will particularly emphasize developing a strong synergism between process-oriented understanding, land surface models and microwave remote sensing. The experimental design is a multi-sensor, multi-scale (1-ha to 160,000 km ^ {2}) approach to providing the comprehensive data set necessary to address several experiment objectives. A description focusing on the microwave remote sensing components (ground, airborne, and spaceborne) of the experiment will be presented.

Providing a Spatial Context for Crop Insurance in Ethiopia: Multiscale Comparisons of Vegetation Metrics in Tigray

NASA Astrophysics Data System (ADS)

Mann, B. F.; Small, C.

2014-12-01

Weather-based index insurance projects are rapidly expanding across the developing world. Many of these projects use satellite-based observations to detect extreme weather events, which inform and trigger payouts to smallholder farmers. While most index insurance programs use precipitation measurements to determine payouts, the use of remotely sensed observations of vegetation is currently being explored. In order to use vegetation indices as a basis for payouts, it is necessary to establish a consistent relationship between the vegetation index and the health and abundance of agriculture on the ground. The accuracy with which remotely sensed vegetation indices can detect changes in agriculture depends on both the spatial scale of the agriculture and the spatial resolution of the sensor. This study analyzes the relationship between meter and decameter scale vegetation fraction estimates derived from linear spectral mixture models with a more commonly used vegetation index (NDVI, EVI) at hectometer spatial scales. In addition, the analysis incorporates land cover/land use field observations collected in Tigray Ethiopia in July 2013. . It also tests the flexibility and utility of a standardized spectral mixture model in which land cover is represented as continuous fields of rock and soil substrate (S), vegetation (V) and dark surfaces (D; water, shadow). This analysis found strong linear relationships with vegetation metrics at 1.6-meter, 30-meter and 250-meter resolutions across spectrally diverse subsets of Tigray, Ethiopia and significantly correlated relationships using the Spearman's rho statistic. The observed linear scaling has positive implications for future use of moderate resolution vegetation indices in similar landscapes; especially index insurance projects that are scaling up across the developing world using remotely-sensed environmental information.

A land use and environmental impact analysis of the Norfolk-Portsmouth SMSA

NASA Technical Reports Server (NTRS)

Mitchel, W. B.; Berlin, G. L.

1973-01-01

The feasibility of using remote sensing techniques for land use and environmental assessment in the Norfolk-Portsmouth area is discussed. Data cover the use of high altitude aircraft and satellite remote sensing data for: (1) identifying various heirarchial levels of land use, (2) monitoring land use changes for repetitive basis, (3) assessing the impact of competing land uses, and (4) identifying areas of potential environmental deterioration. High altitude aircraft photographs (scale 1:120,000) acquired in 1959, 1970, and 1972, plus Earth Resources Technology Satellite (ERTS-1) color composite images acquired in 1972 were used for the land use and environmental assessments. The high altitude aircraft photography, as expected, was successfully used to map Level 1, Level 2, as well as some urban Level 3 land use categories. However, the detail of land use analysis obtainable from the ERTS imagery exceeded the expectations for the U.S. Geological Survey's land use classification scheme. Study results are consistent with the initial investigation which determined Level 1 land use change to be 16.7 square km per year.

Integrating satellite remote sensing data and field data to predict rangeland structural indicators at the continental scale

NASA Astrophysics Data System (ADS)

Zhang, J.; Okin, G.

2016-12-01

Rangelands provide a variety of important ecosystem goods and services across drylands globally. They are also the most important emitters of dust across the globe. Field data collection based on points does not represent spatially continuous information about surface variables and, given the vast size of the world's rangelands, cannot cover even a small fraction of their area. Remote sensing is potentially a labor- and time-saving method to observe important rangeland vegetation variables at both temporal and spatial scales. Information on vegetation cover, bare gap size, and plant height provide key rangeland vegetation variables in arid and semiarid rangelands, in part because they strongly impact dust emission and determine wildlife habitat characteristics. This study reports on relationships between remote sensing in the reflected solar spectrum and field measures related to these three variables, and shows how these relationships can be extended to produce spatially and temporally continuous datasets coupled with quantitative estimates of error. Field data for this study included over 3,800 Assessment, Inventory, and Monitoring (AIM) measurements on Bureau of Land Management (BLM) lands throughout the western US. Remote sensing data were derived from MODIS nadir BRDF-adjusted reflectance (NBAR) and Landsat 8 OLI surface reflectance. Normalized bare gap size, total foliar cover, herbaceous cover and herbaceous height exhibit the greatest predictability from remote sensing variables with physically-reasonable relationships between remote sensing variables and field measures. Data fields produced using these relationships across the western US exhibit good agreement with independent high-resolution imagery.

Urban land-cover change detection through sub-pixel imperviousness mapping using remotely sensed data

USGS Publications Warehouse

Yang, Limin; Xian, George Z.; Klaver, Jacqueline M.; Deal, Brian

2003-01-01

We developed a Sub-pixel Imperviousness Change Detection (SICD) approach to detect urban land-cover changes using Landsat and high-resolution imagery. The sub-pixel percent imperviousness was mapped for two dates (09 March 1993 and 11 March 2001) over western Georgia using a regression tree algorithm. The accuracy of the predicted imperviousness was reasonable based on a comparison using independent reference data. The average absolute error between predicted and reference data was 16.4 percent for 1993 and 15.3 percent for 2001. The correlation coefficient (r) was 0.73 for 1993 and 0.78 for 2001, respectively. Areas with a significant increase (greater than 20 percent) in impervious surface from 1993 to 2001 were mostly related to known land-cover/land-use changes that occurred in this area, suggesting that the spatial change of an impervious surface is a useful indicator for identifying spatial extent, intensity, and, potentially, type of urban land-cover/land-use changes. Compared to other pixel-based change-detection methods (band differencing, rationing, change vector, post-classification), information on changes in sub-pixel percent imperviousness allow users to quantify and interpret urban land-cover/land-use changes based on their own definition. Such information is considered complementary to products generated using other change-detection methods. In addition, the procedure for mapping imperviousness is objective and repeatable, hence, can be used for monitoring urban land-cover/land-use change over a large geographic area. Potential applications and limitations of the products developed through this study in urban environmental studies are also discussed.

Use of geostationary satellite imagery in optical and thermal bands for the estimation of soil moisture status and land evapotranspiration

NASA Astrophysics Data System (ADS)

Ghilain, N.; Arboleda, A.; Gellens-Meulenberghs, F.

2009-04-01

For water and agricultural management, there is an increasing demand to monitor the soil water status and the land evapotranspiration. In the framework of the LSA-SAF project (http://landsaf.meteo.pt), we are developing an energy balance model forced by remote sensing products, i.e. radiation components and vegetation parameters, to monitor in quasi real-time the evapotranspiration rate over land (Gellens-Meulenberghs et al, 2007; Ghilain et al, 2008). The model is applied over the full MSG disk, i.e. including Europe and Africa. Meteorological forcing, as well as the soil moisture status, is provided by the forecasts of the ECMWF model. Since soil moisture is computed by a forecast model not dedicated to the monitoring of the soil water status, inadequate soil moisture input can occur, and can cause large effects on evapotranspiration rates, especially over semi-arid or arid regions. In these regions, a remotely sensed-based method for the soil moisture retrieval can therefore be preferable, to avoid too strong dependency in ECMWF model estimates. Among different strategies, remote sensing offers the advantage of monitoring large areas. Empirical methods of soil moisture assessment exist using remotely sensed derived variables either from the microwave bands or from the thermal bands. Mainly polar orbiters are used for this purpose, and little attention has been paid to the new possibilities offered by geosynchronous satellites. In this contribution, images of the SEVIRI instrument on board of MSG geosynchronous satellites are used. Dedicated operational algorithms were developed for the LSA-SAF project and now deliver images of land surface temperature (LST) every 15-minutes (Trigo et al, 2008) and vegetations indices (leaf area index, LAI; fraction of vegetation cover, FVC; fraction of absorbed photosynthetically active radiation, FAPAR) every day (Garcia-Haro et al, 2005) over Africa and Europe. One advantage of using products derived from geostationary satellites is the close monitoring of the diurnal variation of the land surface temperature. This feature reinforced the statistical strength of empirical methods. An empirical method linking land surface morning heating rates and the fraction of the vegetation cover, also known as a ‘Triangle method' (Gillies et al, 1997) is examined. This method is expected to provide an estimation of a root-zone soil moisture index. The sensitivity of the method to wind speed, soil type, vegetation type and climatic region is explored. Moreover, the impact of the uncertainty of LST and FVC on the resulting soil moisture estimates is assessed. A first impact study of using remotely sensed soil moisture index in the energy balance model is shown and its potential benefits for operational monitoring of evapotranspiration are outlined. References García-Haro, F.J., F. Camacho-de Coca, J. Meliá, B. Martínez (2005) Operational derivation of vegetation products in the framework of the LSA SAF project. Proceedings of the EUMETSAT Meteorological Satellite Conference Dubrovnik (Croatia) 19-23 Septembre. Gellens-Meulenberghs, F., Arboleda, A., Ghilain, N. (2007) Towards a continuous monitoring of evapotranspiration based on MSG data. Proceedings of the symposium on Remote Sensing for Environmental Monitoring and Change Detection. IAHS series. IUGG, Perugia, Italy, July 2007, 7 pp. Ghilain, N., Arboleda, A. and Gellens-Meulenberghs, F., (2008) Improvement of a surface energy balance model by the use of MSG-SEVIRI derived vegetation parameters. Proceedings of the 2008 EUMETSAT meteorological satellite data user's conference, Darmstadt, Germany, 8th-12th September, 7 pp. Gillies R.R., Carlson T.N., Cui J., Kustas W.P. and Humes K. (1997), Verification of the triangle method for obtaining surface soil water content and energy fluxes from remote measurements of Normalized Difference Vegetation Index (NDVI) and surface radiant temperature, International Journal of Remote Sensing, 18, pp. 3145-3166. Trigo, I.F., Monteiro I.T., Olesen F. and Kabsch E. (2008) An assessment of remotely sensed land surface temperature. Journal of Geophysical Research, 113, D17108, doi:10.1029/2008JD010035.

Environmental mapping and monitoring of Iceland by remote sensing (EMMIRS)

NASA Astrophysics Data System (ADS)

Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.

2016-04-01

Iceland is exposed to rapid and dynamic landscape changes caused by natural processes and man-made activities, which impact and challenge the country. Fast and reliable mapping and monitoring techniques are needed on a big spatial scale. However, currently there is lack of operational advanced information processing techniques, which are needed for end-users to incorporate remote sensing (RS) data from multiple data sources. Hence, the full potential of the recent RS data explosion is not being fully exploited. The project Environmental Mapping and Monitoring of Iceland by Remote Sensing (EMMIRS) bridges the gap between advanced information processing capabilities and end-user mapping of the Icelandic environment. This is done by a multidisciplinary assessment of two selected remote sensing super sites, Hekla and Öræfajökull, which encompass many of the rapid natural and man-made landscape changes that Iceland is exposed to. An open-access benchmark repository of the two remote sensing supersites is under construction, providing high-resolution LIDAR topography and hyperspectral data for land-cover and landform classification. Furthermore, a multi-temporal and multi-source archive stretching back to 1945 allows a decadal evaluation of landscape and ecological changes for the two remote sensing super sites by the development of automated change detection techniques. The development of innovative pattern recognition and machine learning-based approaches to image classification and change detection is one of the main tasks of the EMMIRS project, aiming to extract and compute earth observation variables as automatically as possible. Ground reference data collected through a field campaign will be used to validate the implemented methods, which outputs are then inferred with geological and vegetation models. Here, preliminary results of an automatic land-cover classification based on hyperspectral image analysis are reported. Furthermore, the EMMIRS project investigates the complex landscape dynamics between geological and ecological processes. This is done through cross-correlation of mapping results and implementation of modelling techniques that simulate geological and ecological processes in order to extrapolate the landscape evolution

Natural Resource Information System. Volume I. Overall Description.

ERIC Educational Resources Information Center

Boeing Computer Services, Inc., Seattle, WA.

Recognizing the need for the development of a computer based information system which would handle remote sensing as well as conventional mapping data, the Bureau of Indian Affairs and the Bureau of Land Management contracted with Boeing Computer Services for the design and construction of a prototype Natural Resource Information System. The…

Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information

USDA-ARS?s Scientific Manuscript database

A significant challenge in ecological studies has been defining scales of observation that correspond to the relevant ecological scales for organisms or processes of interest. Remote sensing has become commonplace in ecological studies and management, but the default resolution of imagery often used...

Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information

USDA-ARS?s Scientific Manuscript database

A challenge in ecological studies is defining scales of observation that correspond to relevant ecological scales for organisms or processes. Image segmentation has been proposed as an alternative to pixel-based methods for scaling remotely-sensed data into ecologically-meaningful units. However, to...

REMOTE SENSING AND SPATIALLY EXPLICIT LANDSCAPE-BASED NITROGEN MODELING METHODS DEVELOPMENT IN THE NEUSE RIVER BASIN, NC

EPA Science Inventory

The objective of this research was to model and map the spatial patterns of excess nitrogen (N) sources across the landscape within the Neuse River Basin (NRB) of North
Carolina. The process included an initial land cover characterization effort to map landscape "patches" at ...

Estuaries Matter: Connecting Recreation and Sense of Place to Resilience on Cape Cod

EPA Science Inventory

People have been living on Cape Cod or visiting its beautiful land and seascapes for generations, and it holds a special place in New England’s heart with many people greatly attached to it. Part of the attraction of the area is the access to water-based recreation in ponds...

Application of the Combination Approach for Estimating Evapotranspiration in Puerto Rico

NASA Technical Reports Server (NTRS)

Harmsen, Eric; Luvall, Jeffrey; Gonzalez, Jorge

2005-01-01

The ability to estimate short-term fluxes of water vapor from the land surface is important for validating latent heat flux estimates from high resolution remote sensing techniques. A new, relatively inexpensive method is presented for estimating t h e ground-based values of the surface latent heat flux or evapotranspiration.

Remote Sensing Technologies Mitigate Drought

NASA Technical Reports Server (NTRS)

2015-01-01

Ames Research Center has partnered with the California Department of Water Resources to develop satellite-based technologies to mitigate drought conditions. One project aims to help water managers adjust their irrigation to match the biological needs of each crop, and another involves monitoring areas where land is fallow so emergency relief can more quickly aid affected communities.

Deriving urban dynamic evolution rules from self-adaptive cellular automata with multi-temporal remote sensing images

NASA Astrophysics Data System (ADS)

He, Yingqing; Ai, Bin; Yao, Yao; Zhong, Fajun

2015-06-01

Cellular automata (CA) have proven to be very effective for simulating and predicting the spatio-temporal evolution of complex geographical phenomena. Traditional methods generally pose problems in determining the structure and parameters of CA for a large, complex region or a long-term simulation. This study presents a self-adaptive CA model integrated with an artificial immune system to discover dynamic transition rules automatically. The model's parameters are allowed to be self-modified with the application of multi-temporal remote sensing images: that is, the CA can adapt itself to the changed and complex environment. Therefore, urban dynamic evolution rules over time can be efficiently retrieved by using this integrated model. The proposed AIS-based CA model was then used to simulate the rural-urban land conversion of Guangzhou city, located in the core of China's Pearl River Delta. The initial urban land was directly classified from TM satellite image in the year 1990. Urban land in the years 1995, 2000, 2005, 2009 and 2012 was correspondingly used as the observed data to calibrate the model's parameters. With the quantitative index figure of merit (FoM) and pattern similarity, the comparison was further performed between the AIS-based model and a Logistic CA model. The results indicate that the AIS-based CA model can perform better and with higher precision in simulating urban evolution, and the simulated spatial pattern is closer to the actual development situation.

Integrating geospatial and ground geophysical information as guidelines for groundwater potential zones in hard rock terrains of south India.

PubMed

Rashid, Mehnaz; Lone, Mahjoor Ahmad; Ahmed, Shakeel

2012-08-01

The increasing demand of water has brought tremendous pressure on groundwater resources in the regions were groundwater is prime source of water. The objective of this study was to explore groundwater potential zones in Maheshwaram watershed of Andhra Pradesh, India with semi-arid climatic condition and hard rock granitic terrain. GIS-based modelling was used to integrate remote sensing and geophysical data to delineate groundwater potential zones. In the present study, Indian Remote Sensing RESOURCESAT-1, Linear Imaging Self-Scanner (LISS-4) digital data, ASTER digital elevation model and vertical electrical sounding data along with other data sets were analysed to generate various thematic maps, viz., geomorphology, land use/land cover, geology, lineament density, soil, drainage density, slope, aquifer resistivity and aquifer thickness. Based on this integrated approach, the groundwater availability in the watershed was classified into four categories, viz. very good, good, moderate and poor. The results reveal that the modelling assessment method proposed in this study is an effective tool for deciphering groundwater potential zones for proper planning and management of groundwater resources in diverse hydrogeological terrains.

Mass spectrometry based on a coupled Cooper-pair box and nanomechanical resonator system

NASA Astrophysics Data System (ADS)

Jiang, Cheng; Chen, Bin; Li, Jin-Jin; Zhu, Ka-Di

2011-10-01

Nanomechanical resonators (NRs) with very high frequency have a great potential for mass sensing with unprecedented sensitivity. In this study, we propose a scheme for mass sensing based on the NR capacitively coupled to a Cooper-pair box (CPB) driven by two microwave currents. The accreted mass landing on the resonator can be measured conveniently by tracking the resonance frequency shifts because of mass changes in the signal absorption spectrum. We demonstrate that frequency shifts induced by adsorption of ten 1587 bp DNA molecules can be well resolved in the absorption spectrum. Integration with the CPB enables capacitive readout of the mechanical resonance directly on the chip.

Estimation of Land Surface Temperature from GCOM-W1 AMSR2 Data over the Chinese Landmass

NASA Astrophysics Data System (ADS)

Zhou, Ji; Dai, Fengnan; Zhang, Xiaodong

2016-04-01

As one of the most important parameter at the interface between the earth's surface and atmosphere, land surface temperature (LST) plays a crucial role in many fields, such as climate change monitoring and hydrological modeling. Satellite remote sensing provides the unique possibility to observe LST of large regions at diverse spatial and temporal scales. Compared with thermal infrared (TIR) remote sensing, passive microwave (PW) remote sensing has a better ability in overcoming the influences of clouds; thus, it can be used to improve the temporal resolution of current satellite TIR LST. However, most of current methods for estimation LST from PW remote sensing are empirical and have unsatisfied generalization. In this study, a semi-empirical method is proposed to estimate LST from the observation of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1st-WATER "SHIZUKU" satellite (GCOM-W1). The method is based on the PW radiation transfer equation, which is simplified based on (1) the linear relationship between the emissivities of horizontal and vertical polarization channels at the same frequency and (2) the significant relationship between atmospheric parameters and the atmospheric water vapor content. An iteration approach is used to best fit the pixel-based coefficients in the simplified radiation transfer equation of the horizontal and vertical polarization channels at each frequency. Then an integration approach is proposed to generate the ensemble estimation from estimations of multiple frequencies for different land cover types. This method is trained with the AMSR2 brightness temperature and MODIS LST in 2013 over the entire Chinese landmass and then it is tested with the data in 2014. Validation based on in situ LSTs measured in northwestern China demonstrates that the proposed method has a better accuracy than the polarization radiation method, with a root-mean squared error of 3 K. Although the proposal method is applied to AMSR2 data, it has good ability to extend to other satellite PW sensors, such as the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on board the Aqua satellite and the Special Sensor Microwave/Imager (SSM/I) on board the Defense Meteorological Satellite Program (DMSP) satellite. It would be beneficial in providing LST to applications at continental and global scales.

Nasa's Land Remote Sensing Plans for the 1980's

NASA Technical Reports Server (NTRS)

Higg, H. C.; Butera, K. M.; Settle, M.

1985-01-01

Research since the launch of LANDSAT-1 has been primarily directed to the development of analysis techniques and to the conduct of applications studies designed to address resource information needs in the United States and in many other countries. The current measurement capabilities represented by MSS, TM, and SIR-A and B, coupled with the present level of remote sensing understanding and the state of knowledge in the discipline earth sciences, form the foundation for NASA's Land Processes Program. Science issues to be systematically addressed include: energy balance, hydrologic cycle, biogeochemical cycles, biological productivity, rock cycle, landscape development, geological and botanical associations, and land surface inventory, monitoring, and modeling. A global perspective is required for using remote sensing technology for problem solving or applications context. A successful model for this kind of activity involves joint research with a user entity where the user provides a test site and ground truth and NASA provides the remote sensing techniques to be tested.

Use of remote sensing for land use policy formulation

NASA Technical Reports Server (NTRS)

1987-01-01

The overall objectives and strategies of the Center for Remote Sensing remain to provide a center for excellence for multidisciplinary scientific expertise to address land-related global habitability and earth observing systems scientific issues. Specific research projects that were underway during the final contract period include: digital classification of coniferous forest types in Michigan's northern lower peninsula; a physiographic ecosystem approach to remote classification and mapping; land surface change detection and inventory; analysis of radiant temperature data; and development of methodologies to assess possible impacts of man's changes of land surface on meteorological parameters. Significant progress in each of the five project areas has occurred. Summaries on each of the projects are provided.

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2013 CFR

2013-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2011 CFR

2011-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2014 CFR

2014-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2012 CFR

2012-01-01

... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq...

15 CFR 960.1 - Purpose.

Code of Federal Regulations, 2010 CFR

2010-01-01

... LICENSING OF PRIVATE REMOTE SENSING SYSTEMS General § 960.1 Purpose. (a) The regulations in this part set... sensing space system under Title II of the Land Remote Sensing Policy Act of 1992 (15 U.S.C. 5601 et seq... remote sensing satellite industry. (Available from NOAA, National Environmental Satellite Data and...

A web-based land cover classification system based on ontology model of different classification systems

NASA Astrophysics Data System (ADS)

Lin, Y.; Chen, X.

2016-12-01

Land cover classification systems used in remote sensing image data have been developed to meet the needs for depicting land covers in scientific investigations and policy decisions. However, accuracy assessments of a spate of data sets demonstrate that compared with the real physiognomy, each of the thematic map of specific land cover classification system contains some unavoidable flaws and unintended deviation. This work proposes a web-based land cover classification system, an integrated prototype, based on an ontology model of various classification systems, each of which is assigned the same weight in the final determination of land cover type. Ontology, a formal explication of specific concepts and relations, is employed in this prototype to build up the connections among different systems to resolve the naming conflicts. The process is initialized by measuring semantic similarity between terminologies in the systems and the search key to produce certain set of satisfied classifications, and carries on through searching the predefined relations in concepts of all classification systems to generate classification maps with user-specified land cover type highlighted, based on probability calculated by votes from data sets with different classification system adopted. The present system is verified and validated by comparing the classification results with those most common systems. Due to full consideration and meaningful expression of each classification system using ontology and the convenience that the web brings with itself, this system, as a preliminary model, proposes a flexible and extensible architecture for classification system integration and data fusion, thereby providing a strong foundation for the future work.

Web-based interactive access, analysis and comparison of remotely sensed and in situ measured temperature data

NASA Astrophysics Data System (ADS)

Eberle, Jonas; Urban, Marcel; Hüttich, Christian; Schmullius, Christiane

2014-05-01

Numerous datasets providing temperature information from meteorological stations or remote sensing satellites are available. However, the challenging issue is to search in the archives and process the time series information for further analysis. These steps can be automated for each individual product, if the pre-conditions are complied, e.g. data access through web services (HTTP, FTP) or legal rights to redistribute the datasets. Therefore a python-based package was developed to provide data access and data processing tools for MODIS Land Surface Temperature (LST) data, which is provided by NASA Land Processed Distributed Active Archive Center (LPDAAC), as well as the Global Surface Summary of the Day (GSOD) and the Global Historical Climatology Network (GHCN) daily datasets provided by NOAA National Climatic Data Center (NCDC). The package to access and process the information is available as web services used by an interactive web portal for simple data access and analysis. Tools for time series analysis were linked to the system, e.g. time series plotting, decomposition, aggregation (monthly, seasonal, etc.), trend analyses, and breakpoint detection. Especially for temperature data a plot was integrated for the comparison of two temperature datasets based on the work by Urban et al. (2013). As a first result, a kernel density plot compares daily MODIS LST from satellites Aqua and Terra with daily means from GSOD and GHCN datasets. Without any data download and data processing, the users can analyze different time series datasets in an easy-to-use web portal. As a first use case, we built up this complimentary system with remotely sensed MODIS data and in situ measurements from meteorological stations for Siberia within the Siberian Earth System Science Cluster (www.sibessc.uni-jena.de). References: Urban, Marcel; Eberle, Jonas; Hüttich, Christian; Schmullius, Christiane; Herold, Martin. 2013. "Comparison of Satellite-Derived Land Surface Temperature and Air Temperature from Meteorological Stations on the Pan-Arctic Scale." Remote Sens. 5, no. 5: 2348-2367. Further materials: Eberle, Jonas; Clausnitzer, Siegfried; Hüttich, Christian; Schmullius, Christiane. 2013. "Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia." ISPRS Int. J. Geo-Inf. 2, no. 3: 553-576.

An Investigation of Land-Atmosphere Coupling from Local to Regional Scales

NASA Astrophysics Data System (ADS)

Brunsell, N. A.; Van Vleck, E.; Rahn, D. A.

2017-12-01

The exchanges of mass and energy between the surface and atmosphere have been shown to depend upon both local and regional climatic influences. However, the degree of control exerted by the land surface on the coupling metrics is not well understood. In particular, we lack an understanding of the relationship between the local microclimate of a site and the regional forces responsible for land-atmosphere coupling. To address this, we investigate a series of metrics calculated from eddy covariance data and ceilometer data, land surface modeling and remotely sensed observations in the central United States to diagnose these interactions and predict the change from one coupling regime (e.g. wet/dry coupling) to another state. The stability of the coupling is quantified using a Lyapunov exponent based methodology. Through the use of a wavelet information theoretic approach, we isolate the roles local energy partitioning, as well as the temperature and moisture gradients on controlling and changing the coupling regime. Taking a multi-scale observational approach, we first examine the relationship at the tower scale. Using land surface models, we quantify to what extent current models are capable of properly diagnosing the dynamics of the coupling regime. In particular, we focus on the role of the surface moisture and vegetation to initiate and maintain precipitation feedbacks. We extend this analysis to the regional scale by utilizing reanalysis and remotely sensed observations. Thus, we are able to quantify the changes in observed coupling patterns with linkages to local interactions to address the question of the local control that the surface exerts over the maintenance of land-atmosphere coupling.

Remote sensing of a dynamic sub-arctic peatland reservoir using optical and synthetic aperture radar data

NASA Astrophysics Data System (ADS)

Larter, Jarod Lee

Stephens Lake, Manitoba is an example of a peatland reservoir that has undergone physical changes related to mineral erosion and peatland disintegration processes since its initial impoundment. In this thesis I focused on the processes of peatland upheaval, transport, and disintegration as the primary drivers of dynamic change within the reservoir. The changes related to these processes are most frequent after initial reservoir impoundment and decline over time. They continue to occur over 35 years after initial flooding. I developed a remote sensing approach that employs both optical and microwave sensors for discriminating land (Le. floating peatlands, forested land, and barren land) from open water within the reservoir. High spatial resolution visible and near-infrared (VNIR) optical data obtained from the QuickBird satellite, and synthetic aperture radar (SAR) microwave data obtained from the RADARSAT-1 satellite were implemented. The approach was facilitated with a Geographic Information System (GIS) based validation map for the extraction of optical and SAR pixel data. Each sensor's extracted data set was first analyzed separately using univariate and multivariate statistical methods to determine the discriminant ability of each sensor. The initial analyses were followed by an integrated sensor approach; the development of an image classification model; and a change detection analysis. Results showed excellent (> 95%) classification accuracy using QuickBird satellite image data. Discrimination and classification of studied land cover classes using SAR image texture data resulted in lower overall classification accuracies (˜ 60%). SAR data classification accuracy improved to > 90% when classifying only land and water, demonstrating SAR's utility as a land and water mapping tool. An integrated sensor data approach showed no considerable improvement over the use of optical satellite image data alone. An image classification model was developed that could be used to map both detailed land cover classes and the land and water interface within the reservoir. Change detection analysis over a seven year period indicated that physical changes related to mineral erosion, peatland upheaval, transport, and disintegration, and operational water level variation continue to take place in the reservoir some 35 years after initial flooding. This thesis demonstrates the ability of optical and SAR satellite image remote sensing data sets to be used in an operational context for the routine discrimination of the land and water boundaries within a dynamic peatland reservoir. Future monitoring programs would benefit most from a complementary image acquisition program in which SAR images, known for their acquisition reliability under cloud cover, are acquired along with optical images given their ability to discriminate land cover classes in greater detail.

Advances in soil erosion modelling through remote sensing data availability at European scale

NASA Astrophysics Data System (ADS)

Panagos, Panos; Karydas, Christos; Borrelli, Pasqualle; Ballabio, Cristiano; Meusburger, Katrin

2014-08-01

Under the European Union's Thematic Strategy for Soil Protection, the European Commission's Directorate-General for the Environment (DG Environment) has identified the mitigation of soil losses by erosion as a priority area. Policy makers call for an overall assessment of soil erosion in their geographical area of interest. They have asked that risk areas for soil erosion be mapped under present land use and climate conditions, and that appropriate measures be taken to control erosion within the legal and social context of natural resource management. Remote sensing data help to better assessment of factors that control erosion, such as vegetation coverage, slope length and slope angle. In this context, the data availability of remote sensing data during the past decade facilitates the more precise estimation of soil erosion risk. Following the principles of the Universal Soil Loss Equation (USLE), various options to calculate vegetative cover management (C-factor) have been investigated. The use of the CORINE Land Cover dataset in combination with lookup table values taken from the literature is presented as an option that has the advantage of a coherent input dataset but with the drawback of static input. Recent developments in the Copernicus programme have made detailed datasets available on land cover, leaf area index and base soil characteristics. These dynamic datasets allow for seasonal estimates of vegetation coverage, and their application in the G2 soil erosion model which represents a recent approach to the seasonal monitoring of soil erosion. The use of phenological datasets and the LUCAS land use/cover survey are proposed as auxiliary information in the selection of the best methodology.

Remote Sensing of the Environmental Impacts of Utility-Scale Solar Energy Plants

NASA Astrophysics Data System (ADS)

Edalat, Mohammad Masih

Solar energy has many environmental benefits compared with fossil fuels but solar farming can have environmental impacts especially during construction and development. Thus, in order to enhance environmental sustainability, it is imperative to understand the environmental impacts of utility-scale solar energy (USSE) plants. During recent decades, remote sensing techniques and geographic information systems have become standard techniques in environmental applications. In this study, the environmental impacts of USSE plants are investigated by analyzing changes to land surface characteristics using remote sensing. The surface characteristics studied include land cover, land surface temperature, and hydrological response whereas changes are mapped by comparing pre-, syn-, and post- construction conditions. In order to study the effects of USSE facilities on land cover, the changes in the land cover are measured and analyzed inside and around two USSE facilities. The principal component analysis (PCA), minimum noise fraction (MNF), and spectral mixture analysis (SMA) of remote sensing images are used to estimate the subpixel fraction of four land surface endmembers: high-albedo, low-albedo, shadow, and vegetation. The results revealed that USSE plants do not significantly impact land cover outside the plant boundary. However, land-cover radiative characteristics within the plant area are significantly affected after construction. During the construction phase, site preparation practices including shrub removal and land grading increase high-albedo and decrease low-albedo fractions. The thermal effects of USSE facilities are studied by the time series analysis of remote sensing land surface temperature (LST). A statistical trend analysis of LST, with seasonal trends removed is performed with a particular consideration of panel shadowing by analyzing sun angles for different times of year. The results revealed that the LST outside the boundary of the solar plant does not change, whereas it significantly decreases inside the plant at 10 AM after the construction. The decrease in LST mainly occurred in winters due to lower sun's altitude, which casts longer shadows on the ground. In order to study the hydrological impacts of PV plants, pre- and post-installation hydrological response over single-axis technology is compared. A theoretical reasoning is developed to explain flows under the influence of PV panels. Moreover, a distributed parametric hydrologic model is used to estimate runoff before and after the construction of PV plants. The results revealed that peak flow, peak flow time, and runoff volume alter after panel installation. After panel installation, peak flow decreases and is observed to shift in time, which depends on orientation. Likewise, runoff volume increases irrespective of panel orientation. The increase in the tilt angle of panel results in decrease in the peak flow, peak flow time, and runoff. This study provides an insight into the environmental impacts of USSE development using remote sensing. The research demonstrates that USSE plants are environmentally sustainable due to minimal impact on land cover and surface temperature in their vicinity. In addition, this research explains the role of rainfall shadowing on hydrological behavior at USSE plants.

The relation between land use and subsidence in the Vietnamese Mekong delta.

PubMed

Minderhoud, P S J; Coumou, L; Erban, L E; Middelkoop, H; Stouthamer, E; Addink, E A

2018-09-01

The Vietnamese Mekong delta is subsiding due to a combination of natural and human-induced causes. Over the past several decades, large-scale anthropogenic land-use changes have taken place as a result of increased agricultural production, population growth and urbanization in the delta. Land-use changes can alter the hydrological system or increase loading of the delta surface, amplifying natural subsidence processes or creating new anthropogenic subsidence. The relationships between land use histories and current rates of land subsidence have so far not been studied in the Mekong delta. We quantified InSAR-derived subsidence rates for the various land-use classes and past land-use changes using a new, optical remote sensing-based, 20-year time series of land use. Lowest mean subsidence rates were found for undeveloped land-use classes, like marshland and wetland forest (~6-7mmyr -1 ), and highest rates for areas with mixed-crop agriculture and cities (~18-20mmyr -1 ). We assessed the relationship strength between current land use, land-use history and subsidence by predicting subsidence rates during the measurement period solely based on land-use history. After initial training of all land-use sequences with InSAR-derived subsidence rates, the land-use-based approach predicted 65-92% of the spatially varying subsidence rates within the measurement error range of the InSAR observations (RMSE=5.8mm). As a result, the spatial patterns visible in the observed subsidence can largely be explained by land use. We discuss in detail the dominant land-use change pathways and their indirect, causal relationships with subsidence. Our spatially explicit evaluation of these pathways provides valuable insights for policymakers concerned with land-use planning in both subsiding and currently stable areas of the Mekong delta and similar systems. Copyright © 2018 Elsevier B.V. All rights reserved.

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

The Goddard Snow Radiance Assimilation Project: An Integrated Snow Radiance and Snow Physics Modeling Framework for Snow/cold Land Surface Modeling

NASA Technical Reports Server (NTRS)

Kim, E.; Tedesco, M.; Reichle, R.; Choudhury, B.; Peters-Lidard C.; Foster, J.; Hall, D.; Riggs, G.

2006-01-01

Microwave-based retrievals of snow parameters from satellite observations have a long heritage and have so far been generated primarily by regression-based empirical "inversion" methods based on snapshots in time. Direct assimilation of microwave radiance into physical land surface models can be used to avoid errors associated with such retrieval/inversion methods, instead utilizing more straightforward forward models and temporal information. This approach has been used for years for atmospheric parameters by the operational weather forecasting community with great success. Recent developments in forward radiative transfer modeling, physical land surface modeling, and land data assimilation are converging to allow the assembly of an integrated framework for snow/cold lands modeling and radiance assimilation. The objective of the Goddard snow radiance assimilation project is to develop such a framework and explore its capabilities. The key elements of this framework include: a forward radiative transfer model (FRTM) for snow, a snowpack physical model, a land surface water/energy cycle model, and a data assimilation scheme. In fact, multiple models are available for each element enabling optimization to match the needs of a particular study. Together these form a modular and flexible framework for self-consistent, physically-based remote sensing and water/energy cycle studies. In this paper we will describe the elements and the integration plan. All modules will operate within the framework of the Land Information System (LIS), a land surface modeling framework with data assimilation capabilities running on a parallel-node computing cluster. Capabilities for assimilation of snow retrieval products are already under development for LIS. We will describe plans to add radiance-based assimilation capabilities. Plans for validation activities using field measurements will also be discussed.

Environmental application of remote sensing methods to coastal zone land use and marine resource management, appendices G to J. [in southeastern Virginia

NASA Technical Reports Server (NTRS)

1972-01-01

Important data were compiled for use with the Richmond-Cape Henry Environmental Laboratory (RICHEL) remote sensing project in coastal zone land use and marine resources management, and include analyses and projections of population characteristics, formulation of soil loss prediction techniques, and sources and quantity analyses of air and water effluents.

Monitoring the ecology and environment using remote sensing in the Jinta area/Middle Reaches of Heihe River Basin

NASA Astrophysics Data System (ADS)

Lu, Anxin; Wang, Lihong; Chen, Xianzhang

2003-07-01

A major monitoring area, a part of the middle reaches of Heihe basin, was selected. The Landsat TM data in summer of 1990 and 2000 were used with interpretation on the computer screen, classification and setting up environmental investigation database (1:100000) combined with DEM, land cover/land use, land type data and etc., according to the environmental classification system. Then towards to the main problems of environment, the spatial statistical analysis and dynamic comparisons were carried out using the database. The dynamic monitoring results of 1999 and 2000 show that the changing percentage with the area of 6 ground objects are as follows: land use and agriculture land use increased by 34.17% and 19.47% respectively, wet land and water-body also increased by 6.29% and 8.03% respectively; unused land increased by 1.73% and the biggest change is natural/semi-natural vegetation area, decreased by 42.78%, the main results above meat with the requirements of precise and practical conditions by the precise exam and spot check. With the combinations of using TM remote sensing data and rich un-remote sensing data, the investigations of ecology and environment and the dynamic monitoring would be carried out efficiently in the arid area. It is a dangerous signal of large area desertification if the area of natural/semi-natural vegetation is reduced continuously and obviously.

Remote sensing new model for monitoring the east Asian migratory locust infections based on its breeding circle

NASA Astrophysics Data System (ADS)

Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai

2006-12-01

Currently the function of operational locust monitor system mainly focused on after-hazards monitoring and assessment, and to found the way effectively to perform early warning and prediction has more practical meaning. Through 2001, 2002 two years continuously field sample and statistics for locusts eggs hatching, nymph growth, adults 3 phases observation, sample statistics and calculation, spectral measurements as well as synchronically remote sensing data processing we raise the view point of Remote Sensing three stage monitor the locust hazards. Based on the point of view we designed remote sensing monitor in three stages: (1) during the egg hitching phase remote sensing can retrieve parameters of land surface temperature (LST) and soil moisture; (2) during nymph growth phase locust increases appetite greatly and remote sensing can calculate vegetation index, leaf area index, vegetation cover and analysis changes; (3) during adult phase the locust move and assembly towards ponds and water ditches as well as less than 75% vegetation cover areas and remote sensing combination with field data can monitor and predicts potential areas for adult locusts to assembly. In this way the priority of remote sensing technology is elaborated effectively and it also provides technique support for the locust monitor system. The idea and techniques used in the study can also be used as reference for other plant diseases and insect pests.

The U.S. Geological Survey land remote sensing program

USGS Publications Warehouse

Saunders, T.; Feuquay, J.; Kelmelis, J.A.

2003-01-01

The U.S. Geological Survey has been a provider of remotely sensed information for decades. As the availability and use of satellite data has grown, USGS has placed increasing emphasis on expanding the knowledge about the science of remote sensing and on making remotely sensed data more accessible. USGS encourages widespread availability and distribution of these data and through its programs, encourages and enables a variety of research activities and the development of useful applications of the data. The science of remote sensing has great potential for assisting in the monitoring and assessment of the impacts of natural disasters, management and analysis of environmental, biological, energy, and mineral investigations, and supporting informed public policy decisions. By establishing the Land Remote Sensing Program (LRS) as a major unit of the USGS Geography Program, USGS has taken the next step to further increase support for the accessibility, understanding, and use of remotely sensed data. This article describes the LRS Program, its mission and objectives, and how the program has been structured to accomplish its goals.

Hazard avoidance via descent images for safe landing

NASA Astrophysics Data System (ADS)

Yan, Ruicheng; Cao, Zhiguo; Zhu, Lei; Fang, Zhiwen

2013-10-01

In planetary or lunar landing missions, hazard avoidance is critical for landing safety. Therefore, it is very important to correctly detect hazards and effectively find a safe landing area during the last stage of descent. In this paper, we propose a passive sensing based HDA (hazard detection and avoidance) approach via descent images to lower the landing risk. In hazard detection stage, a statistical probability model on the basis of the hazard similarity is adopted to evaluate the image and detect hazardous areas, so that a binary hazard image can be generated. Afterwards, a safety coefficient, which jointly utilized the proportion of hazards in the local region and the inside hazard distribution, is proposed to find potential regions with less hazards in the binary hazard image. By using the safety coefficient in a coarse-to-fine procedure and combining it with the local ISD (intensity standard deviation) measure, the safe landing area is determined. The algorithm is evaluated and verified with many simulated descent downward looking images rendered from lunar orbital satellite images.

Remote sensing applications program

NASA Technical Reports Server (NTRS)

1984-01-01

The activities of the Mississippi Remote Sensing Center are described in addition to technology transfer and information dissemination, remote sensing topics such as timber identification, water quality, flood prevention, land use, erosion control, animal habitats, and environmental impact studies are also discussed.

Comparison of Pixel-Based and Object-Based Classification Using Parameters and Non-Parameters Approach for the Pattern Consistency of Multi Scale Landcover

NASA Astrophysics Data System (ADS)

Juniati, E.; Arrofiqoh, E. N.

2017-09-01

Information extraction from remote sensing data especially land cover can be obtained by digital classification. In practical some people are more comfortable using visual interpretation to retrieve land cover information. However, it is highly influenced by subjectivity and knowledge of interpreter, also takes time in the process. Digital classification can be done in several ways, depend on the defined mapping approach and assumptions on data distribution. The study compared several classifiers method for some data type at the same location. The data used Landsat 8 satellite imagery, SPOT 6 and Orthophotos. In practical, the data used to produce land cover map in 1:50,000 map scale for Landsat, 1:25,000 map scale for SPOT and 1:5,000 map scale for Orthophotos, but using visual interpretation to retrieve information. Maximum likelihood Classifiers (MLC) which use pixel-based and parameters approach applied to such data, and also Artificial Neural Network classifiers which use pixel-based and non-parameters approach applied too. Moreover, this study applied object-based classifiers to the data. The classification system implemented is land cover classification on Indonesia topographic map. The classification applied to data source, which is expected to recognize the pattern and to assess consistency of the land cover map produced by each data. Furthermore, the study analyse benefits and limitations the use of methods.

Technical note: A simple approach for efficient collection of field reference data for calibrating remote sensing mapping of northern wetlands

NASA Astrophysics Data System (ADS)

Gålfalk, Magnus; Karlson, Martin; Crill, Patrick; Bousquet, Philippe; Bastviken, David

2018-03-01

The calibration and validation of remote sensing land cover products are highly dependent on accurate field reference data, which are costly and practically challenging to collect. We describe an optical method for collection of field reference data that is a fast, cost-efficient, and robust alternative to field surveys and UAV imaging. A lightweight, waterproof, remote-controlled RGB camera (GoPro HERO4 Silver, GoPro Inc.) was used to take wide-angle images from 3.1 to 4.5 m in altitude using an extendable monopod, as well as representative near-ground (< 1 m) images to identify spectral and structural features that correspond to various land covers in present lighting conditions. A semi-automatic classification was made based on six surface types (graminoids, water, shrubs, dry moss, wet moss, and rock). The method enables collection of detailed field reference data, which is critical in many remote sensing applications, such as satellite-based wetland mapping. The method uses common non-expensive equipment, does not require special skills or training, and is facilitated by a step-by-step manual that is included in the Supplement. Over time a global ground cover database can be built that can be used as reference data for studies of non-forested wetlands from satellites such as Sentinel 1 and 2 (10 m pixel size).

Coastal and Inland Aquatic Data Products for the Hyperspectral Infrared Imager (HyspIRI)

NASA Technical Reports Server (NTRS)

Abelev, Andrei; Babin, Marcel; Bachmann, Charles; Bell, Thomas; Brando, Vittorio; Byrd, Kristin; Dekker , Arnold; Devred, Emmanuel; Forget, Marie-Helene; Goodman, James;

Application of remote sensing data to land use and land cover assessment in the Tubarao River coastal plain, Santa Catarina, Brazil

NASA Technical Reports Server (NTRS)

1982-01-01

By means of aerial photography and MSS-LANDSAT data a land use/land cover classification was applied to the Tubarao River coastal plain. The following classes were identified: coal related areas, permanently flooded wetlands, periodically flooded wetlands, agricultural lands, bare soils, water bodies, urban areas, forestlands.

Land Cover Change Detection using Neural Network and Grid Cells Techniques

NASA Astrophysics Data System (ADS)

Bagan, H.; Li, Z.; Tangud, T.; Yamagata, Y.

2017-12-01

In recent years, many advanced neural network methods have been applied in land cover classification, each of which has both strengths and limitations. In which, the self-organizing map (SOM) neural network method have been used to solve remote sensing data classification problems and have shown potential for efficient classification of remote sensing data. In SOM, both the distribution and the topology of features of the input layer are identified by using an unsupervised, competitive, neighborhood learning method. The high-dimensional data are then projected onto a low-dimensional map (competitive layer), usually as a two-dimensional map. The neurons (nodes) in the competitive layer are arranged by topological order in the input space. Spatio-temporal analyses of land cover change based on grid cells have demonstrated that gridded data are useful for obtaining spatial and temporal information about areas that are smaller than municipal scale and are uniform in size. Analysis based on grid cells has many advantages: grid cells all have the same size allowing for easy comparison; grids integrate easily with other scientific data; grids are stable over time and thus facilitate the modelling and analysis of very large multivariate spatial data sets. This study chose time-series MODIS and Landsat images as data sources, applied SOM neural network method to identify the land utilization in Inner Mongolia Autonomous Region of China. Then the results were integrated into grid cell to get the dynamic change maps. Land cover change using MODIS data in Inner Mongolia showed that urban area increased more than fivefold in recent 15 years, along with the growth of mining area. In terms of geographical distribution, the most obvious place of urban expansion is Ordos in southwest Inner Mongolia. The results using Landsat images from 1986 to 2014 in northeastern part of the Inner Mongolia show degradation in grassland from 1986 to 2014. Grid-cell-based spatial correlation analysis also confirmed a strong negative correlation between grassland and barren land, indicating that grassland degradation in this region is due to the urbanization and coal mining activities over the past three decades.

INFLUENCE OF REMOTE SENSING IMAGERY SOURCE ON QUANTIFICATION OF RIPARIAN LAND COVER/LAND USE

EPA Science Inventory

This paper compares approaches to quantifying land cover/land use (LCLU) in riparian corridors of 23 watersheds in Oregon's Willamette Valley using aerial photography (AP) and Thematic Mapper (TM) imagery. For each imagery source, we quantified LCLU adjacent to stream networks ac...

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

NASA Astrophysics Data System (ADS)

Midekisa, Alemayehu; Senay, Gabriel B.; Wimberly, Michael C.

2014-11-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

USGS Publications Warehouse

Midekisa, Alemayehu; Senay, Gabriel; Wimberly, Michael C.

2014-01-01

Malaria is a major global public health problem, particularly in Sub-Saharan Africa. The spatial heterogeneity of malaria can be affected by factors such as hydrological processes, physiography, and land cover patterns. Tropical wetlands, for example, are important hydrological features that can serve as mosquito breeding habitats. Mapping and monitoring of wetlands using satellite remote sensing can thus help to target interventions aimed at reducing malaria transmission. The objective of this study was to map wetlands and other major land cover types in the Amhara region of Ethiopia and to analyze district-level associations of malaria and wetlands across the region. We evaluated three random forests classification models using remotely sensed topographic and spectral data based on Shuttle Radar Topographic Mission (SRTM) and Landsat TM/ETM+ imagery, respectively. The model that integrated data from both sensors yielded more accurate land cover classification than single-sensor models. The resulting map of wetlands and other major land cover classes had an overall accuracy of 93.5%. Topographic indices and subpixel level fractional cover indices contributed most strongly to the land cover classification. Further, we found strong spatial associations of percent area of wetlands with malaria cases at the district level across the dry, wet, and fall seasons. Overall, our study provided the most extensive map of wetlands for the Amhara region and documented spatiotemporal associations of wetlands and malaria risk at a broad regional level. These findings can assist public health personnel in developing strategies to effectively control and eliminate malaria in the region.

Evidence for Crater Ejecta on Venus Tessera Terrain from Earth-Based Radar Images

NASA Technical Reports Server (NTRS)

Campbell, Bruce A.; Campbell, Donald B.; Morgan, Gareth A.; Carter, Lynn M.; Nolan, Michael C.; Chandler, John F.

2014-01-01

We combine Earth-based radar maps of Venus from the 1988 and 2012 inferior conjunctions, which had similar viewing geometries. Processing of both datasets with better image focusing and co-registration techniques, and summing over multiple looks, yields maps with 1-2 km spatial resolution and improved signal to noise ratio, especially in the weaker same-sense circular (SC) polarization. The SC maps are unique to Earth-based observations, and offer a different view of surface properties from orbital mapping using same-sense linear (HH or VV) polarization. Highland or tessera terrains on Venus, which may retain a record of crustal differentiation and processes occurring prior to the loss of water, are of great interest for future spacecraft landings. The Earth-based radar images reveal multiple examples of tessera mantling by impact ''parabolas'' or ''haloes'', and can extend mapping of locally thick material from Magellan data by revealing thinner deposits over much larger areas. Of particular interest is an ejecta deposit from Stuart crater that we infer to mantle much of eastern Alpha Regio. Some radar-dark tessera occurrences may indicate sediments that are trapped for longer periods than in the plains. We suggest that such radar information is important for interpretation of orbital infrared data and selection of future tessera landing sites.

Exploring geo-tagged photos for land cover validation with deep learning

NASA Astrophysics Data System (ADS)

Xing, Hanfa; Meng, Yuan; Wang, Zixuan; Fan, Kaixuan; Hou, Dongyang

2018-07-01

Land cover validation plays an important role in the process of generating and distributing land cover thematic maps, which is usually implemented by high cost of sample interpretation with remotely sensed images or field survey. With an increasing availability of geo-tagged landscape photos, the automatic photo recognition methodologies, e.g., deep learning, can be effectively utilised for land cover applications. However, they have hardly been utilised in validation processes, as challenges remain in sample selection and classification for highly heterogeneous photos. This study proposed an approach to employ geo-tagged photos for land cover validation by using the deep learning technology. The approach first identified photos automatically based on the VGG-16 network. Then, samples for validation were selected and further classified by considering photos distribution and classification probabilities. The implementations were conducted for the validation of the GlobeLand30 land cover product in a heterogeneous area, western California. Experimental results represented promises in land cover validation, given that GlobeLand30 showed an overall accuracy of 83.80% with classified samples, which was close to the validation result of 80.45% based on visual interpretation. Additionally, the performances of deep learning based on ResNet-50 and AlexNet were also quantified, revealing no substantial differences in final validation results. The proposed approach ensures geo-tagged photo quality, and supports the sample classification strategy by considering photo distribution, with accuracy improvement from 72.07% to 79.33% compared with solely considering the single nearest photo. Consequently, the presented approach proves the feasibility of deep learning technology on land cover information identification of geo-tagged photos, and has a great potential to support and improve the efficiency of land cover validation.

[The impact of load density differences on land subsidence based on build-up index and PS-InSAR technology].

PubMed

Chen, Bei-Bei; Gong, Hui-Li; Li, Xiao-Juan; Lei, Kun-Chao; Lin, Zhu; Wang, Yan-Bing

2013-08-01

The excessive mining for underground water is the main reason inducing the land subsidence in Beijing, while, increasing of load brought by the urban construction aggravate the local land subsidence in a certain degree. As an international metropolis, the problems of land subsidence that caused by urban construction are becoming increasingly highlights, so revealing the relationship between regional load increase and the response of land subsidence also becomes one of the key problems in the land subsidence research field. In order to analyze the relationship between the load changes in construction and the land subsidence quantitatively, the present study selected the TM remote sensing image covering Beijing plain and used Erdas Modeler tool to invert the index based on building site (IBI), acquired the spatial and temporal change information in research area further; Based on results monitored by PS-InSAR (permanent scatterer interferometry) and IBI index method, and combined with the GIS spatial analysis method in the view of pixels in different scales, this paper analyzes the correlation between typical area load change and land subsidence, The conclusions show that there is a positive correlation between the density of load and the homogeneity of subsidence, especially in area which has a high sedimentation rate. Owing to such characteristics as the complexity and hysteretic nature of soil and geological structure, it is not obvious that the land subsidence caused by the increase of load in a short period. But with the increasing of local land load made by high density buildings and additional settlement of each monomer building superposed with each other, regional land subsidence is still a question that cannot be ignored and needs long-term systematic research and discussion.

Hydrological Responses of Climate and Land Use/Cover Changes in Tao'er River Basin Based on the SWAT Model

NASA Astrophysics Data System (ADS)

Liu, J.; Kou, L.

2015-12-01

Abstract: The changes of both climate and land use/cover have some impact on the water resources. For Tao'er River Basin, these changes have a direct impact on the land use pattern adjustment, wetland protection, connection project between rivers and reservoirs, local social and economic development, etc. Therefore, studying the impact of climate and land use/cover changes is of great practical significance. The Soil and Water Assessment Tool (SWAT) is used as the research method. With historical actual measured runoff data and the yearly land use classification caught by satellite remote sensing maps, analyze the impact of climate change on the runoff of Tao'er River. And according to the land use/cover classification of 1990, 2000 and 2010, analyze the land use/cover change in the recent 30 years, the impact of the land use/cover change on the river runoff and the contribution coefficient of farmland, woodland, grassland and other major land-use types to the runoff. These studies can provide some references to the rational allocation of water resource and adjustment of land use structure in this area.

Scaling water and energy fluxes in climate systems - Three land-atmospheric modeling experiments

NASA Technical Reports Server (NTRS)

Wood, Eric F.; Lakshmi, Venkataraman

1993-01-01

Three numerical experiments that investigate the scaling of land-surface processes - either of the inputs or parameters - are reported, and the aggregated processes are compared to the spatially variable case. The first is the aggregation of the hydrologic response in a catchment due to rainfall during a storm event and due to evaporative demands during interstorm periods. The second is the spatial and temporal aggregation of latent heat fluxes, as calculated from SiB. The third is the aggregation of remotely sensed land vegetation and latent and sensible heat fluxes using TM data from the FIFE experiment of 1987 in Kansas. In all three experiments it was found that the surface fluxes and land characteristics can be scaled, and that macroscale models based on effective parameters are sufficient to account for the small-scale heterogeneities investigated.

HABSEED: a Simple Spatially Explicit Meta-Populations Model Using Remote Sensing Derived Habitat Quality Data

NASA Astrophysics Data System (ADS)

Heumann, B. W.; Guichard, F.; Seaquist, J. W.

2005-05-01

The HABSEED model uses remote sensing derived NPP as a surrogate for habitat quality as the driving mechanism for population growth and local seed dispersal. The model has been applied to the Sahel region of Africa. Results show that the functional response of plants to habitat quality alters population distribution. Plants more tolerant of medium quality habitat have greater distributions to the North while plants requiring only the best habitat are limited to the South. For all functional response types, increased seed production results in diminishing returns. Functional response types have been related to life history tradeoffs and r-K strategies based on the results. Results are compared to remote sensing derived vegetation land cover.

Generating land cover boundaries from remotely sensed data using object-based image analysis: overview and epidemiological application.

PubMed

Maxwell, Susan K

2010-12-01

Satellite imagery and aerial photography represent a vast resource to significantly enhance environmental mapping and modeling applications for use in understanding spatio-temporal relationships between environment and health. Deriving boundaries of land cover objects, such as trees, buildings, and crop fields, from image data has traditionally been performed manually using a very time consuming process of hand digitizing. Boundary detection algorithms are increasingly being applied using object-based image analysis (OBIA) technology to automate the process. The purpose of this paper is to present an overview and demonstrate the application of OBIA for delineating land cover features at multiple scales using a high resolution aerial photograph (1 m) and a medium resolution Landsat image (30 m) time series in the context of a pesticide spray drift exposure application. Copyright © 2010. Published by Elsevier Ltd.

Beyond trend analysis: How a modified breakpoint analysis enhances knowledge of agricultural production after Zimbabwe's fast track land reform

NASA Astrophysics Data System (ADS)

Hentze, Konrad; Thonfeld, Frank; Menz, Gunter

2017-10-01

In the discourse on land reform assessments, a significant lack of spatial and time-series data has been identified, especially with respect to Zimbabwe's ;Fast-Track Land Reform Programme; (FTLRP). At the same time, interest persists among land use change scientists to evaluate causes of land use change and therefore to increase the explanatory power of remote sensing products. This study recognizes these demands and aims to provide input on both levels: Evaluating the potential of satellite remote sensing time-series to answer questions which evolved after intensive land redistribution efforts in Zimbabwe; and investigating how time-series analysis of Normalized Difference Vegetation Index (NDVI) can be enhanced to provide information on land reform induced land use change. To achieve this, two time-series methods are applied to MODIS NDVI data: Seasonal Trend Analysis (STA) and Breakpoint Analysis for Additive Season and Trend (BFAST). In our first analysis, a link of agricultural productivity trends to different land tenure regimes shows that regional clustering of trends is more dominant than a relationship between tenure and trend with a slightly negative slope for all regimes. We demonstrate that clusters of strong negative and positive productivity trends are results of changing irrigation patterns. To locate emerging and fallow irrigation schemes in semi-arid Zimbabwe, a new multi-method approach is developed which allows to map changes from bimodal seasonal phenological patterns to unimodal and vice versa. With an enhanced breakpoint analysis through the combination of STA and BFAST, we are able to provide a technique that can be applied on large scale to map status and development of highly productive cropping systems, which are key for food production, national export and local employment. We therefore conclude that the combination of existing and accessible time-series analysis methods: is able to achieve both: overcoming demonstrated limitations of MODIS based trend analysis and enhancing knowledge of Zimbabwe's FTLRP.

MODIS Land Data Products: Generation, Quality Assurance and Validation

NASA Technical Reports Server (NTRS)

Masuoka, Edward; Wolfe, Robert; Morisette, Jeffery; Sinno, Scott; Teague, Michael; Saleous, Nazmi; Devadiga, Sadashiva; Justice, Christopher; Nickeson, Jaime

2008-01-01

The Moderate Resolution Imaging Spectrometer (MODIS) on-board NASA's Earth Observing System (EOS) Terra and Aqua Satellites are key instruments for providing data on global land, atmosphere, and ocean dynamics. Derived MODIS land, atmosphere and ocean products are central to NASA's mission to monitor and understand the Earth system. NASA has developed and generated on a systematic basis a suite of MODIS products starting with the first Terra MODIS data sensed February 22, 2000 and continuing with the first MODIS-Aqua data sensed July 2, 2002. The MODIS Land products are divided into three product suites: radiation budget products, ecosystem products, and land cover characterization products. The production and distribution of the MODIS Land products are described, from initial software delivery by the MODIS Land Science Team, to operational product generation and quality assurance, delivery to EOS archival and distribution centers, and product accuracy assessment and validation. Progress and lessons learned since the first MODIS data were in early 2000 are described.

A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS

PubMed Central

Benza, Magdalena; Weeks, John R.; Stow, Douglas A.; López-Carr, David; Clarke, Keith C.

2016-01-01

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user’s accuracy (commission errors) being lower than producer’s accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient. PMID:27867227

A Pattern-Based Definition of Urban Context Using Remote Sensing and GIS.

PubMed

Benza, Magdalena; Weeks, John R; Stow, Douglas A; López-Carr, David; Clarke, Keith C

2016-09-15

In Sub-Saharan Africa rapid urban growth combined with rising poverty is creating diverse urban environments, the nature of which are not adequately captured by a simple urban-rural dichotomy. This paper proposes an alternative classification scheme for urban mapping based on a gradient approach for the southern portion of the West African country of Ghana. Landsat Enhanced Thematic Mapper Plus (ETM+) and European Remote Sensing Satellite-2 (ERS-2) synthetic aperture radar (SAR) imagery are used to generate a pattern based definition of the urban context. Spectral mixture analysis (SMA) is used to classify a Landsat scene into Built, Vegetation and Other land covers. Landscape metrics are estimated for Built and Vegetation land covers for a 450 meter uniform grid covering the study area. A measure of texture is extracted from the SAR imagery and classified as Built/Non-built. SMA based measures of Built and Vegetation fragmentation are combined with SAR texture based Built/Non-built maps through a decision tree classifier to generate a nine class urban context map capturing the transition from unsettled land at one end of the gradient to the compact urban core at the other end. Training and testing of the decision tree classifier was done using very high spatial resolution reference imagery from Google Earth. An overall classification agreement of 77% was determined for the nine-class urban context map, with user's accuracy (commission errors) being lower than producer's accuracy (omission errors). Nine urban contexts were classified and then compared with data from the 2000 Census of Ghana. Results suggest that the urban classes appropriately differentiate areas along the urban gradient.

The real world and lunar base activation scenarios

NASA Technical Reports Server (NTRS)

Schmitt, Harrison H.

1992-01-01

A lunar base or a network of lunar bases may have highly desirable support functions in a national or international program to explore and settle Mars. In addition, He-3 exported from the Moon could be the basis for providing much of the energy needs of humankind in the twenty-first century. Both technical and managerial issues must be addressed when considering the establishment of a lunar base that can serve the needs of human civilization in space. Many of the technical issues become evident in the consideration of hypothetical scenarios for the activation of a network of lunar bases. Specific and realistic assumptions must be made about the conduct of various types of activities in addition to the general assumptions given above. These activities include landings, crew consumables, power production, crew selection, risk management, habitation, science station placement, base planning, science, agriculture, resource evaluation, readaptation, plant activation and test, storage module landings, resource transport module landings, integrated operations, maintenance, Base 2 activation, and management. The development of scenarios for the activation of a lunar base or network of bases will require close attention to the 'real world' of space operations. That world is defined by the natural environment, available technology, realistic objectives, and common sense.

Land Use and Change

NASA Technical Reports Server (NTRS)

Irwin, Daniel E.

2004-01-01

The overall purpose of this training session is to familiarize Central American project cooperators with the remote sensing and image processing research that is being conducted by the NASA research team and to acquaint them with the data products being produced in the areas of Land Cover and Land Use Change and carbon modeling under the NASA SERVIR project. The training session, therefore, will be both informative and practical in nature. Specifically, the course will focus on the physics of remote sensing, various satellite and airborne sensors (Landsat, MODIS, IKONOS, Star-3i), processing techniques, and commercial off the shelf image processing software.

75 FR 32360 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-08

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and.... Abstract NOAA has established requirements for the licensing of private operators of remote-sensing space... Land Remote- Sensing Policy Act of 1992 and with the national security and international obligations of...

78 FR 44536 - Proposed Information Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-24

... Collection; Comment Request; Licensing of Private Remote-Sensing Space Systems AGENCY: National Oceanic and... for the licensing of private operators of remote-sensing space systems. The information in applications and subsequent reports is needed to ensure compliance with the Land Remote- Sensing Policy Act of...

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

USGS Publications Warehouse

Van Linn, Peter F.; Nussear, Kenneth E.; Esque, Todd C.; DeFalco, Lesley A.; Inman, Richard D.; Abella, Scott R.

2013-01-01

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Geothermal area detection using Landsat ETM+ thermal infrared data and its mechanistic analysis—A case study in Tengchong, China

NASA Astrophysics Data System (ADS)

Qin, Qiming; Zhang, Ning; Nan, Peng; Chai, Leilei

2011-08-01

Thermal infrared (TIR) remote sensing is an important technique in the exploration of geothermal resources. In this study, a geothermal survey is conducted in Tengchong area of Yunnan province in China using TIR data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. Based on radiometric calibration, atmospheric correction and emissivity calculation, a simple but efficient single channel algorithm with acceptable precision is applied to retrieve the land surface temperature (LST) of study area. The LST anomalous areas with temperature about 4-10 K higher than background area are discovered. Four geothermal areas are identified with the discussion of geothermal mechanism and the further analysis of regional geologic structure. The research reveals that the distribution of geothermal areas is consistent with the fault development in study area. Magmatism contributes abundant thermal source to study area and the faults provide thermal channels for heat transfer from interior earth to land surface and facilitate the present of geothermal anomalies. Finally, we conclude that TIR remote sensing is a cost-effective technique to detect LST anomalies. Combining TIR remote sensing with geological analysis and the understanding of geothermal mechanism is an accurate and efficient approach to geothermal area detection.

A new multi-angle remote sensing framework for scaling vegetation properties from tower-based spectro-radiometers to next generation "CubeSat"-satellites.

NASA Astrophysics Data System (ADS)

Hilker, T.; Hall, F. G.; Dyrud, L. P.; Slagowski, S.

2014-12-01

Frequent earth observations are essential for assessing the risks involved with global climate change, its feedbacks on carbon, energy and water cycling and consequences for live on earth. Often, satellite-remote sensing is the only practical way to provide such observations at comprehensive spatial scales, but relationships between land surface parameters and remotely sensed observations are mostly empirical and cannot easily be scaled across larger areas or over longer time intervals. For instance, optically based methods frequently depend on extraneous effects that are unrelated to the surface property of interest, including the sun-server geometry or background reflectance. As an alternative to traditional, mono-angle techniques, multi-angle remote sensing can help overcome some of these limitations by allowing vegetation properties to be derived from comprehensive reflectance models that describe changes in surface parameters based on physical principles and radiative transfer theory. Recent results have shown in theoretical and experimental research that multi-angle techniques can be used to infer and scale the photosynthetic rate of vegetation, its biochemical and structural composition robustly from remote sensing. Multi-angle remote sensing could therefore revolutionize estimates of the terrestrial carbon uptake as scaling of primary productivity may provide a quantum leap in understanding the spatial and temporal complexity of terrestrial earth science. Here, we introduce a framework of next generation tower-based instruments to a novel and unique constellation of nano-satellites (Figure 1) that will allow us to systematically scale vegetation parameters from stand to global levels. We provide technical insights, scientific rationale and present results. We conclude that future earth observation from multi-angle satellite constellations, supported by tower based remote sensing will open new opportunities for earth system science and earth system modeling.

Radio/FADS/IMU integrated navigation for Mars entry

NASA Astrophysics Data System (ADS)

Jiang, Xiuqiang; Li, Shuang; Huang, Xiangyu

2018-03-01

Supposing future orbiting and landing collaborative exploration mission as the potential project background, this paper addresses the issue of Mars entry integrated navigation using radio beacon, flush air data sensing system (FADS), and inertial measurement unit (IMU). The range and Doppler information sensed from an orbiting radio beacon, the dynamic pressure and heating data sensed from flush air data sensing system, and acceleration and attitude angular rate outputs from an inertial measurement unit are integrated in an unscented Kalman filter to perform state estimation and suppress the system and measurement noise. Computer simulations show that the proposed integrated navigation scheme can enhance the navigation accuracy, which enables precise entry guidance for the given Mars orbiting and landing collaborative exploration mission.

Research for applications of remote sensing to state and local governments (ARSIG)

NASA Technical Reports Server (NTRS)

Foster, K. E.; Johnson, J. D.

1973-01-01

Remote sensing and its application to problems confronted by local and state planners are reported. The added dimension of remote sensing as a data gathering tool has been explored identifying pertinent land use factors associated with urban growth such as soil associations, soil capability, vegetation distribution, and flood prone areas. Remote sensing within rural agricultural setting has also been utilized to determine irrigation runoff volumes, cropping patterns, and land use. A variety of data sources including U-2 70 mm multispectral black and white photography, RB-57 9-inch color IR, HyAC panoramic color IR and ERTS-1 imagery have been used over selected areas of Arizona including Tucson, Arizona (NASA Test Site #30) and the Sulphur Springs Valley.

Spatially distributed storm runoff modeling using remote sensing and geographic information systems

NASA Astrophysics Data System (ADS)

Melesse, Assefa Mekonnen

Advances in scientific knowledge and new techniques of remote sensing permit a better understanding of the physical land features governing hydrologic processes, and make possible efficient, large-scale hydrologic modeling. The need for land-cover and hydrologic response change detection at a larger scale and at times of the year when hydrologic studies are critical makes satellite imagery the most cost effective, efficient and reliable source of data. The use of a Geographic Information System (GIS) to store, manipulate and visualize these data, and ultimately to estimate runoff from watersheds, has gained increasing attention in recent years. In this work, remotely-sensed data and GIS tools were used to estimate the changes in land-cover, and to estimate runoff response, for three watersheds (Etonia, Econlockhatchee, and S-65A sub-basins) in Florida. Land-use information from Digital Orthophoto Quarter Quadrangles (DOQQ), Landsat Thematic Mapper (TM), and Enhanced Thematic Mapper Plus (ETM+) were analyzed for the years 1973, 1984, 1990, 1995, and 2000. Spatial distribution of land-cover was assessed over time. The corresponding infiltration excess runoff response of the study areas due to these changes was estimated using the United States Department of Agriculture, Natural Resources Conservation Service Curve Number (USDA-NRCS-CN) method. A Digital Elevation Model (DEM)-GIS technique was developed to predict stream response to runoff events based on the travel time from each grid cell to the watershed outlet. The method was tested on a representative watershed (Simms Creek) in the Etonia sub-basin. Simulated and observed runoff volume and hydrographs were compared for 17 storm events. Isolated storms, with volumes of not less than 12.75 mm (0.5 inch) were selected. This is the minimum amount of rainfall volume recommended for the NRCS-CN method. Results show that the model predicts the runoff response of the study area with an average efficiency of 57%. Comparison of the runoff prediction to Snyder's synthetic Unit hydrograph method and TOPMODEL shows the spatially distributed infiltration excess travel time model performs better than both the Snyder's method and TOPMODEL. The model is applicable to ungaged watersheds and useful for predicting runoff hydrographs resulting from changes in the land-cover.

Shore zone land use and land cover: Central Atlantic Regional Ecological Test Site

USGS Publications Warehouse

Dolan, R.; Hayden, B.P.; Vincent, C.L.

1974-01-01

Anderson's 1972 United States Geological Survey classification in modified form was applied to the barrier-island coastline within the CARETS region. High-altitude, color-infrared photography of December, 1972, and January, 1973, served as the primary data base in this study. The CARETS shore zone studied was divided into six distinct geographical regions; area percentages for each class in the modified Anderson classification are presented. Similarities and differences between regions are discussed within the framework of man's modification of these landscapes. The results of this study are presented as a series of 19 maps of land-use categories. Recommendations are made for a remote-sensing system for monitoring the CARETS shore zone within the context of the dynamics of the landscapes studied.

Integrated monitoring of ecological conditions in wetland-upland landscapes

USGS Publications Warehouse

Gallant, Alisa; Sadinski, Walt

2012-01-01

Landscapes of interwoven wetlands and uplands offer a rich set of ecosystem goods and services. Managing lands to maximize ecosystem services requires information that distinguishes change caused by local actions from broader-scale shifts in climate, land use, and other forms of global change. Satellite and airborne sensors collect valuable data for this purpose, especially when the data are analyzed along with data collected from ground-based sensors. The U.S. Geological Survey (USGS) is using remote sensing technology in this way as part of the Terrestrial Wetland Global Change Research Network to assess effects of climate change interacting with land-use change and other potential stressors along environmental gradients of wetland-upland landscapes in the United States and Canada.

Landsat—Earth observation satellites

USGS Publications Warehouse

,

2015-11-25

Since 1972, Landsat satellites have continuously acquired space-based images of the Earth’s land surface, providing data that serve as valuable resources for land use/land change research. The data are useful to a number of applications including forestry, agriculture, geology, regional planning, and education. Landsat is a joint effort of the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). NASA develops remote sensing instruments and the spacecraft, then launches and validates the performance of the instruments and satellites. The USGS then assumes ownership and operation of the satellites, in addition to managing all ground reception, data archiving, product generation, and data distribution. The result of this program is an unprecedented continuing record of natural and human-induced changes on the global landscape.

Urban land use: Remote sensing of ground-basin permeability

NASA Technical Reports Server (NTRS)

Tinney, L. R.; Jensen, J. R.; Estes, J. E.

1975-01-01

A remote sensing analysis of the amount and type of permeable and impermeable surfaces overlying an urban recharge basin is discussed. An effective methodology for accurately generating this data as input to a safe yield study is detailed and compared to more conventional alternative approaches. The amount of area inventoried, approximately 10 sq. miles, should provide a reliable base against which automatic pattern recognition algorithms, currently under investigation for this task, can be evaluated. If successful, such approaches can significantly reduce the time and effort involved in obtaining permeability data, an important aspect of urban hydrology dynamics.

Natural Resource Information System, remote sensing studies

NASA Technical Reports Server (NTRS)

Leachtenauer, J.; Hirsch, R.; Williams, V.; Tucker, R.

1972-01-01

Potential applications of remote sensing data were reviewed, and available imagery was interpreted to provide input to a demonstration data base. A literature review was conducted to determine the types and qualities of imagery required to satisfy identified data needs. Ektachrome imagery available over the demonstration areas was reviewed to establish the feasibility of interpreting cultural features, range condition, and timber type. Using the same imagery, a land use map was prepared for the demonstration area. The feasibility of identifying commercial timber areas using a density slicing technique was tested on multispectral imagery available for a portion of the demonstration area.

Application of remote sensing to state and regional problems. [mississippi

NASA Technical Reports Server (NTRS)

Miller, W. F.; Powers, J. S.; Clark, J. R.; Solomon, J. L.; Williams, S. G. (Principal Investigator)

1981-01-01

The methods and procedures used, accomplishments, current status, and future plans are discussed for each of the following applications of LANDSAT in Mississippi: (1) land use planning in Lowndes County; (2) strip mine inventory and reclamation; (3) white-tailed deer habitat evaluation; (4) remote sensing data analysis support systems; (5) discrimination of unique forest habitats in potential lignite areas; (6) changes in gravel operations; and (7) determining freshwater wetlands for inventory and monitoring. The documentation of all existing software and the integration of the image analysis and data base software into a single package are now considered very high priority items.

Can Hyperspectral Remote Sensing Detect Species Specific Biochemicals ?

NASA Astrophysics Data System (ADS)

Vanderbilt, V. C.; Daughtry, C. S.

2011-12-01

Discrimination of a few plants scattered among many plants is a goal common to detection of agricultural weeds, invasive plant species and illegal Cannabis clandestinely grown outdoors, the subject of this research. Remote sensing technology provides an automated, computer based, land cover classification capability that holds promise for improving upon the existing approaches to Cannabis detection. In this research, we investigated whether hyperspectral reflectance of recently harvested, fully turgid Cannabis leaves and buds depends upon the concentration of the psychoactive ingredient Tetrahydrocannabinol (THC) that, if present at sufficient concentration, presumably would allow species-specific identification of Cannabis.

Utility of a scanning densitometer in analyzing remotely sensed imagery

NASA Technical Reports Server (NTRS)

Dooley, J. T.

1976-01-01

The utility of a scanning densitometer for analyzing imagery in the NASA Lewis Research Center's regional remote sensing program was evaluated. Uses studied include: (1) quick-look screening of imagery by means of density slicing, magnification, color coding, and edge enhancement; (2) preliminary category classification of both low- and high-resolution data bases; and (3) quantitative measurement of the extent of features within selected areas. The densitometer was capable of providing fast, convenient, and relatively inexpensive preliminary analysis of aerial and satellite photography and scanner imagery involving land cover, water quality, strip mining, and energy conservation.

Remote sensing of solar radiation absorbed and reflected by vegetated land surfaces

NASA Technical Reports Server (NTRS)

Myneni, Ranga B.; Asrar, Ghassem; Tanre, Didier; Choudhury, Bhaskar J.

1992-01-01

1D and 3D radiative-transfer models have been used to investigate the problem of remotely sensed determination of vegetated land surface-absorbed and reflected solar radiation. Calculations were conducted for various illumination conditions to determine surface albedo, soil- and canopy-absorbed photosynthetically active and nonactive radiation, and normalized difference vegetation index. Simple predictive models are developed on the basis of the relationships among these parameters.

Remote Sensing Monitoring of Changes in Soil Salinity: A Case Study in Inner Mongolia, China.

PubMed

Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain

2008-11-07

This study used archived remote sensing images to depict the history of changes in soil salinity in the Hetao Irrigation District in Inner Mongolia, China, with the purpose of linking these changes with land and water management practices and to draw lessons for salinity control. Most data came from LANDSAT satellite images taken in 1973, 1977, 1988, 1991, 1996, 2001, and 2006. In these years salt-affected areas were detected using a normal supervised classification method. Corresponding cropped areas were detected from NVDI (Normalized Difference Vegetation Index) values using an unsupervised method. Field samples and agricultural statistics were used to estimate the accuracy of the classification. Historical data concerning irrigation/drainage and the groundwater table were used to analyze the relation between changes in soil salinity and land and water management practices. Results showed that: (1) the overall accuracy of remote sensing in detecting soil salinity was 90.2%, and in detecting cropped area, 98%; (2) the installation/innovation of the drainage system did help to control salinity; and (3) a low ratio of cropped land helped control salinity in the Hetao Irrigation District. These findings suggest that remote sensing is a useful tool to detect soil salinity and has potential in evaluating and improving land and water management practices.

An investigation of current and future satellite and in-situ data for the remote sensing of the land surface energy balance

NASA Technical Reports Server (NTRS)

Diak, George R.

1994-01-01

This final report from the University of Wisconsin-Madison Cooperative Institute for Meteorological Satellite Studies (CIMSS) summarizes a research program designed to improve our knowledge of the water and energy balance of the land surface through the application of remote sensing and in-situ data sources. The remote sensing data source investigations to be detailed involve surface radiometric ('skin') temperatures and also high-spectral-resolution infrared radiance data from atmospheric sounding instruments projected to be available at the end of the decade, which have shown promising results for evaluating the land-surface water and energy budget. The in-situ data types to be discussed are measurements of the temporal changes of the height of the planetary boundary layer and measurements of air temperature within the planetary boundary layer. Physical models of the land surface, planetary boundary layer and free atmosphere have been used as important tools to interpret the in-situ and remote sensing signals of the surface energy balance. A prototype 'optimal' system for combining multiple data sources into a three-dimensional estimate of the surface energy balance was developed and first results from this system will be detailed. Potential new sources of data for this system and suggested continuation research will also be discussed.

Use of multi-temporal SPOT-5 satellite images for land degradation assessment in Cameron Highlands, Malaysia using Geospatial techniques

NASA Astrophysics Data System (ADS)

Nampak, Haleh; Pradhan, Biswajeet

2016-07-01

Soil erosion is the common land degradation problem worldwide because of its economic and environmental impacts. Therefore, land-use change detection has become one of the major concern to geomorphologists, environmentalists, and land use planners due to its impact on natural ecosystems. The objective of this paper is to evaluate the relationship between land use/cover changes and land degradation in the Cameron highlands (Malaysia) through multi-temporal remotely sensed satellite images and ancillary data. Land clearing in the study area has resulted increased soil erosion due to rainfall events. Also unsustainable development and agriculture, mismanagement and lacking policies contribute to increasing soil erosion rates. The LULC distribution of the study area was mapped for 2005, 2010, and 2015 through SPOT-5 satellite imagery data which were classified based on object-based classification. A soil erosion model was also used within a GIS in order to study the susceptibility of the areas affected by changes to overland flow and rain splash erosion. The model consists of four parameters, namely soil erodibility, slope, vegetation cover and overland flow. The results of this research will be used in the selection of the areas that require mitigation processes which will reduce their degrading potential. Key words: Land degradation, Geospatial, LULC change, Soil erosion modelling, Cameron highlands.

Application of SAR Remote Sensing in Land Surface Processes Over Tropical region

NASA Technical Reports Server (NTRS)

Saatchi, Sasan S.

1996-01-01

This paper outlines the potential applications of polarimetric SAR systems over tropical regions such as mapping land use and deforestation, forest regeneration, wetland and inundation studies, and mapping land cover types for biodiversity and habitat conservation studies.

Resource analysis and land use planning with space and high altitude photography

NASA Technical Reports Server (NTRS)

Schrumpf, B. J.

1972-01-01

Photographic scales providing resource data for decision making processes of land use and a legend system for barren lands, water resources, natural vegetation, agricultural, urban, and industrial lands in hierarchical framework are applied to various remote sensing techniques. Two natural vegetation resource and land use maps for a major portion of Maricopa County, Arizona are also produced.

Simulation of the spatial stresses due to territorial land development on Yellow River Delta Nature Reserve using a GIS-based assessment model.

PubMed

Zhang, Baolei; Zhang, Qiaoyun; Feng, Qingyu; Cui, Bohao; Zhang, Shumin

2017-07-01

This study aimed at assessing the stresses from land development in or around Yellow River Delta Nature Reserve (YRDNR) and identifying the impacted areas. Major land development types (reservoirs, pond, aquafarm, salt pan, road, residential land, industry land, farming land, and fishing land) in or around the YRDNR from 1995 to 2014 were identified using spatial data sets derived from remote sensing imageries. The spatial stresses were simulated by considering disturbance due to land development activities and accessibility of disturbance using a geographic information system based model. The stresses were then used to identify the impacted area by land development (IALD). The results indicated that main increasing land development types in the study area from 1995 to 2014 were salt pan and construction land. The 98.2% of expanded land development area and 93.7% of increased pump number showed a good control of reserve function zone on land development spread. The spatial stress values and percentages of IALD increased from 1995 to 2014, and IALD percentage exceeded 50% for both parts of YRDNR in 2014. The results of this study also provided the information that detailed planning of the YRDNR (2014-2020) could decrease the spatial stress and IALD percentage of the whole YRDNR on the condition that the area of land development activities increased by 24.4 km 2 from 2014 to 2020. Effective measures should be taken to protect such areas from being further disturbed in order to achieve the goal of a more effective conservation of the YRDNR, and attention should be paid to the disordered land development activities in or around the natural reserves.

Measuring land-use and land-cover change using the U.S. department of agriculture's cropland data layer: Cautions and recommendations

NASA Astrophysics Data System (ADS)

Lark, Tyler J.; Mueller, Richard M.; Johnson, David M.; Gibbs, Holly K.

2017-10-01

Monitoring agricultural land is important for understanding and managing food production, environmental conservation efforts, and climate change. The United States Department of Agriculture's Cropland Data Layer (CDL), an annual satellite imagery-derived land cover map, has been increasingly used for this application since complete coverage of the conterminous United States became available in 2008. However, the CDL is designed and produced with the intent of mapping annual land cover rather than tracking changes over time, and as a result certain precautions are needed in multi-year change analyses to minimize error and misapplication. We highlight scenarios that require special considerations, suggest solutions to key challenges, and propose a set of recommended good practices and general guidelines for CDL-based land change estimation. We also characterize a problematic issue of crop area underestimation bias within the CDL that needs to be accounted for and corrected when calculating changes to crop and cropland areas. When used appropriately and in conjunction with related information, the CDL is a valuable and effective tool for detecting diverse trends in agriculture. By explicitly discussing the methods and techniques for post-classification measurement of land-cover and land-use change using the CDL, we aim to further stimulate the discourse and continued development of suitable methodologies. Recommendations generated here are intended specifically for the CDL but may be broadly applicable to additional remotely-sensed land cover datasets including the National Land Cover Database (NLCD), Moderate Resolution Imaging Spectroradiometer (MODIS)-based land cover products, and other regional, national, and global land cover classification maps.

REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEMS FOR DECISION ANALYSIS IN PUBLIC RESOURCE ADMINISTRATION: A CASE STUDY OF 25 YEARS OF LANDSCAPE CHANGE IN A SOUTHWESTERN WATERSHED

EPA Science Inventory

Alternative futures analysis is a scenario-based approach to regional land planning that attempts to synthesize existing scientific information in a format useful to community decision-makers. Typically, this approach attempts to investigate the impacts of several alternative set...

Development of a Methodology for Predicting Forest Area for Large-Area Resource Monitoring

Treesearch

William H. Cooke

2001-01-01

The U.S. Department of Agriculture, Forest Service, Southcm Research Station, appointed a remote-sensing team to develop an image-processing methodology for mapping forest lands over large geographic areds. The team has presented a repeatable methodology, which is based on regression modeling of Advanced Very High Resolution Radiometer (AVHRR) and Landsat Thematic...

Developing New Coastal Forest Restoration Products Based on Landsat, ASTER, and MODIS Data

DTIC Science & Technology

2010-06-01

hydrology, wildfire, and conversion to non-forest land use. In some cases, such forest disturbance has led to forest loss or loss of regeneration capacity...classification of bald cypress and tupelo gum trees in Thematic Mapper imagery,” Photogrammetric Engineering and Remote Sensing, vol. 63, pp. 717–725, 1997. [14

Utility of an image-based canopy reflectance modeling tool for remote estimation and LAI and leaf chlorophyll content at regional scales

USDA-ARS?s Scientific Manuscript database

Radiance data recorded by remote sensors function as a unique source for monitoring the terrestrial biosphere and vegetation dynamics at a range of spatial and temporal scales. A key challenge is to relate the remote sensing signal to critical variables describing land surface vegetation canopies su...

Estimating and validating surface energy fluxes at field scale over a heterogeneous land surfaces based on two-source energy balance model (TSEB)

USDA-ARS?s Scientific Manuscript database

Accurate estimation of surface energy fluxes at field scale over large areas has the potential to improve agricultural water management in arid and semiarid watersheds. Remote sensing may be the only viable approach for mapping fluxes over heterogeneous landscapes. The Two-Source Energy Balance mode...

Spatially Complete Global Spectral Surface Albedos: Value-Added Datasets Derived from Terra MODIS Land Products

NASA Technical Reports Server (NTRS)

Moody, Eric G.; King, Michael D.; Platnick, Steven; Schaaf, Crystal B.; Gao, Feng

2004-01-01

Land surface albedo is an important parameter in describing the radiative properties of the earth s surface as it represents the amount of incoming solar radiation that is reflected from the surface. The amount and type of vegetation of the surface dramatically alters the amount of radiation that is reflected; for example, croplands that contain leafy vegetation will reflect radiation very differently than blacktop associated with urban areas. In addition, since vegetation goes through a growth, or phenological, cycle, the amount of radiation that is reflected changes over the course of a year. As a result, albedo is both temporally and spatially dependant upon global location as there is a distribution of vegetated surface types and growing conditions. Land surface albedo is critical for a wide variety of earth system research projects including but not restricted to remote sensing of atmospheric aerosol and cloud properties from space, ground-based analysis of aerosol optical properties from surface-based sun/sky radiometers, biophysically-based land surface modeling of the exchange of energy, water, momentum, and carbon for various land use categories, and surface energy balance studies. These projects require proper representation of the surface albedo s spatial, spectral, and temporal variations, however, these representations are often lacking in datasets prior to the latest generation of land surface albedo products.

Land-use change and its ecological responses: a pilot study of typical agro-pastoral region in the Heihe River, northwest China

NASA Astrophysics Data System (ADS)

Du, Ziqiang; Shen, Yudan; Wang, Jian; Cheng, Wenshi

2009-10-01

Although rapid land-use change has taken place in many arid and semi-arid regions of northwestern China, relatively less attention has been paid to studying the characteristics of land use change, as well as the ecological responses of land use change in these regions, especially in fragile agro-pastoral regions. This paper analyzes the land use change and its ecological responses during 1985-2005 based on the landscape metrics change and transition matrix of land use types by the combined use of satellite remote sensing and geographical information systems in Shandan County, a typical agro-pastoral region in the middle and upper reaches of Heihe River, northwest China. The results indicate significant changes in land use have occurred and the landscape has become more continuous, clumped and more homogeneous within the examined area. Land use change was mainly characterized by remarkable expansion of barred land and water area, slight increase of cropland and urbanized land, and evident shrinkage of grassland and woodland. The study also demonstrates that the land cover suffered severe degeneration and the ecological environment tended to deteriorate over the study period, mainly as follows: grassland degradation, land desertification and ecosystem services decline.