Eilander, Dirk; Annor, Frank; Iannini, Lorenzo; van de Giesen, Nick
A new 'growing' maximum likelihood classification algorithm for small reservoir delineation has been developed and is tested with Radarsat-2 data for reservoirs in the semi-arid Upper East Region, Ghana. The delineation algorithm is able to find the land-water boundary from SAR imagery for different weather and environmental conditions. As such, the algorithm allows for remote sensed operational monitoring of small reservoirs. Multipurpose small reservoirs (1-100 ha) are important for many livelihoods in rural semi-arid West Africa. In order to manage and plan these reservoirs and to assess their hydrological impact at a river basin scale, it is important to monitor their water storage fluctuation. Several studies on remotely sensed reservoir mapping have recently been published, but no single method yields good results for all weather and environmental conditions. Detection of small reservoirs from optical satellite imagery using supervised maximum likelihood classification is a well proved method. The application of this method for the monitoring of small reservoirs is however limited because of its dependence on cloud-free day-acquisitions. Delineation from SAR images is promising, but because of difficulties with wind induced Bragg-scattering and low contrast between the water surface and the dried-out surroundings at the end of the dry season, only quasi manual methods have been applied successfully. A smart combination of optical satellite based detection combined with a delineation method for SAR imagery is proposed. From the optical satellite based small reservoir detection the reservoir window is determined in which the 'growing' maximum likelihood classification on SAR images is performed. A water-class seed and land-class seed are implemented and grown dependent on the likelihood of a pixel to belong to one class. The likelihood is calculated based on the probability distributions of the growing land and water populations. Combinations of single
The paper outlines recent developments in using optical remote sensing (ORS) instruments for air quality monitoring both for gaseous pollutants and airborne particulate matter (PM). The U.S. Environmental Protection Agency (EPA) has been using open-path Fourier transform infrared...
Remote sensing technology has the potential to enhance the engagement of communities and managers in the implementation and performance of best management practices. This presentation will use examples from U.S. numeric criteria development and state water quality monitoring prog...
Brown, R. L. (Principal Investigator)
The author has identified the following significant results. A limited study was conducted to determine the applicability of remote sensing for evaluating water quality conditions in the San Francisco Bay and delta. Considerable supporting data were available for the study area from other than overflight sources, but short-term temporal and spatial variability precluded their use. The study results were not sufficient to shed much light on the subject, but it did appear that, with the present state of the art in image analysis and the large amount of ground truth needed, remote sensing has only limited application in monitoring water quality.
The remote sensing technology has many practical applications in different fields of science and industry. There is also a need to examine its usefulness for the purpose of land border surveillance. This research started with analysis of potential direct use of Earth Observation technology for monitoring migrations of people and preventing smuggling. The research, however, proved that there are still many fields within which the EO technology needs to be improved. From that point the analysis focused on improving Border Permeability Index which utilizes EO techniques as a source of information. The result of BPI analysis with use of high resolution data provides new kind of information which can support and make more effective work of authorities from security domain.
I. Remote Sensing Basics
A. The electromagnetic spectrum demonstrates what we can see both in the visible and beyond the visible part of the spectrum through the use of various types of sensors.
B. Resolution refers to what a remote sensor can see and how often.
Genco, S.; Bortoli, D.; Ravegnani, F.
The use of CFCs, which are the main responsible for the ozone depletion in the upper atmosphere and the formation of the so-called "ozone hole" over Antarctic Region, was phase out by Montreal Protocol (1989). CFCs' concentration is recently reported to decrease in the free atmosphere, but severe episodes of ozone depletion in both Arctic and Antarctic regions are still occurring. Nevertheless the complete recovery of the Ozone layer is expected by about 2050. Recent simulation of perturbations in stratospheric chemistry highlight that circulation, temperature and composition are strictly correlated and they influence the global climate changes. Chemical composition plays an important role in the thermodynamic of the atmosphere, as every gaseous species can absorb and emit in different wavelengths, so their different concentration is responsible for the heating or cooling of the atmosphere. Therefore long-term observations are required to monitor the evolution of the stratospheric ozone layer. Measurements from satellite remote sensing instruments, which provide wide coverage, are supplementary to selective ground-based observations which are usually better calibrated, more stable in time and cover a wider time span. The combination of the data derived from different space-borne instruments calibrated with ground-based sensors is needed to produce homogeneous and consistent long-term data records. These last are required for robust investigations and especially for trend analysis. Here, we perform a review of the major remote-sensing techniques and of the principal datasets available to study the evolution of ozone layer in the past decades and predict future behavio
Epiphanio, J. C. N.; Vitorelli, I.
The use of remote sensing for irrigated areas, especially in the region of Guaira, Brazil (state of Sao Paulo), is examined. Major principles of utilizing LANDSAT data for the detection and mapping of irrigated lands are discussed. In addition, initial results obtained by computer processing of digital data, use of MSS (Multispectral Scanner System)/LANDSAT products, and the availability of new remote sensing products are highlighted. Future activities include the launching of the TM (Thematic Mapper)/LANDSAT 4 with 30 meters of resolution and SPOT (Systeme Probatorie d'Observation de la Terre) with 10 to 20 meters of resolution, to be operational in 1984 and 1986 respectively.
Hydrilla is an important submerged aquatic vegetation because it has a large capacity to absorb pollutants and it is an indicator of the eutrophic status of a waterbody. Monitoring and restoration of submerged aquatic vegetation is key for the preservation and restoration of the Chesapeake Bay. Remote sensing techniques have been used for assessing wetlands and non-invasive aquatic species, but there is limited studies of hydrilla monitoring combined with space-borne, airborne and in-situ remote sensing measurements for detecting and mapping hydrilla infestation. The first objective of this research was to establish a database of hydrilla spectral signatures from an experimental tank and from a field setting using a handheld spectrometer. The spectral signatures collected will be used to identify the optimal spectral and spatial characteristics that are required to identify and classify the distribution of hydrilla canopies in water bodies. The second objective is to process and analyze two hyperspectral images from a space-borne (Hyperion) and airborne (AISA) sensors with ENVI for detecting and mapping the infestation of hydrilla vertillicata in a coastal estuary in Chesapeake Bay. The third objective was to validate the satellite and airborne hyperspectral images with the spectral signatures collected with the in-situ field measurements. In addition, the Hyperion and AISA imaging results were compared with ground surveys and aerial photos collected by the Maryland Department of Natural Resources and the Virginia Institute of Marine Sciences for verifying the extent and the location of the hydrilla canopies. The hyperspectral analysis of both sensors provided for a dual results, one is the identification and classification of hydrilla from hyperspectral imaging sensors and secondly the identification of algae blooms in very productive waters. A hydrilla spectral signature database was established and housed in GMU's EastFIRE Lab of Environmental Science and
Jiang, Xuhui; Han, Lei; Dong, Liang; Cui, Lulu; Bie, Jun; Fan, Xuewei
In the north China Sea district, sea ice disaster is very serious every winter, which brings a lot of adverse effects to shipping transportation, offshore oil exploitation, and coastal engineering. In recent years, along with the changing of global climate, the sea ice situation becomes too critical. The monitoring of sea ice is playing a very important role in keeping human life and properties in safety, and undertaking of marine scientific research. The methods to monitor sea ice mainly include: first, shore observation; second, icebreaker monitoring; third, satellite remote sensing; and then aerial remote sensing monitoring. The marine station staffs use relevant equipments to monitor the sea ice in the shore observation. The icebreaker monitoring means: the workers complete the test of the properties of sea ice, such as density, salinity and mechanical properties. MODIS data and NOAA data are processed to get sea ice charts in the satellite remote sensing means. Besides, artificial visual monitoring method and some airborne remote sensors are adopted in the aerial remote sensing to monitor sea ice. Aerial remote sensing is an important means in sea ice monitoring because of its strong maneuverability, wide watching scale, and high resolution. In this paper, several methods in the sea ice monitoring using aerial remote sensing technology are discussed.
The application of remotely sensed data in conjunction with in situ data greatly enhances the ability of the USDA Forest Service to meet the demands of field staff, customers, and others for groundwater information. Generally, the use of remotely sensed data to inventory and monitor groundwater reso...
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...
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...
Wang, Li-Wen; Wei, Ya-Xing
Global climate warming has become the focus question of international global climate change research, and is an important factor influencing world economy, political situation, and ecological environment. Produced carbon emission gases such as CO2, CH4, N2O, etc. caused by human activity are the main reason for global warming. In order to forecast future climate change and construct accurate carbon cycle model, monitoring accuracy of gas concentration from carbon emission must be improved. In the present paper, the newest progress in the international research results about monitoring gas concentration from carbon emissions by remote sensing was considered, monitoring method for carbon emissions was introduced, and remotely sensed monitoring technology about gas concentration from carbon emissions (including thermal infrared, sun spectrum, active remote sensing monitoring technology) was stated. In detail, several present and future satellite sensors were introduced (including TOVS, AIRS, IASI, SCIAMACHY, GOSAT, OCO, A-SCOPE and ASCENDS), and monitoring results achieved by these sensors were analyzed. PMID:22870656
The report gives results of an evaluation, involving field tests, of passive infrared methods for use in remotely monitoring the efficiency of industrial flares. The tests utilized a general infrared measurement device, the EPA ROSE (Remote Optical Sensing of Emissions), a Fourie...
Summers, R. A.; Smith, W. L.; Short, N. M.
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.
Heller, A. N.; Bryson, J. C.; Vasuki, N. C.
The applications of remote sensing in atmospheric monitoring programs are described. The organization, operations, and functions of an air quality monitoring network at New Castle County, Delaware is discussed. The data obtained by the air quality monitoring network ground stations and the equipment used to obtain atmospheric data are explained. It is concluded that correlation of the information obtained by the network will make it possible to anticipate air pollution problems in the Chesapeake Bay area before a crisis develops.
Mettas, Christodoulos; Themistocleous, Kyriacos; Neocleous, Kyriacos; Christofe, Andreas; Pilakoutas, Kypros; Hadjimitsis, Diofantos
One of the main issues in the maintenance plans of road agencies or governmental organizations is the early detection of damaged asphalt pavements. The development of a smart and non-destructive systematic technique for monitoring damaged asphalt pavements is considered a main priority to fill this gap. During the 1970's, remote sensing was used to map road surface distress, while during the last decade, remote sensing became more advanced, thereby assisting in the evolution of the identification and mapping of roads. Various techniques were used in order to explore condition, age, weaknesses and imperfections of asphalted pavements. These methods were fairly successful in the classification of asphalted surfaces and in the detection of some of their characteristics. This paper explores the state of the art of using remote sensing techniques for monitoring damaged pavements and some typical spectral profiles of various asphalt pavements in Cyprus area acquired using the SVC1024 field spectroradiometer.
Wen, Qi; Xu, Feng; Chen, Shirong
Yushu Earthquake of magnitude 7.1 Richter in 2010 has brought a huge loss of personal lives and properties to China. National Disaster Reduction Center of China implemented the disaster assessment by using remote sensing images and field investigation. Preliminary judgment of disaster scope and damage extent was acquired by change detection. And the building region of hard-hit area Jiegu town was partitioned into 3-level grids in airborne remote sensing images by street, type of use, structure, and about 685 girds were numbered. Hazard assessment expert group were sent to implement field investigation according to each grid. The housing damage scope and extent of loss were defined again integrated field investigation data and local government reported information. Though remote sensing technology has played an important role in huge disaster monitoring and assessment, the automatic capability of disaster information extraction flow, three-dimensional disaster monitoring mode and bidirectional feedback mechanism of products and services should still be further improved.
Bolton, W.; Lapp, M.; Vitko, J. Jr.; Phipps, G.
This report documents the results of a Laboratory Directed Research and Development (LDRD) program to explore how best to utilize Sandia`s defense-related sensing expertise to meet the Department of Energy`s (DOE) ever-growing needs for environmental monitoring. In particular, we focused on two pressing DOE environmental needs: (1) reducing the uncertainties in global warming predictions, and (2) characterizing atmospheric effluents from a variety of sources. During the course of the study we formulated a concept for using unmanned aerospace vehicles (UAVs) for making key 0798 climate measurements; designed a highly accurate, compact, cloud radiometer to be flown on those UAVs; and established the feasibility of differential absorption Lidar (DIAL) to measure atmospheric effluents from waste sites, manufacturing processes, and potential treaty violations. These concepts have had major impact since first being formulated in this ,study. The DOE has adopted, and DoD`s Strategic Environmental Research Program has funded, much of the UAV work. And the ultraviolet DIAL techniques have already fed into a major DOE non- proliferation program.
Xie, Caixiang; Song, Jingyuan; Suo, Fengmei; Li, Xiwen; Li, Ying; Yu, Hua; Xu, Xiaolan; Luo, Kun; Li, Qiushi; Xin, Tianyi; Guan, Meng; Xu, Xiuhai; Miki, Eiji; Takeda, Osami; Chen, Shilin
Remote sensing has been extensively applied in agriculture for its objectiveness and promptness. However, few applications are available for monitoring natural medicinal plants. In the paper, a multilevel monitoring system, which includes satellite and aerial remote sensing, as well as ground investigation, was initially proposed to monitor natural Rheum tanguticum resource in Baihe Pasture, Zoige County, Sichuan Province. The amount of R. tanguticum from images is M = S*ρ and S is vegetation coverage obtained by satellite imaging, whereas ρ is R. tanguticum density obtained by low-altitude imaging. Only the R. tanguticum which coverages exceeded 1 m2 could be recognized from the remote sensing image because of the 0.1 m resolution of the remote sensing image (called effective resource at that moment), and the results of ground investigation represented the amounts of R. tanguticum resource in all sizes (called the future resource). The data in paper showed that the present available amount of R. tanguticum accounted for 4% to 5% of the total quantity. The quantity information and the population structure of R. tanguticum in the Baihe Pasture were initially confirmed by this system. It is feasible to monitor the quantitative distribution for natural medicinal plants with scattered distribution. PMID:25101134
“How can remote sensing address information needs and gaps in water quality and quantity management?” was a workshop convened during the biennial National Water Quality Monitoring Conference 2014, held in Cincinnati, OH. The focus of this workshop was to provide an o...
The extent of past and anticipated plantings of transgenic corn in the United States requires a new approach to monitor this important crop for the development of pest resistance. Remote sensing by aerial and/or satellite images may provide a method of identifying transgenic pest...
Current plantings of 25+ million acres of transgenic corn in the United States require a new approach to monitor this important crop for the development of pest resistance. Remote sensing by aerial or satellite images may provide a method of identifying transgenic pesticidal cro...
Rhee, Jinyoung; Im, Jungho; Park, Seonyoung
Drought originates from the deficit of precipitation and impacts environment including agriculture and hydrological resources as it persists. The assessment and monitoring of drought has traditionally been performed using a variety of drought indices based on meteorological data, and recently the use of remote sensing data is gaining much attention due to its vast spatial coverage and cost-effectiveness. Drought information has been successfully derived from remotely sensed data related to some biophysical and meteorological variables and drought monitoring is advancing with the development of remote sensing-based indices such as the Vegetation Condition Index (VCI), Vegetation Health Index (VHI), and Normalized Difference Water Index (NDWI) to name a few. The Scaled Drought Condition Index (SDCI) has also been proposed to be used for humid regions proving the performance of multi-sensor data for agricultural drought monitoring. In this study, remote sensing-based hydro-meteorological variables related to drought including precipitation, temperature, evapotranspiration, and soil moisture were examined and the SDCI was improved by providing multiple blends of the multi-sensor indices for different types of drought. Multiple indices were examined together since the coupling and feedback between variables are intertwined and it is not appropriate to investigate only limited variables to monitor each type of drought. The purpose of this study is to verify the significance of each variable to monitor each type of drought and to examine the combination of multi-sensor indices for more accurate and timely drought monitoring. The weights for the blends of multiple indicators were obtained from the importance of variables calculated by non-linear optimization using a Machine Learning technique called Random Forest. The case study was performed in the Republic of Korea, which has four distinct seasons over the course of the year and contains complex topography with a variety
Jones, Cathleen E.
Radar remote sensing offers great potential for high resolution monitoring of ground surface changes over large areas at one time to detect movement on and near levees and for location of seepage through levees. Our NASA-funded projects to monitor levees in the Sacramento Delta and the Mississippi River have developed and demonstrated methods to use radar remote sensing to measure quantities relevant to levee health and of great value to emergency response. The DHS-funded project will enable us is to define how to optimally monitor levees in this new way and set the stage for transition to using satellite SAR (synthetic aperture radar) imaging for better temporal and spatial coverage at lower cost to the end users.
Drought assessment is a complex endeavor, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture (SM), ground and surface water anomalies reflect deficiencies in moist...
Adsavakulchai, Suwannee; Panichayapichet, Paweena
There has been a rapid growth of shrimp farm around Kung Krabaen Bay in the past decade. This has caused enormous rise in generation of domestic and industrial wastes. Most of these wastes are disposed in the Kung Krabaen Bay. There is a serious need to retain this glory by better water quality management of this river. Conventional methods of monitoring of water quality have limitations in collecting information about water quality parameters for a large region in detailed manner due to high cost and time. Satellite based technologies have offered an alternate approach for many environmental monitoring needs. In this study, the high-resolution satellite data (LANDSAT TM) was utilized to develop mathematical models for monitoring of chlorophyll-a. Comparison between empirical relationship of spectral reflectance with chl-a and band ratio between the near infrared (NIR) and red was suggested to detect chlorophyll in water. This concept has been successfully employed for marine zones and big lakes but not for narrow rivers due to constraints of spatial resolution of satellite data. This information will be very useful in locating point and non-point sources of pollution and will help in designing and implementing controlling structures.
Various types of remote sensing are now available or will be in the future for snowpack monitoring. Aircraft reconnaissance is now used in a conventional manner by various water resources agencies to obtain information on snowlines, depth, and melting of the snowpack for forecasting purposes. The use of earth resources satellites for mapping snowcovered area, snowlines, and changes in snowcover during the spring has increased during the last five years. Gamma ray aircraft flights, although confined to an extremely low altitude, provide a means for obtaining valuable information on snow water equivalent. The most recently developed remote sensing technology for snow, namely, microwave monitoring, has provided initial results that may eventually allow us to infer snow water equivalent or depth, snow wetness, and the hydrologic condition of the underlying soil.
Faisal, K.; AlAhmad, M.; Shaker, A.
The disposal of the solid wastes in landfill sites should be properly monitored by analyzing samples from soil, water, and landfill gases within the landfill site. Nevertheless, ground monitoring systems require intensive efforts and cost. Furthermore, ground monitoring may be difficult to be achieved in large geographic extent. Remote sensing technology has been introduced for waste disposal management and monitoring effects of the landfill sites on the environment. In this paper, two case studies are presented in the Trail Road landfill, Ottawa, Canada and the Al-Jleeb landfill, Al-Farwanyah, Kuwait to evaluate the use of multi-temporal remote sensing images to monitor the landfill sites. The work objectives are: 1) to study the usability of multi-temporal Landsat images for landfill site monitoring by studying the land surface temperature (LST) in the Trail Road landfill, 2) to investigate the relationship between the LST and the amount of the landfill gas emitted in the Trail Road landfill, and 3) to use the multi-temporal LST images to detect the suspicious dumping areas within the Al-Jleeb landfill site. Free archive of multi-temporal Landsat images are obtained from the USGS EarthExplorer. The Landsat images are then atmospherically corrected and the LST images are derived from the thermal band of the corrected Landsat images. In the Trail Road landfill, the results reveal that the LST of the landfill site is always higher than the air temperature by 10°C in average as well as the surroundings. A correlation is also observed between the recorded emitted methane (CH4) from the ground monitoring stations and the LST derived from the Landsat images. Based on the findings in the Al-Jleeb landfill, five locations are identified as suspicious dumping areas by overlaying the highest LST contours generated from the multi-temporal LST images. The study demonstrates that the use of multi-temporal remote sensing images can provide supplementary information for
Klemas, V. V.
Tracking and Monitoring Oil Slicks Using Remote Sensing Victor Klemas, Ph.D. , College of Earth, Ocean and Environment, University of Delaware, Newark, DE 19716 Abstract Oil spills can harm marine life in the ocean, estuaries and wetlands. To limit the damage by a spill and facilitate cleanup efforts, emergency managers need information on spill location, size and extent, direction and speed of oil movement, wind, current, and wave information for predicting oil drift and dispersion. The main operational data requirements are fast turn-around time and frequent imaging to monitor the dynamics of the spill. Radar and multispectral remote sensors on satellites and aircraft meet most of these requirements by tracking the spilled oil at various resolutions, over wide areas and at frequent intervals. They also provide key inputs to drift prediction models and facilitate targeting of skimming and booming efforts. Satellite data are frequently supplemented by information provided by aircraft, ships and remotely controlled underwater robots. The Sea Princess tanker grounding off the coast of Wales and the explosion on the Deepwater Horizon rig in the Gulf of Mexico provide two representative, yet different, scenarios for evaluating the effectiveness of remote sensors during oil spill emergencies. Session NH17: Remote Sensing of Natural Hazards Session Chair: Ramesh P. Singh Sponsor: Natural Hazards (NH)
Rothery, D. A.; Francis, P. W.; Wood, C. A.
The use of short wavelength IR Landsat TM data for volcano monitoring is examined. By determining the pixel-integrated from the TM data, it is possible to estimate the temperature and size of hot areas which occupy less than one complete pixel. Examples of volcano monitoring with remote sensing data are discussed. It is suggested that the entire volcanic temperature range (100-1200 C) could be accomplished by decreasing the band 6 gain by just one order of magnitude so that it was sensitive to radiance from 1 to 100 mW/sq cm/sr/micron.
Li, Zhanqing; Cihlar, J.; Chen, Jing
A suite of remote sensing techniques intended for northern terrestrial environment monitoring are described. Algorithms designed to derive land cover type, leaf area index, canopy absorbed photosynthetically active radiation, net primary productivity, active fires, and annual total burned area from remote sensing are also outlined. Prototype products of these parameters across Canada from single day and 10-day composite satellite measurements, some of which have been validated, are presented and discussed. The system used for data acquisition and processing includes three major components: data preprocessing, removal of artifacts, and inversion of surface parameters. The preprocessing includes satellite data calibration, registration, and clear sky compositing. Artifacts introduced by the presence of clouds, atmosphere, and angular dependence are eliminated or alleviated using various correction models. 69 refs., 1 fig.
McKellip, Rodney; Prados, Donald; Ryan, Robert; Ross, Kenton; Spruce, Joseph; Gasser, Gerald; Greer, Randall
The Time Series Product Tool (TSPT) is software, developed in MATLAB , which creates and displays high signal-to- noise Vegetation Indices imagery and other higher-level products derived from remotely sensed data. This tool enables automated, rapid, large-scale regional surveillance of crops, forests, and other vegetation. TSPT temporally processes high-revisit-rate satellite imagery produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and by other remote-sensing systems. Although MODIS imagery is acquired daily, cloudiness and other sources of noise can greatly reduce the effective temporal resolution. To improve cloud statistics, the TSPT combines MODIS data from multiple satellites (Aqua and Terra). The TSPT produces MODIS products as single time-frame and multitemporal change images, as time-series plots at a selected location, or as temporally processed image videos. Using the TSPT program, MODIS metadata is used to remove and/or correct bad and suspect data. Bad pixel removal, multiple satellite data fusion, and temporal processing techniques create high-quality plots and animated image video sequences that depict changes in vegetation greenness. This tool provides several temporal processing options not found in other comparable imaging software tools. Because the framework to generate and use other algorithms is established, small modifications to this tool will enable the use of a large range of remotely sensed data types. An effective remote-sensing crop monitoring system must be able to detect subtle changes in plant health in the earliest stages, before the effects of a disease outbreak or other adverse environmental conditions can become widespread and devastating. The integration of the time series analysis tool with ground-based information, soil types, crop types, meteorological data, and crop growth models in a Geographic Information System, could provide the foundation for a large-area crop-surveillance system that could identify
Wang, Wen; Yan, Jing; Chen, Yonghua; Niu, Zheng; Wang, Changyao
High spectral remote sensing is a hopeful technology in diagnosing crop nutrition background. With surface spectral measurement and laboratory biochemical analysis, the relationship between crop properties and spectral remote sensing data has been established. Seven chemical components - total chlorophyll, water crude protein, soluble sugar, N, P, K - were analyzed by laboratory chemical analyzing instrument. Foliar spectral property was detected outdoors by surface spectrometer. Chemical concentrations have been related to foliar spectral properties through stepwise multiple regression. The statistical equations between the chemical concentrations and reflectance as well as its several transformations were established. They underscored good estimation performance for chlorophyll, water crude protein, N and K with high squared multiple correlation coefficients (R2) values and high believable level. Especially R2 value of the equation between crude protein concentration and the first derivative of reflectance is 0.9564, which is the best result in the study of the fresh leave biochemistry up to now. On the basis of field experiment, an airborne remote sensing for crop nutrition monitoring was conducted in Shunyi County, Beijing, PR China. The sensor, made by Chinese Academy of Sciences, is in visible and near IR band. By image processing, the crop biochemistry map is obtained.
Pedersen, Gro B. M.; Vilmundardóttir, Olga K.; Falco, Nicola; Sigurmundsson, Friðþór S.; Rustowicz, Rose; Belart, Joaquin M.-C.; Gísladóttir, Gudrun; Benediktsson, Jón A.
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
Ecological and crop condition monitoring requires high temporal and spatial resolution remote sensing data. Due to technical limitations and budget constraints, remote sensing instruments trade spatial resolution for swath width. As a result, it is difficult to acquire remotely sensed data with both...
Fang, Yi; Ganguly, Auroop R; Singh, Nagendra; Vijayaraj, Veeraraghavan; Feierabend, Robert Neal; Potere, David T
We present a fast and statistically principled approach to land cover change detection. A reference statistical distribution is fitted to prior data based on off-line analysis, and an adaptive metric based on the exponentially weighted moving average (EWMA) of normal scores derived from p-values are tracked for new or streaming data, leading to alarms for large or sustained change. Methods which can track the origin of the change are also discussed. The approach is illustrated with a geographic application which involves monitoring remotely sensed data to detect changes in the normalized difference vegetation index (NDVI) in near real-time. We use Wal-Mart store openings as a nontraditional way to monitor and validate known cases of NDVI change. The proposed approach performs well on this validation dataset.
La Loggia, Goffredo; Capodici, Fulvio; Ciraolo, Giuseppe; Drago, Aldo; Maltese, Antonino
Human activities contaminate both coastal areas and open seas, even though impacts are different in terms of pollutants, ecosystems and recovery time. In particular, Mediterranean offshore pollution is mainly related to maritime transport of oil, accounting for 25% of the global maritime traffic and, during the last 25 years, for nearly 7% of the world oil accidents, thus causing serious biological impacts on both open sea and coastal zone habitats. This paper provides a general review of maritime pollution monitoring using integrated approaches of remote sensing and hydrodynamic modeling; focusing on the main results of the MAPRES (Marine pollution monitoring and detection by aerial surveillance and satellite images) research project on the synergistic use of remote sensing, forecasting, cleanup measures and environmental consequences. The paper also investigates techniques of oil spill detection using SAR images, presenting the first results of "Monitoring of marine pollution due to oil slick", a COSMO-SkyMed funded research project where X-band SAR constellation images provided by the Italian Space Agency are used. Finally, the prospect of using real time observations of marine surface conditions is presented through CALYPSO project (CALYPSO-HF Radar Monitoring System and Response against Marine Oil Spills in the Malta Channel), partly financed by the EU under the Operational Programme Italia-Malta 2007-2013. The project concerns the setting up of a permanent and fully operational HF radar observing system, capable of recording surface currents (in real-time with hourly updates) in the stretch of sea between Malta and Sicily. A combined use of collected data and numerical models, aims to optimize intervention and response in the case of marine oil spills.
Wang, Guangjun; Fu, Meichen; Xiao, Qiuping; Wang, Zeng
Because of the capability of remote sensing to acquire synoptic coverage and repetitive data acquisition it has become a widely used technique for monitoring the effects of human activity on terrestrial ecosystems. This paper presents the spatial extent, magnitude and temporal behavior of land desertification around Holinguole caused by city expansion. The selected test area, Huoliguole City, is a typical grassland city in China that is located in the northeast of China. A time-series of Landsat TM images covering a period of 20 years (1987-2006) were used. The data sets were geometrically and radiometrically pre-processed in a rigorous fashion, followed by a linear spectral mixture unmixing model to extract feature images of vegetation and sandy soil. The biomass images were derived using a polynomial regression model based on the ground-based observations of the amount of grass and a vegetation index based on satellite remote sensing. By combing the vegetation fraction images, the sandy soil fraction images, biomass images, and PC (principal components) images, the grassland desertification information around the built-up area of the city was extracted based on BP (Back-Propagation) neural network algorithm. The results of our studies indicate significant expansion of the city over the last 20 years, and a similar trend was also observed in the temporal magnitude behavior of severe grassland desertification away from the city.
Sharapov, Ruslan; Varlamov, Alexey
In paper considered the problem of using remote sensing monitoring of the exogenous processes. The satellite observations can used in tasks of detection of newly formed landslides, landslips and karst collapses. Practice shows that the satellite images of the same area, taken at different times, can have significant differences from each other. For this reason, it is necessary to perform the images correction to bring them into the same species, removing impact of changes in weather conditions, etc. In addition, it is needed to detect the clouds in the images. Clouds interfere with the analysis of images. The detection of exogenous processes manifestations can be make after these actions. For image correction and object detection can be used the neural networks. In paper are given the algorithm for image correction and the structure of a neural network.
Tang, Xu-guang; Liu, Dian-wei; Zhang, Bai; Du, Jia; Lei, Xiao-chun; Zeng, Li-hong; Wang, Yuan-dong; Song, Kai-shan
Contaminants in the snow can be used to reflect regional and global environmental pollution caused by human activities. However, so far, the research on space-time monitoring of snow contamination concentration for a wide range or areas difficult for human to reach is very scarce. In the present paper, based on the simulated atmospheric deposition experiments, the spectroscopy technique method was applied to analyze the effect of different contamination concentration on the snow reflectance spectra. Then an evaluation of snow contamination concentration (SCC) retrieval methods was conducted using characteristic index method (SDI), principal component analysis (PCA), BP neural network and RBF neural network method, and the estimate effects of four methods were compared. The results showed that the neural network model combined with hyperspectral remote sensing data could estimate the SCC well. PMID:21800591
Dennison, P. E.; Hultine, K. R.; Nagler, P. L.; Miura, T.; Glenn, E. P.; Ehleringer, J. R.
Non-native tamarisk (Tamarix spp.) has invaded riparian ecosystems throughout the Western United States. Another non-native species, the saltcedar leaf beetle (Diorhabda elongata), has been released in an attempt to control tamarisk infestations. Most efforts directed towards monitoring tamarisk defoliation by Diorhabda have focused on changes in leaf area or sap flux, but these measurements only give a local view of defoliation impacts. We are assessing the ability of remote sensing data for monitoring tamarisk defoliation and measuring resulting changes in evapotranspiration over space and time. Tamarisk defoliation by Diorhabda has taken place during the past two summers along the Colorado River and its tributaries near Moab, Utah. We are using 15 meter spatial resolution Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and 250 meter spatial resolution Moderate Resolution Imaging Spectrometer (MODIS) data to monitor tamarisk defoliation. An ASTER normalized difference vegetation index (NDVI) time series has revealed large drops in index values associated with loss of leaf area due to defoliation. MODIS data have superior temporal monitoring abilities, but at the sacrifice of much lower spatial resolution. A MODIS enhanced vegetation index time series has revealed that for pixels where the percentage of riparian cover is moderate or high, defoliation is detectable even at 250 meter spatial resolution. We are comparing MODIS vegetation index time series to site measurements of leaf area and sap flux. We are also using an evapotranspiration model to scale potential water savings resulting from the biocontrol of tamarisk.
McNairn, H.; Pacheco, A.
Soil organic carbon is fundamental to the sustainability of agricultural soils and soils play an important role in the global carbon balance. Estimating soil carbon levels and monitoring changes in these levels over time requires extensive data on climate, soil properties, land cover and land management. Remote sensing technologies are capable of providing some of the data needed in modeling soil organic carbon concentrations and in tracking changes in soil carbon. The characteristics of the vegetation cover influence the amount of organic matter in the soil and cultivation impacts the rate of organic matter decomposition. Consequently land management decisions, which include cropping and tillage practices, play a vital role in determining soil carbon levels. Agriculture and Agri-Food Canada (AAFC) has developed several methods to map land management practices from multispectral and Synthetic Aperture Radar (SAR) satellite sensors. These include identification of crops grown, estimation of crop residue cover left post-harvest and identification of tillage activities. Optical and SAR data are capable of identifying crop types to accuracies consistently above 85%. Knowledge of crop type also provides information needed to establish biomass levels and residue type, both of which influence the amounts and decomposition rates of organic matter. Scientists with AAFC have also extensively validated a method to estimate percent residue cover using spectral unmixing analysis applied to multispectral satellite data. Percentages for corn, soybean and small grain residues can be estimated to accuracies of 83%, 80% and 82%, respectively. Tillage activity influences residue decomposition and AAFC is investigating methods to identify tillage occurrence using advanced polarimetric SAR information. This presentation will provide an overview of methods and results from research ongoing at AAFC. The potential contribution of these remote sensing approaches to support wide area carbon
Singh, Upendra N. (Editor); Itabe, Toshikazu (Editor); Sugimoto, Nobuo (Editor)
Contents include the following: 1. Keynote paper: Overview of lidar technology for industrial and environmental monitoring in Japan. 2. lidar technology I: NASA's future active remote sensing mission for earth science. Geometrical detector consideration s in laser sensing application (invited paper). 3. Lidar technology II: High-power femtosecond light strings as novel atmospheric probes (invited paper). Design of a compact high-sensitivity aerosol profiling lidar. 4. Lasers for lidars: High-energy 2 microns laser for multiple lidar applications. New submount requirement of conductively cooled laser diodes for lidar applications. 5. Tropospheric aerosols and clouds I: Lidar monitoring of clouds and aerosols at the facility for atmospheric remote sensing (invited paper). Measurement of asian dust by using multiwavelength lidar. Global monitoring of clouds and aerosols using a network of micropulse lidar systems. 6. Troposphere aerosols and clouds II: Scanning lidar measurements of marine aerosol fields at a coastal site in Hawaii. 7. Tropospheric aerosols and clouds III: Formation of ice cloud from asian dust particles in the upper troposphere. Atmospheric boundary layer observation by ground-based lidar at KMITL, Thailand (13 deg N, 100 deg. E). 8. Boundary layer, urban pollution: Studies of the spatial correlation between urban aerosols and local traffic congestion using a slant angle scanning on the research vessel Mirai. 9. Middle atmosphere: Lidar-observed arctic PSC's over Svalbard (invited paper). Sodium temperature lidar measurements of the mesopause region over Syowa Station. 10. Differential absorption lidar (dIAL) and DOAS: Airborne UV DIAL measurements of ozone and aerosols (invited paper). Measurement of water vapor, surface ozone, and ethylene using differential absorption lidar. 12. Space lidar I: Lightweight lidar telescopes for space applications (invited paper). Coherent lidar development for Doppler wind measurement from the International Space
Chen, Fulong; Jiang, Aihui; Ishwaran, Natarajan
Angkor, in the northern province of Siem Reap, Cambodia, is one of the most important world heritage sites of Southeast Asia. Seasonal flood and ground sinking are two representative hazards in Angkor site. Synthetic Aperture Radar (SAR) remote sensing has played an important role for the Angkor site monitoring and management. In this study, 46 scenes of TerraSAR data acquired in the span of February, 2011 to December, 2013 were used for the time series analysis and hazard evaluation; that is, two-fold classification for flood area extracting and Multi-Temporal SAR Interferometry (MT-InSAR) for ground subsidence monitoring. For the flood investigation, the original Single Look Complex (SLC) TerraSAR-X data were transferred into amplitude images. Water features in dry and flood seasons were firstly extracted using a proposed mixed-threshold approach based on the backscattering; and then for the correlation analysis between water features and the precipitation in seasonally and annually. Using the MT-InSAR method, the ground subsidence was derived with values ranging from -50 to +12 mm/yr in the observation period of February, 2011 to June, 2013. It is clear that the displacement on the Angkor site was evident, implying the necessity of continuous monitoring.
Williams, Richard S., Jr.; Southworth, C. Scott
The Landsat Program became the major event of 1982 in geological remote sensing with the successful launch of Landsat 4. Other 1982 remote sensing accomplishments, research, publications, (including a set of Landsat worldwide reference system index maps), and conferences are highlighted. (JN)
Agoltsov, Alexander; Sizov, Oleg; Rubtsova, Natalia
Today we don't have full and reliable information about forests in Russia, so it is impossible to make any well-timed decision for forest management. Update of all this information by means of traditional methods (fieldwork) is a time-consuming and in fact impossible task. Also we do not think that using of the reports without objective information for cameral data actualization is an appropriate method in such situation. So our company uses remote sensing data and technologies to resolve this problem. Nowadays numerous satellites record numerous images every day. Remote sensing data are widespread and accessible, so we can use them as the source of actual and reliable information about current status of the Forest Fund. Furthermore regular monitoring allows extracting the information about the location and intensity of forests' changes like degradation and destruction. First of all we create a georeferenced data set to cover the area of interest with orthomosaic in targeting scale depending on the goals of the project (1:25 000 - 1:10 000). For example, we can do a mosaic from RapidEye (Germany) imagery with GSD = 6.5 m or from WorldView-2 (USA) imagery with GSD = 0.5 m. The next step is to create vector layers to describe the content of images. We use visual and contemporary automatic interpretation techniques. The benefit of such approach that we can extract not only information about forests (like boundary) but also the information about roads, hydrographic objects, power lines and so on. During vectorization except relevant orthomosaic we can use multi-temporal composites of images based on archive of satellite imagery. This helps us not only to detect general changes but detect illegal logging, areas affected by fires, windfalls. Then this information can be used for different products e.g. forest cover statistics, forest cover change statistics, maps of forest management and also we can analyze transport accessibility and economic assessment of forests.
Li, Yan; Wang, Jun-De; Huang, Zhong-Hua; Xu, Hou-Qian; Zhou, Xue-Tie
Measurement of leaking gases using Open Path Fourier Transform Infrared (OP-FTIR) spectroscopy was carried out in this study to acquire Path Integrated Concentration (PIC) data. Three hazardous Volatile Organic Compounds (VOCs) namely methylene chloride, chloroform and acetone were analyzed. For the two-component leaking source, the PIC data were easily obtained through ordinary calculation and compared to those obtained from Artificial Neural Network (ANN). When the leaking source was composed of three VOCs whose characteristic peaks interfere with each other, it was necessary to do spectral correction for multicomponent analysis with ANN. The Absorbance-Wavenumber-Time 3D spectra of the leaking sources and concentration variations with the leaking time were plotted. The results showed that OP-FTIR is a good quantitative analytical method for indoor or field air pollution. Further more, the remote sensing OP-FTIR system could be utilized to continuously monitor many more toxic gases and work as an alert system for the real time monitoring of hazardous gases beyond normal working conditions of various kinds of areas, such as living or industrial areas. PMID:12369638
Crow, W. T.; Bolten, J. D.
Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Such systems, however, are prone to random error associated with: incorrect process model physics, poor parameter choices and noisy meteorological inputs. The presentation will describe attempts to remediate these sources of error via the assimilation of remotely-sensed surface soil moisture retrievals from satellite-based passive microwave sensors into a global soil water balance model. Results demonstrate the ability of satellite-based soil moisture retrieval products to significantly improve the global characterization of root-zone soil moisture - particularly in data-poor regions lacking adequate ground-based rain gage instrumentation. This success has lead to an on-going effort to implement an operational land data assimilation system at the United States Department of Agriculture's Foreign Agricultural Service (USDA FAS) to globally monitor variations in root-zone soil moisture availability via the integration of satellite-based precipitation and soil moisture information. Prospects for improving the performance of the USDA FAS system via the simultaneous assimilation of both passive and active-based soil moisture retrievals derived from the upcoming NASA Soil Moisture Active/Passive mission will also be discussed.
Yang, Ruixia; Peng, Yanyan
Yin Xu, dates back more than 3,300 years, is the first relic of the capital of the Shang Dynasty literally recorded and confirmed by oracle bone scripts and the archaeological excavation in China. Located in Anyang City of Henan Province(northwestern suburbs of Huanhe banks) it covers an area of around 36 km2. According to the characteristics of Yin Xu, remote sensing has shown its great capabilities to solve many issues in different fields, e.g. visual interpretations of aerial photo were used to identify the feature of Yin Xu site in 1972, 1984, 1998, 2005 and 2010. Using the classification validated by field investigations,the change information such as the monitoring index of settlements, riverway, main roads, factory and green area can be extracted in heritage site. According to the monitoring results of land cover and the surrounding environment, we conclude that the protection planning system is effective, and the rapid expansion of neighboring building area has playing a negative role in Yin Xu protection.
Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status: soil surface temperature increases with decreasing water content, while moisture depletion in the plant root zone leads to stomatal closure, reduced transpirat...
Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status affecting evapotranspiration and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as...
Thermal infrared (TIR) remote sensing of land-surface temperature (LST) provides valuable information about the sub-surface moisture status for estimating evapotranspiration and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g...
Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge (Euphorbia esula L.), whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery [NASA JPL’s Airborne Visible Infra...
Bhattacharya, A.; Reddy, C.S.S.; Srivastav, S.K. )
The Barren Island Volcano, situated in the Andaman Sea of the Bay of Bengal, erupted recently (March, 1991) after a prolonged period of quiescence of about 188 years. This resumed activity coincides with similar outbreaks in the Philippines and Japan, which are located in an identical tectonic environment. This study addresses (1) remote sensing temporal monitoring of the volcanic activity, (2) detecting hot lava and measuring its pixel-integrated and subpixel temperatures, and (3) the importance of SWIR bands for high temperature volcanic feature detection. Seven sets of TM data acquired continuously from 3 March 1991 to 8 July 1991 have been analyzed. It is concluded that detectable pre-eruption warming took place around 25 March 1991 and volcanic activity started on 1 April 1991. It is observed that high temperature features, such as an erupting volcano, can register emitted thermal radiance in SWIR bands. Calculation of pixel-integrated and sub-pixel temperatures related to volcanic vents has been made, using the dual-band method. 6 refs.
Fink, Jonathan; Wessels, Rick; Eisinger, Chris; Ramsey, Michael; Hellman, Melanie; Kuhn, Sally
This report details the final progress on the Solid Earth and Natural Hazards project: Monitoring of Hazards of Silicic Volcanoes with Remote Sensing (SENH99-0000-0159). The original award went to Arizona State University (ASU) with Dr. Jonathan Fink as the P.I. and Dr. Michael Ramsey as the Co-I. In May 2000, Dr. Ramsey left ASU to take a tenure-track faculty position at the University of Pittsburgh. The principle investigators and NASA Headquarters agreed to split the grant award at the HQ level and therefore avoid the double overhead charges that would arise from a university subcontract. The objectives of the science were divided, and coordinated yearly progress reports have been submitted from each University. This report details the final progress on work carried out at Arizona State. A report by Dr. Ramsey at the University of Pittsburgh has already been submitted. The work from both institutions is closely related and this report will reflect that connection.
The applications are reported of new remote sensing techniques for earth resources surveys and environmental monitoring. Applications discussed include: vegetation systems, environmental monitoring, and plant protection. Data processing systems are described.
Aten, Michelle L.
Lakes and reservoirs are important resources that provide water for critical needs, such as drinking water, agriculture, recreation, fisheries, wildlife, and other uses. However, there is increasing concern that anthropogenic eutrophication threatens the usability of these natural resources. Therefore, this research investigates these complex hydrologic ecosystems and recommends a methodology for monitoring the trophic state of lakes and reservoirs using remote sensing data. The Mississippi Department of Environmental Quality provided in situ data for seven Mississippi lakes including, Arkabutla, Bay Springs, Enid, Grenada, Okatibbee, Ross Barnett, and Sardis lakes. This research explored the relationships between the Secchi depth (SD), chlorophyll-a (CHL), and total phosphorus (TP) in situ data and Moderate Resolution Imaging Spectroradiometer (MODIS) spectral reflectance data. This was accomplished by deriving Carlson Trophic State Index values for each in situ measurements and using these TSI(SD), TSI(CHL), and TSI(TP) values to evaluate potential predictive methods. Simple linear regression was performed to quantify the strength of the relationships between the in situ data and MODIS surface reflectance values. However, R-square values were too low and inconsistent to justify additional analyses. Therefore, machine learning models from the Waikato Environment for Knowledge Analysis (WEKA) software workbench were explored and tested. Optimal predictive models and settings were investigated for two meta-learner classifiers, three Bayesian classifiers and three decision tree classifiers. The Classification Via Regression yielded the best results when using large datasets, the all-but-one iteration setting, MODIS A1 individual bands as predictors, and TSI(SD) as targets. For this model and these settings, the percentages of correctly classified instances ranged from 77.74% to 81.98% and kappa values ranged from 0.41 to 0.48. The percentage of correctly classified
Simoniello, T.; Carone, M. T.; Loperte, A.; Satriani, A.; Imbrenda, V.; D'Emilio, M.; Guariglia, A.
Coastal areas are of great environmental, economic, social, cultural and recreational relevance; therefore, the implementation of suitable monitoring and protection actions is fundamental for their preservation and for assuring future use of this resource. Such actions have to be based on an ecosystem perspective for preserving coastal environment integrity and functioning and for planning sustainable resource management of both the marine and terrestrial components (ICZM-EU initiative). We implemented an integrated study based on remote sensing and geophysical techniques for monitoring a coastal area located along the Ionian side of Basilicata region (Southern Italy). This area, between the Bradano and Basento river mouths, is mainly characterized by a narrow shore (10-30 m) of fine sandy formations and by a pine forest planted in the first decade of 50's in order to preserve the coast and the inland cultivated areas. Due to drought and fire events and saltwater intrusion phenomena, such a forest is affected by a strong decline with consequent environmental problems. Multispectral satellite data were adopted for evaluating the spatio-temporal features of coastal vegetation and the structure of forested patterns. The increase or decrease in vegetation activity was analyzed from trends estimated on a time series of NDVI (Normalized Difference Vegetation Index) maps. The fragmentation/connection levels of vegetated patterns was assessed form a set of landscape ecology metrics elaborated at different structure scales (patch, class and landscape) on satellite cover classifications. Information on shoreline changes were derived form a multi-source data set (satellite data, field-GPS surveys and Aerial Laser Scanner acquisitions) by taking also into account tidal effects. Geophysical campaigns were performed for characterizing soil features and limits of salty water infiltrations. Form vertical resistivity soundings (VES), soil resistivity maps at different a deeps (0
Tamás, János; Nagy, Attila; Fehér, János
There is a close quality relationship between the harmful levels of all three drought indicator groups (meteorological, hydrological and agricultural). However, the numerical scale of the relationships between them is unclear and the conversion of indicators is unsolved. Different areas or an area with different forms of drought cannot be compared. For example, from the evaluation of meteorological drought using the standardized precipitation index (SPI) values of a river basin, it cannot be stated how many tonnes of maize will be lost during a given drought period. A reliable estimated rate of yield loss would be very important information for the planned interventions (i.e. by farmers or river basin management organisations) in terms of time and cost. The aim of our research project was to develop a process which could provide information for estimating relevant drought indexes and drought related yield losses more effectively from remotely sensed spectral data and to determine the congruency of data derived from spectral data and from field measurements. The paper discusses a new calculation method, which provides early information on physical implementation of drought risk levels. The elaborated method provides improvement in setting up a complex drought monitoring system, which could assist hydrologists, meteorologists and farmers to predict and more precisely quantify the yield loss and the role of vegetation in the hydrological cycle. The results also allow the conversion of different-purpose drought indices, such as meteorological, agricultural and hydrological ones, as well as allow more water-saving agricultural land use alternatives to be planned in the river basins. PMID:26676009
Environmental managers need current, accurate information upon which to base decisions. Viable information, especially in developing countries, is often unavailable. Satellite remote sensing is an appropriate and effective data source for mapping the surface of the earth, including a variety of environmental features. Remote-sensing-derived information is enhanced by being one component within a geographic information system (GIS). These techniques were employed to study an expanding delta in East Africa.The Omo River flows from the Ethiopian Highlands into the northern end of Lake Turkana, creating a large delta extending between Ethiopia and Kenya. This isolated and unique wetland feature has expanded by over 500 sq km in the last 15 years as measured by space-borne remote sensing techniques and corroborated by low-altitude aircraft reconnaissance flights.The growth of the delta appears to be a function of both increased sedimentation and decreased lake levels and river flows. Within the delta there has been a selective decline in wildlife and an increase in human activity, both pastoral and agricultural. The uniqueness of this isolated delta suggests that consideration be given to its possible protection and management. PMID:8661611
Active ground optical remote sensing (AGORS) devices mounted on overhead irrigation booms could help to improve seedling quality by autonomously monitoring seedling stress. In contrast to traditionally used passive optical sensors, AGORS devices operate independently of ambient light conditions and ...
Pickles, W; Cover, W
This project's goal is to develop remote sensing methods for early detection and spatial mapping, over whole regions simultaneously, of any surface areas under which there are significant CO2 leaks from deep underground storage formations. If large amounts of CO2 gas percolated up from a storage formation below to within plant root depth of the surface, the CO2 soil concentrations near the surface would become elevated and would affect individual plants and their local plant ecologies. Excessive soil CO2 concentrations are observed to significantly affect local plant and animal ecologies in our geothermal exploration, remote sensing research program at Mammoth Mountain CA USA. We also know from our geothermal exploration remote sensing programs, that we can map subtle hidden faults by spatial signatures of altered minerals and of plant species and health distributions. Mapping hidden faults is important because in our experience these highly localized (one to several centimeters) spatial pathways are good candidates for potentially significant CO2 leaks from deep underground formations. The detection and discrimination method we are developing uses primarily airborne hyperspectral, high spatial (3 meter) with 128 band wavelength resolution, visible and near infrared reflected light imagery. We also are using the newly available ''Quickbird'' satellite imagery that has high spatial resolution (0.6 meter for panchromatic images, 2.4 meters for multispectral). We have a commercial provider, HyVista Corp of Sydney Australia, of airborne hyperspectral imagery acquisitions and very relevant image data post processing, so that eventually the ongoing surveillance of CO2 storage fields can be contracted for commercially. In this project we have imaged the Rangely Colorado Oil field and surrounding areas with an airborne hyperspectral visible and near infrared reflected light sensor. The images were analyzed by several methods using the suite of tools available in the ENVI
Wang, H.; Lin, H.; Liu, D.
Abstract: Effectively monitoring vegetation drought is of great significance in ecological conservation and agriculture irrigation at the regional scale. Combining meteorological drought indices with remotely sensed drought indices can improve tracking vegetation dynamic under the threat of drought. This study analyzes the dynamics of spatially-defined Temperature Vegetation Dryness Index (TVDI) and temporally-defined Vegetation Health Index (VHI) from remotely sensed NDVI and LST datasets in the dry spells in Southwest China. We analyzed the correlation between remotely sensed drought indices and meteorological drought index of different time scales. The results show that TVDI was limited by the spatial variations of LST and NDVI, while VHI was limited by the temporal variations of LST and NDVI. Station-based buffering analysis indicates that the extracted remotely sensed drought indices and Standard Precipitation Index (SPI) could reach stable correlation with buffering radius larger than 35 km. Three factors affect the spatiotemporal relationship between remotely sensed drought indices and SPI: i) different vegetation types; ii) the timescale of SPI; and iii) remote sensing data noise. Vegetation responds differently to meteorological drought at various time scales. The correlation between SPI6 and VHI is more significant than that between SPI6 and TVDI. Spatial consistency between VHI and TVDI varies with drought aggravation. In early drought period from October to December, VHI and TVDI show limited consistency due to the low quality of remotely sensed images. The study helps to improve monitoring vegetation drought using both meteorological drought indices and remotely sensed drought indices.
Slonecker, Terrence; Jones, John W.; Price, Susan D.; Hogan, Dianna
'Remote sensing' is a generic term for monitoring techniques that collect information without being in physical contact with the object of study. Overhead imagery from aircraft and satellite sensors provides the most common form of remotely sensed data and records the interaction of electromagnetic energy (usually visible light) with matter, such as the Earth's surface. Remotely sensed data are fundamental to geographic science. The Eastern Geographic Science Center (EGSC) of the U.S. Geological Survey (USGS) is currently conducting and promoting the research and development of three different aspects of remote sensing science: spectral analysis, automated orthorectification of historical imagery, and long wave infrared (LWIR) polarimetric imagery (PI).
Stoms, D. M.; Estes, J. E.
A remote sensing research agenda designed to expand the knowledge of the spatial distribution of species richness and its ecological determinants and to predict its response to global change is proposed. Emphasis is placed on current methods of mapping species richness of both plants and animals, hypotheses concerning the biophysical factors believed to determine patterns of species richness, and anthropogenic processes causing the accelerating rate of extinctions. It is concluded that biodiversity should be incorporated more prominently into the global change and earth system science paradigms.
Bernardes, T.; Rosa, V. G.; Rudorf, B. F.; Adami, M.
Remote sensing studies applied to coffee crop have shown the complexity and difficulty to extract information from satellite imagery. The accuracy of automatic classification for coffee areas was considered only intermediate by several authors. The errors were attributed to topographic effects and low spatial resolution of Landsat images. Besides the difficulties to map coffee crop, there are few cloud cover free Landsat images over the growing season. Despite the low spatial resolution, high temporal coverage of MODIS data makes it possible to obtain cloud free images on several dates over the year providing additional information for monitoring coffee crops. Our hypothesis is that the range of foliar biomass of coffee plots over the growing season, assumed to be estimated through MODIS vegetation indices, is related to coffee yield. We assess the feasibility of monitoring coffee yield by using time-series of MODIS 250m normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) data. The study area is situated in the south of the Minas Gerais State which produces about thirty percent of the Brazilian coffee production. We used NDVI and EVI products from MODIS spanning from 2006 to 2009 to assess the feasibility of detecting relationships between vegetation indices and coffee yield. Landsat images were used to obtain a reference map of coffee areas and to identify MODIS 250m pure pixels overlapping homogeneous coffee crops. Only MODIS pixels with 100% coffee were included in the analysis. A wavelet-based filter was used to smooth NDVI and EVI time profiles. The next step was the acquisition of coffee yield data directly from farmers on the test site. Those data are being statistically related to vegetation indices and range values per year. The study region presents nearly 452.000 hectares of coffee mapped by on-screen digitalization of Landsat imagery from which about 10.000 hectares match plots likely to be monitored from 250 meters MODIS
Huang, Qing; Zhou, Qing-bo; Zhang, Li
China is a large agricultural country. To understand the agricultural production condition timely and accurately is related to government decision-making, agricultural production management and the general public concern. China Agriculture Remote Sensing Monitoring System (CHARMS) can monitor crop acreage changes, crop growing condition, agriculture disaster (drought, floods, frost damage, pest etc.) and predict crop yield etc. quickly and timely. The basic principles, methods and regular operation of crop growing condition monitoring in CHARMS are introduced in detail in the paper. CHARMS can monitor crop growing condition of wheat, corn, cotton, soybean and paddy rice with MODIS data. An improved NDVI difference model was used in crop growing condition monitoring in CHARMS. Firstly, MODIS data of every day were received and processed, and the max NDVI values of every fifteen days of main crop were generated, then, in order to assessment a certain crop growing condition in certain period (every fifteen days, mostly), the system compare the remote sensing index data (NDVI) of a certain period with the data of the period in the history (last five year, mostly), the difference between NDVI can indicate the spatial difference of crop growing condition at a certain period. Moreover, Meteorological data of temperature, precipitation and sunshine etc. as well as the field investigation data of 200 network counties were used to modify the models parameters. Last, crop growing condition was assessment at four different scales of counties, provinces, main producing areas and nation and spatial distribution maps of crop growing condition were also created.
Rader, M. L. (Principal Investigator)
Activities and results are reported of a project to investigate the application of remote sensing technology developed for the LACIE, AgRISTARS, Forestry and other NASA remote sensing projects for the environmental monitoring of strip mining, industrial pollution, and acid rain. Following a remote sensing workshop for EPA personnel, the EOD clustering algorithm CLASSY was selected for evaluation by EPA as a possible candidate technology. LANDSAT data acquired for a North Dakota test sight was clustered in order to compare CLASSY with other algorithms.
Nuclear power plants typically use waste heat rejection systems such as cooling lakes and natural draft cooling towers. These systems are designed to reduce cooling water temperatures sufficiently to allow full power operation even during adverse meteorological conditions. After the power plant is operational, the performance of the cooling system is assessed. These assessments usually rely on measured temperatures of the cooling water after it has lost heat to the environment and is being pumped back into the power plant (cooling water inlet temperature). If the cooling system performance is not perceived to be optimal, the utility will collect additional data to determine why. This paper discusses the use of thermal imagery collected from aircraft and satellites combined with numerical simulation to better understand the dynamics and thermodynamics of nuclear power plant waste heat dissipation systems. The ANS meeting presentation will discuss analyses of several power plant cooling systems based on a combination of remote sensing data and hydrodynamic modeling.
Brosius, C. A.; Gervin, J. C.; Ragusa, J. M.
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.
Aldea, Mihaela; Petrescu, Florian; Sercaianu, Mihai; Gaman, Florian; Iacoboaea, Cristina
Monitoring urban growth process and its patterns for the city of Bucharest was the starting point in our attempt to identify and propose a more general procedure for monitoring this type of process in Romania using remote sensing data. Several important technical aspects such as comparable data sources, comparable algorithms, interoperability issues as well as final presentation standards are discussed. The paper is meant to be considered as a basis for promoting a unitary technical approach concerning urban growth monitoring in Romania using remote sensing data.
Zhang, A.; Jia, G.
Remote sensing drought indices derived from optical and infrared bands have been successfully used in monitoring drought throughout the world; however the application of microwave remote sensing sensor in drought monitoring has not been thoroughly investigated. In this study, we propose a microwave remote sensing drought index, the Microwave Integrated Drought Index (MIDI), especially for short-term drought monitoring over northern China. The index combined three variables: the Tropical Rainfall Measuring Mission (TRMM) precipitation, land surface temperature (LST) and soil moisture (SM) obtained by the Vrije Universiteit Amsterdam and NASA Goddard Space Flight Center (VUA-NASA) from the Advanced Microwave Scanning Radiometer (AMSR-E) on-board Aqua satellite. The microwave remotely sensed variables were linearly scaled from 0 to 1 for each pixel based on absolute minimum and maximum values for each variable over time, in order to discriminate the weather-related component from the ecosystem component. The microwave indices were evaluated with the Standardized Precipitation Index (SPI), an in-situ meteorological data based drought index. Pearson correlation analyses were performed between the remotely sensed drought indices values and different time scale SPI values for the growing season from 2003 to 2010 to assess the capability of remotely sensed drought indices in monitoring drought over three different climate regions in northern China. There was significant spatial variability in the correlations between remote sensing drought indices and SPI, generally, the Precipitation Condition Index (PCI) showed the highest correlation with 1-month SPI (r around 0.70) whether compared to remote sensing drought indices or different time scale SPI; while correlations between Soil Moisture Condition Index (SMCI), Land Surface Temperature (TCI) and SPI exhibited different trends among three climate regions. The MIDI with proper weights of three components nearly possessed the
Yang, Guijun; Yang, Hao; Jin, Xiuliang; Pignatti, Stefano; Casa, Raffaele; Pascucci, Simone; Silvesrtro, Paolo Cosmo
Since the Kick-off of the Dragon-3 project Farmland Drought Monitoring and Prediction Based on Multi-source Remote Sensing Data (ID: 10448), our research focuses on three points including 1) the monitoring of key biophysical variables of crop and soil in farmland drought by optical and radar remote sensing data, 2) the risk assessment of farmland drought by time series remote sensing and meteorological data, and 3) the crop loss evaluation under farmland drought mainly based on AquaCrop crop model. Our study area is mainly located in Beijing, and Shaanxi Province (semi-arid region), China. Experiment campaign and data analysis were carried out and some new methods aiming at farmland drought monitoring and prediction were developed, which highlighting the importance of ESA-NRSCC Dragon cooperation.
Remote Sensing is a scientific discipline of non-contact monitoring. It includes a range of technologies that span from aerial photography to advanced spectral imaging and analytical methods. This Session is designed to demonstrate contemporary practical applications of remote se...
Zou, Chunhui; Chen, Huailiang; Yin, Qing
Satellite remote sensing monitoring of forest fire-points is a routine operation of weather service. By taking advantage of remote sensing information's characteristics such as relatively fixed resolution, little geometric distortion and quite stable data quality, the thesis establishes Henan Satellite Remote Sensing Forest Fire-points Automatic Monitoring System in the way of automatic geography registration based on gray correlation and control point database, which can realize automation of the whole process including automatic monitoring,automatic geography registration,automatic fire-points monitoring,automatic production releasing and cell phone short-message notice of fire-points warning information. The system could greatly improve service efficiency. Automatic registration of remote sensing information based on gray correlation and control point database features simpleness and quickness. Through automatic geography registration testing of sunny EOS/MODIS data (at daytime and nightime) during 18 periods from February 2008 to May 2008 in Henan Province with average error of registration is 0.637 pixels at daytime and 0.319 at nighttime, it can fully meet ordinary operation requirements. Fire-point identification and fire-point area estimate method in the system can be applied to monitoring different fires at daytime and at nighttime. Besides, it can automatically screen effective fire-points according to background geographic information, and thus it can improve monitoring accuracy.
Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael
Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge. PMID:26437413
Kim, Duk-jin; Jung, Jungkyo; Kang, Ki-mook; Kim, Seung Hee; Xu, Zhen; Hensley, Scott; Swan, Aaron; Duersch, Michael
Coastal lands and nearshore marine areas are productive and rapidly changing places. However, these areas face many environmental challenges related to climate change and human-induced impacts. Space-borne remote sensing systems may be restricted in monitoring these areas because of their spatial and temporal resolutions. In situ measurements are also constrained from accessing the area and obtaining wide-coverage data. In these respects, airborne remote sensing sensors could be the most appropriate tools for monitoring these coastal areas. In this study, a cost-effective airborne remote sensing system with synthetic aperture radar and thermal infrared sensors was implemented to survey coastal areas. Calibration techniques and geophysical model algorithms were developed for the airborne system to observe the topography of intertidal flats, coastal sea surface current, sea surface temperature, and submarine groundwater discharge. PMID:26437413
Haiyang, Yu; Yanmei, Liu; Guijun, Yang; Xiaodong, Yang; Dong, Ren; Chenwei, Nie
To achieve dynamic winter wheat quality monitoring and forecasting in larger scale regions, the objective of this study was to design and develop a winter wheat quality monitoring and forecasting system by using a remote sensing index and environmental factors. The winter wheat quality trend was forecasted before the harvest and quality was monitored after the harvest, respectively. The traditional quality-vegetation index from remote sensing monitoring and forecasting models were improved. Combining with latitude information, the vegetation index was used to estimate agronomy parameters which were related with winter wheat quality in the early stages for forecasting the quality trend. A combination of rainfall in May, temperature in May, illumination at later May, the soil available nitrogen content and other environmental factors established the quality monitoring model. Compared with a simple quality-vegetation index, the remote sensing monitoring and forecasting model used in this system get greatly improved accuracy. Winter wheat quality was monitored and forecasted based on the above models, and this system was completed based on WebGIS technology. Finally, in 2010 the operation process of winter wheat quality monitoring system was presented in Beijing, the monitoring and forecasting results was outputted as thematic maps.
Hively, Wells; Sjoerd Duiker; Greg McCarty; Prabhakara, Kusuma
In the Chesapeake Bay Watershed, winter cereal cover crops are often planted in rotation with summer crops to reduce the loss of nutrients and sediment from agricultural systems. Cover crops can also improve soil health, control weeds and pests, supplement forage needs, and support resilient cropping systems. In southeastern Pennsylvania, cover crops can be successfully established following corn (Zea mays L.) silage harvest and are strongly promoted for use in this niche. They are also planted following corn grain, soybean (Glycine max L.), and vegetable harvest. In Pennsylvania, the use of winter cover crops for agricultural conservation has been supported through a combination of outreach, regulation, and incentives. On-farm implementation is thought to be increasing, but the actual extent of cover crops is not well quantified. Satellite imagery can be used to map green winter cover crop vegetation on agricultural fields and, when integrated with additional remote sensing data products, can be used to evaluate wintertime vegetative groundcover following specific summer crops. This study used Landsat and SPOT (System Probatoire d’ Observation de la Terre) satellite imagery, in combination with the USDA National Agricultural Statistics Service Cropland Data Layer, to evaluate the extent and amount of green wintertime vegetation on agricultural fields in four Pennsylvania counties (Berks, Lebanon, Lancaster, and York) from 2010 to 2013. In December of 2010, a windshield survey was conducted to collect baseline data on winter cover crop implementation, with particular focus on identifying corn harvested for silage (expected earlier harvest date and lower levels of crop residue), versus for grain (expected later harvest date and higher levels of crop residue). Satellite spectral indices were successfully used to detect both the amount of green vegetative groundcover and the amount of crop residue on the surveyed fields. Analysis of wintertime satellite imagery
Yuping, Ma; Shili, Wang; Li, Zhang; Yingyu, Hou; Liwei, Zhuang; Yanbo, He; Futang, Wang
Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL-PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.
Remote sensing is measuring something without touching it. Most methods measure a portion of the electro-magnetic spectrum using energy reflected from or emitted by a material. Moving the instrument away makes it easier to see more at one time. Airplanes are good but satellites are much better. Many things can not be easily measured on the scale of an individual person. Example - measuring all the vegetation growing at one time in even the smallest country. A satellite can see things over large areas repeatedly and in a consistent way. Data from the detector is reported as digital values for a grid that covers some portion of the Earth. Because it is digital and consistent a computer can extract information or enhance the data for a specific purpose.
With a changing climate, drought has become more intensified, of which agriculture is the major affected sector. Satellite observations have proven great utilities for real-time drought monitoring as well as crop yield estimation, and many remotely sensed indicators have been developed for drought monitoring based on vegetation growth conditions, surface temperature and evapotranspiration information. However, those current drought indicators typically don't take into account the different responses of various input information and the drought impacts during the growing season, revealing some limitations for effective agricultural drought monitoring and impact analysis. Therefore, the goal of this research is to build a framework for the development of an impact-oriented and remote sensing based agricultural drought indicator. Firstly, the global agricultural drought risk was characterized to provide an overview of the agricultural drought prone areas in the world. Then, the responses of different remotely sensed indicators to drought and the impacts of drought on crop yield from the remote sensing perspective during the growing season were explored. Based on previous works on drought risk, drought indicator response and drought impact analysis, an impact-oriented drought indicator will be prototyped from the integration of the drought responses of different indicators and the drought impacts during the growing season. This research can inform an impact-oriented agricultural drought indicator, help prototype an impact-oriented agricultural drought monitoring system, and thus provide valuable inputs for effective agricultural management.
Understanding vegetation response to changing climate patterns is an important element of rangeland management and supports the use and development of ecological site descriptions. Monitoring of rangeland conditions with remote sensing can be misleading if ground measurements are not used to interpr...
Huang, He; Zhou, Hongjian; Wang, Ping; Wu, Wei; Yang, Siquan
Drought is one major nature disaster in the world. The affected population and agriculture loss caused by drought are the largest in all natural disasters. Drought has the characteristics of wide affected areas, long duration and periodic strong feature. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster monitoring and forecasting. So, remote sensing data have been widely used and delivered significant benefits in drought prevention and reduction in China. Three drought monitor models including Vegetation Condition Index (VCI), Temperature Condition Index (TCI) and Temperature Vegetation Dryness Index (TVDI) had been used to monitor southwest drought occurred in China from 2009 to 2011 based on the small satellite constellation for environment and disaster monitoring and forecasting A/B satellites (HJ-1AB) and Landsat remote sensing data. The results shown that five regions including Sichuan province, Chongqing, Guizhou province, Yunnan province, Guangxi province in southwest of China had suffered different degrees agricultural drought disaster in 2010 and 2011. The comprehensive agricultural disaster situation of five affected areas in 2010 was more serious than drought events occurred in 2011. The many regions in Guizhou province were hardest-hit areas cased by the two consecutive year drought events in southwest China.
Rains, Dominik; Lievens, Hans; Vernieuwe, Hilde; De Baets, Bernard; Hostache, Renaud; Chini, Marco; Pfister, Laurent; Matgen, Patrick; He, Guowei; Vereecken, Harry; Han, Xujun; Montzka, Carsten; Verhoest, Niko
Given the expected increase in extreme events due to climate change, more drought events can be expected in the future. These events have often devastating impacts on society and the environment. Adequate monitoring of these events within disaster management is therefore of utmost importance. Remote sensing can provide important information, though does not allow for a complete assessment of droughts as (1) only measurements of the surface are obtained and (2) the spatial and temporal resolutions are often too coarse. Combining remote sensing with land surface models is generally opted for, and is already in place in many drought monitoring systems. However, prediction of drought events (occurrence, intensity, frequency) can be improved by improving modelling approaches via the assimilation of multiple sources of remote sensing data. If both remote sensing observation and model reliability and accuracy can be enhanced, a more precise monitoring and modelling is expected, and therefore improved drought forecast is possible. Within the recently initiated BELSPO/FNR funded HYDRAS+ project, research on these domains is carried out demonstrating the benefits of jointly assimilating several remote sensing sources (e.g. Sentinel 1, SMOS, SMAP) in land surface models for improved drought monitoring and prediction. It furthermore aims at assessing whether conceptual models (SUPERFLEX) can be used instead of complex and computation-expensive land surface models (CLM 4.5). If such models can be used, a faster computation of droughts at very large scale becomes possible. The findings will not be used to set up a standalone drought monitoring system but rather be used to potentially improve currently existing systems. Any improvement in the currently available systems will have important positive consequences with respect to disaster management as it will allow for an improved management of resources.
Johnson, R. W.; Ohlhorst, C. W.
Remotely sensed wide area synoptic data provides information on ocean dumping that is not readily available by other means. A qualitative approach has been used to map features, such as river plumes. Results of quantitative analyses have been used to develop maps showing quantitative distributions of one or more water quality parameters, such as suspended solids or chlorophyll a. Joint NASA/NOAA experiments have been conducted at designated dump areas in the U.S. coastal zones to determine the applicability of aircraft remote sensing systems to map plumes resulting from ocean dumping of sewage sludge and industrial wastes. A second objective is related to the evaluation of previously developed quantitative analysis techniques for studying dispersion of materials in these plumes. It was found that plumes resulting from dumping of four waste materials have distinctive spectral characteristics. The development of a technology for use in a routine monitoring system, based on remote sensing techniques, is discussed.
The use of different remote sensing data is demonstrated by example of the world mountain, Mt. Damavand (5671 m) in the Alborz Mountains, Iran. Several types of satellite data were required to master the complex task of preparing a monograph of this mountain: SSEOP images of NASA, Russian KFA-1000 pictures, CORONA panoramic images of NASA and Russian KVR-1000 orthoimages. Examples of climatic studies, transportation routes, water resources, conservation areas and relicts of human land-use are presented in order to show the potential of remote sensing data. The right choice of image data is a top priority in applied remote sensing in order to obtain significant results in the documentation and monitoring of human activities.
Stumpf, Richard P.; Tomlinson, Michelle C.
Harmful algal blooms (HABs) have impacts on coastal economies, public health, and various endangered species. HABs are caused by a variety of organisms, most commonly dinoflagellates, diatoms, and cyanobacteria. In the late 1970's, optical remote sensing was found to have a potential for detecting the presence of blooms of Karenia brevis on the US Florida coast. Due to the nearly annual frequency of these blooms and the ability to note them with ocean color imagery, K. brevis blooms have strongly influenced the field of HAB remote sensing. However, with the variability between phytoplankton blooms, heir environment and their relatively narrow range of pigment types, particularly between toxic and non-toxic dinoflagellates and diatoms, techniques beyond optical detection are required for detecting and monitoring HABs. While satellite chlorophyll has some value, ecological or environmental characteristics are required to use chlorophyll. For example, identification of new blooms can be an effective means of identifying HABs that are quie intense, also blooms occurring after specific rainfall or wind events can be indicated as HABs. Several HAB species do not bloom in the traditional sense, in that they do not dominate the biomass. In these cases, remote sensing of SST or chlorophyll can be coupled with linkages to seasonal succession, changes in circulation or currents, and wind-induced transport--including upwelling and downwelling, to indicate the potential for a HAB to occur. An effective monitoring and forecasting system for HABs will require the coupling of remote sensing with an environmental and ecological understanding of the organism.
Ma, Yi; Zhang, Jie; Zhang, Jingyu
The coastal wetland, a transitional zone between terrestrial ecosystems and marine ecosystems, is the type of great value to ecosystem services. For the recent 3 decades, area of the coastal wetland is decreasing and the ecological function is gradually degraded with the rapid development of economy, which restricts the sustainable development of economy and society in the coastal areas of China in turn. It is a major demand of the national reality to carry out the monitoring of coastal wetlands, to master the distribution and dynamic change. UAV, namely unmanned aerial vehicle, is a new platform for remote sensing. Compared with the traditional satellite and manned aerial remote sensing, it has the advantage of flexible implementation, no cloud cover, strong initiative and low cost. Image-spectrum merging is one character of high spectral remote sensing. At the same time of imaging, the spectral curve of each pixel is obtained, which is suitable for quantitative remote sensing, fine classification and target detection. Aimed at the frontier and hotspot of remote sensing monitoring technology, and faced the demand of the coastal wetland monitoring, this paper used UAV and the new remote sensor of high spectral imaging instrument to carry out the analysis of the key technologies of monitoring coastal wetlands by UAV on the basis of the current situation in overseas and domestic and the analysis of developing trend. According to the characteristic of airborne hyperspectral data on UAV, that is "three high and one many", the key technology research that should develop are promoted as follows: 1) the atmosphere correction of the UAV hyperspectral in coastal wetlands under the circumstance of complex underlying surface and variable geometry, 2) the best observation scale and scale transformation method of the UAV platform while monitoring the coastal wetland features, 3) the classification and detection method of typical features with high precision from multi scale
Recent developments in on-vehicle electronics makes practical remote monitoring of vehicle emissions compliance with CARB and EPA regulations. A system consisting of emission controls malfunction sensors, an on-board computer (OBC), and vehicle-to-roadside communications (VRC) would enable enforcement officials to remotely and automatically detect vehicle out-of-compliance status. Remote sensing could be accomplished at highway speeds as vehicles pass a roadside RF antenna and reader unit which would interrogate the on- vehicle monitoring and recording system. This paper will focus on the hardware system components require to achieve this goal with special attention to the VRC; a key element for remote monitoring. this remote sensing concept piggybacks on the development of inexpensive VRC equipment for automatic vehicle identification for electronic toll collection and intelligent transportation applications. Employing an RF transponder with appropriate interface to the OBC and malfunction sensors, a practical monitoring system can be developed with potentially important impact on air quality and enforcement. With such a system in place, the current -- and costly and ineffective -- emission control strategy of periodic smog checking could be replaced or modified.
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.
Dowling, David R.; Sabra, Karim G.
Acoustic waves carry information about their source and collect information about their environment as they propagate. This article reviews how these information-carrying and -collecting features of acoustic waves that travel through fluids can be exploited for remote sensing. In nearly all cases, modern acoustic remote sensing involves array-recorded sounds and array signal processing to recover multidimensional results. The application realm for acoustic remote sensing spans an impressive range of signal frequencies (10-2 to 107 Hz) and distances (10-2 to 107 m) and involves biomedical ultrasound imaging, nondestructive evaluation, oil and gas exploration, military systems, and Nuclear Test Ban Treaty monitoring. In the past two decades, approaches have been developed to robustly localize remote sources; remove noise and multipath distortion from recorded signals; and determine the acoustic characteristics of the environment through which the sound waves have traveled, even when the recorded sounds originate from uncooperative sources or are merely ambient noise.
Yuliang, Qiao; Ying, Wang; Junyou, Tang
Taking for example Daning County, a key pilot area of ``the Three North Protection Forest Project'' in China's Loess Plateau, this article is to explore the method of using remote sensing technology to monitoring the dynamic change information of forest vegetation. It uses LANDSAT TM, CBERS-1 data and aerial remote sensing and ground investigation to monitoring the dynamic change of forest vegetation information of Daning County in three different periods - 1978, 1987 and 2000. The results of the research prove that, this method is worth widely popularized, by which the dynamic change information of the forest vegetation can be monitored simply and quickly so as to explore a scientific, rational and effective road for us to rectify the territory of China's Loess Plateau, change the poor physiognomy of this area, improve the ecological environment and promote the development of national economy.
Iacoboaea, Cristina; Petrescu, Florian
Landfill monitoring is one of the most important components of waste management. This article presents a case study on landfill monitoring using remote sensing technology. The study area was the Glina landfill, one of the largest municipal waste disposal sites in Romania. The methodology consisted of monitoring the differences of temperature computed for several distinct waste disposal zones with respect to a ground reference area, all of them located within the landfill site. The remote sensing data used were Landsat satellite multi-temporal data. The differences of temperature were computed using Landsat thermal infrared data. The study confirmed the use of multi-temporal Landsat imagery as a complementary data source. PMID:23660748
Yang, Shiqi; Tang, Yunhui; Gao, Yanghua; Xu, Yongjin
There are regional and periodic droughts in Chongqing, which impacted seriously on agricultural production and people's lives. This study attempted to monitor the drought in Chongqing with complex terrain using MODIS data. First, we analyzed and compared three remote sensing methods for drought monitoring (time series of vegetation index, temperature vegetation dryness index (TVDI), and vegetation supply water index (VSWI)) for the severe drought in 2006. Then we developed a remote sensing based drought monitoring model for Chongqing by combining soil moisture data and meteorological data. The results showed that the three remote sensing based drought monitoring models performed well in detecting the occurrence of drought in Chongqing on a certain extent. However, Time Series of Vegetation Index has stronger sensitivity in time pattern but weaker in spatial pattern; although TVDI and VSWI can reflect inverse the whole process of severe drought in 2006 summer from drought occurred - increased - relieved - increased again - complete remission in spatial domain, but TVDI requires the situation of extreme drought and extreme moist both exist in study area which it is more difficult in Chongqing; VSWI is simple and practicable, which the correlation coefficient between VSWI and soil moisture data reaches significant levels. In summary, VSWI is the best model for summer drought monitoring in Chongqing.
Sandoz, A.; Chauvelon, P.; Pichaud, M.
We show limits and potential applications of satellite images linked with agricultural and natural habitats and flooded duration problematic. Satellite images could play a major role in the study and monitoring context. When we started our satellite images collection in 1975, it allowed us to map annual variations of habitats and flooded areas. Since the year 2004, we've acquired an important quantity of Spot 5 images through a special programming (ISIS program), which cover the area during all the hydrological year. Using them, the knowledge of spatiotemporal dynamics of habitats and flooded areas, could then, be formalised in a much better way. We present results of inventory and monitoring in the Rhone delta context, South of France, an area of high wetland biodiversity in a Mediterranean catchment area. Our objective is to propose an operational methodology for inventory and monitoring of wetland habitats and wetland flooded duration. The exceptional spatial and temporal resolution sharpness is demonstrated.
Root-zone soil moisture information is a valuable diagnostic for detecting the onset and severity of agricultural drought. Current attempts to globally monitor root-zone soil moisture are generally based on the application of soil water balance models driven by observed meteorological variables. Suc...
elmi, omid; javad tourian, mohammad; sneeuw, nico
Monitoring the variation of water storage in a long period is a primary issue for understanding the impact of climate change and human activities on earth water resources. In order to obtain the change in water volume in a lake and reservoir, in addition to water level, water extent must be repeatedly determined in an appropriate time interval. Optical satellite imagery as a passive system is the main source of determination of coast line change as it is easy to interpret. Optical sensors acquire the reflected energy from the sunlight in various bands from visible to near infrared. Also, panchromatic mode provides more geometric details. Establishing a ratio between visible bands is the most common way of extract coastlines because with this ratio, water and land can be separated directly. Also, since the reflectance value of water is distinctly less than soil in infrared bands, applying a histogram threshold on this band is a effective way of coastline extraction. However, optical imagery is highly vulnerable to occurrence of dense clouds and fog. Moreover, the coastline is hard to detect where it is covered by dense vegetation. Synthetic aperture radar (SAR) as an active system provides an alternative source for monitoring the spatial change in coastlines. Two methods for monitoring the shoreline with SAR data have been published. First, the backscatter difference is calculated between two images acquired at different times. Second, the change in coastline is detected by computing the coherence of two SAR images acquired at different times. A SAR system can operate in all weather, so clouds and fog don't impact its efficiency. Also, it can penetrate into the plant canopy. However, in comparison with optical imagery, interpretation of SAR image in this case is relatively hard because of limitation in the number of band and polarization modes, also due to effects caused by speckle noises, slant-range imaging and shadows. The primary aim of this study is a
Furtney, M.; Pritchard, M. E.; Carn, S. A.; McCormick, B.; Ebmeier, S. K.; Jay, J.
Volcanoes exhibit variable eruption frequencies and styles, from near-continuous eruptions of effusive lavas to more intermittent, explosive eruptions. The monitoring frequency necessary to capture precursory signals at any volcano remains uncertain, as some warnings allot hours for evacuation. Likewise, no precursory signal appears deterministic for each volcano. Volcanic activity manifests in a variety of ways (i.e. tremor, deformation), thus requiring multiple monitoring mechanisms (i.e. geodetic, geochemical, geothermal). We are developing databases to compare relationships among remotely sensed volcanic unrest signals and eruptions. Satellite remote sensing utilizes frequent temporal measurements (daily to bi-weekly), an essential component of worldwide volcano monitoring. Remote sensing methods are also capable of detecting diverse precursory signals such as ground deformation from satellite interferometric synthetic aperture radar—InSAR— (multiple space agencies), degassing from satellite spectroscopy (i.e. OMI SO2 from NASA), and hot spots from thermal infrared (i.e. MODIS from NASA). We present preliminary results from seven SAR satellites and two thermal infrared satellites for 24 volcanoes with prominent SO2 emissions. We find near-continuous emissions at Ibu (Indonesia) since 2008 corresponded with hotspots and 10 cm of subsidence, with degassing and comparable subsidence observed at Pagan (Marianas). A newcomer to volcano monitoring, remote sensing data are only beginning to be utilized on a global scale, let alone as a synthesized dataset for monitoring developing eruptions. We foresee a searchable tool for rapidly accessing basic volcanic unrest characteristics for different types of volcanoes and whether or not they resulted in eruption. By including data from multiple satellite sensors in our database we hope to develop quantitative assessments for calculating the likelihood of eruption from individual events.
Howkins, John, Ed.
Four of the articles in this publication discuss the remote sensing of the Earth and its resources by satellites. Among the topics dealt with are the development and management of remote sensing systems, types of satellites used for remote sensing, the uses of remote sensing, and issues involved in using information obtained through remote…
Lan, Guoxin; Ma, Long; Li, Ying; Liu, Bingxin
Remote Sensing surveillance constitutes an important component of oil spill disaster management system, but subject to monitoring accuracy and ability, which suffered from resolution, environmental conditions, and look-alikes. So this article aims to provide information of identification and distinguishing of look-alikes for optical sensors, and then improve the monitoring precision. Although limited by monitoring conditions of the atmosphere and night, optical satellite remote sensing can provide the intrinsic spectral information of the film and the background sea, then affords the potentiality for detailed identification of the film thickness, oil type classification (crude/light oil), trends, and sea surface roughness by multi-type data products. This paper focused on optical sensors and indicated that these false targets of sun glint, bottom feature, cloud shadow, suspend bed sediment and surface bioorganic are the main factors for false alarm in optical images. Based on the detailed description of the theory of oil spill detection in optical images, depending on the preliminary summary of the feature of look-alikes in visible-infrared bands, a discriminate criteria and work-flow for slicks identification are proposed. The results are helpful to improve the remote sensing monitoring ability and the contingency planning.
Han, Xiuzhen; Ma, Jianwen; Bao, Yuhai
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.
Liu, Sanchao; Li, Wenbo
Drought is one of major nature disaster in the world and China. China has a vast territory and very different spatio-temporal distribution weather condition. Therefore, drought disasters occur frequently throughout China, which may affect large areas and cause great economic loss every year. In this paper, geostationary meteorological remote sensing data, FY-2C/D/E VISSR and three quantitative remotely sensed models including Cloud Parameters Method (CPM), Vegetation Supply Water Index (VSWI), and Temperature Vegetation Dryness Index (TVDI) have been used to dynamically monitor severe drought in southwest China from 2009 to 2010. The results have effectively revealed the occurrence, development and disappearance of this drought event. The monitoring results can be used for the relevant disaster management departments' decision-making works.
Fang, Chengyin; Chen, Xue; Ma, Jianwen
In order to follow up the concepts of "Green Olympics, High-tech Olympics and People's Olympics", Chinese Academy of Sciences set up a project to monitor the land cover and use change of Beijing city especially inner sixth ring road driven by the venues construction use remote sensing images after the successful bid for the 2008 Olympic Games. Landsat TM and airborne remote sensing temporal data were used in this paper to monitor the construction of Olympic main venues as well as the effluences on neighboring land use and urban growth. This research forms a complete set of urban growth model analysis, records the develop situation of Beijing driven by Olympic games and also provides decision and research reference of Olympic construction.
Tilton, James C.; Lawrence, William T.
NASA's Goddard Space Flight Center has developed a fast and effective method for generating image segmentation hierarchies. These segmentation hierarchies organize image data in a manner that makes their information content more accessible for analysis. Image segmentation enables analysis through the examination of image regions rather than individual image pixels. In addition, the segmentation hierarchy provides additional analysis clues through the tracing of the behavior of image region characteristics at several levels of segmentation detail. The potential for extracting the information content from imagery data based on segmentation hierarchies has not been fully explored for the benefit of the Earth and space science communities. This paper explores the potential of exploiting these segmentation hierarchies for the analysis of multi-date data sets, and for the particular application of change monitoring.
Jeong, Seungtaek; Ko, Jonghan; Kim, Mijeong; Kim, Jongkwon
We constructed a lightweight unmanned aerial vehicle (UAV) remote sensing system and determined the ideal method for equipment setup, image acquisition, and image processing. Fields of rice paddy (Oryza sativa cv. Unkwang) grown under three different nitrogen (N) treatments of 0, 50, or 115 kg/ha were monitored at Chonnam National University, Gwangju, Republic of Korea, in 2013. A multispectral camera was used to acquire UAV images from the study site. Atmospheric correction of these images was completed using the empirical line method, and three-point (black, gray, and white) calibration boards were used as pseudo references. Evaluation of our corrected UAV-based remote sensing data revealed that correction efficiency and root mean square errors ranged from 0.77 to 0.95 and 0.01 to 0.05, respectively. The time series maps of simulated normalized difference vegetation index (NDVI) produced using the UAV images reproduced field variations of NDVI reasonably well, both within and between the different N treatments. We concluded that the UAV-based remote sensing technology utilized in this study is potentially an easy and simple way to quantitatively obtain reliable two-dimensional remote sensing information on crop growth.
Marshall, M.; Tu, K.; Funk, C.; Michaelsen, J.; Williams, Pat; Williams, C.; Ardö, J.; Marie, B.; Cappelaere, B.; Grandcourt, A.; Nickless, A.; Noubellon, Y.; Scholes, R.; Kutsch, W.
Climate change is expected to have the greatest impact on the world's poor. In the Sahel, a climatically sensitive region where rain-fed agriculture is the primary livelihood, expected decreases in water supply will increase food insecurity. Studies on climate change and the intensification of the water cycle in sub-Saharan Africa are few. This is due in part to poor calibration of modeled actual evapotranspiration (AET), a key input in continental-scale hydrologic models. In this study, a model driven by dynamic canopy AET was combined with the Global Land Data Assimilation System realization of the NOAH Land Surface Model (GNOAH) wet canopy and soil AET for monitoring purposes in sub-Saharan Africa. The performance of the hybrid model was compared against AET from the GNOAH model and dynamic model using eight eddy flux towers representing major biomes of sub-Saharan Africa. The greatest improvements in model performance are at humid sites with dense vegetation, while performance at semi-arid sites is poor, but better than individual models. The reduction in errors using the hybrid model can be attributed to the integration of a dynamic vegetation component with land surface model estimates, improved model parameterization, and reduction of multiplicative effects of uncertain data.
Noomen, Marleen; Hakkarainen, Annika; van der Meijde, Mark; van der Werff, Harald
In recent years, several studies focused on the detection of hydrocarbon pollution in the environment using hyperspectral remote sensing. Particularly the indirect detection of hydrocarbon pollution, using vegetation reflectance in the red edge region, has been studied extensively. Bioremediation is one of the methods that can be applied to clean up polluted sites. So far, there have been no studies on monitoring of bioremediation using (hyperspectral) remote sensing. This study evaluates the feasibility of hyperspectral remote sensing for monitoring the effect of bioremediation over time. Benzene leakage at connection points along a pipeline was monitored by comparing the red edge position (REP) in 2005 and 2008 using HyMap airborne hyperspectral images. REP values were normalized in order to enhance local variations caused by a change in benzene concentrations. 11 out of 17 locations were classified correctly as remediated, still polluted, or still clean, with a total accuracy of 65%. When only polluted locations that were remediated were taken into account, the (user's) accuracy was 71%.
Yuliang Qiao, Pro.
As an important city in the southern part of Chu Chiang Delta, Zhuhai is one of the four special economic zones which are opening up to the outside at the earliest in China. With pure and fresh air and trees shading the street, Zhuhai is a famous beach port city which is near the mountain and by the sea. On the basis of Garden City, the government of Zhuhai decides to build National Forest City in 2011, which firstly should understand the situation of greenbelt in Zhuhai in short term. Traditional methods of greenbelt investigation adopt the combination of field surveying and statistics, whose efficiency is low and results are not much objective because of artificial influence. With the adventure of the information technology such as remote sensing to earth observation, especially the launch of many remote sensing satellites with high resolution for the past few years, kinds of urban greenbelt information extraction can be carried out by using remote sensing technology; and dynamic monitoring to spatial pattern evolvement of forest and greenbelt in Zhuhai can be achieved by the combination of remote sensing and GIS technology. Taking Landsat5 TM data in 1995, Landsat7 ETM+ data in 2002, CCD and HR data of CBERS-02B in 2009 as main information source, this research firstly makes remote sensing monitoring to dynamic change of forest and greenbelt in Zhuhai by using the combination of vegetation coverage index and three different information extraction methods, then does a driving force analysis to the dynamic change results in 3 months. The results show: the forest area in Zhuhai shows decreasing tendency from 1995 to 2002, increasing tendency from 2002 to 2009; overall, the forest area show a small diminution tendency from 1995 to 2009. Through the comparison to natural and artificial driving force, the artificial driving force is the leading factor to the change of forest and greenbelt in Zhuhai. The research results provide a timely and reliable scientific basis
Asante, K.O.; Macuacua, R.D.; Artan, G.A.; Lietzow, R.W.; Verdin, J.P.
This paper describes the application of remotely sensed precipitation to the monitoring of floods in a region that regularly experiences extreme precipitation and flood events, often associated with cyclonic systems. Precipitation data, which are derived from spaceborne radar aboard the National Aeronautics and Space Administration's Tropical Rainfall Measuring Mission and from National Oceanic and Atmospheric Administration's infrared-based products, are used to monitor areas experiencing extreme precipitation events that are defined as exceedance of a daily mean areal average value of 50 mm over a catchment. The remotely sensed precipitation data are also ingested into a hydrologic model that is parameterized using spatially distributed elevation, soil, and land cover data sets that are available globally from remote sensing and in situ sources. The resulting stream-flow is classified as an extreme flood event when flow anomalies exceed 1.5 standard deviations above the short-term mean. In an application in the Limpopo basin, it is demonstrated that the use of satellite-derived precipitation allows for the identification of extreme precipitation and flood events, both in terms of relative intensity and spatial extent. The system is used by water authorities in Mozambique to proactively initiate independent flood hazard verification before generating flood warnings. The system also serves as a supplementary information source when in situ gauging systems are disrupted. This paper concludes that remotely sensed precipitation and derived products greatly enhance the ability of water managers in the Limpopo basin to monitor extreme flood events and provide at-risk communities with early warning information. ?? 2007 IEEE.
Handcock, Rebecca N.; Swain, Dave L.; Bishop-Hurley, Greg J.; Patison, Kym P.; Wark, Tim; Valencia, Philip; Corke, Peter; O'Neill, Christopher J.
Remote monitoring of animal behaviour in the environment can assist in managing both the animal and its environmental impact. GPS collars which record animal locations with high temporal frequency allow researchers to monitor both animal behaviour and interactions with the environment. These ground-based sensors can be combined with remotely-sensed satellite images to understand animal-landscape interactions. The key to combining these technologies is communication methods such as wireless sensor networks (WSNs). We explore this concept using a case-study from an extensive cattle enterprise in northern Australia and demonstrate the potential for combining GPS collars and satellite images in a WSN to monitor behavioural preferences and social behaviour of cattle. PMID:22412327
Roundy, Joshua K.; Santanello, Joseph A.
Drought causes significant economic impact to society that can be reduced through preparations made possible by monitoring and prediction. Most drought monitoring systems utilize a variety of metrics to assess and understand drought. Feedbacks induced through land-atmosphere interactions are an important mechanism of drought intensification and persistence that is often not considered in current drought monitors due to a lack of spatially consistent observations. Recent work has developed a new classification of land-atmosphere interactions that summarizes the net impact of these interactions on drought intensification and recovery through the Coupling Drought Index (CDI). One thing that makes the CDI unique is that it can be calculated based on estimates from satellite remote sensing, which makes it particularly useful for global drought monitoring. Furthermore, the persistent nature of these coupling regimes provides a means of prediction through a Markov Chain Coupling Statistical Model (CSM). Previous work has shown that the CDI based on satellite remote sensing compares well with the U.S. Drought monitor in terms of drought intensification and recovery. On the other hand, the skill of the CSM forecasts over the U.S. is limited and still needs improvement. In this work the extent to which the CDI and CSM can be extended to other areas of the globe are explored. In particular, the ability of the satellite remote sensing based CDI to capture drought intensification and recovery over Africa and Europe are assessed. The benefits and limitations of using a metric of land-atmosphere interactions for global drought monitoring are also discussed.
The application of remote sensing techniques to land management, urban planning, agriculture, oceanography, and environmental monitoring is discussed. The results of various projects are presented along with cost effective considerations.
Imhoff, Marc L.; Rosenquist, A.; Milne, A. K.; Dobson, M. C.; Qi, J.
An International workshop was held to address how remote sensing technology could be used to support the environmental monitoring requirements of the Kyoto Protocol. An overview of the issues addressed and the findings of the workshop are discussed.
Shen, Xuhui; Zhang, Xuemin; Hong, Shunying; Jing, Feng; Zhao, Shufan
In the last ten years, a few national research plans and scientific projects on remote sensing application in Earthquake monitoring research are implemented in China. Focusing on advancing earthquake monitoring capability searching for the way of earthquake prediction, satellite electromagnetism, satellite infrared and D-InSAR technology were developed systematically and some remarkable progress were achieved by statistical research on historical earthquakes and summarized initially the space precursory characters, which laid the foundation for gradually promoting the practical use. On the basis of these works, argumentation on the first space-based platform has been finished in earthquake stereoscope observation system in China, and integrated earthquake remote sensing application system has been designed comprehensively. To develop the space-based earthquake observational system has become a major trend of technological development in earthquake monitoring and prediction. We shall pay more emphasis on the construction of the space segment of China earthquake stereoscope observation system and Imminent major scientific projects such as earthquake deformation observation system and application research combined INSAR, satellite gravity and GNSS with the goal of medium and long term earthquake monitoring and forcasting, infrared observation and technical system and application research with the goal of medium and short term earthquake monitoring and forcasting, and satellite-based electromagnetic observation and technical system and application system with the goal of short term and imminent earthquake monitoring.
Bhat, Nagaraj; Gouda, Krushna Chandra; Vh, Manumohan; Bhat, Reshma
Water Reservoirs are the main source of water supply for many settlements as well as power generation. So the water volume and extent of the reservoirs needs to be monitored at regular time intervals for efficient usage as well as to avoid disasters like extreme rainfall events and flood etc. Generally the reservoirs are remotely located so it is difficult to well monitor the water volume and extent. But with growing of Remote sensing and GIS in HPC environment and modeling techniques it is possible to monitor, estimate even predict the reservoir water volumes in advance by using the numerical modeling and satellite Remote sensing data. In this work the monitoring and estimation of the volume of water in the Krishna Raja Sagar(KRS) water reservoir in Karnataka state of India. In this work multispectral images from different sources like Landsat TRS and Digital Elevation Model(DEM) using IRS LISS III (IRS- Indian Remote Sensing, LISS- Linear Imaging Self-Scanning) and ASTER(Advanced Spaceborne Thermal Emission and Reflectance Radiometer) are being used .The methodology involves GIS and image processing techniques such as mosaicing and georeferencing the raw data from satellite, identifying the reservoir water level, segmentation of waterbody using the pixel level analysis. Calculating area and depth per each pixel, the total water volume calculations are done based on the empirical model developed using the past validated data. The water spreaded area calculated by using water indexing is converted in to vector polygon using ArcGIS tools. Water volume obtained by this method is compared with ground based observed values of a reservoir and the comparison well matches for 80% of cases.
Zhou, M.; Yuan, X.; Sun, L.
Wetland is important natural resource. The main method to monitor the landcover change in wetland natural reserve is to extract and analyze information from remote sensing image. In this paper, the landcover information is extracted, summarized and analyzed by using multi-temporal HJ and Landsat satellite image in Zhalong natural reserve, Heilongjiang, China. The method can monitor the wetland landcover change accurately in real time and long term. This paper expounds the natural factors and human factors influence on wetland land use type, for scientific and effective support for the development of the rational use of wetlands in Zhalong natural wetland reserve.
Song, Xiaoyu; Wang, Jihua; Huang, Wenjiang
An uneven growing winter wheat will be slower to reach full ground cover and will be lead to uneven yield and quality for cropland. The traditional investigation of crop uniformity is mainly depends on manpower. Remote sensing technique is a potentially useful tool for monitoring the crop uniformity status for it can provide an area global view for entire field within the crop growth season with scathelessness. The objective of this study was to use remote sensing imagery to evaluate the crop growth uniformity, as well as the yield and grain quality variation for a winter wheat study area. One Quickbird image on winter wheat booting stage was collected and processed to monitoring the uniformity of wheat growth. The results indicated that the spectrum parameters of Quickbird image can reflect the spatial uniformity of winter wheat growth in the study areas. Meanwhile the spatial uniformity of wheat growth in early stage can reflect the uniformity of yield and grain quality. The wheat growth information at the booting stage has strong positive correlations with yield, and strong negative correlation with grain protein. The correlation coefficient between OSAVI (optimized soil adjusted vegetation index) and wheat yield was 0.536. It was -0.531 for GNDVI (Greeness-normalized difference vegetation index) and grain protein content. The study also indicated that diverse spectrum parameters had different sensitivity to the wheat growth spatial variance. So it is feasible to use remote sensing data to investigate the crop growth and quality spatial uniformity.
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
Gu, Xing-Fa; Chen, Xing-Feng; Yin, Qiu; Li, Zheng-Qiang; Xu, Hua; Shao, Yun; Li, Zi-Wei
In the summer 2008, Enteromorpha Prolifra broke out in Yellow Sea and East Sea on a large scale for the first time, and became a marine disaster. The authors constructed a stereoscopic monitoring system which monitored the disaster continuously, dynamically and in real time. The present paper introduced the construction of the stereoscopic monitoring system; through analyzing the spectral characteristics of Enteromorpha Prolifra and ocean water which were acquired in a field experiment, confirmed Enteromorpha Prolifra retrieval models based on multi-platform multi-sensor and multi-spectral remote sensing data, contrasted the different scale monitoring results, and analyzed the evolvement rules with time-series analysis. This system was applied to the Enteromorpha Prolifra emergency monitoring in the 29th Olympic sailing area. It was proved feasible and valuable for the Olympic safeguard. PMID:21847947
Johnson, J. D.; Foster, K. E.; Mouat, D. A.; Miller, D. A.; Conn, J. S.
The activities and accomplishments of the Applied Remote Sensing Program during FY 1975-1976 are reported. The principal objective of the Applied Remote Sensing Program continues to be designed projects having specific decision-making impacts as a principal goal. These projects are carried out in cooperation and collaboration with local, state and federal agencies whose responsibilities lie with planning, zoning and environmental monitoring and/or assessment in the application of remote sensing techniques. The end result of the projects is the use by the involved agencies of remote sensing techniques in problem solving.
Guzzetti, F.; Candela, L.; Carlà, R.; Fornaro, G.; Lanari, R.; Mondini, A.; Ober, G.; Fiorucci, F.; Zeni, G.
MORFEO, an Italian acronym for Monitoring Landslide Risk exploiting Earth Observation Technology, is a 3-year research and development project of the Italian Space Agency, carried out in the framework of the Italian national earth observation programme. The project primary contract is Carlo Gavazzi Space, a leading enterprise in space technology and remote sensing applications in Italy. The project research team is composed by seven research institutes of the Italian National Research Council, and six university departments. The team has consolidated experience in landslide detection and mapping, landslide hazard assessment and risk evaluation, remote sensing technology (e.g., laser, optical, radar, GPS) for landslide detection, mapping and monitoring. MORFEO aims at the design, development and demonstration of a prototype system that exploits multiple satellite technologies to support the Italian national civil protection offices to manage landslide risk in Italy. Research activities conducted within the MORFEO project consist chiefly in testing, evaluating and improving EO technologies to increase the current capabilities to detect, map, monitor and forecast landslides in Italy. More precisely, the activities include: (i) detection and mapping landslides exploiting medium-resolution to very-high resolution satellite optical images, (ii) landslide monitoring, through the integration of ground based and satellite technologies, including GPS and DInSAR, (iii) landslide susceptibility, hazard and risk modelling using information obtained processing optical and radar data, (iv) vulnerability and damage assessment, exploiting optical and radar sensors, and (v) landslides forecasting, using thresholds, models and remote sensing data. We provide examples of some of the preliminary results obtained in the MOFEO project.
Keafer, L. S., Jr. (Editor)
The long term role of airborne/spaceborne passive remote sensing systems for tropospheric air quality research and the identification of technology advances required to improve the performance of passive remote sensing systems were discussed.
This task provides remote sensing technical support to the Superfund program. Support includes the collection, processing, and analysis of remote sensing data to characterize hazardous waste disposal sites and their history. Image analysis reports, aerial photographs, and assoc...
Li, Su-wen; Xie, Pin-hua; Jiang, En-hua; Zhang, Yong; Dai, Hai-feng
Huaibei is an energy city. Coal as the primary energy consumption brings a large number of regional pollution in Huaibei area. Differential optical absorption spectroscopy (DOAS) as optical remote sensing technology has been applied to monitor regional average concentrations and inventory of nitrogen dioxide, sulfur dioxide and ozone. DOAS system was set up and applied to monitor the main air pollutants in Huaibei area. Monitoring data were obtained from 7 to 28 August, 2011. Monitoring results show measurements in controlling pollution are effective, and emissions of pollutants are up to the national standard in Huaibei area. Prediction model was also created to track changing trend of pollutions. These will provide raw data support for effective evaluation of environmental quality in Huaibei area.
Zheng, You-Fei; Cheng, Jin-Xin; Wu, Rong-Jun; Guan, Fu-Lai; Yao, Shu-Ran
From the viewpoint of land surface evapotranspiration, and by using the semi-empirical evapotranspiration model based on the Priestley-Taylor equation and the land surface temperature-vegetation index (LST-VI) triangle algorithm, the current monitoring technology of agricultural drought based on remote sensing was improved, and a simplified Evapotranspiration Stress Index (SESI) was derived. With the application of the MODIS land products from March to November in 2008 and 2009, the triangle algorithm modeling with three different schemes was constructed to calculate the SESI to monitor the agricultural drought in the plain areas of Beijing, Tianjin, and Hebei, in comparison with the Temperature Vegetation Dryness Index (TVDI). The results showed that SESI could effectively simplify the remote sensing drought monitoring method, and there was a good agreement between SESI and surface soil (10 and 20 cm depth) moisture content. Moreover, the performance of SESI was better in spring and autumn than in summer, and the SESI during different periods was more comparable than TVDI. It was feasible to apply the SESI to the continuous monitoring of a large area of agricultural drought. PMID:24417121
Wang, Difeng; Pan, Delu; Li, Ning
The State Development and Planning Commission has approved nuclear power projects with the total capacity of 23,000 MW. The plants will be built in Zhejiang, Jiangsu, Guangdong, Shandong, Liaoning and Fujian Province before 2020. However, along with the nuclear power policy of accelerated development in our country, the quantity of nuclear plants and machine sets increases quickly. As a result the environment influence of thermal discharge will be a problem that can't be slid over. So evaluation of the environment influence and engineering simulation must be performed before station design and construction. Further more real-time monitoring of water temperature need to be arranged after fulfillment, reflecting variety of water temperature in time and provided to related managing department. Which will help to ensure the operation of nuclear plant would not result in excess environment breakage. At the end of 2007, an airborne thermal discharge monitoring experiment has been carried out by making use of MAMS, a marine multi-spectral scanner equipped on the China Marine Surveillance Force airplane. And experimental subject was sea area near Qin Shan nuclear plant. This paper introduces the related specification and function of MAMS instrument, and decrypts design and process of the airborne remote sensing experiment. Experiment showed that applying MAMS to monitoring thermal discharge is viable. The remote sensing on a base of thermal infrared monitoring technique told us that thermal discharge of Qin Shan nuclear plant was controlled in a small scope, never breaching national water quality standard.
Fu, Jun'e.; Lu, Jingxuan; Pang, Zhiguo
Regional river basins, transboundary rivers in particular, are shared water resources among multiple users. The tempo-spatial distribution and utilization potentials of water resources in these river basins have a great influence on the economic layout and the social development of all the interested parties in these basins. However, due to the characteristics of cross borders and multi-users in these regions, especially across border regions, basic data is relatively scarce and inconsistent, which bring difficulties in basin water resources management. Facing the basic data requirements in regional river management, the overall technical framework for remote sensing monitoring and data service system in China's regional river basins was designed in the paper, with a remote sensing driven distributed basin hydrologic model developed and integrated within the frame. This prototype system is able to extract most of the model required land surface data by multi-sources and multi-temporal remote sensing images, to run a distributed basin hydrological simulation model, to carry out various scenario analysis, and to provide data services to decision makers.
Chawira, M.; Dube, T.; Gumindoga, W.
Lakes Chivero and Manyame are amongst Zimbabwe’s most polluted inland water bodies. MEdium Resolution Imaging Spectrometry level 1b full resolution imagery for 2011 and 2012 were used to derive chlorophyll-a (chl_a) and phycocyanin (blue-green algae) concentrations using a semi-empirical band ratio model; total suspended matter (TSM) concentrations were derived from the MERIS processor. In-situ measured chl_a was used to validate the remotely sensed values. Results indicate that remote sensing measurements are comparable with in situ measurements. A strong positive correlation (R2 = 0.91; MAE = 2.75 mg/m3 (8.5%)) and p < 0.01 (highly significant)) between measured and modeled chl_a concentrations was obtained. Relationships between optically active water constituents were assessed. Measured chl_a correlated well with MERIS modeled phycocyanin (PC) concentration (R2 = 0.9458; p < 0.01 (highly significant)) whilst chl_a and TSM gave (R2 = 0.7344; p < 0.05 (significant)). Modeled TSM and PC concentrations manifested a good relationship with each other (R2 = 9047; p < 0.001 (very highly significant)). We conclude that remote sensing data allow simultaneous retrieval of different water quality parameters as well as providing near real time and space results that can be used by water managers and policy makers to monitor water bodies.
Paska, Eva Petra
The widespread use of digital technologies, combined with rapid sensor advancements resulted in a paradigm shift in geospatial technologies the end of the last millennium. The improved performance provided by the state-of-the-art airborne remote sensing technology created opportunities for new applications that require high spatial and temporal resolution data. Transportation activities represent a major segment of the economy in industrialized nations. As such both the transportation infrastructure and traffic must be carefully monitored and planned. Engineering scale topographic mapping has been a long-time geospatial data user, but the high resolution geospatial data could also be considered for vehicle extraction and velocity estimation to support traffic flow analysis. The objective of this dissertation is to provide an assessment on what state-of-the-art remote sensing technologies can offer in both areas: first, to further improve the accuracy and reliability of topographic, in particular, roadway corridor mapping systems, and second, to assess the feasibility of extracting primary data to support traffic flow computation. The discussion is concerned with airborne LiDAR (Light Detection And Ranging) and digital camera systems, supported by direct georeferencing. The review of the state-of-the-art remote sensing technologies is dedicated to address the special requirements of the two transportation applications of airborne remotely sensed data. The performance characteristics of the geospatial sensors and the overall error budget are discussed. The error analysis part is focused on the overall achievable point positioning accuracy performance of directly georeferenced remote sensing systems. The QA/QC (Quality Assurance/Quality Control) process is a challenge for any airborne direct georeferencing-based remote sensing system. A new method to support QA/QC is introduced that uses the road pavement markings to improve both sensor data accuracy as well as the
Pettersson, L.H.; Johannessen, O.M.; Frette, O. )
During the late spring of 1988 an extensive bloom of the toxic algae Chrysocromulina polylepis occurred in the Skagerrak region influencing most life in the upper 30 meter of the ocean. The algal front was advected northward with the Norwegian Coastal Current along the coast of southern Norway, where it became a severe threat to the Norwegian seafarming industry. An ad-hoc expert team was established to monitor and forecast the movement of the algae front. Remote sensing of sea surface temperature from the operational US NOAA satellites monitored the movement of the algal front, consistent with a warm ocean front. The lack of any optical remote sensing instrumentation was recognized as a major de-efficiency during this algal bloom. To prepare for similar events in the future Nansen Remote Sensing Center initiated a three week pilot study in the Oslofjord and Skagerrak region, during May 1989. The Canadian Compact Airborne Spectrographic Imager (CASI) was installed in the surveillance aircraft. Extensive in situ campaigns was also carried out by the Norwegian Institute for Water Research and Institute of Marine Research. A ship-borne non-imaging spectrometer was operated from the vessels participating in the field campaign. As a contribution from a joint campaign (EISAC '89) between the Joint Research Centre (JRC) of the European Community and the European Space Agency (ESA) both the Canadian Fluorescence Line Imager (FLI) and the US 64-channel GER scanner was operated simultaneously at the NORSMAP 89 test site. Regions of different biological and physical conditions were covered during the pilot study and preliminary analysis are obtained from oil slicks, suspended matter from river, as well as minor algal bloom. The joint analysis of the data collected during the NORSMAP 89 campaign and conclussions will be presented, as well as suggestions for future utilization of airborne spectroscopy systems for operational monitoring of algal bloom and water pollution.
Washington-Allen, R. A.
Drylands cover 41% of the terrestrial surface and provide > $1 trillion in ecosystem services to one-third of the global population, yet are not well studied with estimates of degradation ranging from 10 - 80%. Here I will present an abbreviated history of the use of remote sensing (RS) to monitor Dryland degradation, review contemporary applications, and provide guidance for future directions. These early monitoring attempts (and some recent efforts) assumed the social model of "Tragedy of the Commons" and the ecological model of "the Balance of Nature". These assumptions justified a monitoring approach rather than an assessment, where land degradation was understood to be primarily a function of human action through livestock grazing management. The perceived linear impact of grazing on grassland biomass led to the early development of a remote sensing-based proxy of vegetation response: the normalized difference vegetation index (NDVI). Many RS studies of Drylands are biased towards the NDVI or variants, whereas the contemporary view of Drylands as complex systems has led to a new synthesis of approaches from ecological modeling, ecohydrology, landscape ecology, and remote sensing that now explicitly confront both multiple drivers that include land-use policy, droughts & floods, fire, and responses that include increased soil erosion and changes in soil quality, landscape composition, pattern, and structure. However, problems still abound including 1) a consensus on the definition of Drylands, 2) the need for time series of drivers to conduct assessments, 3) a lack of understanding of below-ground biomass dynamics, 4) improved mapping of grassland, shrubland, and savanna dryland cover types and their 3D structure. There are new technologies in Dryland RS including multi-frequency ground penetrating radar (GPR), RADAR, IFSAR, LIDAR, and MISR that may lead to the development of new indicators to address these issues.
Budde, M. E.; Rowland, J.; Senay, G. B.; Funk, C. C.; Husak, G. J.; Magadzire, T.; Verdin, J. P.
Global agriculture monitoring is a crucial aspect of monitoring food security in the developing world. The Famine Early Warning Systems Network (FEWS NET) has a long history of using remote sensing and crop modeling to address food security threats in the form of drought, floods, pests, and climate change. In recent years, it has become apparent that FEWS NET requires the ability to apply monitoring and modeling frameworks at a global scale to assess potential impacts of foreign production and markets on food security at regional, national, and local levels. Scientists at the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center and the University of California Santa Barbara (UCSB) Climate Hazards Group have provided new and improved data products as well as visualization and analysis tools in support of the increased mandate for remote monitoring. We present our monitoring products for measuring actual evapotranspiration (ETa), normalized difference vegetation index (NDVI) in a near-real-time mode, and satellite-based rainfall estimates and derivatives. USGS FEWS NET has implemented a Simplified Surface Energy Balance (SSEB) model to produce operational ETa anomalies for Africa and Central Asia. During the growing season, ETa anomalies express surplus or deficit crop water use, which is directly related to crop condition and biomass. We present current operational products and provide supporting validation of the SSEB model. The expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) production system provides FEWS NET with an improved NDVI dataset for crop and rangeland monitoring. eMODIS NDVI provides a reliable data stream with a relatively high spatial resolution (250-m) and short latency period (less than 12 hours) which allows for better operational vegetation monitoring. We provide an overview of these data and cite specific applications for crop monitoring. FEWS NET uses satellite rainfall estimates as inputs for
Remote sensing technologies applications research supports the ORD Landscape Sciences Program (LSP) in two separate areas: operational remote sensing, and remote sensing research and development. Operational remote sensing is provided to the LSP through the use of current and t...
Bai, Hongwu; Teng, Guanghui; Ma, Liang; Li, Zhizhong; Yuan, Zhengdong; Li, Minzan; Yang, Xiuslayerg
A remote sensing and monitor system for a large poultry layer farm is developed based on distributed data acquisition and internet control. The supervising system applied patent techniques known as arc orbit movable vidicon, wireless video transmission and telecommunications. It features supervising at all orientations, and digital video telecommunicating through internet. All measured and control information is sent to a central computer, which is in charge of storing, displaying, analyzing and serving to internet, where managers can monitor real time production scene anywhere and customers can also see the healthy layers through internet. This paper primarily discusses how to design the remote sensing and monitor system (RSMS), and its usage in a large poultry farm, Deqingyuan Healthy Breeding Ecological Garden, Yanqing County, Beijing, China. The system applied web service technology and the middleware using XML language and Java language. It preponderated in data management, data exchange, expansibility, security, and compatibility. As a part of poultry sustainable development management system, it has been applied in a large farm with 1,200,000 layers. Tests revealed that there was distinct decline in the death ratio of chicken with 2. 2%, as the surroundings of layers had been ameliorated. At the same time, there was definite increase in the laying ratio with 3. 5%.
Wang, Li-Tao; Wang, Shi-Xin; Zhou, Yi; Liu, Wen-Liang; Wang, Fu-Tao
The vegetation is one of main drying carriers. The change of Vegetation Water Content (VWC) reflects the spatial-temporal distribution of drought situation and the degree of drought. In the present paper, a method of retrieving the VWC based on remote sensing data is introduced and analyzed, including the monitoring theory, vegetation water content indicator and retrieving model. The application was carried out in the region of Southwest China in the spring, 2010. The VWC data was calculated from MODIS data and spatially-temporally analyzed. Combined with the meteorological data from weather stations, the relationship between the EWT and weather data shows that precipitation has impact on the change in vegetation moisture to a certain extent. However, there is a process of delay during the course of vegetation absorbing water. So precipitation has a delaying impact on VWC. Based on the above analysis, the probability of drought monitoring and evaluation based on multi-spectral VWC data was discussed. Through temporal synthesis and combined with auxiliary data (i. e. historical data), it will help overcome the limitation of data itself and enhance the application of drought monitoring and evaluation based on the multi-spectral remote sensing. PMID:22250560
Wang, Fu-tao; Wang, Shi-xin; Zhou, Yi; Wang, Li-tao; Yan, Fu-li; Li, Wen-jun; Liu, Xiong-fei
The secondary geological disasters triggered by the Lushan earthquake on April 20, 2013, such as landslides, collapses, debris flows, etc., had caused great casualties and losses. We monitored the number and spatial distribution of the secondary geological disasters in the earthquake-hit area from airborne remote sensing images, which covered areas about 3 100 km2. The results showed that Lushan County, Baoxing County and Tianquan County were most severely affected; there were 164, 126 and 71 secondary geological disasters in these regions. Moreover, we analyzed the relationship between the distribution of the secondary geological disasters, geological structure and intensity. The results indicate that there were 4 high-hazard zones in the monitored area, one focused within six kilometers from the epicenter, and others are distributed along the two main fault zones of the Longmen Mountain. More than 97% secondary geological disasters occurred in zones with a seismic intensity of VII to IX degrees, a slope between 25 A degrees and 50 A degrees, and an altitude of between 800 and 2 000 m. At last, preliminary suggestions were proposed for the rehabilitation and reconstruction planning of Lushan earthquake. According to the analysis result, airborne and space borne remote sensing can be used accurately and effectively in almost real-time to monitor and assess secondary geological disasters, providing a scientific basis and decision making support for government emergency command and post-disaster reconstruction. PMID:27228764
Nikolakopoulos, Konstantinos G.; Kavoura, Katerina; Depountis, Nikolaos; Argyropoulos, Nikolaos; Koukouvelas, Ioannis; Sabatakakis, Nikolaos
An active landslide can be monitored using many different methods: Classical geotechnical measurements like inclinometer, topographical survey measurements with total stations or GPS and photogrammetric techniques using airphotos or high resolution satellite images. As the cost of the aerial photo campaign and the acquisition of very high resolution satellite data is quite expensive the use of cameras on board UAV could be an identical solution. Small UAVs (Unmanned Aerial Vehicles) have started their development as expensive toys but they currently became a very valuable tool in remote sensing monitoring of small areas. The purpose of this work is to demonstrate a cheap but effective solution for an active landslide monitoring. We present the first experimental results of the synergistic use of UAV, GPS measurements and remote sensing data. A six-rotor aircraft with a total weight of 6 kg carrying two small cameras has been used. Very accurate digital airphotos, high accuracy DSM, DGPS measurements and the data captured from the UAV are combined and the results are presented in the current study.
Keskin, Göksu; Teutsch, Caroline D.; Lenz, Andreas; Middelmann, Wolfgang
Areas occupied by oil pipelines and storage facilities are prone to severe contamination due to leaks caused by natural forces, poor maintenance or third parties. These threats have to be detected as quickly as possible in order to prevent serious environmental damage. Periodical and emergency monitoring activities need to be carried out for successful disaster management and pollution minimization. Airborne remote sensing stands out as an appropriate choice to operate either in an emergency or periodically. Hydrocarbon Index (HI) and Hydrocarbon Detection Index (HDI) utilize the unique absorption features of hydrocarbon based materials at SWIR spectral region. These band ratio based methods require no a priori knowledge of the reference spectrum and can be calculated in real time. This work introduces a flexible airborne pipeline monitoring system based on the online quasi-operational hyperspectral remote sensing system developed at Fraunhofer IOSB, utilizing HI and HDI for oil leak detection on the data acquired by an SWIR imaging sensor. Robustness of HI and HDI compared to state of the art detection algorithms is evaluated in an experimental setup using a synthetic dataset, which was prepared in a systematic way to simulate linear mixtures of selected background and oil spectra consisting of gradually decreasing percentages of oil content. Real airborne measurements in Ettlingen, Germany are used to gather background data while the crude oil spectrum was measured with a field spectrometer. The results indicate that the system can be utilized for online and offline monitoring activities.
Goettelman, R. C.; Grass, L. B.; Millard, J. P.; Nixon, P. R.
The following multispectral remote-sensing techniques were compared to determine the most suitable method for routinely monitoring agricultural subsurface drain conditions: airborne scanning, covering the visible through thermal-infrared (IR) portions of the spectrum; color-IR photography; and natural-color photography. Color-IR photography was determined to be the best approach, from the standpoint of both cost and information content. Aerial monitoring of drain conditions for early warning of tile malfunction appears practical. With careful selection of season and rain-induced soil-moisture conditions, extensive regional surveys are possible. Certain locations, such as the Imperial Valley, Calif., are precluded from regional monitoring because of year-round crop rotations and soil stratification conditions. Here, farms with similar crops could time local coverage for bare-field and saturated-soil conditions.
El Vilaly, Mohamed Abd salam M.
Droughts are a recurrent part of our climate, and are still considered to be one of the most complex and least understood of all natural hazards in terms of their impact on the environment. In recent years drought has become more common and more severe across the world. For more than a decade, the US southwest has faced extensive and persistent drought conditions that have impacted vegetation communities and local water resources. The focus of this work is achieving a better understanding of the impact of drought on the lands of the Hopi Tribe and Navajo Nation, situated in the Northeastern corner of Arizona. This research explores the application of remote sensing data and geospatial tools in two studies to monitor drought impacts on vegetation productivity. In both studies we used land surface phenometrics as the data tool. In a third related study, I have compared satellite-derived land surface phenology (LSP) to field observations of crop stages at the Maricopa Agricultural Center to achieve a better understanding of the temporal sensitivity of satellite derived phenology of vegetation and understand their accuracy as a tool for monitoring change. The first study explores long-term vegetation productivity responses to drought. The paper develops a framework for drought monitoring and assessment by integrating land cover, climate, and topographical data with LSP. The objective of the framework is to detect long-term vegetation changes and trends in the Normalized Difference Vegetation Index (NDVI) related productivity. The second study examines the major driving forces of vegetation dynamics in order to provide valuable spatial information related to inter-annual variability in vegetation productivity for mitigating drought impacts. The third study tests the accuracy of remote sensing-derived LSP by comparing them to the actual seasonal phases of crop growth. This provides a way to compare and validate the various LSP algorithms, and more crucially, helps to
Hong, Yang; Adler, Robert F.; Huffman, George J.
Landslides triggered by rainfall can possibly be foreseen in real time by jointly using rainfall intensity-duration thresholds and information related to land surface susceptibility. However, no system exists at either a national or a global scale to monitor or detect rainfall conditions that may trigger landslides due to the lack of extensive ground-based observing network in many parts of the world. Recent advances in satellite remote sensing technology and increasing availability of high-resolution geospatial products around the globe have provided an unprecedented opportunity for such a study. In this paper, a framework for developing an experimental real-time monitoring system to detect rainfall-triggered landslides is proposed by combining two necessary components: surface landslide susceptibility and a real-time space-based rainfall analysis system (http://trmm.gsfc.nasa.aov). First, a global landslide susceptibility map is derived from a combination of semi-static global surface characteristics (digital elevation topography, slope, soil types, soil texture, and land cover classification etc.) using a GIs weighted linear combination approach. Second, an adjusted empirical relationship between rainfall intensity-duration and landslide occurrence is used to assess landslide risks at areas with high susceptibility. A major outcome of this work is the availability of a first-time global assessment of landslide risk, which is only possible because of the utilization of global satellite remote sensing products. This experimental system can be updated continuously due to the availability of new satellite remote sensing products. This proposed system, if pursued through wide interdisciplinary efforts as recommended herein, bears the promise to grow many local landslide hazard analyses into a global decision-making support system for landslide disaster preparedness and risk mitigation activities across the world.
Savastano, K. J.; Leming, T. D.
An investigation was conducted to establish the feasibility of utilizing remotely sensed data acquired from aircraft and satellite platforms to provide information concerning the distribution and abundance of oceanic gamefish. The data from the test area was jointly acquired by NASA, the Navy, the Air Force and NOAA/NMFS elements and private and professional fishermen in the northeastern Gulf of Mexico. The data collected has made it possible to identify fisheries significant environmental parameters for white marlin. Prediction models, based on catch data and surface truth information, were developed and demonstrated a potential for significantly reducing search by identifying areas that have a high probability of productivity. Three of the parameters utilized by the models, chlorophyll-a, sea surface temperature, and turbidity were inferred from aircraft sensor data and were tested. Effective use of Skylab data was inhibited by cloud cover and delayed delivery. Initial efforts toward establishing the feasibility of utilizing remotely sensed data to assess and monitor the distribution of oceanic gamefish has successfully identified fisheries significant oceanographic parameters and demonstrated the capability of remotely measuring most of the parameters.
di, L.; Yu, G.; Han, W.; Deng, M.
Group on Earth Observations (GEO) is a voluntary partnership of governments and international organizations. GEO is coordinating the implementation of the Global Earth Observation System of Systems (GEOSS), a worldwide effort to make Earth observation resources more useful to the society. As one of the important technical contributors to GEOSS, the Center for Spatial Information Science and Systems (CSISS), George Mason University, is implementing a remote sensing-based global agricultural drought monitoring and forecasting system (GADMFS) as a GEOSS societal benefit areas (agriculture and water) prototype. The goals of the project are 1) to establish a system as a component of GEOSS for providing global on-demand and systematic agriculture drought information to users worldwide, and 2) to support decision-making with improved monitoring, forecasting, and analyses of agriculture drought. GADMFS has adopted the service-oriented architecture and is based on standard-compliant interoperable geospatial Web services to provide online on-demand drought conditions and forecasting at ~1 km spatial and daily and weekly temporal resolutions for any part of the world to world-wide users through the Internet. Applicable GEOSS recommended open standards are followed in the system implementation. The system’s drought monitoring relies on drought-related parameters, such as surface and root-zone soil moisture and NDVI time series derived from remote sensing data, to provide the current conditions of agricultural drought. The system links to near real-time satellite remote sensing data sources from NASA and NOAA for the monitoring purpose. For drought forecasting, the system utilizes a neural-network based modeling algorithm. The algorithm is trained with inputs of current and historic vegetation-based and climate-based drought index data, biophysical characteristics of the environment, and time-series weather data. The trained algorithm will establish per-pixel model for
Thermal infrared remote sensing over vegetated land surfaces is expected to provide valuable information for documenting soil-vegetation-atmosphere exchanges about heat, water and mass. On the one hand, the community benefits of various TIR remote sensing observations according to four dimensions: s...
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
Albright, T.P.; Ode, D.J.
Potamogeton crispus L. (curly pondweed) is a cosmopolitan aquatic macrophyte considered invasive in North America and elsewhere. Its range is expanding and, on individual water bodies, its coverage can be dynamic both within and among years. In this study, we evaluate the use of free and low-cost satellite remote sensing data to monitor a problematic emergent macrophyte community dominated by P. crispus. Between 2000 and 2006, we acquired eight satellite images of 24,000-ha Lake Sharpe, South Dakota (USA). During one of the dates for which satellite imagery was acquired, we sampled the lake for P. crispus and other emergent macrophytes using GPS and photography for documentation. We used cluster analysis to assist in classification of the satellite imagery and independently validated results using the field data. Resulting estimates of emergent macrophyte coverage ranged from less than 20 ha in 2002 to 245 ha in 2004. Accuracy assessment indicated 82% of image pixels were correctly classified, with errors being primarily due to failure to identify emergent macrophytes. These results emphasize the dynamic nature of P. crispus-dominated macrophyte communities and show how they can be effectively monitored over large areas using low-cost remote sensing imagery. While results may vary in other systems depending on water quality and local flora, such an approach could be applied elsewhere and for a variety of macrophyte communities. ?? Springer Science+Business Media B.V. 2010.
Sarna, Karolina; Russchenberg, Herman W. J.
A new method for continuous observation of aerosol-cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of the change of the cloud droplet size due to the change in the aerosol concentration. We use high-resolution measurements from a lidar, a radar and a radiometer, which allow us to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example case studies were chosen from the Atmospheric Radiation Measurement (ARM) Program deployment on Graciosa Island, Azores, Portugal, in 2009 to present the method. We use the cloud droplet effective radius (re) to represent cloud microphysical properties and an integrated value of the attenuated backscatter coefficient (ATB) below the cloud to represent the aerosol concentration. All data from each case study are divided into bins of the liquid water path (LWP), each 10 g m-2 wide. For every LWP bin we present the correlation coefficient between ln re and ln ATB, as well as ACIr (defined as ACIr = -d ln re/d ln ATB, change in cloud droplet effective radius with aerosol concentration). Obtained values of ACIr are in the range 0.01-0.1. We show that ground-based remote sensing instruments used in synergy can efficiently and continuously monitor aerosol-cloud interactions.
Markogianni, V; Dimitriou, E; Karaouzas, I
Degradation of water quality is a major problem worldwide and often leads to serious environmental impacts and concerns about public health. In this study, the water quality monitoring and assessment of the Koumoundourou Lake, a brackish urban shallow lake located in the northeastern part of Elefsis Bay (Greece), were evaluated. A number of water quality parameters (pH, temperature, dissolved oxygen concentration, electrical conductivity, turbidity, nutrients, and chlorophyll-a concentration) were analyzed in water samples collected bimonthly over a 1-year period from five stations throughout the lake. Moreover, biological quality elements were analysed seasonally over the 1-year period (benthic fauna). Statistical analysis was performed in order to evaluate the water quality of the lake and distinguish sources of variation measured in the samples. Furthermore, the chemical and trophic status of the lake was evaluated according to the most widely applicable classification schemes. Satellite images of Landsat 5 Thematic Mapper were used in order for algorithms to be developed and calculate the concentration of chlorophyll-a (Chl-a). The trophic status of the lake was characterized as oligotrophic based on phosphorus and as mesotrophic-eutrophic based on Chl-a concentrations. The results of the remote sensing application indicated a relatively high coefficient of determination (R (2)) among point sampling results and the remotely sensed data, which implies that the selected algorithm is reliable and could be used for the monitoring of Chl-a concentration in the particular water body when no field data are available. PMID:24705815
Jaud, M.; Delacourt, C.; Allemand, P.; Deschamps, A.; Cancouët, R.; Ammann, J.; Grandjean, P.; Suanez, S.; Fichaut, B.; Cuq, V.
Because the anthropogenic pressure on the coastal fringe is continuously increasing, the comprehension of morphological coastal changes is a key problem. An efficient, practical and affordable monitoring strategy is essential to investigate the physical processes that are on the origin of these changes and to model the changes to come. This paper presents an assessment of several very high resolution remote sensing techniques (DGPS, stereo-photogrammetry by drone, Terrestrial Laser Scanning and shallow-water multi-beam echo-sounder) which have been jointly used to survey a beach in French Brittany. These techniques allow an integrated approach for Digital Elevation Model (DEM) differencing in order to quantify morphological changes and to monitor the beach evolution. Gathering topographic and bathymetric data enables to draw up the sediment budget of a complete sediment compartment.
Schmid, Thomas; Rico, Celia; Rodríguez-Rastrero, Manuel; José Sierra, María; Javier Díaz-Puente, Fco; Pelayo, Marta; Millán, Rocio
The Almadén area in Spain has a long history of mercury mining with prolonged human-induced activities that are related to mineral extraction and metallurgical processes before the closure of the mines and a more recent post period dominated by projects that reclaim the mine dumps and tailings and recuperating the entire mining area. Furthermore, socio-economic alternatives such as crop cultivation, livestock breeding and tourism are increasing in the area. Up till now, only scattered information on these activities is available from specific studies. However, improved acquisition systems using satellite borne data in the last decades opens up new possibilities to periodically study an area of interest. Therefore, comparing the influence of these activities on the environment and monitoring their impact on the ecosystem vastly improves decision making for the public policy makers to implement appropriate land management measures and control environmental degradation. The objective of this work is to monitor environmental changes affected by human-induced activities within the Almadén area occurring before, during and after the mine closure over a period of nearly three decades. To achieve this, data from numerous sources at different spatial scales and time periods are implemented into a methodology based on advanced remote sensing techniques. This includes field spectroradiometry measurements, laboratory analyses and satellite borne data of different surface covers to detect land cover and use changes throughout the mining area. Finally, monitoring results show that the distribution of areas affected by mercury mining is rapidly diminishing since activities ceased and that rehabilitated mining areas form a new landscape. This refers to mine tailings that have been sealed and revegetated as well as an open pit mine that has been converted to an "artificial" lake surface. Implementing a methodology based on remote sensing techniques that integrate data from
Hurtt, G. C.; Zhao, M.; Dubayah, R.; Huang, C.; Swatantran, A.; ONeil-Dunne, J.; Johnson, K. D.; Birdsey, R.; Fisk, J.; Flanagan, S.; Sahajpal, R.; Huang, W.; Tang, H.; Armstrong, A. H.
As part of its Phase 1 Carbon Monitoring System (CMS) activities, NASA initiated a Local-Scale Biomass Pilot study. The goals of the pilot study were to develop protocols for fusing high-resolution remotely sensed observations with field data, provide accurate validation test areas for the continental-scale biomass product, and demonstrate efficacy for prognostic terrestrial ecosystem modeling. In Phase 2, this effort was expanded to the state scale. Here, we present results of this activity focusing on the use of remote sensing in high-resolution ecosystem modeling. The Ecosystem Demography (ED) model was implemented at 90 m spatial resolution for the entire state of Maryland. We rasterized soil depth and soil texture data from SSURGO. For hourly meteorological data, we spatially interpolated 32-km 3-hourly NARR into 1-km hourly and further corrected them at monthly level using PRISM data. NLCD data were used to mask sand, seashore, and wetland. High-resolution 1 m forest/non-forest mapping was used to define forest fraction of 90 m cells. Three alternative strategies were evaluated for initialization of forest structure using high-resolution lidar, and the model was used to calculate statewide estimates of forest biomass, carbon sequestration potential, time to reach sequestration potential, and sensitivity to future forest growth and disturbance rates, all at 90 m resolution. To our knowledge, no dynamic ecosystem model has been run at such high spatial resolution over such large areas utilizing remote sensing and validated as extensively. There are over 3 million 90 m land cells in Maryland, greater than 43 times the ~73,000 half-degree cells in a state-of-the-art global land model.
Li, Fei; Zhao, Ying; Zheng, Jiajia; Luo, Juhua; Zhang, Xiaoqiang
The quantification of grassland grazing intensity (GI) and its detailed spatial distribution are important for grassland management and ecological protection. Remote sensing has great potential in these areas, but its use is still limited. This study analyzed the impacts of grazing on biophysical properties of vegetation and suggested using biomass to quantify GI because of its stability and interpretability. In comparison to a single spectral index, such as the red edge index (REI), combining REI and a cellulose absorption ratio index calculated from hyperspectral data performs better for biomass estimation. Further, an auxiliary spectral index, called the grazing monitoring index (GMI), was developed based on differences in spectral reflectance in the infrared range. Experiments in a grazing area of the Inner Mongolia grassland indicated that GMI can identify GI, with three range intervals (GMI <0, 0-1, and ≥1) used to describe the biomass distribution. The results showed that combining GMI and biomass was more successful than existing approaches for identifying the grassland variability resulting from the spatial heterogeneity of grazing behavior. The thresholds of biomass for four GI levels (ungrazed, lightly grazed, moderately grazed, and heavily grazed) could be determined by the intersections of biomass distributions. In addition, the approach developed at the on-ground canopy scale was extended to remotely sensed Hyperion data. The results showed that the approach could successfully identify the grazing treatments of blocks in the experimental grazing area. Overall, our study provides inspiration and ideas for using satellite remote sensing for evaluating plant production, standing biomass, and livestock impacts.
Gadallah, Fawziah L.
Human environmental impact has occurred on a global scale. Effective management of problems occurring over broad regions requires monitoring and intervention over large extents of space and time. Remote sensing provides an attractive data source, particularly as satellite data have been consistently collected over both space and time and present a readily available, inexpensive archive. At best, however, remote sensing provides proxy data for the underlying variables of interest. Here remotely sensed data are used to measure habitat degradation at a lesser snow goose colony. An increase in goose numbers has led to a loss of forage vegetation in the arctic and sub-arctic marshes where the geese nest and raise their young. In particular, isostatic rebound has generated extensive coastal marshes along the west coast of Hudson Bay, and lesser snow geese colonized such a marsh at La Perouse Bay in the late 1950's. This well-studied colony is used to assess the feasibility of mapping decadal change with Landsat imagery. A baseline map is developed using satellite data, aerial photography, and a knowledge of vegetation dynamics at the site. Calibration equations, relating the quantity of above-ground vegetation and its reflectance, are developed using cross-validation and goodness-of-prediction measures for field data collected on-site. To detect changes in vegetation state, tree-classification and cross-validation were applied to ground data. Using satellite imagery, changes in vegetation quantity and type could be detected against a background of mineral soil, but not against a background of mosses. Even in this site with low topographic variability, few species and few strong driving forces (i.e. isostatic rebound and herbivory), multiple change trajectories are possible. As different trajectories have different influences on both the reflectance of the surface and the expected behaviour and functioning of the system, each must be accounted for separately. Failure to
Kunte, Pravin D.; M. A., Aswini
The present study addresses an intense sandstorm event over the Persian Gulf and its transport over the Arabian Sea region and the Indian sub-continent using satellite observations and measurements. MODIS data are used to analyze the temporal variation of the dust events that occurred from 17 to 24 March 2012 with the strongest intensity on 20 March over the Arabian Sea. MODIS images are examined to provide an independent assessment of dust presence and plume location and its migration over the Arabian Sea to the Indian sub-continent. Dust enhancement and dust detection procedure is attempted to demarcate the dust event. Dust source, formation, transportation path, and dissipation is studied using source-back-tracking, surface wind, and surface pressure, wind speed and direction, geo-potential height for different pressure level, and remote sensing methods. Finally, an attempt is made to investigate the impact of super sandstorm on the Arabian Sea by studying sea surface temperature and chlorophyll a variability during the events. It is noted that sea surface temperature is decreased and chlorophyll a concentration increased during the post-event period. The present study demonstrates the use of remote sensing data and geospatial techniques in detecting and mapping of dust events and monitoring dust transport along specific regional transport pathways over land and ocean.
Wang, Wei; Zhao, Li; Wu, Yanbin
Based on remote sensing images, the panoramic views of land coverage distribution across a large geographic area can be accessed conveniently. In order to improve the accuracy of monitoring land use changes, the Chaos Genetic Algorithm was proposed. Chaos Immune Algorithm has capability of self-organizing, self-learning, self-recognition and self-memory, hence through the input samples the global optimization clustering center was found. And then the clustering center was employed to classify the view picture of remote sensing image. In this process, the ergodic property of chaos phenomenon was used to optimize the initial antibody population, so it could accelerate the convergence of Immune Algorithm. Through the clone selection operator, mutation operator and recruited antibody, local optimums were avoid. Chaos Immune Algorithm was applied to classify land use in Huainan -based on TM image. Based on confusion matrix, the classification of the Parallelepiped and Maximum likelihood methods were contrasted with Chaos Immune Algorithm. It is demonstrated that Chaos Immune Algorithm is superior to the two traditional algorithms, and its overall accuracy and Kappa coefficient reach 88.26% and 0.853respectively.
In this paper, a method to interpret the high, mid, low salinized ploughland and the salinized wasteland using comprehensive aerophoto interpretation principles will be described for Xinding Basin, Shanxi Province. The dynamic change of salinized soil during 7 years from 1980 to 1987 will be compared with the typical Dingxiang County. The map and data obtained, with an accuracy of more than 90%, are provided to the local government as the scientific grounds to instruct agricultural productivity. Soil salinization is a worldwide problem. With the sharp increase in world population and modern industrialisation development, the natural resource consumption is increasing day and day, and bringing about a lack of land resource worldwide. As a kind of back-up land resource, salinized land has not only attracted the concern and study of the agricultural scientists in all countries, but also by the whole society. Shanxi is such a province in China where more than 1/3 of its total area of irrigation land is salinized. The statistics used to monitor this salinized area lack objectivity and accuracy. In 1987, the government of Shanxi Province began to investigate the salinized area of the whole province, using remote sensing technology. We selected the Xinding Basin in central Shanxi as the test district to perform the aerial remote sensing investigation, and, at the same time, studied the salinization dynamic change on the Dingxiang County used as the typical district.
Imen, Sanaz; Chang, Ni-Bin; Yang, Y Jeffrey
Adjustment of the water treatment process to changes in water quality is a focus area for engineers and managers of water treatment plants. The desired and preferred capability depends on timely and quantitative knowledge of water quality monitoring in terms of total suspended solids (TSS) concentrations. This paper presents the development of a suite of nowcasting and forecasting methods by using high-resolution remote-sensing-based monitoring techniques on a daily basis. First, the integrated data fusion and mining (IDFM) technique was applied to develop a near real-time monitoring system for daily nowcasting of the TSS concentrations. Then a nonlinear autoregressive neural network with external input (NARXNET) model was selected and applied for forecasting analysis of the changes in TSS concentrations over time on a rolling basis onward using the IDFM technique. The implementation of such an integrated forecasting and nowcasting approach was assessed by a case study at Lake Mead hosting the water intake for Las Vegas, Nevada, in the water-stressed western U.S. Long-term monthly averaged results showed no simultaneous impact from forest fire events on accelerating the rise of TSS concentration. However, the results showed a probable impact of a decade of drought on increasing TSS concentration in the Colorado River Arm and Overton Arm. Results of the forecasting model highlight the reservoir water level as a significant parameter in predicting TSS in Lake Mead. In addition, the R-squared value of 0.98 and the root mean square error of 0.5 between the observed and predicted TSS values demonstrates the reliability and application potential of this remote sensing-based early warning system in terms of TSS projections at a drinking water intake. PMID:26093101
Huang, Ming-Fang; Plant, Genevieve; Tanaka, Akihiro; Cvijetic, Neda; Tian, Yue; Wysocki, Gerard; Wang, Ting
We propose an optical gas sensing network directly overlaid onto optical access networks, hybrid TDM/WDM-PONs. Centralized remote gas monitoring is demonstrated using three different sensing technologies: Chirp Laser Dispersion Spectroscopy (CLaDS), Direct Laser Absorption Spectroscopy (DLAS) and tunable diode laser absorption spectroscopy (TDLS). DLAS performs fast threshold detection while CLaDS provides quantitative information about the gas. Additionally, TDLS utilizes a cost-effective solution for multiple gases detection. The results confirm that centralized remote gas sensing can be realized in optical communication networks using standard single-mode fiber (SMF), which provides a real time, low cost, and maintenance-free solution.
Agapiou, Athos; Papadavid, George; Hadjimitsis, Diofantos G.
This paper aims to highlight the benefits from the integration of wireless sensor network / meteorological data and remote sensing for monitoring and determine irrigation demand in Cyprus. Estimating evapotranspiration in Cyprus will help, in taking measures for an effective irrigation water management in the future in the island. For this purpose both multi-spectral satellite images (Landsat 7 ETM+ and ASTER) and hydro-meteorological data from wireless sensors and automatic meteorological stations have been used. The wireless sensor network, which consist approximately twenty wireless nodes, was placed in our case study. The wireless sensor network acts as a wide area distributed data collection system deployed to collect and reliably transmit soil and air environmental data to a remote base-station hosted at Cyprus University of Technology. Furthermore auxiliary meteorological field data, from an automatic meteorological station, nearby our case study, where used such as solar radiation, air temperature, air humidity and wind speed. These data were used in conjunction with remote sensing results. Satellite images where used in ERDAS Imagine Software after the necessary processing: geometric rectification, radiometric calibration and atmospheric corrections. The satellite images were atmospheric corrected and calibrated using spectro-radiometers and sun-photometers measurements taken in situ, in an agricultural area, south-west of the island of Cyprus. Evapotranspiration is difficult to determine since it combines various meteorological and field parameters while in literature quite many different models for estimating ET are indicated. For estimating evapotranspiration from satellite images and the hydro-meteorological data different methods have been evaluated such as FAO Penman-Monteith, Carlson-Buffum and Granger methods. These results have been compared with E-pan methods. Finally a water management irrigation schedule has been applied. The final results are
Nádor, G.; Fényes, D.; Vasas, L.; Surek, G.
The gradual dispersion of western corn rootworm (WCR) is becoming a serious maize pest in Europe, and all over the world. In 2008 using remote sensing data, the Remote Sensing Centre of Institute of Geodesy, Cartography and Remote Sensing (FÖMI RSC) carried out this project to identify WCR larval damage. Our goal with the present project is to assess and identify the disorder and structural changes caused by WCR larvae using optical (IRS-P6 AWiFS, IRS-P6 LISS, SPOT4 and SPOT5) and polarimetic radar (ALOS PALSAR) satellite images. We used 3 different individual features (Mono-maize feature, Optical feature, Radar feature) derived from remote sensing data to accomplish this goal. Findings were tested against on-the-spot ground assessments. Using radar polarimetry increased the accuracy significantly. The final results have implications for plant protection strategy, farming practices, pesticide producers, state authorities and research institutes.
Nagy, Attila; Tamás, János; Fehér, János
The World Meteorological Organization (WMO) and Global Water Partnership (GWP) have launched a joint Integrated Drought Management Programme (IDMP) to improve monitoring and prevention of droughts. In the frame of this project this study focuses on identification of agricultural drought characteristics and elaborates a monitoring method (with application of remote sensing data), which could result in appropriate early warning of droughts before irreversible yield loss and/or quality degradation occur. The spatial decision supporting system to be developed will help the farmers in reducing drought risk of the different regions by plant specific calibrated drought indexes. The study area was the Tisza River Basin, which is located in Central Europe within the Carpathian Basin. For the investigations normalized difference vegetation index (NDVI) was used calculated from 16 day moving average chlorophyll intensity and biomass quantity data. The results offer concrete identification of remote sensing and GIS data tools for agricultural drought monitoring and forecast, which eventually provides information on physical implementation of drought risk levels. In the first step, we statistically normalized the crop yield maps and the MODIS satellite data. Then the drought-induced crop yield loss values were classified. The crop yield loss data were validated against the regional meteorological drought index values (SPI), the water management and soil physical data. The objective of this method was to determine the congruency of data derived from spectral data and from field measurements. As a result, five drought risk levels were developed to identify the effect of drought on yields: Watch, Early Warning, Warning, Alert and Catastrophe. In the frame of this innovation such a data link and integration, missing from decision process of IDMP, are established, which can facilitate the rapid spatial and temporal monitoring of meteorological, agricultural drought phenomena and its
Becerril, R.; González Sosa, E.; Diaz-Delgado, C.; Mastachi-Loza, C. A.; Hernández-Tellez, M.
Desertification is defined as land degradation in arid, semi-arid and sub-humid areas due to climatic variations and human activities. Therefore there is a need to monitor the desertification process in the spatiotemporal scale in order to develop strategies to fight against desertification (Wu and Ci, 2002). In this sense, data provided by remote sensing is an important source for spatial and temporal information, which allows monitoring changes in the environment at low cost and high effectiveness. In Mexico, drylands hold 65% of the area, with about 1,280,494 km2 (UNESCO, 2010), where is located 46% of the national population (SEMARNAT, 2008). Given these facts, there is interest in monitoring the degradation of these lands, especially in Mexico because no specific studies have identified trends and progress of desertification in the country so far. However, it has been considered land degradation as an indicator of desertification process. Thus, it has been determined that 42% of soils in Mexico present some degradation degree. The aim of this study was to evaluate the spatial and temporal dynamics of desertification for 1993, 2000 and 2011 in the semiarid central plateau in Mexico based on demographic, climatic and satellite data. It took into consideration: 1) the Anthropogenic Impact Index (HII), based on the spatial population distribution and its influence on the use of resources and 2) the Aridity Index (AI), calculated with meteorological station records for annual rainfall and potential evapotranspiration. Mosaics were made with Landsat TM scenes; considering they are a data source that allows evaluate surface processes regionally and with high spectral resolution. With satellite information five indices were estimated to assess the vegetation and soil conditions: Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Weighted Difference Vegetation Index (WDVI), Grain Size Index (GSI) and Bare Soil Index (BSI). The rates
We report on findings from ongoing polarization lidar research at the University of Utah Facility for Atmospheric Remote Sensing (FARS). This facility was established in 1987, and the current total of lidar and radiometric measurements is approx. 2,900-h. Research at FARS has been applied to the climatological investigation of cirrus cloud properties for basic research and satellite measurement validation (currently in its 13th year), and studies of contrails, mixed phase clouds, and volcanic and Asian dust aerosols. Among the techniques utilized for monitoring cloud and aerosol properties are triple-wave length linear depolarization measurements, and high (1.5-m by 10-Hz) resolution scanning observations. The usefulness of extended time lidar studies for atmospheric and climate research is illustrated.
Pencheva, Vasilka H.; Penchev, S.; Naboko, Vassily N.; Naboko, Sergei V.
We report new aspects of application of pulsed GaAs diode lasers, concerning absorption spectroscopy of water vapor of third oscillatory molecular overtone 8990 - 9012 angstroms, and Mie-scattering lidar signal in the 15 km range. It is accessible by the power characteristics of a system utilizing the powerful `chip-stack' GaAs diode lasers, employing optimal photodetection technique based on an analyzing system with computer operated boxcar. Data on atmospheric aerosol backscatter signal acquired by DL lidar are presented with relevance to the potential of complex atmospheric remote sensing. GaAs diode lasers, with radiation matching water vapor spectrum of absorption- coefficients of 0.5 - 5 km-1 in Beer's law, are shown feasible for DIAL monitoring of atmospheric humidity.
Eitel, Jan U. H.; Keefe, Robert F.; Long, Dan S.; Davis, Anthony S.; Vierling, Lee A.
Active ground optical remote sensing (AGORS) devices mounted on overhead irrigation booms could help to improve seedling quality by autonomously monitoring seedling stress. In contrast to traditionally used passive optical sensors, AGORS devices operate independently of ambient light conditions and do not require spectral reference readings. Besides measuring red (590–670 nm) and near-infrared (>760 nm) reflectance AGORS devices have recently become available that also measure red-edge (730 nm) reflectance. We tested the hypothesis that the additional availability of red-edge reflectance information would improve AGORS of plant stress induced chlorophyll breakdown in Scots pine (Pinus sylvestris). Our results showed that the availability of red-edge reflectance information improved AGORS estimates of stress induced variation in chlorophyll concentration (r2 > 0.73, RMSE < 1.69) when compared to those without (r2 = 0.57, RMSE = 2.11). PMID:22319275
Liu, Anlin; Li, Xingmin; He, Yanbo; Deng, Fengdong
Based on the principle of energy balance, the method for calculating latent evaporation was simplified, and hence, the construction of the drought remote sensing monitoring model of crop water shortage index was also simplified. Since the modified model involved fewer parameters and reduced computing times, it was more suitable for the operation running in the routine services. After collecting the concerned meteorological elements and the NOAA/AVHRR image data, the new model was applied to monitor the spring drought in Guanzhong, Shanxi Province. The results showed that the monitoring results from the new model, which also took more considerations of the effects of the ground coverage conditions and meteorological elements such as wind speed and the water pressure, were much better than the results from the model of vegetation water supply index. From the view of the computing times, service effects and monitoring results, the simplified crop water shortage index model was more suitable for practical use. In addition, the reasons of the abnormal results of CWSI > 1 in some regions in the case studies were also discussed in this paper. PMID:15146625
Lin, Jiayuan; Wang, Zhiliang; Wang, Yangchun; Lin, Yi; Du, Xiaolin
High-speed railway construction will produce a large amount of abandoned dregs, so it is necessary to build enough dreg deposition fields along the railway. The task of the department of soil and water conservation is to monitor the construction and usage of abandoned dreg fields according to the design in the whole process of railway construction. As long linear construction projects, many high-speed railways go through regions of complex terrain, which poses great difficulties to monitoring current status of abandoned dreg fields. With the advantages of low cost, flexible launch and landing, safety, under-cloud-flying, hyperspatial image resolution, Unmanned Aerial Vehicles (UAVs) are very suitable for obtaining remote sensing imagery along the railway. One segment of the high-speed railway from Chongqing to Wanzhou and its neighborhood was chosen as the study area to demonstrate key technologies and specific procedures of monitoring abandoned dreg fields using the UAV system. The UAV system and its components are introduced along with the flight trajectories, acquired UAV imagery, and attitude data. Image preprocessing and generation of DEM and DOM are described in detail followed by image-based measurement accuracy assessment and abandoned dreg field status investigation on the resulting DOM and DEM. Results prove the feasibility and effectiveness of applying the fixed wing UAV system to rapidly monitoring the construction and usage of abandoned dreg fields
Yao, Yuan; Ding, Jian-Li; Kelimul, Ardak; Zhang, Fang; Lei, Lei
In the present study, the delta oasis between the Weigan River and the Kuqa River was selected as our study area. Firstly, the measured hyperspectral data related to different soil salinization extent was combined with electromagnetic induction instrument (EM38) in order to establish a soil salinization monitoring model; Secondly, by using the scaling transformation method, the model was adopted to calibrate the soil salinity index calculated from Landsat-TM images. Thirdly, the calibrated Landsat-TM images were used for the retrieval of regional soil salinity, and the retrieved data was verified based on the measured data. We found that at wavelengths of 456, 533, 686 and 1 373 nm, the interpretated data of EM38 were highly correlated with soil spectral reflectance (obtained via first order differentiation transformation of the spectra). Additionally, the soil salinity index model constructed from the combination of 456, 686 and 1 373 nm waveband was the best model among the different saliniza tion monitoring models. The authors' conclusion is that with R2 = 0.799 3 (p < 0.01), extracting the salinity information at regional scale by combining the electromagnetic and multispectral data performed better than those monitoring models with only salinity index extracted from multispectral remote sensing method (R2 = 0.587 4, p < 0 01). Our findings provides scientific bases for the future studies related to more accurate monitoring and prediction of soil salinization. PMID:24059201
Hepner, George F.
The University of Utah, Department of Geography has developed a research and instructional program in satellite remote sensing and image processing. The University requested funds for the purchase of software licenses, mass storage for massive hyperspectral imager data sets, upgrades for the central data server to handle the additional storage capacity, a spectroradiometer for field data collection. These purchases have been made. This equipment will support research in one of the newest and most rapidly expanding areas of remote sensing.
Macomber, S.A.; Woodcock, C.E. )
A prolonged drought in the western US has resulted in alarming levels of mortality in conifer forests. Satellite remote sensing holds the potential for mapping and monitoring the effects of such environmental changes over large geographic areas in a timely manner. Results from the application of a forest canopy reflectance model using multitemporal Landsat TM imagery and field measurements, indicate conifer mortality can be effectively mapped and inventoried. The test area for this project is the Lake Tahoe Basin Management Unit in the Sierra Nevada of California. The Landsat TM images are from the summers of 1988 and 1991. The Li-Strahler canopy model estimates several forest stand parameters, including tree size and canopy cover for each conifer stand, from reflectance values in satellite imagery. The difference in cover estimates between the dates forms the basis for stratifying stands into mortality classes, which are used as both themes in a map and the basis of the field sampling design. Field measurements from 61 stands collected in the summer of 1992 indicate 15% of the original timber volume in the true fir zone died between 1988 and 1992. The resulting low standard error of 11% for this estimate indicates the utility of these mortality classes for detecting areas of high mortality. Also, the patterns in the estimated mean timber volume loss for each class follow the expected trends. The results of this project are immediately useful for fire hazard management, by providing both estimates of the degree of overall mortality and maps showing its location. They also indicate current remote sensing technology may be useful for monitoring the changes in vegetation that are expected to result from climate change.
Sarna, Karolina; Russchenberg, Herman W. J.
A new method for continuous observation of aerosol–cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of the change of the cloud droplet size due to the change in the aerosol concentration. We use high-resolution measurements from a lidar, a radar and a radiometer, which allow us to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example case studies were chosen from the Atmospheric Radiation Measurementmore » (ARM) Program deployment on Graciosa Island, Azores, Portugal, in 2009 to present the method. We use the cloud droplet effective radius (re) to represent cloud microphysical properties and an integrated value of the attenuated backscatter coefficient (ATB) below the cloud to represent the aerosol concentration. All data from each case study are divided into bins of the liquid water path (LWP), each 10 g m–2 wide. For every LWP bin we present the correlation coefficient between ln re and ln ATB, as well as ACIr (defined as ACIr = –d ln re/d ln ATB, change in cloud droplet effective radius with aerosol concentration). Obtained values of ACIr are in the range 0.01–0.1. Lastly, we show that ground-based remote sensing instruments used in synergy can efficiently and continuously monitor aerosol–cloud interactions.« less
Hamada, Y.; O'Connor, B. L.
Development in arid environments often results in the loss and degradation of the ephemeral streams that provide habitat and critical ecosystem functions such as water delivery, sediment transport, and groundwater recharge. Quantification of these ecosystem functions is challenging because of the episodic nature of runoff events in desert landscapes and the large spatial scale of watersheds that potentially can be impacted by large-scale development. Low-impact development guidelines and regulatory protection of ephemeral streams are often lacking due to the difficulty of accurately mapping and quantifying the critical functions of ephemeral streams at scales larger than individual reaches. Renewable energy development in arid regions has the potential to disturb ephemeral streams at the watershed scale, and it is necessary to develop environmental monitoring applications for ephemeral streams to help inform land management and regulatory actions aimed at protecting and mitigating for impacts related to large-scale land disturbances. This study focuses on developing remote sensing methodologies to identify and monitor impacts on ephemeral streams resulting from the land disturbance associated with utility-scale solar energy development in the desert southwest of the United States. Airborne very high resolution (VHR) multispectral imagery is used to produce stereoscopic, three-dimensional landscape models that can be used to (1) identify and map ephemeral stream channel networks, and (2) support analyses and models of hydrologic and sediment transport processes that pertain to the critical functionality of ephemeral streams. Spectral and statistical analyses are being developed to extract information about ephemeral channel location and extent, micro-topography, riparian vegetation, and soil moisture characteristics. This presentation will demonstrate initial results and provide a framework for future work associated with this project, for developing the necessary
Corp, Lawrence A.; Middleton, Elizabeth M.; Campbell, Petya K. E.; Huemmrich, K. Fred; Cheng, Yen-Ben; Daughtry, Craig S. T.
Patterns of change in vegetation growth and condition are one of the primary indicators of the present and future ecological status of the globe. Nitrogen (N) is involved in photochemical processes and is one of the primary resources regulating plant growth. As a result, biological carbon (C) sequestration is driven by N availability. Large scale monitoring of photosynthetic processes are currently possible only with remote sensing systems that rely heavily on passive reflectance (R) information. Unlike R, fluorescence (F) emitted from chlorophyll is directly related to photochemical reactions and has been extensively used for the elucidation of the photosynthetic pathways. Recent advances in passive fluorescence instrumentation have made the remote acquisition of solar-induced fluorescence possible. The goal of this effort is to evaluate existing reflectance and emerging fluorescence methodologies for determining vegetation parameters related to photosynthetic function and carbon sequestration dynamics in plants. Field corn N treatment levels of 280, 140, 70, and 0 kg N / ha were sampled from an intensive test site for a multi-disciplinary project, Optimizing Production Inputs for Economic and Environmental Enhancement (OPE). Aircraft, near-ground, and leaf-level measurements were used to compare and contrast treatment effects within this experiment site assessed with both reflectance and fluorescence approaches. A number of spectral indices including the R derivative index D730/D705, the normalized difference of R750 vs. R705, and simple ratio R800/R750 differentiated three of the four N fertilization rates and yielded high correlations to three important carbon parameters: C:N, light use efficiency, and grain yield. These results advocate the application of hyperspectral sensors for remotely monitoring carbon cycle dynamics in terrestrial ecosystems.
Under contract to the US Air Force and Navy, Pacific Advanced Technology has developed a very sensitive hyperspectral imaging infrared camera that can perform remote imaging spectro-radiometry. One of the most exciting applications for this technology is in the remote monitoring of gas plume emissions. Pacific Advanced Technology (PAT) currently has the technology available to detect and identify chemical species in gas plumes using a small light weight infrared camera the size of a camcorder. Using this technology as a remote sensor can give advanced warning of hazardous chemical vapors undetectable by the human eye as well as monitor the species concentrations in a gas plume from smoke stack and fugitive leaks. Some of the gas plumes that have been measured and species detected using an IMSS imaging spectrometer are refinery smoke stacks plumes with emission of CO2, CO, SO2, NOx. Low concentration vapor unseen by the human eye that has been imaged and measured is acetone vapor evaporating at room temperature. The PAT hyperspectral imaging sensor is called 'Image Multi-spectral Sensing or IMSS.' The IMSS instrument uses defractive optic technology and exploits the chromatic aberrations of such lenses. Using diffractive optics for both imaging and dispersion allows for a very low cost light weight robust imaging spectrometer. PAT has developed imaging spectrometers that span the spectral range from the visible, midwave infrared (3 to 5 microns) and longwave infrared (8 to 12 microns) with this technology. This paper will present the imaging spectral data that we have collected on various targets with our hyperspectral imaging instruments as will also describe the IMSS approach to imaging spectroscopy.
Lehnert, Lukas; Meyer, Hanna; Thies, Boris; Reudenbach, Christoph; Bendix, Jörg
The degradation of the grasslands on the Tibetan Plateau (TP) is seen as ongoing process. This general assumption is based on small scale studies which are not comparable because field methods and indicators differ among the investigations. Thus, especially for remote areas, a remotely sensed monitoring system is critically needed to monitor degradation. Additionally, a single and comparable product for the entire TP is of urgent concern to evaluate the ecological consequences of the assumed ongoing degradation on ecosystem services provided by the grasslands on the TP. As indicator for degradation plant cover was used in this study because a close link between degradation and plant cover has been identified by several previous studies on the TP. Thus, we implemented a four-scale remote sensing approach to derive plant cover. As reference and validation data, field records were taken between 2011 and 2013 at 15 locations spanning the entire TP and covering all grazed grassland vegetation types. Plant cover was measured in situ at up to 210 plots per location using standardized taken digital photos. To classify green vegetated parts in the digital photos, simple threshold classifications were applied to the ratio of red and green color values. The geographical position of all plots was recorded using a differential GPS. Plant cover was derived from satellite data at three scales using spectral angle mapper (SAM), normalized difference band indices and linear spectral unmixing. The result of the first two approaches was transferred to plant cover using multiple linear regression techniques. Reference spectra and endmember spectra for SAM and linear spectral unmixing were recorded using a field spectrometer. The hyperspectral information was resampled to satellite bands using the spectral response functions of the sensors. To derive plant cover at local scale, we classified 35 high resolution WorldView-2, Quickbird and RapidEye satellite images using the in situ
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.
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.
Anderson, M. C.; Hain, C.; Otkin, J.; Zhan, X.
Drought assessment is a complex endeavor, requiring monitoring of deficiencies in multiple components of the hydrologic budget. Precipitation anomalies reflect variability in water supply to the land surface, while soil moisture (SM), ground and surface water anomalies reflect deficiencies in moisture storage. In contrast, evapotranspiration (ET) anomalies provide unique yet complementary information, reflecting variations in actual water use by crops and direct evaporation from the soil. For example, precipitation- and ET-based anomalies may differ significantly in regions of intensive irrigation, shallow water table, or deep rooting depth - areas where plants may be more resilient to soil moisture deficiencies inferred from rainfall patterns. In addition, an ET-based index can better capture impacts of hot, windy conditions leading to "flash droughts", where anomalously high water use precipitates rapid decay in soil moisture and crop condition. Here we describe a remotely sensed Evaporative Stress Index (ESI) based on anomalies in actual-to-reference ET ratio, and compare with patterns in precipitation-based drought indicators. Actual ET is derived from thermal remote sensing, using the morning land-surface temperature (LST) rise observed with geostationary satellites. In comparison with vegetation indices, LST is a fast-response variable, with the potential for providing early warning of crop stress reflected in increasing canopy temperatures. Spatiotemporal patterns in ESI reasonably match those in precipitation-based indices (such as SPI and modeled SM) and patterns in the U.S. Drought Monitor. However, because ESI does not use precipitation as an input, it provides an independent assessment of evolving drought conditions, and is more portable to data-sparse parts of the world lacking dense rain-gauge and Doppler radar networks. Integrating LST information from polar orbiting systems, the ESI has unique potential for sensing moisture stress at field scale
Zhang, C., Sr.; Huang, J.; Li, L.; Wang, H.; Zhu, D.
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 firstname.lastname@example.org
Huang, He; Yang, Siquan; Li, Suju; He, Haixia; Liu, Ming; Xu, Feng; Lin, Yueguan
Earthquake is one major nature disasters in the world. At 8:02 on 20 April 2013, a catastrophic earthquake with Ms 7.0 in surface wave magnitude occurred in Sichuan province, China. The epicenter of this earthquake located in the administrative region of Lushan County and this earthquake was named the Lushan earthquake. The Lushan earthquake caused heavy casualties and property losses in Sichuan province. After the earthquake, various emergency relief supplies must be transported to the affected areas. Transportation network is the basis for emergency relief supplies transportation and allocation. Thus, the road losses of the Lushan earthquake must be monitoring. The road losses monitoring results for Lushan earthquake disaster utilization multisource remote sensing images were reported in this paper. The road losses monitoring results indicated that there were 166 meters' national roads, 3707 meters' provincial roads, 3396 meters' county roads, 7254 meters' township roads, and 3943 meters' village roads were damaged during the Lushan earthquake disaster. The damaged roads mainly located at Lushan County, Baoxing County, Tianquan County, Yucheng County, Mingshan County, and Qionglai County. The results also can be used as a decision-making information source for the disaster management government in China.
Soil water sensing methods are widely used to characterize the rhizosphere and below, but only a few are capable of delivering water content data with accuracy for the entire soil profile such that evapotranspiration (ET) can be determined by soil water balance with minimal error. One such is the ne...
Thomas, J. P.
Some of the findings of the Superflux program relative to fishery research and monitoring are reviewed. The actual and potential influences of the plume on the shelf ecosystem contiguous to the mouth of Chesapeake Bay are described and insights derived from the combined use of in situ and remotely sensed data are presented.
Piscini, Alessandro; Lombardo, Valerio
Neural networks are an effective and well-established technique for the classification of satellite images. In addition, once well trained, they prove to be very fast in the application stage. Furthermore satellite remote sensing is a very effective and safe way to monitor volcanic eruptions in order to safeguard the environment and the people affected by such natural hazards. In our study a Back Propagation Neural Network was used for the recognition of thermal anomalies affecting hot lava pixels in multispectral remote sensed images. The network was trained using the three thermal channels of the Advanced Very High Resolution Radiometer (AVHHR) sensor as inputs and the corresponding values of heat flux, estimated using a two thermal component model, as reference outputs. As a case study the volcano Etna (Eastern Sicily, Italy) was chosen and the neural network was trained with a time series of AVHRR images belonging to an effusive eruption which took place during the month of July 2006, and validated on three independent data sets of images of the same eruption and on two relative to an eruption occurred the following month. Whilst for both night-time and day-time validation images the neural network identified the image pixels affected by hot lava with a 100% success rate, for the daytime images also adjacent pixels were included, apparently not interested by lava flow. Despite these performance differences under different illumination conditions, the proposed method can be considered effective both in terms of classification accuracy and generalization capability. In particular our approach proved to be robust in the rejection of false positives, often corresponding to noisy or cloudy pixels, whose presence in multispectral images can often undermine the performance of traditional classification algorithms. Future work shall address application of the proposed method to data from different eruptions provided by the MODIS sensor aboard the Terra and Aqua
Soares da Silva, Natália; Sánchez-Román, Rodrigo; Marchamalo Sacristán, Miguel; Rodriguez-Sinobas, Leonor
Nowadays, the concern of the effect of climate change on water availability on a global scale is getting bigger and bigger. In average, about 65 % of the world water consumption is devoted to irrigated agriculture. In countries such as Brazil, water scarcity has been a main issue in populated areas (i.e. São Paulo) in the last two years. This has affected not only water availability for the population but also irrigation water to maintain crop yield and Brazilian economy. Remote sensing is a tool broadly used in multiple fields of science such as water management in irrigated agriculture. Actually, there are several satellites moving around the earth, and they take images of every place in a weekly or biweekly basis. The images can be downloaded from the internet site at no cost by the users. Then, they are used to determine the vegetation index NDVI which is based in the energy reflected in red and infrared spectrum and it depends on the vegetation photosynthetic activity. Within the above context, this study focus on remote sensing monitoring of a bean crop located in the basin of Boi Branco, São Paulo - Brazil, which is irrigated by pivot center. The images from the Landsat and Modis satellites were downloaded throughout the bean growing period and then, they were processed and analyzed with the Qgis software. In addition, soil moisture was measured by several TDR probe sensors deployed in the irrigated area, and the leaf area index was measured as well in the field. Both variables were used to estimate the Normalized Difference Vegetation Index (NDVI) for each bean phenology state.
Zhang, Yuanzhi; Chen, Zhengyi; Zhu, Boqin; Luo, Xiuyue; Guan, Yanning; Guo, Shan; Nie, Yueping
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. PMID:18197462
Roller, N. E. G.
The concept of using remote sensing to inventory wetlands and the related topics of proper inventory design and data collection are discussed. The material presented shows that aerial photography is the form of remote sensing from which the greatest amount of wetlands information can be derived. For extensive, general-purpose wetlands inventories, however, the use of LANDSAT data may be more cost-effective. Airborne multispectral scanners and radar are, in the main, too expensive to use - unless the information that these sensors alone can gather remotely is absolutely required. Multistage sampling employing space and high altitude remote sensing data in the initial stages appears to be an efficient survey strategy for gathering non-point specific wetlands inventory data over large areas. The operational role of remote sensing insupplying inventory data for application to several typical wetlands management problems is illustrated by summary descriptions of past ERIM projects.
Product amount and quality monitoring in agricultural fields with remote sensing satellite and radio-control helicopter is proposed. In particular, tealeaves and rice crop quality and amoujnt monitorings are peoposed as examples. Nitrogen rich tealeaves tasts good. Therefore, quality of tealeaves can be estimated with nitrogen content which is related with near infrared reflectance of the tealeves in concern. Also, rice crop quality depends on protein content in rice grain which is related to near infrared reflectance of rice leaves. Therefore, product quality can be estimated with observation of near infrared reflectance of the leaves in concern. Near infared reflectance is provided by near infrared radiometers onboard remote sensing satellites and by near infrared cameras onboard radio-control helicopter. This monitoring system is applicable to the other agricultural plant products. Through monitoring near ingfrared reflectance, it is possible to estimate quality as well as product amount.
Torres, R. C.; Mouginis-Mark, P.; Wright, R.; Garbeil, H.; Craig, B.
The Philippines has one of the world's fastest disappearing forest cover, which is being lost to natural processes and landscape-modifying human activities. Currently, forested landscape covers 24% (i.e., 7.2 million hectares) of the Philippines' total land area, of which only 800,000 hectares are considered as old-growth forests. Occasionally, volcanic activities and earthquakes cause large-scale impacts on the forest cover, but the systematic reduction of the country's forest has been sustained through unregulated logging operations and other human-induced landscape modification. Reforestation and watershed protection have become important public policy programs as forest denudation is linked to recent devastating landslides, debris flows and flashfloods. However, many watershed areas that are at risk to deforestation are hardly accessible to ground-based monitoring. A spaced-based monitoring system facilitates an efficient and timely response to changes in the quality and extent of the Philippine forest cover. This monitoring system relies in the generation of Normalized Difference Vegetation Index (NDVI) products from the red and infrared bands of remote sensing data, which correlates with the amount of chlorophyll in the vegetation. Given the existing forest classification maps, non-forested regions are masked in the data analysis, so that only forest-related changes in the vegetation are shown in the NDVI image difference products. A combination of two MODIS-bearing satellites, i.e., Terra and Aqua, acquire high temporal and moderate spatial resolution data, enabling the countrywide detection of vegetation changes within a certain observation period. MODIS data are calibrated for setting the pixel quality thresholds, which minimize the artifact of clouds and haze in the analysis. Areas showing dramatic changes are further investigated using higher resolution data, such as ASTER and Landsat 7 ETM. Sequential NDVI products of remote sensing data provide
Remote sensing is one of the best ways to be able to monitor and see changes in the Earth. The use of satellite images in the classroom can be a practical way to help students understand the importance and use of remote sensing and Geographic Information Systems (GIS). It is essential in helping students to understand that underlying individual data points are converted to a broad spatial form. The use of actual remote sensing data makes this more understandable to the students e.g. an online map of recent earthquake events, geologic maps, satellite imagery. For change detection, images of years ten or twenty years apart of the same area can be compared and observations recorded. Satellite images of different places can be available on the Internet or from the local space agency. In groups of mixed abilities, students can observe changes in land use over time and also give possible reasons and explanations to those changes. Students should answer essential questions like, how does satellite imagery offer valuable information to different faculties e.g. military, weather, environmental departments and others. Before and after images on disasters for example, volcanoes, floods and earthquakes should be obtained and observed. Key questions would be; how can scientists use these images to predict, or to change the future outcomes over time. How to manage disasters and how the archived images can assist developers in planning land use around that area in the future. Other material that would be useful includes maps and aerial photographs of the area. A flight should be organized over the area for students to acquire aerial photographs of their own; this further enhances their understanding of the concept "remote sensing". Environmental issues such as air, water and land pollution can also be identified on satellite images. Key questions for students would include causes, effects and possible solutions to the problem. Conducting a fieldwork exercise around the area would
Qu, Jianhe; Kafatos, Menas; Yang, Ruixin; Chiu, Long S.; Riebau, Allen R.
Global pollution aerosol monitoring is a very important climatic and environmental problem. It affects not only human health but also ecological systems. Because most pollution aerosols are concentrated in the atmospheric boundary layer where human, animal and vegetation live, global pollution aerosol stuides have been an important topic since about a decade ago. Recently, many new chemistry remote sensing satellite systems, such as NASA's Aura (EOS-CHEM), have been established. However, pollution aerosols in the atmospheric boundary layer cannot be detected using current remote sensing technologies. George Mason University (GMU) proposes to design scientific algorithms and technologies to monitor the atmospheric boundary layer pollution aerosols, using both satellite remote sensing measurements and ground measurements, collaborating with NASA and the United States Department of Agriculture (USDA)/Forest Services (FS). Boundary layer pollution aerosols result from industrial pollution, desert dust storms, smoke from wildfires and biomass burning, volcanic eruptions, and from other trace gases. The current and next generation satellite instruments, such as The Ozone Mapping and Profiler Suite (OMPS), Ozone Monitoring Instrument (OMI), Thermal Emission Spectrometer (TES), and High Resolution Dynamics Limb Sounder (HIRDLS) can be used for this study. Some surface measurements from USDA/FS and other agencies may also be used in this study. We will discuss critical issues for GPAM in the boundary layer using Earth observing satellite remote sensing in detail in this paper.
Guo, Jianmao; Lu, Weisong; Zhang, Guoping; Qian, Yonglan; Yu, Qiang; Zhang, Jiahua
Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial information about model inputs, such as the value of some of their parameters and initial conditions, which may have great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.
This presentation surveys current and emerging drought monitoring approaches using satellite remote sensing observations from climatological and ecosystem perspectives. We argue that satellite observations not currently used for operational drought monitoring, such as near-surface air relative humidity data from the Atmospheric Infrared Sounder (AIRS) mission, provide opportunities to improve early drought warning. Current and future satellite missions offer opportunities to develop composite and multi-indicator drought models. While there are immense opportunities, there are major challenges including data continuity, unquantified uncertainty, sensor changes, and community acceptability. One of the major limitations of many of the currently available satellite observations is their short length of record. A number of relevant satellite missions and sensors (e.g., the Gravity Recovery and Climate Experiment, GRACE) provide only a decade of data, which may not be sufficient to study droughts from a climate perspective. However, they still provide valuable information about relevant hydrologic and ecological processes linked to this natural hazard. Therefore, there is a need for models and algorithms that combine multiple datasets and/or assimilate satellite observations into model simulations to generate long-term climate data records. Finally, the study identifies a major gap in indicators for describing drought impacts on the carbon and nitrogen cycle, which are fundamental to assessing drought impacts on ecosystems.
Egan, Walter G.
Various aspects of polarization in remote sensing are presented including mathematical treatments and selected experimental observations. The observations are of the percent polarization from Haleakala volcanic ash, basalt powder, rhyolytic oumice. rose quartz, niccolite, ilmenite, black oak leaves. dried red pine needles, a New Haven red pine stand, moist soil, the sky above Mauna Loa Observatory, the sky above Long Island in summer and winter, and cirrus clouds. Also, space based shuttle photographic observations of polarization are described. Instrumental polarization from a Cassegrainian telescope is described as well as the design of an imaging soectropolarimeter for remote sensing. A list is presented of twelve polarimetric properties associated with remote sensing.
Philipson, W. R. (Principal Investigator)
Built on Cornell's thirty years of experience in aerial photographic studies, the NASA-sponsored remote sensing program strengthened instruction and research in remote sensing, established communication links within and beyond the university community, and conducted research projects for or with town, county, state, federal, and private organizations in New York State. The 43 completed applied research projects are listed as well as 13 spinoff grants/contracts. The curriculum offered, consultations provided, and data processing facilities available are described. Publications engendered are listed including the thesis of graduates in the remote sensing program.
Contreras, Sergio; Hunink, Johannes E.
We present a satellite-based Drought Monitoring System that provides weekly updates of maps and bulletins with vegetation drought indices over the Iberian Peninsula. The web portal InfoSequía (http://infosequia.es) aims to complement the current Spanish Drought Monitoring System which relies on a hydrological drought index computed at the basin level using data on river flows and water stored in reservoirs. Drought indices computed by InfoSequia are derived from satellite data provided by MODIS sensors (TERRA and AQUA satellites), and report the relative anomaly observed in the Normalized Difference Vegetation Index (NDVI), Land Surface Temperature (LST), and in an additive combination of both. Similar to the U.S. Drought Monitoring System by NOAA, the indices include the Vegetation Condition Index (VCI, relative NDVI anomaly), the Temperature Condition Index (TCI, relative LST anomaly) and the Vegetation Health Index (VHI, relative NDVI-LST anomaly). Relative anomalies are codified into four warning levels, and all of them are provided for short periods of time (8-day windows), or longer periods (e.g. 1 year) in order to capture the cumulative effects of droughts in the state variables. Additionally, InfoSequia quantifies the seasonal trajectories of the cumulative deviation of the observed NDVI in relation with the averaged seasonal trajectory observed over a reference period. Through the weekly bulletins, the Drought Monitoring System InfoSequia aims to provide practical information to stakeholders on the sensitivity and resilience of native ecosystems and rainfed agrosystems during drought periods. Also, the remote sensed indices can be used as drought impact indicator to evaluate the skill of seasonal agricultural drought forecasting systems. InfoSequia is partly funded by the Spanish Ministry of Economy and Competiveness through a Torres-Quevedo grant.
Hashim, M.; Pour, A. B.; Onn, C. H.
Remote sensing technology is an important tool to analyze vegetation dynamics, quantifying vegetation fraction of Earth's agricultural and natural vegetation. In optical remote sensing analysis removing atmospheric interferences, particularly distribution of cloud contaminations, are always a critical task in the tropical climate. This paper suggests a fast and alternative approach to remove cloud and shadow contaminations for Landsat Enhanced Thematic Mapper+ (ETM+) multi temporal datasets. Band 3 and Band 4 from all the Landsat ETM+ dataset are two main spectral bands that are very crucial in this study for cloud removal technique. The Normalise difference vegetation index (NDVI) and the normalised difference soil index (NDSI) are two main derivatives derived from the datasets. Change vector analysis is used in this study to seek the vegetation dynamics. The approach developed in this study for cloud optimizing can be broadly applicable for optical remote sensing satellite data, which are seriously obscured with heavy cloud contamination in the tropical climate.
North, G. W.
Environmental pollution is a problem of international scope and concern. It can be subdivided into problems relating to water, air, or land pollution. Many of the problems in these three categories lend themselves to study and possible solution by remote sensing. Through the use of remote sensing systems and techniques, it is possible to detect and monitor, and in some cases, identify, measure, and study the effects of various environmental pollutants. As a guide for making decisions regarding the use of remote sensors for pollution studies, a special five-dimensional sensor/applications matrix has been designed. The matrix defines an environmental goal, ranks the various remote sensing objectives in terms of their ability to assist in solving environmental problems, lists the environmental problems, ranks the sensors that can be used for collecting data on each problem, and finally ranks the sensor platform options that are currently available.
Dardanelli, Gino; La Loggia, Goffredo; Perfetti, Nicola; Capodici, Fulvio; Puccio, Luigi; Maltese, Antonino
deformations monitoring of the Castello dam. Displacements of different sections of the dam reveal different behaviour (in time and periodicity) that looks to be related with water surface (and level) retrieved from remote sensing.
Rickman, Douglas L.
This viewgraph presentation reviews the classification of Remote Sensing data in relation to epidemiology. Classification is a way to reduce the dimensionality and precision to something a human can understand. Classification changes SCALAR data into NOMINAL data.
Sever, Thomas L.
Remotely sensed data allows archeologists and historic preservationists the ability to non-destructively detect phenomena previously unobservable to them. Archeologists have successfully used aerial photography since the turn of the century and it continues to be an important research tool today. Multispectral scanners and computer-implemented analysis techniques extend the range of human vision and provides the investigator with innovative research designs at scales previously unimaginable. Pioneering efforts in the use of remote sensing technology have demonstrated its potential, but it is the recent technological developments in remote sensing instrumentation and computer capability that provide for unlimited, cost-effective applications in the future. The combination of remote sensing, Global Positioning System (GPS) technology, and Geographic Information Systems (GIS) are radically altering survey, inventory, and modelling approaches.
Washington-Allen, R. A.; West, N. E.; Ramsey, R. D.; Efroymson, R. A.
Because the condition and trend of drylands in the United States at sub-regional to national scales is unknown, this paper presents a protocol for the monitoring and assessment of dryland degradation using historical remote sensing. Traditional field-based monitoring has proven inadequate for capturing the changes in spatial and temporal heterogeneity that may adversely affect drylands at sub-regional to national spatial scales. Continuous monitoring data of 14 to 25 years is required to detect and separate the effects of climatic processes from those induced by land management practices. However, few field-based datasets exist that have measured the trend of appropriate ecological indicators at regional to national scales. Landsat data has been collected and archived at least once-a month since 1972, and includes complete coverage of US drylands. Five assessment endpoints that relate to land degradation can be retrospectively measured for the last 33 years using surrogate indicators derived from Landsat imagery: change in vegetation physiognomy, decline in vegetation productivity, accelerated soil erosion, decline in soil quality, and change in landscape composition and configuration. Consequently, this protocol considers (1) how land degradation is characterized, i.e., what indicators are assessed, (2) which indicators are measured, (3) what reference conditions are appropriate, (4) which temporal and spatial scales are of concern, and finally, (5) which statistics and/or analyses are appropriate for determining the significance of a change? Aspects of two retrospective studies are presented as examples of application of the protocol to considerations of the land use impacts from military training and testing, and commercial grazing activities on drylands.
Zhang, J.; Huang, J.; Mu, Q.
With a warming climate, the world has experienced frequent droughts during the past few decades. A remotely sensed Drought Severity Index (DSI), which integrates both vegetation growth condition and evapotranspiration, has been recently proposed for drought monitoring at the global scale. However, there has been little research on its utility for regional application, especially on agricultural drought. As an important winter wheat producing region, the North China has suffered from frequent droughts in recent years. In this study, the capability of the DSI for drought monitoring and impact analysis in five wheat producing provinces of North China was investigated. First, the DSI was compared with precipitation and soil moisture to show its ability for characterizing moisture status. Then specifically for agricultural drought, the DSI was evaluated against agricultural drought severity and the impacts of drought on crop yield during the growing season were also explored using the 8-day DSI data. The main conclusions are: (1) The DSI shows generally good ability for characterizing moisture conditions at the province level with varying ability during winter wheat main growing season (March-June), and the best relationship was found in April. (2) Despite varying capability, the DSI is quite effective in characterizing agricultural drought severity at the province level. (3) Drought shows generally increasing agricultural impacts during winter wheat main growing season (March-June), with little impacts in March (green-up stage), emerging impacts in April (jointing and booting stages) and significant drought impacts in May (heading and filling stages). (4) Based on the spatial pattern of agricultural drought impacts, densely winter wheat planted areas such as South Hebei, Central/West Shandong and North/East Henan are identified as drought vulnerable regions and comprehensive monitoring in these hotspots is highly recommended.
Yan, Hongxiang; Moradkhani, Hamid
Current two datasets provide spatial and temporal resolution of soil moisture at large-scale: the remotely-sensed soil moisture retrievals and the model-simulated soil moisture products. Drought monitoring using remotely-sensed soil moisture is emerging, and the soil moisture simulated using land surface models (LSMs) have been used operationally to monitor agriculture drought in United States. Although these two datasets yield important drought information, their drought monitoring skill still needs further quantification. This study provides a comprehensive assessment of the potential of remotely-sensed and model-simulated soil moisture data in monitoring agricultural drought over the Columbia River Basin (CRB), Pacific Northwest. Two satellite soil moisture datasets were evaluated, the LPRM-AMSR-E (unscaled, 2002-2011) and ESA-CCI (scaled, 1979-2013). The USGS Precipitation Runoff Modeling System (PRMS) is used to simulate the soil moisture from 1979-2011. The drought monitoring skill is quantified with two indices: drought area coverage (the ability of drought detection) and drought severity (according to USDM categories). The effects of satellite sensors (active, passive), multi-satellite combined, length of climatology, climate change effect, and statistical methods are also examined in this study.
Abd Salam El Vilaly, Mohamed; El Vilaly, Audra; Badiane, Ousmane
Understanding the complex feedbacks between climate, environmental change, and human activities is essential to the development of sustainable agricultural systems. A key aspect of crop production that shows immediate response to climate change is crop phenology, which defines the shape and progress of the growing season and is an integrator of all environmental factors controlling crop production. This research aims to characterize remote sensing-based land surface phenology of cropped areas and compare them to the actual crop growing seasons recorded by farmers: planting, emergences, flowering, fruiting, and harvest date. We use the 2000-2013 MODIS Terra 16-day record of vegetation index to extract 4 phenometrics (Start and Length of Growing Season, Date of Growing Season Peak, and the Growing Season Cumulative Signal). Most of these metrics are simple time-related phenometrics. A spatiotemporal phenological characterization of cropped/managed lands in the basin already shows distinct response patterns and trajectories along climate gradients. This permits us to monitor cropped lands and their responses to disturbances, such as drought, fire, flooding, and human activities. In turn, interviewing farmers in the basin and consulting their phenological records. This study will allow for robust validation of remote sensing LSP algorithms, and more crucially, will help characterize any remote sensing-based metrics that contrast with the actual biological phenophases of monitored crops. In terms of its larger significance, this study demonstrates the fundamental role that remote sensing plays in global agriculture in informing conservation and management practices.
Harb, Mostapha; De Vecchi, Daniele; Dell'Acqua, Fabio
The paper introduces a novel approach for the geometric co registration of optical remote sensing imagery. In the context of disaster mitigation and preparedness, a multi-temporal set of several remote sensing images often has to be processed separately to extract the required information. Then, a comparison among the obtained results would provide clues towards the time-evolving extent and distribution of risk. Therefore, it is of significant importance to achieve a proper geometric matching among the compared images. The traditional procedure of using manually-determined ground control points is not viable for large stacks of images, and automated methods may fail short of ensuring image conformity. The established method uses image data itself to effectively perform the co registration among the images relying on feature extraction and matching, without the necessity of using ground control points (GCPs). The approach has been tested using both high and medium resolution images on different test cases in a context of multi-risk vulnerability monitoring. The obtained results were highly promising in resolving the mismatching problem of objects in images taken from different dates and allowing smooth extraction of vulnerability proxies from multi-temporal moderate resolution optical satellite images. In conclusion, the methodology would be a useful contribution towards easing the tracking of temporal variation of ground features in the wide domain of risk-related application of remote sensing (e.g. urban development, deforestation, wild fire, damage assessment...) Keywords: Risk monitoring, remote sensing, optical imagery, geometric co registration
BasinTools Module 1 processes remotely sensed raster data, including multi- and hyper-spectral data products, via a Web site with no downloads and no plug-ins required. The interface provides standardized algorithms designed so that a user with little or no remote-sensing experience can use the site. This Web-based approach reduces the amount of software, hardware, and computing power necessary to perform the specified analyses. Access to imagery and derived products is enterprise-level and controlled. Because the user never takes possession of the imagery, the licensing of the data is greatly simplified. BasinTools takes the "just-in-time" inventory control model from commercial manufacturing and applies it to remotely-sensed data. Products are created and delivered on-the-fly with no human intervention, even for casual users. Well-defined procedures can be combined in different ways to extend verified and validated methods in order to derive new remote-sensing products, which improves efficiency in any well-defined geospatial domain. Remote-sensing products produced in BasinTools are self-documenting, allowing procedures to be independently verified or peer-reviewed. The software can be used enterprise-wide to conduct low-level remote sensing, viewing, sharing, and manipulating of image data without the need for desktop applications.
Zhang, J.; Becker-Reshef, I.; Justice, C. O.
Although agricultural production has been rising in the past years, drought remains the primary cause of crop failure, leading to food price instability and threatening food security. The recent 'Global Food Crisis' in 2008, 2011 and 2012 has put drought and its impact on crop production at the forefront, highlighting the need for effective agricultural drought monitoring. Satellite observations have proven a practical, cost-effective and dynamic tool for drought monitoring. However, most satellite based methods are not specially developed for agriculture and their performances for agricultural drought monitoring still need further development. Wheat is the most widely grown crop in the world, and the recent droughts highlight the importance of drought monitoring in major wheat producing areas. As the largest wheat producing state in the US, Kansas plays an important role in both global and domestic wheat markets. Thus, the objective of this study is to investigate the capabilities of remotely sensed crop indicators for effective agricultural drought monitoring in Kansas wheat-grown regions using MODIS data and crop yield statistics. First, crop indicators such as NDVI, anomaly and cumulative metrics were calculated. Second, the varying impacts of agricultural drought at different stages were explored by examining the relationship between the derived indicators and yields. Also, the starting date of effective agricultural drought early detection and the key agricultural drought alert period were identified. Finally, the thresholds of these indicators for agricultural drought early warning were derived and the implications of these indicators for agricultural drought monitoring were discussed. The preliminary results indicate that drought shows significant impacts from the mid-growing-season (after Mid-April); NDVI anomaly shows effective drought early detection from Late-April, and Late-April to Early-June can be used as the key alert period for agricultural
The world is suffering from rapid changes in both climate and land cover which are the main factors affecting global biodiversity. These changes may affect ecosystems by altering species distributions, population sizes, and community compositions, which emphasizes the need for a rapid assessment of biodiversity status for conservation and management purposes. Current approaches on monitoring biodiversity rely mainly on long term observations of predetermined sites, which require large amounts of time, money and personnel to be executed. In order to overcome problems associated with current field monitoring methods, the main objective of this dissertation is the development of framework for inferential monitoring of the impact of global change on biodiversity based on remotely sensed data coupled with species distribution modeling techniques. Several research pieces were performed independently in order to fulfill this goal. First, species distribution modeling was used to identify the ranges of 6362 birds, mammals and amphibians in South America. Chapter 1 compares the power of different presence-only species distribution methods for modeling distributions of species with different response curves to environmental gradients and sample sizes. It was found that there is large variability in the power of the methods for modeling habitat suitability and species ranges, showing the importance of performing, when possible, a preliminary gradient analysis of the species distribution before selecting the method to be used. Chapter 2 presents a new methodology for the redefinition of species range polygons. Using a method capable of establishing the uncertainty in the definition of existing range polygons, the automated procedure identifies the relative importance of bioclimatic variables for the species, predicts their ranges and generates a quality assessment report to explore prediction errors. Analysis using independent validation data shows the power of this
Kontoes, C.; Papoutsis, I.; Michail, D.; Herekakis, Th.; Koubarakis, M.; Kyzirakos, K.; Karpathiotakis, M.; Nikolaou, C.; Sioutis, M.; Garbis, G.; Vassos, S.; Keramitsoglou, I.; Kersten, M.; Manegold, S.; Pirk, H.
In the Institute for Space Applications and Remote Sensing of the National Observatory of Athens (ISARS/NOA) volumes of Earth Observation images of different spectral and spatial resolutions are being processed on a systematic basis to derive thematic products that cover a wide spectrum of applications during and after wildfire crisis, from fire detection and fire-front propagation monitoring, to damage assessment in the inflicted areas. The processed satellite imagery is combined with auxiliary geo-information layers, including land use/land cover, administrative boundaries, road and rail network, points of interest, and meteorological data to generate and validate added-value fire-related products. The service portfolio has become available to institutional End Users with a mandate to act on natural disasters and that have activated Emergency Support Services at a European level in the framework of the operational GMES projects SAFER and LinkER. Towards the goal of delivering integrated services for fire monitoring and management, ISARS/NOA employs observational capacities which include the operation of MSG/SEVIRI and NOAA/AVHRR receiving stations, NOA's in-situ monitoring networks for capturing meteorological parameters to generate weather forecasts, and datasets originating from the European Space Agency and third party satellite operators. The qualified operational activity of ISARS/NOA in the domain of wildfires management is highly enhanced by the integration of state-of-the-art Information Technologies that have become available in the framework of the TELEIOS (EC/ICT) project. TELEIOS aims at the development of fully automatic processing chains reliant on a) the effective storing and management of the large amount of EO and GIS data, b) the post-processing refinement of the fire products using semantics, and c) the creation of thematic maps and added-value services. The first objective is achieved with the use of advanced Array Database technologies, such
Shafaie, M.; Ghodosi, H.; Mostofi, K. H.
Whereas the tank volume and dehydrating digits from kinds of tanks are depended on repository sludge, so calculating the sediments is so important in tank planning and hydraulic structures. We are worry a lot about soil erosion in the basin area leading to deposit in rivers and lakes. It holds two reasons: firstly, because the surface soil of drainage would lose its fertility and secondly, the capacity of the tank decreases also it causes the decrease of water quality in downstream. Several studies have shown that we can estimate the rate of suspension sediments through remote sensing techniques. Whereas using remote sensing methods in contrast to the traditional and current techniques is faster and more accurate then they can be used as the effective techniques. The intent of this study has already been to estimate the rate of sediments in Karaj watershed through remote sensing and satellite images then comparing the gained results to the sediments data to use them in gauge-hydraulic station. We mean to recognize the remote sensing methods in calculating sediment and use them to determine the rate of river sediments so that identifying their accuracies. According to the results gained of the shown relations at this article, the amount of annual suspended sedimentary in KARAJ watershed have been 320490 Tones and in hydrologic method is about 350764 Tones .
Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. n this study, the authors compared forest cover estimates from a recently developed ...
Turner, D P; Koerper, G; Gucinski, H; Peterson, C; Dixon, R K
Satellite-based remote sensing offers great potential for frequent assessment of forest cover over broad spatial scales, however, calibration and validation using ground-based surveys are needed. In this study, forest cover estimates for the United States from a recently developed land surface cover map generated from satellite remote sensing data were compared to state-level inventory data from the U.S. National Resources Planning Act Timber Database. The land cover map was produced at the U.S. Geological Survey EROS Data Center and is based on imagery from the AVHRR sensor (spatial resolution ∼1.1 km). Vegetation type was classified using the temporal signal in the Normalized Difference Vegetation Index derived from AVHRR data. Comparisons revealed close agreement in the estimate of forest cover for extensively forested states with large polygons of relatively similar vegetation such as Oregon. Larger forest cover differences were observed in other states with some regional patterns in the level of agreement apparent.Comparisons in inventory- and remote sensing-based estimates of current forested area with potential vegetation maps indicated the magnitude of past land use change and the potential for future changes. The remote sensing approach appears to hold promise for conducting surveys of forest cover where inventory data are limited or where rates of vegetation change, due to human or climatic factors, are rapid. PMID:24220843
The Evaporative Stress Index (ESI) describes temporal anomalies in evapotranspiration (ET), highlighting areas with anomalously high or low rates of water use across the land surface. ET is retrieved via energy balance using remotely sensed land-surface temperature (LST) time-change signals. LST ...
Remote sensing technology has the potential to inform and accelerate the engagement of communities and managers in the implementation and performance of best management practices. Over the last few decades, satellite technology has allowed measurements on a global scale over long...
Remote sensing is used to show the actual distribution of distinctive invasive weeds such as leafy spurge (Euphorbia esula L.), whereas landscape modeling can show the potential distribution over an area. Geographic information system data and hyperspectral imagery [NASA JPL’s Airborne Visible Infra...
Cheng, Peng-gen; Tong, Cheng-zhuo; Chen, Xiao-yong; Nie, Yun-ju
At present, the dynamic change monitoring of urban ecological environment has became an important guarantee measure for urban management, planning and construction. In this paper, taking Nanchang city as a case study, the remote sensing ecological index (RSEI) which is based on the natural factors is used to study the changes of the urban ecological environment. The Landsat images in the three different time periods of 1996, 2005, and 2013 in Nanchang were selected. To extract the four factors of green level, moisture, dryness and heat respectively as sub-indexs of the ecological assessment, in which the single window algorithm was used to calculate the heat. Based on the four factors, the RSEI in each year was finally calculated. The results show that the ecological environment in Nanchang deteriorated in the past 17 years, the value of the RSEI has decreased from 0.385 in 1996 to 0.267 in 2005, falling by 30.65%, but the ecological environment has improved in the later period, with the value of RSEI value rising to 0.413, increased by 54.68% compared with the results in 2005. It is indicates that the urban ecological environment of Nanchang has been significantly improved after some effective measures such as urban greening, pollution control, environmental protection were taken.
Yuhas, R.H.; Dolan, P.H.; Goetz, A.F.H. )
Increasing concern over the threat of global warming has precipitated the need for study sites which can be scientifically monitored to detect and follow the effects of environmental landscape change. Extensive eolian dune deposits in northeastern Colorado provide an ideal study site. These dune complexes, found along the South Platte River, are currently stabilized by a thin cover of shortgrass prairie vegetation. However, stratigraphic evidence demonstrates that during at least four times in the past 10,000 years, the dunes were actively migrating across the landscape. In addition, climate models indicate that the High Plains could be one of the first areas to react to climate changes when they occur. The scaling relationships that contribute to the evolution of the landscape are nearly impossible to understand without the regional perspective that remote sensing and geographical information system (GIS) techniques provide. Imagery acquired with the NASA/JPL Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is processed to detect the amount of sand exposed, as well as the percent vegetation cover that is currently stabilizing the dunes. Excellent discrimination is found between areas of low and no vegetation, something not possible with traditional analysis methods. Seasonal changes are also emphasized. This information is incorporated into the GIS database the authors created, which also has information on parameters that influence the landscape: elevation, soil type, surface/subsurface hydrology, etc. With these data areas that are susceptible to climate change are highlighted, but more importantly, the reasons for the susceptibility are determined using the GIS's analytical capabilities.
Kozhoridze, Giorgi; Orlovsky, Leah; Orlovsky, Nikolai
The Aral Sea ecological crisis resulted from the USSR government decision in 1960s to deploy agricultural project for cotton production in Central Asia. Consequently water flow in the Aral Sea decreased drastically due to the regulation of Amydarya and Syrdarya Rivers for irrigation purposes from 55-60 km3 in 1950s to 43 km3 in 1970s, 4 km3 in 1980s and 9-10 km3 in 2000s. Expert land cover classification approach gives the opportunity to use the unlimited variable for classification purposes. The band algebra (band5/band4 and Band4/Band3) and remote sensing indices (Normalized differential Salinity Index (NDSI), Salt Pan Index (SPI), Salt Index (SI), Normalized difference Vegetation Index (NDVI), Albedo, Crust Index) utilized for the land cover classification has shown satisfactory result with classification overall accuracy 86.9 % and kappa coefficient 0.85. Developed research algorithm and obtained results can support monitoring system, contingency planning development, and improvement of natural resources rational management.
Drolon, Vanessa; Maisongrande, Philippe; Berthier, Etienne; Swinnen, Else
Mass balance is a key variable to describe the state of health of glaciers, their contribution to sea level rise and, in a few dry regions, their role in water resource. We explore here a new method to retrieve seasonal glacier mass balances from low resolution optical remote sensing. We derive winter and summer snow maps for each year during 1998-2014, using the Normalized Difference Snow Index (NDSI) computed from visible and SWIR channels available with SPOT/VEGETATION. The NDSI dynamic is directly linked to the area percentage of snow in the VGT kilometric pixel. The combination of 15 years of 10-daily NDSI maps with the SRTM DEM allows us to calculate the altitude of the transition between bare soil and snow. Then, we compare the interannual dynamic of this altitude with in situ measurements of mass balance available for 60 alpine glaciers (Huss et al., 2010; Zemp et al., 2009, 2013) and find promising relationships for winter mass balance. We also explore the possibility of a real-time monitoring of winter mass balance for a selection of alpine glaciers. Finally, we discuss the robustness and genericity of these relationships for their future application in regions where in situ glaciers mass balances are scarce or not available.
Sarna, K.; Russchenberg, H. W. J.
A method for continuous observation of aerosol-cloud interactions with ground-based remote sensing instruments is presented. The main goal of this method is to enable the monitoring of cloud microphysical changes due to the changing aerosol concentration. We use high resolution measurements from lidar, radar and radiometer which allow to collect and compare data continuously. This method is based on a standardised data format from Cloudnet and can be implemented at any observatory where the Cloudnet data set is available. Two example study cases were chosen from the Atmospheric Radiation Measurement (ARM) Program deployment at Graciosa Island, Azores, Portugal in 2009 to present the method. We show the Pearson Product-Moment Correlation Coefficient, r, and the Coefficient of Determination, r2 for data divided into bins of LWP, each of 10 g m-2. We explain why the commonly used way of quantity aerosol cloud interactions by use of an ACI index (ACIr,τ = dln re,τ/dlnα) is not the best way of quantifying aerosol-cloud interactions.
Gao, Zhihai; del Barrio, Gabriel; Li, Xiaosong; Wang, Bengyu; Puigdefabregas, Juan; Sanjuan, Maria E.; Bai, Lina; Wu, Junjun; Sun, Bin; Li, Changlong
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.
Becker, M.; Bejannin, S.; Papa, F.; Frappart, F.; Calmant, S.; Santos Da Silva, J.
Despite the global importance of the Congo Basin, which is the second largest river basin in the world, only a small number of studies to date have focused on its hydro-climatic variability. The limited understanding of climate dynamics in the Congo Basin is in part due to the lack of the in situ monitoring of climate variables in that area. Given the vast size of the Congo Basin, remote sensing observations provide the only viable approach to understanding the spatial and temporal variability of the basin's hydro-climatic patterns. To apprehend the water cycle of the Congo basin it is important to know how the freshwater is stored in this basin and its spatial and temporal dynamics. In this study, a multi-satellite approach is proposed to estimate the water stored in the floodplains of the Congo Basin at monthly time-scale using the surface water extent from the Global Inundation Extent Multi-Satellite (GIEMS) and the water level fluctuations derived from ENVISAT Radar Altimeter. The combination of these datasets for their overlapping period of record, from 2003 to 2007, enabled us to compute water level maps from which we estimated surface water storage in the Congo Basin.
Robert Paul Breckenridge
Creeping environmental changes are impacting some of the largest remaining intact parcels of sagebrush steppe ecosystems in the western United States, creating major problems for land managers. The Idaho National Laboratory (INL), located in southeastern Idaho, is part of the sagebrush steppe ecosystem, one of the largest ecosystems on the continent. Scientists at the INL and the University of Idaho have integrated existing field and remotely sensed data with geographic information systems technology to analyze how recent fires on the INL have influenced the current distribution of terrestrial vegetation. Three vegetation mapping and classification systems were used to evaluate the changes in vegetation caused by fires between 1994 and 2003. Approximately 24% of the sagebrush steppe community on the INL was altered by fire, mostly over a 5-year period. There were notable differences between methods, especially for juniper woodland and grasslands. The Anderson system (Anderson et al. 1996) was superior for representing the landscape because it includes playa/bare ground/disturbed area and sagebrush steppe on lava as vegetation categories. This study found that assessing existing data sets is useful for quantifying fire impacts and should be helpful in future fire and land use planning. The evaluation identified that data from remote sensing technologies is not currently of sufficient quality to assess the percentage of cover. To fill this need, an approach was designed using both helicopter and fixed wing unmanned aerial vehicles (UAVs) and image processing software to evaluate six cover types on field plots located on the INL. The helicopter UAV provided the best system compared against field sampling, but is more dangerous and has spatial coverage limitations. It was reasonably accurate for dead shrubs and was very good in assessing percentage of bare ground, litter and grasses; accuracy for litter and shrubs is questionable. The fixed wing system proved to be
González-Dugo, Maria P.; Andreu, Ana; Carpintero, Elisabet; Gómez-Giráldez, Pedro; José Polo, María
Drought is one of the major hazards faced by agroforestry systems in southern Europe, and an increase in frequency is predicted under the conditions of climate change for the region. Timely and accurate monitoring of vegetation water stress using remote sensing time series may assist early-warning services, helping to assess drought impacts and the design of management actions leading to reduce the economic and environmental vulnerability of these systems. A holm oak savanna, known as dehesa in Spain and montado in Portugal, is an agro-silvo-pastoral system occupying more than 3 million hectares the Iberian Peninsula and Greece. It consists of widely-spaced oak trees (mostly Quercus ilex L.), combined with crops, pasture and Mediterranean shrubs, and it is considered an example of sustainable land use, with great importance in the rural economy. Soil water dynamics is known to have a central role in current tree decline and the reduction of the forested area that is threatening its conservation. A two-source thermal-based evapotranspiration model (TSEB) has been applied to monitor the effect on vegetation water use of soil moisture stress in a dehesa located in southern Spain. The TSEB model separates the soil and canopy contributions to the radiative temperature and to the exchange of surface energy fluxes, so it is especially suited for partially vegetated landscapes. The integration of remotely sensed data in this model may support an evaluation of the whole ecosystem state at a large scale. During two consecutive summers, in 2012 and 2013, time series of optical and thermal MODIS images, with 250m and 1 km of spatial resolution respectively, have been combined with meteorological data provided by a ground station to monitor the evapotranspiration (ET) of the system. An eddy covariance tower (38°12' N; 4°17' W, 736 m a.s.l), equipped with instruments to measure all the components of the energy balance and 1 km of homogeneous fetch in the predominant wind
Huang, He; Fan, Yida; Yang, Siquan; Wen, Qi; Pan, Donghua; Fan, Chunbo; He, Haixia
Drought is one kind of nature disasters in the world. It has characteristics of temporal-spatial inhomogeneity, wide affected areas and periodic happening. The economic loss and affected population caused by different droughts are the largest in all natural disasters. Remote sensing has the advantages of large coverage, frequent observation, repeatable observation, reliable information source and low cost. These advantages make remote sensing a vital contributor for drought disaster risk assessment and monitoring. In this paper, three drought monitoring models, such as Vegetation Condition Index (VCI), Temperature Vegetation Dryness Index (TVDI), and Water Supplying Vegetation Index (WSVI) had been selected to monitor the drought occurred from January 2012 to June 2012 in Hubei province, China. Two kinds of remote sensing data, including HJ-1A/B CCD/IRS and ZY-3, had been employed to assess the integrated risk of Hubei drought based on three drought monitoring models. The results shown that the risk of northwest regions and middle regions in Hubei province were higher than that in the other regions. The results also indicated that the extreme risk regions were located in Shiyan, Xiangyang, Suizhou and Jingmen.
Mona, Lucia; Caggiano, Rosa; Donvito, Angelo; Giannini, Vincenzo; Papagiannopoulos, Nikolaos; Sarli, Valentina; Trippetta, Serena
The atmospheric aerosols have effects on climate, environment and health. Although the importance of the study of aerosols is well recognized, the current knowledge of the characteristics and their distribution is still insufficient, and there are large uncertainties in the current understanding of the role of aerosols on climate and the environment, both on a regional and local level. Overcoming these uncertainties requires a search strategy that integrates data from multiple platforms (eg, terrestrial, satellite, ships and planes) and the different acquisition techniques (for example, in situ measurements, remote sensing, modeling numerical and data assimilation) (Yu et al., 2006). To this end, in recent years, there have been many efforts such as the creation of networks dedicated to systematic observation of aerosols (eg, European Monitoring and Evaluation Programme-EMEP, European Aerosol Research Lidar NETwork-EARLINET, MicroPulse Lidar Network- MPLNET, and Aerosol Robotic NETwork-AERONET), the development and implementation of new satellite sensors and improvement of numerical models. The recent availability of numerous data to the ground, columnar and profiles of aerosols allows to investigate these aspects. An integrated approach between these different techniques could be able to provide additional information, providing greater insight into the properties of aerosols and their distribution and overcoming the limits of each single technique. In fact, the ground measurements allow direct determination of the physico-chemical properties of aerosols, but cannot be considered representative for large spatial and temporal scales and do not provide any information about the vertical profile of aerosols. On the other hand, the remote sensing techniques from the ground and satellite provide information on the vertical distribution of atmospheric aerosols both in the Planetary Boundary Layer (PBL), mainly characterized by the presence of aerosols originating from
Tourre, Y. M.; Lacaux, J.
Presence (density) of mosquitoes linked to Rift Valley Fever (RVF) epidemics in the Ferlo (Senegal) is evaluated by monitoring the environment from space. Using five SPOT-5 high-resolution images (~10m spatial resolution, on August 17th, 2006) a meridional transect of 290 x 60 km2 is analyzed for the first time. Four major ecozones are thus identified: Senegal River valley; sandy Ferlo; sandy-clayey Ferlo; and steppe/cultivated areas, from north to south, respectively. An integrated/multidisciplinary approach using remote-sensing leads to a composited Zones Potentially Occupied by Mosquitoes (or ZPOMs, with extrema). It is found that at the peak of the rainy season, the area occupied by ponds is of 12,817 ha ± 10% (i.e., ~ 0.8 % of the transect) with a mean ZPOM 17 times larger i.e.: 212,813 ha ± 10 % (or ~14 % of the transect). ZPOMs characteristics (minimum and maximum) at the ecozones levels with different hydrological mechanisms, are presented. Ponds and ZPOMs inter-annual variabilities and RVF risks, are subsequently highlighted by comparing statistics in the so-called Barkedji zone (sandy-clayey Ferlo with a hydrofossil riverbed), for the very humid year of 2003, and the near normal rainfall year of 2006. It is shown that at the end of August 2003/2006, ponds (ZPOMs) areas, were already ~22 (~5) times larger. The key roles played by isolated ponds for animals' exposure to RVF risks are thus identified. These results highlight the importance of monitoring the changing environment when linkages with public health exist. The ZPOM approach is to be adapted for other vector-borne diseases such as malaria, dengue fever, in different places of the world. Results are meant to be included into Health Information Systems (HIS) on an operational basis, in order to minimize socio-economical impacts from epidemics.
Ramoelo, A.; Cho, M. A.; Madonsela, S.; Mathieu, R.; van der Korchove, R.; Kaszta, Z.; Wolf, E.
Global change consisting of land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) is one of the key factors limiting agricultural production and ecosystem functioning. Leaf N can be used as an indicator of rangeland quality which could provide information for the farmers, decision makers, land planners and managers. Leaf N plays a crucial role in understanding the feeding patterns and distribution of wildlife and livestock. Assessment of this vegetation parameter using conventional methods at landscape scale level is time consuming and tedious. Remote sensing provides a synoptic view of the landscape, which engenders an opportunity to assess leaf N over wider rangeland areas from protected to communal areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The objective of this study is to monitor leaf N as an indicator of rangeland quality using WorldView 2 satellite images in the north-eastern part of South Africa. Series of field work to collect samples for leaf N were undertaken in the beginning of May (end of wet season) and July (dry season). Several conventional and red edge based vegetation indices were computed. Simple regression was used to develop prediction model for leaf N. Using bootstrapping, indicator of precision and accuracy were analyzed to select a best model for the combined data sets (May and July). The may model for red edge based simple ratio explained over 90% of leaf N variations. The model developed from the combined data sets with normalized difference vegetation index explained 62% of leaf N variation, and this is a model used to estimate and map leaf N for two seasons. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability.
Ghosh, Manoj Kumer; Kumar, Lalit; Roy, Chandan
A large percentage of the world's population is concentrated along the coastal zones. These environmentally sensitive areas are under intense pressure from natural processes such as erosion, accretion and natural disasters as well as anthropogenic processes such as urban growth, resource development and pollution. These threats have made the coastal zone a priority for coastline monitoring programs and sustainable coastal management. This research utilizes integrated techniques of remote sensing and geographic information system (GIS) to monitor coastline changes from 1989 to 2010 at Hatiya Island, Bangladesh. In this study, satellite images from Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM) were used to quantify the spatio-temporal changes that took place in the coastal zone of Hatiya Island during the specified period. The modified normalized difference water index (MNDWI) algorithm was applied to TM (1989 and 2010) and ETM (2000) images to discriminate the land-water interface and the on-screen digitizing approach was used over the MNDWI images of 1989, 2000 and 2010 for coastline extraction. Afterwards, the extent of changes in the coastline was estimated through overlaying the digitized maps of Hatiya Island of all three years. Coastline positions were highlighted to infer the erosion/accretion sectors along the coast, and the coastline changes were calculated. The results showed that erosion was severe in the northern and western parts of the island, whereas the southern and eastern parts of the island gained land through sedimentation. Over the study period (1989-2010), this offshore island witnessed the erosion of 6476 hectares. In contrast it experienced an accretion of 9916 hectares. These erosion and accretion processes played an active role in the changes of coastline during the study period.
Thomas, J. P.
Both airborne remote sensors and seagoing research vessels were used to study the effects of man's continual use of the Chesapeake Bay offshore environments. The major focus of the study was to: (1) advance the development and transfer of improved remote sensing systems and techniques for monitoring environmental quality and effects on living marine resources; (2) increase understanding of the influence of estuarine outwellings (plumes) on contiguous shelf ecosystems; and (3) provide a synoptic, integrated and timely data base for application to problems of marine resources and environmental quality.
Yang, Qiang; Zhang, Zhi; Chen, Wei-tao; Qian, Li-ping
In this paper, the water quality of Hushan Tailings reservoir in Huji, Zhongxiang of Hubei province, was studied by remote sensing technology. Firstly, radioactive correction of ASTER data was processed by FLAASH Model, then the scatter was produced by SPSS, which was about reflectance or reflectance ratio of remote sensing(ASTER) and measured pollutant (Ag+, Ba2+, Hg2+, Ca2+, Mg2+, F-, Cl- , NO3-, H2PO4-, SO42-, et.)at sampling sites. After experimentation,conversion model was built with significance test, F=68.797(F0.05=11.821), R=0.793. Finally, the data of water reflectance was processed with our model, and the distribution of corresponding pollutant was obtained.
Miller, W. L.
Nonpoint pollution of streams with sediment as a result of runoff from alternative uses of land has become a socially unacceptable product of economic activity. This report describes a research approach to economically achieve correction of the nonpoint pollution problem. The research approach integrates the economic model with those data which may be obtainable from remotely sensed sources. The economic problem involves measurement of the direct benefits and costs associated with the changes in land management activities necessary to reduce the level of nonpoint pollution. Remotely sensed data from ERTS-1 may provide some of the information required for the economic model which indicates efficient solutions to the nonpoint pollution problem. Three classes of data (i.e., soil categories, vegetative cover, and water turbidity) have the potential to be measured by ERTS-1 systems. There is substantial research which indicates the ability of ERTS-1 to measure these classes of data under selected conditions.
Scipal, K.; Wagner, W.
The lack of global soil moisture observations is one of the most glaring and pressing deficiencies in current research activities of related fields, from climate monitoring and ecological applications to the quantification of biogeophysical fluxes. This has implications for important issues of the international political agenda like managing global water resources, securing food production and studying climate change. Currently it is held that only microwave remote sensing offers the potential to produce reliable global scale soil moisture information economically. Recognising the urgent need for a soil moisture mission several international initiatives are planning satellite missions dedicated to monitor the global hydrological cycle among them two European microwave satellites. ESA is planning to launch the Soil Moisture and Ocean Salinity Mission SMOS, in 2006. SMOS will measure soil moisture over land and ocean salinity over the oceans. The mission rests on a passive microwave sensor (radiometer) operated in L-band which is currently believed to hold the largest potential for soil moisture retrieval. One year before (2005) EUMETSAT will launch the Meteorological Operational satellite METOP which carries the active microwave system Advanced Scatterometer ASCAT on board. ASCAT has been designed to retrieve winds over the oceans but recent research has established its capability to retrieve soil moisture. Although currently it is hold that, using active microwave techniques, the effect of surface roughness dominates that of soil moisture (while the converse is true for radiometers), the ERS scatterometer was successfully used to derive global soil moisture information at a spatial resolution of 50 km with weekly to decadal temporal resolution. The quality of the soil moisture products have been assessed by independent experts in several pilot projects funded by the European Space Agency. There is evidence to believe that both missions will provide a flow of
Trelogan, Jessica; Crawford, Melba; Carter, Joseph
In 1998 the University of Texas Institute of Classical Archaeology, in collaboration with the University of Texas Center for Space Research and the National Preserve of Tauric Chersonesos (Ukraine), began a collaborative project, funded by NASA's Solid Earth and Natural Hazards program, to investigate the use of remotely sensed data for the study and protection of the ancient a cultural territory, or chora, of Chersonesos in Crimea, Ukraine.
Bolten, John D.; Crow, Wade T.; Zhan, Xiwu; Jackson, Thomas J.; Reynolds,Curt
Soil moisture is a fundamental data source used by the United States Department of Agriculture (USDA) International Production Assessment Division (IPAD) to monitor crop growth stage and condition and subsequently, globally forecast agricultural yields. Currently, the USDA IPAD estimates surface and root-zone soil moisture using a two-layer modified Palmer soil moisture model forced by global precipitation and temperature measurements. However, this approach suffers from well-known errors arising from uncertainty in model forcing data and highly simplified model physics. Here we attempt to correct for these errors by designing and applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA modified Palmer soil moisture model. An assessment of soil moisture analysis products produced from this assimilation has been completed for a five-year (2002 to 2007) period over the North American continent between 23degN - 50degN and 128degW - 65degW. In particular, a data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing EnKF soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline Palmer model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.
Murthy, A.; Gouda, K. C.; Bhat, R.; Laxmikantha, B. P.; Prabhuraj, D. K.
The Krishna River Basin in the south India experienced a major flood during October 2009, which is the second largest Eastward draining River in Peninsular India covering vast area in the States of Maharashtra, Karnataka and Andhra Pradesh. This River drains approximately 2,58,948 km2 , which is about 8 % of the total geographical area of India. In the present study the lateral extent of river resulted by the flood is monitored and analyzed using the MODIS remote sensing satellite data. The extension of river is derived by processing the data before, during and after the flood event in the river basin. Associated meteorological parameters like rainfall, river run off, rise in water column are also discussed using multi-source satellite (TMI/TRMM, SRTM DEM etc) and observed data. The land cover and Land use analysis of the basin is also carried out for the pre flood and post flood scenarios. It is observed that the elevation tends to decrease from the western part to the eastern part of the basin. The variations of lateral extent is well captured by the GIS analysis, which indicates the extent pattern are different at east and west part of basin due to different topographical features in the river basin. Figure 1 presents the increase in the lateral extent of river due to the flood event. This information can be used by the disaster managers for pro-active disaster mitigation. Figure 1: Increase in the lateral extent of Krishna river due to the October 2009 flood.
Lessel, J.; Ceccato, P.
Agriculture is a vital resource in the country of Uruguay. Here we propose new methods using remotely sensed data for assisting ranchers, land managers, and policy makers in the country to better manage their crops. Firstly, we created a drought severity index based on the climatological anomalies of land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS), precipitation data from the Tropical Rainfall Monitoring Mission (TRMM), and normalized difference water index (NDWI) data also using MODIS. The use of the climatological anomalies on the variables has improved the ability of the index to correlate with known drought indices versus previously published indices, which had not used them. We applied various coefficient schemes and vegetation indices in order to choose the model which best correlated with the drought indices across 10 sites throughout Uruguay's rangelands. The model was tested over summer months from 2009-2013. In years where drought had indeed been a problem in the country (such as 2009) the model showed intense signals of drought. Secondly, we used Landsat images to identify winter and summer crops in Uruguay. We first classified them using ENVI and then used the classifications in an ArcMap model to identify specific crop areas. We first created a polygon of the classifications for soils and vegetation for each month (omitting cloud covered images). We then used the crop growing cycle to identify the times during the year for which specific polygons should be soil and which should be vegetation. By intersecting the soil polygons with the vegetation polygons during their respective time periods during the crop growing cycle we were able to create an accurately identify crops. When compared to a shapefile of proposed crops for the year the model obtained a kappa value of 0.60 with a probability of detection of 0.79 and a false alarm ratio of 0.31 for the south-western study area over the 2013-2014 summer.
Shumate, M. S.
An instrument capable of remotely monitoring trace atmospheric constituents is described. The instrument, called a laser absorption spectrometer, can be operated from an aircraft or spacecraft to measure the concentration of selected gases in three dimensions. This device will be particularly useful for rapid determination of pollutant levels in urban areas.
Wu, Jingwei; Vincent, Bernard; Yang, Jinzhong; Bouarfa, Sami; Vidal, Alain
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.
Lenoble, Jacqueline (Editor); Remer, Lorraine (Editor); Tanre, Didier (Editor)
This book gives a much needed explanation of the basic physical principles of radia5tive transfer and remote sensing, and presents all the instruments and retrieval algorithms in a homogenous manner. For the first time, an easy path from theory to practical algorithms is available in one easily accessible volume, making the connection between theoretical radiative transfer and individual practical solutions to retrieve aerosol information from remote sensing. In addition, the specifics and intercomparison of all current and historical methods are explained and clarified.
The author presents selected case studies to demonstrate theories and practices of remote sensing and its value to the study of the terrestrial environment. Begins with an overview of sensor types and electromagnetic remote sensing, continuing with an examination of photographic and non-photographic systems in the study of the radiation budget, temperature structure and weather conditions of the atmosphere. Includes thorough coverage of the lithosphere, biosphere and hydrosphere, as well as the cartographic problems involved in land use/land cover and topographic mapping. Concludes with a discussion of the impact of electromagnetic computers in the development of geographic information systems.
Lillesand, T. M.; Kiefer, R. W. (Principal Investigator)
A textbook prepared primarily for use in introductory courses in remote sensing is presented. Topics covered include concepts and foundations of remote sensing; elements of photographic systems; introduction to airphoto interpretation; airphoto interpretation for terrain evaluation; photogrammetry; radiometric characteristics of aerial photographs; aerial thermography; multispectral scanning and spectral pattern recognition; microwave sensing; and remote sensing from space.
Thi Van Le, Khoa; Minkman, Ellen; Nguyen Thi Phuong, Thuy; Rutten, Martine; Bastiaanssen, Wim
Remote sensing and citizen science can be utilized to fulfill the gap of conventional monitoring methods. However, how to engage these techniques, principally taking advantage of local capacities and of globally accessible data for satisfying the continuous data requirements and uncertainties are exciting challenges. Previous studies in Vietnam showed that official documents regulated towards responding the vital need of upgrading national water monitoring infrastructures do not put the huge potentials of free satellite images and crowd-based data collection into account, this factor also limits publications related to these techniques. In this research, a new water monitoring approach will be developed friendly with areas suffering poor quality monitoring works. Particularly, algorithms respecting to the relationship between temperature, total suspended sediment (TSS), chlorophyll and information collected by sensors onboard Landsat-8 and Sentinel-2 MSI satellites are built in the study area in Northern Vietnam; additionally, undergraduate student volunteers were sent to the sites with all the measurement activities are designed to coincide with the time when the study area captured by the satellites to compare the results. While conventional techniques are proving their irreplaceable role in the water monitoring network, the utilization of remote sensing techniques and citizen science in this study will demonstrate highly supportive values, saving monitoring costs and time; advantaging local human resources to science; providing an inclusive assessment of water quality changes along with land-use change in the study area, these approaches are excellent alternatives to meet the demand of real-time, continuous data nationwide.
Butler, James J.; Johnson, B. Carol; Barnes, Robert A.
The use of remote sensing instruments on orbiting satellite platforms in the study of Earth Science and environmental monitoring was officially inaugurated with the April 1, 1960 launch of the Television Infrared Observation Satellite (TIROS) . The first TIROS accommodated two television cameras and operated for only 78 days. However, the TIROS program, in providing in excess of 22,000 pictures of the Earth, achieved its primary goal of providing Earth images from a satellite platform to aid in identifying and monitoring meteorological processes. This marked the beginning of what is now over four decades of Earth observations from satellite platforms. reflected and emitted radiation from the Earth using instruments on satellite platforms. These measurements are input to climate models, and the model results are analyzed in an effort to detect short and long-term changes and trends in the Earth's climate and environment, to identify the cause of those changes, and to predict or influence future changes. Examples of short-term climate change events include the periodic appearance of the El Nino-Southern Oscillation (ENSO) in the tropical Pacific Ocean  and the spectacular eruption of Mount Pinatubo on the Philippine island of Luzon in 1991. Examples of long term climate change events, which are more subtle to detect, include the destruction of coral reefs, the disappearance of glaciers, and global warming. Climatic variability can be both large and small scale and can be caused by natural or anthropogenic processes. The periodic El Nino event is an example of a natural process which induces significant climatic variability over a wide range of the Earth. A classic example of a large scale anthropogenic influence on climate is the well-documented rapid increase of atmospheric carbon dioxide occurring since the beginning of the Industrial Revolution . An example of the study of a small-scale anthropogenic influence in climate variability is the Atlanta Land
Kilpatrick, Adam D.; Lewis, Megan M.; Ostendorf, Bertram
A need exists in arid rangelands for effective monitoring of the impacts of grazing management on vegetation cover. Monitoring methods which utilize remotely-sensed imagery may have comprehensive spatial and temporal sampling, but do not necessarily control for spatial variation of natural variables, such as landsystem, vegetation type, soil type and rainfall. We use the inverse of the red band from Landsat TM satellite imagery to determine levels of vegetation cover in a 22,672km2 area of arid rangeland in central South Australia. We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management. The cross-fence comparison methodology generates and solves simultaneous equations of the relationship between each paddock and all other paddocks, derived from pairs of cross-fence sample points. We compare this ranking from two image dates separated by six years, during which management changes are known to have taken place. Changes in paddock rank resulting from the cross-fence comparison method show strong correspondence to those predicted by grazing management in this region, with a significant difference between the two common management types; a change from full stocking rate to light 20% stocking regime (Major Stocking Reduction) and maintenance of full 100% stocking regime (Full Stocking Maintained) (P = 0.00000132). While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities. These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged. For the first time, this method allows us to rank non-adjacent paddocks in a rangeland region relative to each other, while controlling for natural spatio
Kilpatrick, Adam D; Lewis, Megan M; Ostendorf, Bertram
A need exists in arid rangelands for effective monitoring of the impacts of grazing management on vegetation cover. Monitoring methods which utilize remotely-sensed imagery may have comprehensive spatial and temporal sampling, but do not necessarily control for spatial variation of natural variables, such as landsystem, vegetation type, soil type and rainfall. We use the inverse of the red band from Landsat TM satellite imagery to determine levels of vegetation cover in a 22,672 km(2) area of arid rangeland in central South Australia. We interpret this wealth of data using a cross-fence comparison methodology, allowing us to rank paddocks (fields) in the study region according to effectiveness of grazing management. The cross-fence comparison methodology generates and solves simultaneous equations of the relationship between each paddock and all other paddocks, derived from pairs of cross-fence sample points. We compare this ranking from two image dates separated by six years, during which management changes are known to have taken place. Changes in paddock rank resulting from the cross-fence comparison method show strong correspondence to those predicted by grazing management in this region, with a significant difference between the two common management types; a change from full stocking rate to light 20% stocking regime (Major Stocking Reduction) and maintenance of full 100% stocking regime (Full Stocking Maintained) (P = 0.00000132). While no paddocks had a known increase in stocking rate during the study period, many had a reduction or complete removal in stock numbers, and many also experienced removals of pest species, such as rabbits, and other ecosystem restoration activities. These paddocks generally showed an improvement in rank compared to paddocks where the stocking regime remained relatively unchanged. For the first time, this method allows us to rank non-adjacent paddocks in a rangeland region relative to each other, while controlling for natural
Shah-Hosseini, Reza; Homayouni, Saeid; Safari, Abdolreza
In the event of a natural disaster, such as a flood or earthquake, using fast and efficient methods for estimating the extent of the damage is critical. Automatic change mapping and estimating are important in order to monitor environmental changes, e.g., deforestation. Traditional change detection (CD) approaches are time consuming, user dependent, and strongly influenced by noise and/or complex spectral classes in a region. Change maps obtained by these methods usually suffer from isolated changed pixels and have low accuracy. To deal with this, an automatic CD framework-which is based on the integration of change vector analysis (CVA) technique, kernel-based C-means clustering (KCMC), and kernel-based minimum distance (KBMD) classifier-is proposed. In parallel with the proposed algorithm, a support vector machine (SVM) CD method is presented and analyzed. In the first step, a differential image is generated via two approaches in high dimensional Hilbert space. Next, by using CVA and automatically determining a threshold, the pseudo-training samples of the change and no-change classes are extracted. These training samples are used for determining the initial value of KCMC parameters and training the SVM-based CD method. Then optimizing a cost function with the nature of geometrical and spectral similarity in the kernel space is employed in order to estimate the KCMC parameters and to select the precise training samples. These training samples are used to train the KBMD classifier. Last, the class label of each unknown pixel is determined using the KBMD classifier and SVM-based CD method. In order to evaluate the efficiency of the proposed algorithm for various remote sensing images and applications, two different datasets acquired by Quickbird and Landsat TM/ETM+ are used. The results show a good flexibility and effectiveness of this automatic CD method for environmental change monitoring. In addition, the comparative analysis of results from the proposed method