Thematic mapper data analysis

NASA Technical Reports Server (NTRS)

Settle, M.; Chavez, P.; Kieffer, H. H.; Everett, J. R.; Kahle, A. B.; Kitcho, C. A.; Milton, N. M.; Mouat, D. A.

1983-01-01

The geological applications of remote sensing technology are discussed, with emphasis given to the analysis of data from the Thematic Mapper (TM) instrument onboard the Landsat 4 satellite. The flight history and design characteristics of the Landsat 4/TM are reviewed, and some difficulties endountered in the interpretation of raw TM data are discussed, including: the volume of data; residual noise; detector-to-detector striping; and spatial misregistration between measurements. Preliminary results of several geological, lithological, geobotanical mapping experiments are presented as examples of the geological applications of the TM, and some areas for improving the guality of TM imagery are identified.

Utilizing remote sensing of thematic mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

NASA Technical Reports Server (NTRS)

Browder, Joan A.; May, L. Nelson, Jr.; Rosenthal, Alan; Baumann, Robert H.; Gosselink, James G.

1987-01-01

A stochastic spatial computer model addressing coastal resource problems in Lousiana is being refined and validated using thematic mapper (TM) imagery. The TM images of brackish marsh sites were processed and data were tabulated on spatial parameters from TM images of the salt marsh sites. The Fisheries Image Processing Systems (FIPS) was used to analyze the TM scene. Activities were concentrated on improving the structure of the model and developing a structure and methodology for calibrating the model with spatial-pattern data from the TM imagery.

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

NASA Astrophysics Data System (ADS)

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

2008-03-01

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

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

NASA Astrophysics Data System (ADS)

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

2011-12-01

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

Discrimination of active and inactive sand from remote sensing - Kelso dunes, Mojave Desert, California

NASA Technical Reports Server (NTRS)

Paisley, Elizabeth C. I.; Lancaster, Nicholas; Gaddis, Lisa R.; Greeley, Ronald

1991-01-01

Landsat TM images, field data, and laboratoray reflectance spectra were examined for the Kelso dunes, Mojave Desert, California to assess the use of visible and near-infrared (VNIR) remote sensing data to discriminate aeolian sand populations on the basis of spectral brightness. Results show that areas of inactive sand have a larger percentage of dark, fine-grained materials compared to those composed of active sand, which contain less dark fines and a higher percentage of quartz sand-size grains. Both areas are spectrally distinct in the VNIR, suggesting that VNIR spectral data can be used to discriminate active and inactive sand populations in the Mojave Desert. Analysis of laboratory spectra was complicated by the presence of magnetite in the active sands, which decreases their laboratory reflectance values to those of inactive sands. For this application, comparison of TM and laboratory spectra suggests that less than 35 percent vegetation cover does not influence the TM spectra.

Aircraft and satellite remote sensing of desert soils and landscapes

NASA Technical Reports Server (NTRS)

Petersen, G. W.; Connors, K. F.; Miller, D. A.; Day, R. L.; Gardner, T. W.

1987-01-01

Remote sensing data on desert soils and landscapes, obtained by the Landsat TM, Heat Capacity Mapping Mission (HCMM), Simulated SPOT, and Thermal IR Multispectral Scanner (TIMS) aboard an aircraft, are discussed together with the analytical techniques used in the studies. The TM data for southwestern Nevada were used to discriminate among the alluvial fan deposits with different degrees of desert pavement and varnish, and different vegetation cover. Thermal-IR data acquired from the HCMM satellite were used to map the spatial distribution of diurnal surface temperatures and to estimate mean annual soil temperatures in central Utah. Simulated SPOT data for northwestern New Mexico identified geomorphic features, such as differences in eolian sand cover and fluvial incision, while the TIMS data depicted surface geologic features of the Saline Valley in California.

Machine processing of remotely sensed data - quantifying global process: Models, sensor systems, and analytical methods; Proceedings of the Eleventh International Symposium, Purdue University, West Lafayette, IN, June 25-27, 1985

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

Mengel, S.K.; Morrison, D.B.

1985-01-01

Consideration is given to global biogeochemical issues, image processing, remote sensing of tropical environments, global processes, geology, landcover hydrology, and ecosystems modeling. Topics discussed include multisensor remote sensing strategies, geographic information systems, radars, and agricultural remote sensing. Papers are presented on fast feature extraction; a computational approach for adjusting TM imagery terrain distortions; the segmentation of a textured image by a maximum likelihood classifier; analysis of MSS Landsat data; sun angle and background effects on spectral response of simulated forest canopies; an integrated approach for vegetation/landcover mapping with digital Landsat images; geological and geomorphological studies using an image processing technique;more » and wavelength intensity indices in relation to tree conditions and leaf-nutrient content.« less

Hyperspectral Remote Sensing and Ecological Modeling Research and Education at Mid America Remote Sensing Center (MARC): Field and Laboratory Enhancement

NASA Technical Reports Server (NTRS)

Cetin, Haluk

1999-01-01

The purpose of this project was to establish a new hyperspectral remote sensing laboratory at the Mid-America Remote sensing Center (MARC), dedicated to in situ and laboratory measurements of environmental samples and to the manipulation, analysis, and storage of remotely sensed data for environmental monitoring and research in ecological modeling using hyperspectral remote sensing at MARC, one of three research facilities of the Center of Reservoir Research at Murray State University (MSU), a Kentucky Commonwealth Center of Excellence. The equipment purchased, a FieldSpec FR portable spectroradiometer and peripherals, and ENVI hyperspectral data processing software, allowed MARC to provide hands-on experience, education, and training for the students of the Department of Geosciences in quantitative remote sensing using hyperspectral data, Geographic Information System (GIS), digital image processing (DIP), computer, geological and geophysical mapping; to provide field support to the researchers and students collecting in situ and laboratory measurements of environmental data; to create a spectral library of the cover types and to establish a World Wide Web server to provide the spectral library to other academic, state and Federal institutions. Much of the research will soon be published in scientific journals. A World Wide Web page has been created at the web site of MARC. Results of this project are grouped in two categories, education and research accomplishments. The Principal Investigator (PI) modified remote sensing and DIP courses to introduce students to ii situ field spectra and laboratory remote sensing studies for environmental monitoring in the region by using the new equipment in the courses. The PI collected in situ measurements using the spectroradiometer for the ER-2 mission to Puerto Rico project for the Moderate Resolution Imaging Spectrometer (MODIS) Airborne Simulator (MAS). Currently MARC is mapping water quality in Kentucky Lake and vegetation in the Land-Between-the Lakes (LBL) using Landsat-TM data. A Landsat-TM scene of the same day was obtained to relate ground measurements to the satellite data. A spectral library has been created for overstory species in LBL. Some of the methods, such as NPDF and IDFD techniques for spectral unmixing and reduction of effects of shadows in classifications- comparison of hyperspectral classification techniques, and spectral nonlinear and linear unmixing techniques, are being tested using the laboratory.

Evaluating the potential of Landsat TM/ETM+ imagery for assessing fire severity in Alaskan black spruce forests

Treesearch

Elizabeth E. Hoy; Nancy H.F. French; Merritt R. Turetsky; Simon N. Trigg; Eric S. Kasischke

2008-01-01

Satellite remotely sensed data of fire disturbance offers important information; however, current methods to study fire severity may need modifications for boreal regions. We assessed the potential of the differenced Normalized Burn Ratio (dNBR) and other spectroscopic indices and image transforms derived from Landsat TM/ETM+ data for mapping fire severity in Alaskan...

Regional forest cover estimation via remote sensing: the calibration center concept

Treesearch

Louis R. Iverson; Elizabeth A. Cook; Robin L. Graham; Robin L. Graham

1994-01-01

A method for combining Landsat Thematic Mapper (TM), Advanced Very High Resolution Radiometer (AVHRR) imagery, and other biogeographic data to estimate forest cover over large regions is applied and evaluated at two locations. In this method, TM data are used to classify a small area (calibration center) into forest/nonforest; the resulting forest cover map is then...

Frontiers for geological remote sensing from space; Geosat Workshop, 4th, Flagstaff, AZ, June 12-17, 1983, Report

NASA Technical Reports Server (NTRS)

Henderson, F. B. (Editor); Rock, B. N. (Editor)

1983-01-01

Consideration is given to: the applications of near-infrared spectroscopy to geological reconnaissance and exploration from space; imaging systems for identifying the spectral properties of geological materials in the visible and near-infrared; and Thematic Mapper (TM) data analysis. Consideration is also given to descriptions of individual geological remote sensing systems, including: GEO-SPAS; SPOT; the Thermal Infrared Multispectral Scanner (TIMS); and the Shuttle Imaging Radars A and B (SIR-A and SIR-B). Additional topics include: the importance of geobotany in geological remote sensing; achromatic holographic stereograms from Landsat MSS data; and the availability and applications of NOAA's non-Landsat satellite data archive.

[Prediction models of soil organic matter based on spectral curve in the upstream of Heihe basin].

PubMed

Liu, Jiao; Li, Yi; Liu, Shi-Bin

2013-12-01

Benefiting from the high spectral resolution, ground hyperspectral remote sensing technology can express the ground surface feature in detail, meanwhile, multispectral remote sensing has more advantages in studying the features in a large space time region, because of its long time-series images and wide coverage. Investigating the prediction models between the soil organic matter (SOM) content and the hyperspectral data and the sensitive bands based on different indices mathematically obtained from reflectance could combine the advantages of both kinds of spectral data, and provide a new method to search the spatio-temporal characteristics of SOM. Two hundred twenty three soil samples were chosen from the upper reaches of Heihe Basin to measure the SOM content and hyperspectral curve. Taking 181 of them, the stepwise linear regression methods were used to establish models between the SOM and five indices, including reflectance (lambda), reciprocal (REC), logarithm of the reciprocal (LR), continuum-removal (CR) and the first derivative reflectance (FDR). After then, the left 42 samples were used for model validation: firstly, the best model of the same index was chosen by the values of Pearson correlation coefficient (r) and Root mean squared error (RMSE) between the measured value and predicted value; secondly, the best models of different indices were compared. As a result, the model built by reflectance has a better estimation of SOM with the r: 0.863 and RMSE: 4.79. And the sensitive bands of the reflectance model contain 474 nm during TM1, 636 nm during TM3 and 1 632 nm during TM5. This result could be a reference for the retrieval of SOM content of the upper reaches by using the TM remote sensing data.

Urbanization in Pearl River Delta area in past 20 years: remote sensing of impact on water quality

NASA Astrophysics Data System (ADS)

Wang, Yunpeng; Fan, Fenglei; Zhang, Jinqu; Xia, Hao; Ye, Chun

2004-11-01

The Pearl River Delta of Guangdong province in China is one of the world"s largest growths in urbanization for the past 20 years. The objective of this research is to explore the relationship between urbanization and water quality in this area. Present and past remote sensing data including MSS< TM/ETM and ASTER are used to research the urbanization and its impact on water quality. Land use and water quality information are extracted from remote sensing data. Data of population, industrial and agricultural productivity indices are integrated with the thematic maps derived from remote sensing data by GIS method. Spatial analysis methods are applied on these data and the results indicate that population, waste water both from household and industrial and chemical fertilizer consumptions are main controls of the regional water quality and environment.

BOREAS Level-3s Landsat TM Imagery Scaled At-sensor Radiance in LGSOWG Format

NASA Technical Reports Server (NTRS)

Nickeson, Jaime; Knapp, David; Newcomer, Jeffrey A.; Cihlar, Josef; Hall, Forrest G. (Editor)

2000-01-01

For BOReal Ecosystem-Atmosphere Study (BOREAS),the level-3s Landsat Thematic Mapper (TM) data, along with the other remotely sensed images,were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy,detailed land cover, and biophysical parameter maps such as Fraction of Photosynthetically Active Radiation (FPAR) and Leaf area Index (LAI). CCRS collected and supplied the level-3s images to BOREAS for use in the remote sensing research activities. Geographically,the bulk of the level-3s images cover the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA) with a few images covering the area between the NSA and SSA. Temporally,the images cover the period of 22-Jun-1984 to 30-Jul-1996. The images are available in binary,image-format files.

Atmospheric correction for inland water based on Gordon model

NASA Astrophysics Data System (ADS)

Li, Yunmei; Wang, Haijun; Huang, Jiazhu

2008-04-01

Remote sensing technique is soundly used in water quality monitoring since it can receive area radiation information at the same time. But more than 80% radiance detected by sensors at the top of the atmosphere is contributed by atmosphere not directly by water body. Water radiance information is seriously confused by atmospheric molecular and aerosol scattering and absorption. A slight bias of evaluation for atmospheric influence can deduce large error for water quality evaluation. To inverse water composition accurately we have to separate water and air information firstly. In this paper, we studied on atmospheric correction methods for inland water such as Taihu Lake. Landsat-5 TM image was corrected based on Gordon atmospheric correction model. And two kinds of data were used to calculate Raleigh scattering, aerosol scattering and radiative transmission above Taihu Lake. Meanwhile, the influence of ozone and white cap were revised. One kind of data was synchronization meteorology data, and the other one was synchronization MODIS image. At last, remote sensing reflectance was retrieved from the TM image. The effect of different methods was analyzed using in situ measured water surface spectra. The result indicates that measured and estimated remote sensing reflectance were close for both methods. Compared to the method of using MODIS image, the method of using synchronization meteorology is more accurate. And the bias is close to inland water error criterion accepted by water quality inversing. It shows that this method is suitable for Taihu Lake atmospheric correction for TM image.

Reconstruction of time-varying tidal flat topography using optical remote sensing imageries

NASA Astrophysics Data System (ADS)

Tseng, Kuo-Hsin; Kuo, Chung-Yen; Lin, Tang-Huang; Huang, Zhi-Cheng; Lin, Yu-Ching; Liao, Wen-Hung; Chen, Chi-Farn

2017-09-01

Tidal flats (TFs) occupy approximately 7% of the total coastal shelf areas worldwide. However, TFs are unavailable in most global digital elevation models (DEMs) due to water-impermeable nature of existing remote sensing approaches (e.g., radar used for WorldDEM™ and Shuttle Radar Topography Mission DEM and optical stereo-pairs used for ASTER Global Digital Elevation Map Version 2). However, this problem can be circumvented using remote sensing imageries to observe land exposure at different tidal heights during each revisit. This work exploits Landsat-4/-5/-7/-8 Thematic Mapper (TM)/Enhanced TM Plus/Operational Land Imager imageries to reconstruct topography of a TF, namely, Hsiang-Shan Wetland in Taiwan, to unveil its formation and temporal changes since the 1980s. We first classify water areas by applying modified normalized difference water index to each Landsat image and normalize chances of water exposure to create an inundation probability map. This map is then scaled by tidal amplitudes extracted from DTU10 tide model to convert the probabilities into actual elevations. After building DEM at intertidal zone, a water level-area curve is established, and accuracy of DEM is validated by sea level (SL) at the timing of each Landsat snapshot. A 22-year (1992-2013) dataset composed of 227 Landsat scenes are analyzed and compared with tide gauge data. Root-mean-square differences of SL reaches 48 cm with a correlation coefficient of 0.93, indicating that the present technique is useful for constructing accurate coastal DEMs, and that products can be utilized for estimating instant SL. This study shows the possibility of exploring evolution of intertidal zones using an archive of optical remote sensing imageries. The technique developed in the present study potentially helps in quantifying SL from the start of optical remote sensing era.

Remote sensing of soybean stress as an indicator of chemical concentration of biosolid amended surface soils

NASA Astrophysics Data System (ADS)

Sridhar, B. B. Maruthi; Vincent, Robert K.; Roberts, Sheila J.; Czajkowski, Kevin

2011-08-01

The accumulation of heavy metals in the biosolid amended soils and the risk of their uptake into different plant parts is a topic of great concern. This study examines the accumulation of several heavy metals and nutrients in soybeans grown on biosolid applied soils and the use of remote sensing to monitor the metal uptake and plant stress. Field and greenhouse studies were conducted with soybeans grown on soils applied with biosolids at varying rates. The plant growth was monitored using Landsat TM imagery and handheld spectroradiometer in field and greenhouse studies, respectively. Soil and plant samples were collected and then analyzed for several elemental concentrations. The chemical concentrations in soils and roots increased significantly with increase in applied biosolid concentrations. Copper (Cu) and Molybdenum (Mo) accumulated significantly in the shoots of the metal-treated plants. Our spectral and Landsat TM image analysis revealed that the Normalized Difference Vegetative Index (NDVI) can be used to distinguish the metal stressed plants. The NDVI showed significant negative correlation with increase in soil Cu concentrations followed by other elements. This study suggests the use of remote sensing to monitor soybean stress patterns and thus indirectly assess soil chemical characteristics.

Remote sensing strategic exploration of large or superlarge gold ore deposits

NASA Astrophysics Data System (ADS)

Yan, Shouxun; Liu, Qingsheng; Wang, Hongmei; Wang, Zhigang; Liu, Suhong

1998-08-01

To prospect large or superlarge gold ore deposits, blending of remote sensing techniques and modern metallogenitic theories is one of the effective measures. The theory of metallogeny plays a director role before and during remote sensing technique applications. The remote sensing data with different platforms and different resolutions can be respectively applied to detect direct or indirect metallogenic information, and to identify the ore-controlling structure, especially, the ore-controlling structural assemblage, which, conversely, usually are the new conditions to study and to modify the metallogenic model, and to further develop the exploration model of large or superlarge ore deposits. Guidance by an academic idea of 'adjustment structure' which is the conceptual model of transverse structure, an obscured ore- controlling transverse structure has been identified on the refined TM imagery in the Hadamengou gold ore deposit, Setai Hyperspectral Geological Remote Sensing Testing Site (SHGRSTS), Wulashan mountains, Inner Mongolia, China. Meanwhile, The MAIS data has been applied to quickly identify the auriferous alteration rocks with Correspondence Analysis method and Spectral Angle Mapping (SAM) technique. The theoretical system and technical method of remote sensing strategic exploration of large or superlarge gold ore deposits have been demonstrated by the practices in the SHGRSTS.

An approach for detecting five typical vegetation types on the Chinese Loess Plateau using Landsat TM data.

PubMed

Wang, Zhi-Jie; Jiao, Ju-Ying; Lei, Bo; Su, Yuan

2015-09-01

Remote sensing can provide large-scale spatial data for the detection of vegetation types. In this study, two shortwave infrared spectral bands (TM5 and TM7) and one visible spectral band (TM3) of Landsat 5 TM data were used to detect five typical vegetation types (communities dominated by Bothriochloa ischaemum, Artemisia gmelinii, Hippophae rhamnoides, Robinia pseudoacacia, and Quercus liaotungensis) using 270 field survey data in the Yanhe watershed on the Loess Plateau. The relationships between 200 field data points and their corresponding radiance reflectance were analyzed, and the equation termed the vegetation type index (VTI) was generated. The VTI values of five vegetation types were calculated, and the accuracy was tested using the remaining 70 field data points. The applicability of VTI was also tested by the distribution of vegetation type of two small watersheds in the Yanhe watershed and field sample data collected from other regions (Ziwuling Region, Huangling County, and Luochuan County) on the Loess Plateau. The results showed that the VTI can effectively detect the five vegetation types with an average accuracy exceeding 80 % and a representativeness above 85 %. As a new approach for monitoring vegetation types using remote sensing at a larger regional scale, VTI can play an important role in the assessment of vegetation restoration and in the investigation of the spatial distribution and community diversity of vegetation on the Loess Plateau.

Comparison of remote sensing image processing techniques to identify tornado damage areas from Landsat TM data

USGS Publications Warehouse

Myint, S.W.; Yuan, M.; Cerveny, R.S.; Giri, C.P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and objectoriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. ?? 2008 by MDPI.

Comparison of Remote Sensing Image Processing Techniques to Identify Tornado Damage Areas from Landsat TM Data

PubMed Central

Myint, Soe W.; Yuan, May; Cerveny, Randall S.; Giri, Chandra P.

2008-01-01

Remote sensing techniques have been shown effective for large-scale damage surveys after a hazardous event in both near real-time or post-event analyses. The paper aims to compare accuracy of common imaging processing techniques to detect tornado damage tracks from Landsat TM data. We employed the direct change detection approach using two sets of images acquired before and after the tornado event to produce a principal component composite images and a set of image difference bands. Techniques in the comparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment is based on Kappa coefficient calculated from error matrices which cross tabulate correctly identified cells on the TM image and commission and omission errors in the result. Overall, the Object-oriented Approach exhibits the highest degree of accuracy in tornado damage detection. PCA and Image Differencing methods show comparable outcomes. While selected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approach performs significantly better with 15-20% higher accuracy than the other two techniques. PMID:27879757

Exploitation of multi-temporal Earth Observation imagery for monitoring land cover change in mining sites

NASA Astrophysics Data System (ADS)

Petropoulos, G.; Partsinevelos, P.; Mitraka, Z.

2012-04-01

Surface mining has been shown to cause intensive environmental degradation in terms of landscape, vegetation and biological communities. Nowadays, the commercial availability of remote sensing imagery at high spatiotemporal scales, has improved dramatically our ability to monitor surface mining activity and evaluate its impact on the environment and society. In this study we investigate the potential use of Landsat TM imagery combined with diverse classification techniques, namely artificial neural networks and support vector machines for delineating mining exploration and assessing its effect on vegetation in various surface mining sites in the Greek island of Milos. Assessment of the mining impact in the study area is validated through the analysis of available QuickBird imagery acquired nearly concurrently to the TM overpasses. Results indicate the capability of the TM sensor combined with the image analysis applied herein as a potential economically viable solution to provide rapidly and at regular time intervals information on mining activity and its impact to the local environment. KEYWORDS: mining environmental impact, remote sensing, image classification, change detection, land reclamation, support vector machines, neural networks

Assessment of a remote sensing-based model for predicting malaria transmission risk in villages of Chiapas, Mexico

NASA Technical Reports Server (NTRS)

Beck, L. R.; Rodriguez, M. H.; Dister, S. W.; Rodriguez, A. D.; Washino, R. K.; Roberts, D. R.; Spanner, M. A.

1997-01-01

A blind test of two remote sensing-based models for predicting adult populations of Anopheles albimanus in villages, an indicator of malaria transmission risk, was conducted in southern Chiapas, Mexico. One model was developed using a discriminant analysis approach, while the other was based on regression analysis. The models were developed in 1992 for an area around Tapachula, Chiapas, using Landsat Thematic Mapper (TM) satellite data and geographic information system functions. Using two remotely sensed landscape elements, the discriminant model was able to successfully distinguish between villages with high and low An. albimanus abundance with an overall accuracy of 90%. To test the predictive capability of the models, multitemporal TM data were used to generate a landscape map of the Huixtla area, northwest of Tapachula, where the models were used to predict risk for 40 villages. The resulting predictions were not disclosed until the end of the test. Independently, An. albimanus abundance data were collected in the 40 randomly selected villages for which the predictions had been made. These data were subsequently used to assess the models' accuracies. The discriminant model accurately predicted 79% of the high-abundance villages and 50% of the low-abundance villages, for an overall accuracy of 70%. The regression model correctly identified seven of the 10 villages with the highest mosquito abundance. This test demonstrated that remote sensing-based models generated for one area can be used successfully in another, comparable area.

BOREAS RSS-7 Landsat TM LAI IMages of the SSA and NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team used Landsat Thematic Mapper (TM) images processed at CCRS to produce images of Leaf Area Index (LAI) for the BOREAS study areas. Two images acquired on 06-Jun and 09-Aug-1991 were used for the SSA, and one image acquired on 09-Jun-1994 was used for the NSA. The LAI images are based on ground measurements and Landsat TM Reduced Simple Ratio (RSR) images. The data are stored in binary image-format files.

Integration of remote sensing and geographic information systems for Great Lakes water quality monitoring

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

Lathrop, R.G. Jr.

1988-01-01

The utility of three operational satellite remote sensing systems, namely, the Landsat Thematic Mapper (TM), the SPOT High Resolution Visible (HRV) sensors and the NOAA Advanced Very High Resolution Radiometer (AVHRR), were evaluated as a means of estimating water quality and surface temperature. Empirical calibration through linear regression techniques was used to relate near-simultaneously acquired satellite radiance/reflectance data and water quality observations obtained in Green Bay and the nearshore waters of Lake Michigan. Four dates of TM and one date each of SPOT and AVHRR imagery/surface reference data were acquired and analyzed. Highly significant relationships were identified between the TMmore » and SPOT data and secchi disk depth, nephelometric turbidity, chlorophyll a, total suspended solids (TSS), absorbance, and surface temperature (TM only). The AVHRR data were not analyzed independently but were used for comparison with the TM data. Calibrated water quality image maps were input to a PC-based raster GIS package, EPPL7. Pattern interpretation and spatial analysis techniques were used to document the circulation dynamics and model mixing processes in Green Bay. A GIS facilitates the retrieval, query and spatial analysis of mapped information and provides the framework for an integrated operational monitoring system for the Great Lakes.« less

CCRS Landcover Maps From Satellite Data

DOE Data Explorer

Trishchenko, Alexander

2008-01-15

The Canadian Centre for Remote Sensing (CCRS) presents several landcover maps over the SGP CART site area (32-40N, 92-102W) derived from satellite data including AVHRR, MODIS, SPOT vegetation data, and Landsat satellite TM imagery.

Mojave remote sensing field experiment

NASA Technical Reports Server (NTRS)

Arvidson, Raymond E.; Petroy, S. B.; Plaut, J. J.; Shepard, Michael K.; Evans, D.; Farr, T.; Greeley, Ronald; Gaddis, L.; Lancaster, N.

1991-01-01

The Mojave Remote Sensing Field Experiment (MFE), conducted in June 1988, involved acquisition of Thermal Infrared Multispectral Scanner (TIMS); C, L, and P-band polarimetric radar (AIRSAR) data; and simultaneous field observations at the Pisgah and Cima volcanic fields, and Lavic and Silver Lake Playas, Mojave Desert, California. A LANDSAT Thematic Mapper (TM) scene is also included in the MFE archive. TM-based reflectance and TIMS-based emissivity surface spectra were extracted for selected surfaces. Radiative transfer procedures were used to model the atmosphere and surface simultaneously, with the constraint that the spectra must be consistent with field-based spectral observations. AIRSAR data were calibrated to backscatter cross sections using corner reflectors deployed at target sites. Analyses of MFE data focus on extraction of reflectance, emissivity, and cross section for lava flows of various ages and degradation states. Results have relevance for the evolution of volcanic plains on Venus and Mars.

Airborne remote sensing for geology and the environment; present and future

USGS Publications Warehouse

Watson, Ken; Knepper, Daniel H.

1994-01-01

In 1988, a group of leading experts from government, academia, and industry attended a workshop on airborne remote sensing sponsored by the U.S. Geological Survey (USGS) and hosted by the Branch of Geophysics. The purpose of the workshop was to examine the scientific rationale for airborne remote sensing in support of government earth science in the next decade. This report has arranged the six resulting working-group reports under two main headings: (1) Geologic Remote Sensing, for the reports on geologic mapping, mineral resources, and fossil fuels and geothermal resources; and (2) Environmental Remote Sensing, for the reports on environmental geology, geologic hazards, and water resources. The intent of the workshop was to provide an evaluation of demonstrated capabilities, their direct extensions, and possible future applications, and this was the organizational format used for the geologic remote sensing reports. The working groups in environmental remote sensing chose to present their reports in a somewhat modified version of this format. A final section examines future advances and limitations in the field. There is a large, complex, and often bewildering array of remote sensing data available. Early remote sensing studies were based on data collected from airborne platforms. Much of that technology was later extended to satellites. The original 80-m-resolution Landsat Multispectral Scanner System (MSS) has now been largely superseded by the 30-m-resolution Thematic Mapper (TM) system that has additional spectral channels. The French satellite SPOT provides higher spatial resolution for channels equivalent to MSS. Low-resolution (1 km) data are available from the National Oceanographic and Atmospheric Administration's AVHRR system, which acquires reflectance and day and night thermal data daily. Several experimental satellites have acquired limited data, and there are extensive plans for future satellites including those of Japan (JERS), Europe (ESA), Canada (Radarsat), and the United States (EOS). There are currently two national airborne remote sensing programs (photography, radar) with data archived at the USGS' EROS Data Center. Airborne broadband multispectral data (comparable to Landsat MSS and TM but involving several more channels) for limited geographic areas also are available for digital processing and analysis. Narrow-band imaging spectrometer data are available for some NASA experiment sites and can be acquired for other locations commercially. Remote sensing data and derivative images, because of the uniform spatial coverage, availability at different resolutions, and digital format, are becoming important data sets for geographic information system (GIS) analyses. Examples range from overlaying digitized geologic maps on remote sensing images and draping these over topography, to maps of mineral distribution and inferred abundance. A large variety of remote sensing data sets are available, with costs ranging from a few dollars per square mile for satellite digital data to a few hundred dollars per square mile for airborne imaging spectrometry. Computer processing and analysis costs routinely surpass these expenses because of the equipment and expertise necessary for information extraction and interpretation. Effective use requires both an understanding of the current methodology and an appreciation of the most cost-effective solution.

Temporal relationships between spectral response and agronomic variables of a corn canopy

NASA Technical Reports Server (NTRS)

Kimes, D. S.; Markham, B. L.; Tucker, C. J.; Mcmurtrey, J. E., III

1981-01-01

Attention is given to an experiment in which spectral radiance data collected in three spectral regions are related to corn canopy variables. The study extends the work of Tucker et al. (1979) in that more detailed measurements of corn canopy variables were made using quantitative techniques. Wet and dry green leaf biomass is considered along with the green leaf area index, chlorotic leaf biomass, chlorotic leaf area, and leaf water content. In addition, spectral data were collected with a hand-held radiometer having Landsat-D Thematic Mapper (TM) bands TM3 (0.63-0.69 micrometers), TM4 (0.76-0.90 micrometers), and TM5 (1.55-1.75 micrometers). TM3, TM4, and TM5 seem to be well situated spectrally for making remotely sensed measurements related to chlorophyll concentration, leaf density, and leaf water content.

Cluster Method Analysis of K. S. C. Image

NASA Technical Reports Server (NTRS)

Rodriguez, Joe, Jr.; Desai, M.

1997-01-01

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

Nasa's Land Remote Sensing Plans for the 1980's

NASA Technical Reports Server (NTRS)

Higg, H. C.; Butera, K. M.; Settle, M.

1985-01-01

Research since the launch of LANDSAT-1 has been primarily directed to the development of analysis techniques and to the conduct of applications studies designed to address resource information needs in the United States and in many other countries. The current measurement capabilities represented by MSS, TM, and SIR-A and B, coupled with the present level of remote sensing understanding and the state of knowledge in the discipline earth sciences, form the foundation for NASA's Land Processes Program. Science issues to be systematically addressed include: energy balance, hydrologic cycle, biogeochemical cycles, biological productivity, rock cycle, landscape development, geological and botanical associations, and land surface inventory, monitoring, and modeling. A global perspective is required for using remote sensing technology for problem solving or applications context. A successful model for this kind of activity involves joint research with a user entity where the user provides a test site and ground truth and NASA provides the remote sensing techniques to be tested.

The application of geologic remote sensing to vertebrate biostratigraphy - General results from the Wind River Basin, Wyoming

NASA Technical Reports Server (NTRS)

Stucky, Richard K.; Krishtalka, Leonard

1991-01-01

Since 1986, remote sensing images derived from satellite and aircraft-borne sensor data have been used to study the stratigraphy and sedimentology of the vertebrate-bearing Wind River and Wagon Bed formations in the Wind River Basin (Wyoming). Landsat 5 TM and aircraft Thermal Infrared Multispectral Scanner data were combined with conventional geologic analyses. The remote sensing data have contributed significantly to: (1) geologic mapping at the formation, member, and bed levels; (2) stratigraphic correlation; (3) reconstruction of ancient depositional environments; and (4) identification of structural complexity. This information is critical to vertebrate paleontology in providing the stratigraphic, sedimentologic, and structural framework required for evolutionary and paleoecologic studies. Of primary importance is the ability to map at minimal cost the geology of large areas (20,000 sq km or greater) at a high level of precision. Remote sensing data can be especially useful in geologically and paleontologically unexplored or poorly understood regions.

Brazilian remote sensing receiving, recording and processing ground systems in the 1980's

NASA Technical Reports Server (NTRS)

Parada, N. D. J. (Principal Investigator)

1984-01-01

A ground station was built in Brazil to receive, record, and process TM data from LANDSAT satellites. The receiving/recording subsystem and the processing subsystem are discussed. Functional design specifications for the facility are addressed.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon.

PubMed

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E; Moran, Emilio

2008-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin.

A Comparative Study of Landsat TM and SPOT HRG Images for Vegetation Classification in the Brazilian Amazon

PubMed Central

Lu, Dengsheng; Batistella, Mateus; de Miranda, Evaristo E.; Moran, Emilio

2009-01-01

Complex forest structure and abundant tree species in the moist tropical regions often cause difficulties in classifying vegetation classes with remotely sensed data. This paper explores improvement in vegetation classification accuracies through a comparative study of different image combinations based on the integration of Landsat Thematic Mapper (TM) and SPOT High Resolution Geometric (HRG) instrument data, as well as the combination of spectral signatures and textures. A maximum likelihood classifier was used to classify the different image combinations into thematic maps. This research indicated that data fusion based on HRG multispectral and panchromatic data slightly improved vegetation classification accuracies: a 3.1 to 4.6 percent increase in the kappa coefficient compared with the classification results based on original HRG or TM multispectral images. A combination of HRG spectral signatures and two textural images improved the kappa coefficient by 6.3 percent compared with pure HRG multispectral images. The textural images based on entropy or second-moment texture measures with a window size of 9 pixels × 9 pixels played an important role in improving vegetation classification accuracy. Overall, optical remote-sensing data are still insufficient for accurate vegetation classifications in the Amazon basin. PMID:19789716

Improving GLOBALlAND30 Artificial Type Extraction Accuracy in Low-Density Residents

NASA Astrophysics Data System (ADS)

Hou, Lili; Zhu, Ling; Peng, Shu; Xie, Zhenlei; Chen, Xu

2016-06-01

GlobalLand 30 is the first 30m resolution land cover product in the world. It covers the area within 80°N and 80°S. There are ten classes including artificial cover, water bodies, woodland, lawn, bare land, cultivated land, wetland, sea area, shrub and snow,. The TM imagery from Landsat is the main data source of GlobalLand 30. In the artificial surface type, one of the omission error happened on low-density residents' part. In TM images, hash distribution is one of the typical characteristics of the low-density residents, and another one is there are a lot of cultivated lands surrounded the low-density residents. Thus made the low-density residents part being blurred with cultivated land. In order to solve this problem, nighttime light remote sensing image is used as a referenced data, and on the basis of NDBI, we add TM6 to calculate the amount of surface thermal radiation index TR-NDBI (Thermal Radiation Normalized Difference Building Index) to achieve the purpose of extracting low-density residents. The result shows that using TR-NDBI and the nighttime light remote sensing image are a feasible and effective method for extracting low-density residents' areas.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks.

PubMed

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-04-26

With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction.

A revised surface resistance parameterisation for estimating latent heat flux from remotely sensed data

NASA Astrophysics Data System (ADS)

Song, Yi; Wang, Jiemin; Yang, Kun; Ma, Mingguo; Li, Xin; Zhang, Zhihui; Wang, Xufeng

2012-07-01

Estimating evapotranspiration (ET) is required for many environmental studies. Remote sensing provides the ability to spatially map latent heat flux. Many studies have developed approaches to derive spatially distributed surface energy fluxes from various satellite sensors with the help of field observations. In this study, remote-sensing-based λE mapping was conducted using a Landsat Thematic Mapper (TM) image and an Enhanced Thematic Mapper Plus (ETM+) image. The remotely sensed data and field observations employed in this study were obtained from Watershed Allied Telemetry Experimental Research (WATER). A biophysics-based surface resistance model was revised to account for water stress and temperature constraints. The precision of the results was validated using 'ground truth' data obtained by eddy covariance (EC) system. Scale effects play an important role, especially for parameter optimisation and validation of the latent heat flux (λE). After considering the footprint of EC, the λE derived from the remote sensing data was comparable to the EC measured value during the satellite's passage. The results showed that the revised surface resistance parameterisation scheme was useful for estimating the latent heat flux over cropland in arid regions.

Remote sensing research for agricultural applications. [San Joaquin County, California and Snake River Plain and Twin Falls area, Idaho

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator); Wall, S. L.; Beck, L. H.; Degloria, S. D.; Ritter, P. R.; Thomas, R. W.; Travlos, A. J.; Fakhoury, E.

1984-01-01

Materials and methods used to characterize selected soil properties and agricultural crops in San Joaquin County, California are described. Results show that: (1) the location and widths of TM bands are suitable for detecting differences in selected soil properties; (2) the number of TM spectral bands allows the quantification of soil spectral curve form and magnitude; and (3) the spatial and geometric quality of TM data allows for the discrimination and quantification of within field variability of soil properties. The design of the LANDSAT based multiple crop acreage estimation experiment for the Idaho Department of Water Resources is described including the use of U.C. Berkeley's Survey Modeling Planning Model. Progress made on Peditor software development on MIDAS, and cooperative computing using local and remote systems is reported as well as development of MIDAS microcomputer systems.

Comparison of Huanjing and Landsat satellite remote sensing of the spatial heterogeneity of Qinghai-Tibet alpine grassland

NASA Astrophysics Data System (ADS)

Wang, Junbang; Sun, Wenyi

2014-11-01

Remote sensing is widely applied in the study of terrestrial primary production and the global carbon cycle. The researches on the spatial heterogeneity in images with different sensors and resolutions would improve the application of remote sensing. In this study two sites on alpine meadow grassland in Qinghai, China, which have distinct fractal vegetation cover, were used to test and analyze differences between Normalized Difference Vegetation Index (NDVI) and enhanced vegetation index (EVI) derived from the Huanjing (HJ) and Landsat Thematic Mapper (TM) sensors. The results showed that: 1) NDVI estimated from HJ were smaller than the corresponding values from TM at the two sites whereas EVI were almost the same for the two sensors. 2) The overall variance represented by HJ data was consistently about half of that of Landsat TM although their nominal pixel size is approximately 30m for both sensors. The overall variance from EVI is greater than that from NDVI. The difference of the range between the two sensors is about 6 pixels at 30m resolution. The difference of the range in which there is not more corrective between two vegetation indices is about 1 pixel. 3) The sill decreased when pixel size increased from 30m to 1km, and then decreased very quickly when pixel size is changed to 250m from 30m or 90m but slowly when changed from 250m to 500m. HJ can capture this spatial heterogeneity to some extent and this study provides foundations for the use of the sensor for validation of net primary productivity estimates obtained from ecosystem process models.

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

USGS Publications Warehouse

Picotte, J.J.; Robertson, K.

2011-01-01

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78-90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy. ?? 2011 by the authors.

Timing constraints on remote sensing of wildland fire burned area in the southeastern US

USGS Publications Warehouse

Picotte, Joshua J.; Robertson, Kevin

2011-01-01

Remote sensing using Landsat Thematic Mapper (TM) satellite imagery is increasingly used for mapping wildland fire burned area and burn severity, owing to its frequency of collection, relatively high resolution, and availability free of charge. However, rapid response of vegetation following fire and frequent cloud cover pose challenges to this approach in the southeastern US. We assessed these timing constraints by using a series of Landsat TM images to determine how rapidly the remotely sensed burn scar signature fades following prescribed burns in wet flatwoods and depression swamp community types in the Apalachicola National Forest, Florida, USA during 2006. We used both the Normalized Burn Ratio (NBR) of reflectance bands sensitive to vegetation and exposed soil cover, as well as the change in NBR from before to after fire (dNBR), to estimate burned area. We also determined the average and maximum amount of time following fire required to obtain a cloud-free image for burns in each month of the year, as well as the predicted effect of this time lag on percent accuracy of burn scar estimates. Using both NBR and dNBR, the detectable area decreased linearly 9% per month on average over the first four months following fire. Our findings suggest that the NBR and dNBR methods for monitoring burned area in common southeastern US vegetation community types are limited to an average of 78–90% accuracy among months of the year, with individual burns having values as low as 38%, if restricted to use of Landsat 5 TM imagery. However, the majority of burns can still be mapped at accuracies similar to those in other regions of the US, and access to additional sources of satellite imagery would improve overall accuracy.

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

Ellis, J.M.

Remote sensing allows the petroleum industry to make better and quicker interpretations of geological and environmental conditions in areas of present and future operations. Often remote sensing (including aerial photographs) is required because existing maps are out-of-date, too small of scale, or provide only limited information. Implementing remote sensing can lead to lower project costs and reduced risk. The same satellite and airborne data can be used effectively for both geological and environmental applications. For example, earth scientists can interpret new lithologic, structural, and geomorphic information from near-infrared and radar imagery in terrains as diverse as barren desert and tropicalmore » jungle. Environmental applications with these and other imagery include establishing baselines, assessing impact by documenting changes through time, and mapping land-use, habitat, and vegetation. Higher resolution sensors provide an up-to-date overview of onshore and offshore petroleum facilities, whereas sensors capable of oblique viewing can be used to generate topographic maps. Geological application in Yemen involved merging Landsat TM and SPOT imagery to obtain exceptional lithologic discrimination. In the Congo, a topographic map to plan field operations was interpreted from the overlapping radar strips. Landsat MSS and TM, SPOT, and Russian satellite images with new aerial photographs are being used in the Tengiz supergiant oil field of Kazakhstan to help establish an environmental baseline, generate a base map, locate wells, plan facilities, and support a geographical information system (GIS). In the Niger delta, Landsat TM and SPOT are being used to plan pipeline routes and seismic lines, and to monitor rapid shoreline changes and population growth. Accurate coastlines, facility locations, and shoreline types are being extracted from satellite images for use in oil spill models.« less

Assessment of landscape change associated with tropical cyclone phenomena in Baja California Sur, Mexico, using satellite remote sensing

NASA Astrophysics Data System (ADS)

Martinez-Gutierrez, Genaro

Baja California Sur (Mexico), as well as mainland Mexico, is affected by tropical cyclone storms, which originate in the eastern north Pacific. Historical records show that Baja has been damaged by intense summer storms. An arid to semiarid climate characterizes the study area, where precipitation mainly occurs during the summer and winter seasons. Natural and anthropogenic changes have impacted the landscape of southern Baja. The present research documents the effects of tropical storms over the southern region of Baja California for a period of approximately twenty-six years. The goal of the research is to demonstrate how remote sensing can be used to detect the important effects of tropical storms including: (a) evaluation of change detection algorithms, and (b) delineating changes to the landscape including coastal modification, fluvial erosion and deposition, vegetation change, river avulsion using change detection algorithms. Digital image processing methods with temporal Landsat satellite remotely sensed data from the North America Landscape Characterization archive (NALC), Thematic Mapper (TM), and Enhanced Thematic Mapper (ETM) images were used to document the landscape change. Two image processing methods were tested including Image differencing (ID), and Principal Component Analysis (PCA). Landscape changes identified with the NALC archive and TM images showed that the major changes included a rapid change of land use in the towns of San Jose del Cabo and Cabo San Lucas between 1973 and 1986. The features detected using the algorithms included flood deposits within the channels of active streams, erosion banks, and new channels caused by channel avulsion. Despite the 19 year period covered by the NALC data and approximately 10 year intervals between acquisition dates, there were changed features that could be identified in the images. The TM images showed that flooding from Hurricane Isis (1998) produced new large deposits within the stream channels. This research has shown that remote sensing based change detection can delineate the effects of flooding on the landscape at scales down to the nominal resolution of the sensor. These findings indicate that many other applications for change detection are both viable and important. These include disaster response, flood hazard planning, geomorphic studies, water supply management in deserts.

Comparison of hyperspectral transformation accuracies of multispectral Landsat TM, ETM+, OLI and EO-1 ALI images for detecting minerals in a geothermal prospect area

NASA Astrophysics Data System (ADS)

Hoang, Nguyen Tien; Koike, Katsuaki

2018-03-01

Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images (> 95% and > 42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces.

A SUB-PIXEL ACCURACY ASSESSMENT FRAMEWORK FOR DETERMINING LANDSAT TM DERIVED IMPERVIOUS SURFACE ESTIMATES.

EPA Science Inventory

The amount of impervious surface in a watershed is a landscape indicator integrating a number of concurrent interactions that influence a watershed's hydrology. Remote sensing data and techniques are viable tools to assess anthropogenic impervious surfaces. However a fundamental ...

NASA Fluid Lensing & MiDAR: Next-Generation Remote Sensing Technologies for Aquatic Remote Sensing

NASA Technical Reports Server (NTRS)

Chirayath, Ved

2018-01-01

We present two recent instrument technology developments at NASA, Fluid Lensing and MiDAR, and their application to remote sensing of Earth's aquatic systems. Fluid Lensing is the first remote sensing technology capable of imaging through ocean waves in 3D at sub-cm resolutions. MiDAR is a next-generation active hyperspectral remote sensing and optical communications instrument capable of active fluid lensing. Fluid Lensing has been used to provide 3D multispectral imagery of shallow marine systems from unmanned aerial vehicles (UAVs, or drones), including coral reefs in American Samoa and stromatolite reefs in Hamelin Pool, Western Australia. MiDAR is being deployed on aircraft and underwater remotely operated vehicles (ROVs) to enable a new method for remote sensing of living and nonliving structures in extreme environments. MiDAR images targets with high-intensity narrowband structured optical radiation to measure an objectâ€"TM"s non-linear spectral reflectance, image through fluid interfaces such as ocean waves with active fluid lensing, and simultaneously transmit high-bandwidth data. As an active instrument, MiDAR is capable of remotely sensing reflectance at the centimeter (cm) spatial scale with a signal-to-noise ratio (SNR) multiple orders of magnitude higher than passive airborne and spaceborne remote sensing systems with significantly reduced integration time. This allows for rapid video-frame-rate hyperspectral sensing into the far ultraviolet and VNIR wavelengths. Previously, MiDAR was developed into a TRL 2 laboratory instrument capable of imaging in thirty-two narrowband channels across the VNIR spectrum (400-950nm). Recently, MiDAR UV was raised to TRL4 and expanded to include five ultraviolet bands from 280-400nm, permitting UV remote sensing capabilities in UV A, B, and C bands and enabling mineral identification and stimulated fluorescence measurements of organic proteins and compounds, such as green fluorescent proteins in terrestrial and aquatic organics.

Understanding and utilization of Thematic Mapper and other remotely sensed data for vegetation monitoring

NASA Technical Reports Server (NTRS)

Crist, E. P.; Cicone, R. C.; Metzler, M. D.; Parris, T. M.; Rice, D. P.; Sampson, R. E.

1983-01-01

The TM Tasseled Cap transformation, which provides both a 50% reduction in data volume with little or no loss of important information and spectral features with direct physical association, is presented and discussed. Using both simulated and actual TM data, some important characteristics of vegetation and soils in this feature space are described, as are the effects of solar elevation angle and atmospheric haze. A preliminary spectral haze diagnostic feature, based on only simulated data, is also examined. The characteristics of the TM thermal band are discussed, as is a demonstration of the use of TM data in energy balance studies. Some characteristics of AVHRR data are described, as are the sensitivities to scene content of several LANDSAT-MSS preprocessing techniques.

Landsat Thematic Mapper studies of land cover spatial variability related to hydrology

NASA Technical Reports Server (NTRS)

Wharton, S.; Ormsby, J.; Salomonson, V.; Mulligan, P.

1984-01-01

Past accomplishments involving remote sensing based land-cover analysis for hydrologic applications are reviewed. Ongoing research in exploiting the increased spatial, radiometric, and spectral capabilities afforded by the TM on Landsats 4 and 5 is considered. Specific studies to compare MSS and TM for urbanizing watersheds, wetlands, and floodplain mapping situations show that only a modest improvement in classification accuracy is achieved via statistical per pixel multispectral classifiers. The limitations of current approaches to multispectral classification are illustrated. The objectives, background, and progress in the development of an alternative analysis approach for defining inputs to urban hydrologic models using TM are discussed.

Effect of leaf variables on visible, near-infrared and mid-infrared reflectance of excised leaves

NASA Technical Reports Server (NTRS)

Bell, R.; Labovitz, M. L.; Ludwig, R. W.

1983-01-01

Effects of an imposed (excised) leaf orientation, differing species and differing venation patterns on reflectance measurements in the LANDSAT-4 thematic mapper (TM) channels TM3 (0.63 to 0.69 microns), TM4 (0.76 to 0.90 microns), and TM5 (1.55 to 1.75 microns) were investigated. Orientation of leaves (random vs. systematic placement) was found to affect measurements in the TM4 channel, but not the TM3 and TM5 measurements. Venation caused no significant changes for any band. Azimuth of incident radiation was not a significant main effect, but in conjunction with changes in orientation, angle did have a significant effect on reflectance values in TM3, TM4 and TM5. Specific differences were highly significant (P f or = 0.006) in all but one borderline (P F or = 0.0222) case for TM5. For spectral examination of excised leaves, the sampling arrangement of the leaves should as closely approximate in situ positioning as possible (with respect to remote sensing instrumentation). This dictates a random rather than aligned arrangement.

Thematic Mapper Data Quality and Performance Assessment in Renewable Resources/agriculture/remote Sensing

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Prior, H. L.

1985-01-01

Analysis of the early thematic mapper (TM) data indicate the TM sensor and associated ground processing are performing equal to the high expectations and within advertised specifications. The overall TM system with improved resolution, together with additional and more optimumly placed spectral bands shows much promise for benefits in future analysis activities. By selecting man-made features of known dimensions (e.g., highways, airfields, buildings, and isolated water bodies), an assessment was made of the TM performance relative to the specified 30-meter (98-foot) resolution. The increase of spatial resolution of TM (30 m) over MSS (80 M) appears to be significant not only in resolving spectrally distinct classes that were previously undefinable but also in distinguishing within-field variability. An Important result of the early TM evaluation and pre-TM analyses was the development of an integrated system to receive LANDSAT-4 TM (as well as MSS) data and analyze the data via various approaches.

Remote sensing of landscape-level coastal environmental indicators.

PubMed

Klemas, V V

2001-01-01

Advances in technology and decreases in cost are making remote sensing (RS) and geographic information systems (GIS) practical and attractive for use in coastal resource management. They are also allowing researchers and managers to take a broader view of ecological patterns and processes. Landscape-level environmental indicators that can be detected by Landsat Thematic Mapper (TM) and other remote sensors are available to provide quantitative estimates of coastal and estuarine habitat conditions and trends. Such indicators include watershed land cover, riparian buffers, shoreline and wetland changes, among others. With the launch of Landsat 7, the cost of TM imagery has dropped by nearly a factor of 10, decreasing the cost of monitoring large coastal areas and estuaries. New satellites, carrying sensors with much finer spatial (1-5 m) and spectral (200 narrow bands) resolutions are being launched, providing a capability to more accurately detect changes in coastal habitat and wetland health. Advances in the application of GIS help incorporate ancillary data layers to improve the accuracy of satellite land-cover classification. When these techniques for generating, organizing, storing, and analyzing spatial information are combined with mathematical models, coastal planners and managers have a means for assessing the impacts of alternative management practices.

Monitoring the ecology and environment using remote sensing in the Jinta area/Middle Reaches of Heihe River Basin

NASA Astrophysics Data System (ADS)

Lu, Anxin; Wang, Lihong; Chen, Xianzhang

2003-07-01

A major monitoring area, a part of the middle reaches of Heihe basin, was selected. The Landsat TM data in summer of 1990 and 2000 were used with interpretation on the computer screen, classification and setting up environmental investigation database (1:100000) combined with DEM, land cover/land use, land type data and etc., according to the environmental classification system. Then towards to the main problems of environment, the spatial statistical analysis and dynamic comparisons were carried out using the database. The dynamic monitoring results of 1999 and 2000 show that the changing percentage with the area of 6 ground objects are as follows: land use and agriculture land use increased by 34.17% and 19.47% respectively, wet land and water-body also increased by 6.29% and 8.03% respectively; unused land increased by 1.73% and the biggest change is natural/semi-natural vegetation area, decreased by 42.78%, the main results above meat with the requirements of precise and practical conditions by the precise exam and spot check. With the combinations of using TM remote sensing data and rich un-remote sensing data, the investigations of ecology and environment and the dynamic monitoring would be carried out efficiently in the arid area. It is a dangerous signal of large area desertification if the area of natural/semi-natural vegetation is reduced continuously and obviously.

Initial results of the spatial distribution of rubber trees in Peninsular Malaysia using remotely sensed data for biomass estimate

NASA Astrophysics Data System (ADS)

Shidiq, I. P. A.; Ismail, M. H.; Kamarudin, N.

2014-02-01

The preservation and sustainable management of forest and other land cover ecosystems such as rubber trees will help addressing two major recent issues: climate change and bio-resource energy. The rubber trees are dominantly distributed in the Negeri Sembilan and Kedah on the west coast side of Peninsular Malaysia. This study is aimed to analyse the spatial distribution and biomass of rubber trees in Peninsular Malaysia with special emphasis in Negeri Sembilan State. Geospatial data from remote sensors are used to tackle the time and labour consuming problem due to the large spatial coverage and the need of continuous temporal data. Remote sensing imagery used in this study is a Landsat 5 TM. The image from optical sensor was used to sense the rubber trees and further classified rubber tree by different age.

Estimating population size of Pygoscelid Penguins from TM data

NASA Technical Reports Server (NTRS)

Olson, Charles E., Jr.; Schwaller, Mathew R.; Dahmer, Paul A.

1987-01-01

An estimate was made toward a continent wide population of penguins. The results indicate that Thematic Mapper data can be used to identify penguin rookeries due to the unique reflectance properties of guano. Strong correlations exist between nesting populations and rookery area occupied by the birds. These correlations allow estimation of the number of nesting pairs in colonies. The success of remote sensing and biometric analyses leads one to believe that a continent wide estimate of penguin populations is possible based on a timely sample employing ground based and remote sensing techniques. Satellite remote sensing along the coastline may well locate previously undiscovered penguin nesting sites, or locate rookeries which have been assumed to exist for over a half century, but never located. Observations which found that penguins are one of the most sensitive elements in the complex of Southern Ocean ecosystems motivated this study.

A multi-characteristic based algorithm for classifying vegetation in a plateau area: Qinghai Lake watershed, northwestern China

NASA Astrophysics Data System (ADS)

Ma, Weiwei; Gong, Cailan; Hu, Yong; Li, Long; Meng, Peng

2015-10-01

Remote sensing technology has been broadly recognized for its convenience and efficiency in mapping vegetation, particularly in high-altitude and inaccessible areas where there are lack of in-situ observations. In this study, Landsat Thematic Mapper (TM) images and Chinese environmental mitigation satellite CCD sensor (HJ-1 CCD) images, both of which are at 30m spatial resolution were employed for identifying and monitoring of vegetation types in a area of Western China——Qinghai Lake Watershed(QHLW). A decision classification tree (DCT) algorithm using multi-characteristic including seasonal TM/HJ-1 CCD time series data combined with digital elevation models (DEMs) dataset, and a supervised maximum likelihood classification (MLC) algorithm with single-data TM image were applied vegetation classification. Accuracy of the two algorithms was assessed using field observation data. Based on produced vegetation classification maps, it was found that the DCT using multi-season data and geomorphologic parameters was superior to the MLC algorithm using single-data image, improving the overall accuracy by 11.86% at second class level and significantly reducing the "salt and pepper" noise. The DCT algorithm applied to TM /HJ-1 CCD time series data geomorphologic parameters appeared as a valuable and reliable tool for monitoring vegetation at first class level (5 vegetation classes) and second class level(8 vegetation subclasses). The DCT algorithm using multi-characteristic might provide a theoretical basis and general approach to automatic extraction of vegetation types from remote sensing imagery over plateau areas.

Automated extraction of metadata from remotely sensed satellite imagery

NASA Technical Reports Server (NTRS)

Cromp, Robert F.

1991-01-01

The paper discusses research in the Intelligent Data Management project at the NASA/Goddard Space Flight Center, with emphasis on recent improvements in low-level feature detection algorithms for performing real-time characterization of images. Images, including MSS and TM data, are characterized using neural networks and the interpretation of the neural network output by an expert system for subsequent archiving in an object-oriented data base. The data show the applicability of this approach to different arrangements of low-level remote sensing channels. The technique works well when the neural network is trained on data similar to the data used for testing.

Emerging solid-state laser technology by lidar/DIAL remote sensing

NASA Technical Reports Server (NTRS)

Killinger, Dennis

1992-01-01

Significant progress has been made in recent years in the development of new, solid-state laser sources. This talk will present an overview of some of the new developments in solid-state lasers, and their application toward lidar/DIAL measurements of the atmosphere. Newly emerging lasers such as Ho:YAG, Tm:YAG, OPO, and Ti:Sapphire will be covered, along with the spectroscopic parameters required for differential operational modes of atmospheric remote sensing including Doppler-Windshear lidar, Tunable laser detection of water/CO2, and broad linewidth OPO's for open path detection of pollutant hydrocarbon gases. Additional considerations of emerging laser technology for lidar/DIAL will also be covered.

INFLUENCE OF REMOTE SENSING IMAGERY SOURCE ON QUANTIFICATION OF RIPARIAN LAND COVER/LAND USE

EPA Science Inventory

This paper compares approaches to quantifying land cover/land use (LCLU) in riparian corridors of 23 watersheds in Oregon's Willamette Valley using aerial photography (AP) and Thematic Mapper (TM) imagery. For each imagery source, we quantified LCLU adjacent to stream networks ac...

Spectroradiometric calibration of the thematic mapper and multispectral scanner system

NASA Technical Reports Server (NTRS)

Palmer, J. M.; Slater, P. N.

1983-01-01

The results of an analysis that relates thematic mapper (TM) saturation level to ground reflectance, calendar date, latitude, and atmospheric condition is provided. A revised version of the preprint included with the last quarterly report is also provided for publication in the IEEE Transactions on Geoscience and Remote Sensing.

BOREAS RSS-8 Snow Maps Derived from Landsat TM Imagery

NASA Technical Reports Server (NTRS)

Hall, Dorothy; Chang, Alfred T. C.; Foster, James L.; Chien, Janeet Y. L.; Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Smith, David E. (Technical Monitor)

2000-01-01

The Boreal Ecosystem-Atmosphere Study (BOREAS) Remote Sensing Science (RSS)-8 team utilized Landsat Thematic Mapper (TM) images to perform mapping of snow extent over the Southern Study Area (SSA). This data set consists of two Landsat TM images that were used to determine the snow-covered pixels over the BOREAS SSA on 18 Jan 1993 and on 06 Feb 1994. The data are stored in binary image format files. The RSS-08 snow map data are available from the Earth Observing System Data and Information System (EOSDIS) Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC). The data files are available on a CD-ROM (see document number 20010000884).

Remote sensing of temperate coniferous forest lead area index - The influence of canopy closure, understory vegetation and background reflectance

NASA Technical Reports Server (NTRS)

Spanner, Michael A.; Pierce, Lars L.; Running, Steven W.; Peterson, David L.

1990-01-01

Consideration is given to the effects of canopy closure, understory vegetation, and background reflectance on the relationship between Landsat TM data and the leaf area index (LAI) of temperate coniferous forests in the western U.S. A methodology for correcting TM data for atmospheric conditions and sun-surface-sensor geometry is discussed. Strong inverse curvilinear relationships were found between coniferous forest LAI and TM bands 3 and 5. It is suggested that these inverse relationships are due to increased reflectance of understory vegetation and background in open stands of lower LAI and decreased reflectance of the overstory in closed canopy stands with higher LAI.

BOREAS Level-3b Landsat TM Imagery: At-sensor Radiances in BSQ Format

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime; Knapp, David; Newcomer, Jeffrey A.; Cihlar, Josef

2000-01-01

For BOREAS, the level-3b Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar in content to the level-3a Landsat TM products, the level-3b images were created to provide users with a directly usable at-sensor radiance image. Geographically, the level-3b images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 09-Jul-1996. The images are available in binary, image format files.

Airborne remote sensing of forest biomes

NASA Technical Reports Server (NTRS)

Sader, Steven A.

1987-01-01

Airborne sensor data of forest biomes obtained using an SAR, a laser profiler, an IR MSS, and a TM simulator are presented and examined. The SAR was utilized to investigate forest canopy structures in Mississippi and Costa Rica; the IR MSS measured forest canopy temperatures in Oregon and Puerto Rico; the TM simulator was employed in a tropical forest in Puerto Rico; and the laser profiler studied forest canopy characteristics in Costa Rica. The advantages and disadvantages of airborne systems are discussed. It is noted that the airborne sensors provide measurements applicable to forest monitoring programs.

Analysis of data acquired by Shuttle Imaging Radar SIR-A and Landsat Thematic Mapper over Baldwin County, Alabama

NASA Technical Reports Server (NTRS)

Wu, S.-T.

1985-01-01

Seasonally compatible data collected by SIR-A and by Landsat 4 TM over the lower coastal plain in Alabama were coregistered, forming a SIR-A/TM multichannel data set with 30 m x 30 m pixel size. Spectral signature plots and histogram analysis of the data were used to observe data characteristics. Radar returns from pine forest classes correlated highly with the tree ages, suggesting the potential utility of microwave remote sensing for forest biomass estimation. As compared with the TM-only data set, the use of SIR-A/TM data set improved classification accuracy of the seven land cover types studied. In addition, the SIR-A/TM classified data support previous finding by Engheta and Elachi (1982) that microwave data appear to be correlated with differing bottomland hardwood forest vegetation as associated with varying water regimens (i.e., wet versus dry).

Remote detection of forest damage

NASA Technical Reports Server (NTRS)

Rock, B. N.; Vogelmann, J. E.; Vogelmann, A. F.; Hoshizaki, T.; Williams, D. L.

1986-01-01

The use of remote sensing to discriminate, measure, and map forest damage is evaluated. TM spectal coverage, a helicopter-mounted radiometer, and ground-based surveys were utilized to examine the responses of the spruces and firs of Camels Hump Mountain, Vermont to stresses, such as pollution and trace metals. The basic spectral properties of vegetation are described. Forest damage at the site was estimated as 11.8-76.0 percent for the spruces and 19-43.8 percent for the balsam firs. Shifts in the spectra of the conifers in particular in the near IR region are analyzed, and variations in the mesophyll cell anatomy and pigment content of the spruces and firs are investigated. The relations between canopy moisture and damage is studied. The TM data are compared to aircraft data and found to be well correlated.

Combining lake and watershed characteristics with Landsat TM data for remote estimation of regional lake clarity

USGS Publications Warehouse

McCullough, Ian M.; Loftin, Cyndy; Sader, Steven A.

2012-01-01

Water clarity is a reliable indicator of lake productivity and an ideal metric of regional water quality. Clarity is an indicator of other water quality variables including chlorophyll-a, total phosphorus and trophic status; however, unlike these metrics, clarity can be accurately and efficiently estimated remotely on a regional scale. Remote sensing is useful in regions containing a large number of lakes that are cost prohibitive to monitor regularly using traditional field methods. Field-assessed lakes generally are easily accessible and may represent a spatially irregular, non-random sample of a region. We developed a remote monitoring program for Maine lakes >8 ha (1511 lakes) to supplement existing field monitoring programs. We combined Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) brightness values for TM bands 1 (blue) and 3 (red) to estimate water clarity (secchi disk depth) during 1990–2010. Although similar procedures have been applied to Minnesota and Wisconsin lakes, neither state incorporates physical lake variables or watershed characteristics that potentially affect clarity into their models. Average lake depth consistently improved model fitness, and the proportion of wetland area in lake watersheds also explained variability in clarity in some cases. Nine regression models predicted water clarity (R2 = 0.69–0.90) during 1990–2010, with separate models for eastern (TM path 11; four models) and western Maine (TM path 12; five models that captured differences in topography and landscape disturbance. Average absolute difference between model-estimated and observed secchi depth ranged 0.65–1.03 m. Eutrophic and mesotrophic lakes consistently were estimated more accurately than oligotrophic lakes. Our results show that TM bands 1 and 3 can be used to estimate regional lake water clarity outside the Great Lakes Region and that the accuracy of estimates is improved with additional model variables that reflect physical lake characteristics and watershed conditions.

Remote Sensing the Patterns of Vector-borne Disease in El Nino and non-El Nino Years

NASA Technical Reports Server (NTRS)

Wood, B. L.; Chang, J.; Lobitz, B.; Beck, L.; DAntoni, Hector (Technical Monitor)

1997-01-01

The relationship between El Nino and non-El Nino and the patterns of vector-borne disease can be viewed at a variety of spatial and temporal scales. At one extreme are long term predictions of changing precipitation and temperature patterns at continental and global scales. At the opposite extreme are the local or site specific ecological changes associated with the long term events. In order to understand and address the human health consequences of El Nino events, especially the patterns of vector-borne diseases, it is necessary to combine both scales of observation. At a local or regional scale the patterns of vector-borne diseases are determined by temperature, precipitation, and habitat availability. These factors, as well as disease incidence can be altered by El Nino events. Remote sensing data such as that acquired by the NOAA AVHRR and Landsat TM sensors can be used to characterize and monitor changing ecological conditions and therefore predict vector-borne disease patterns. The authors present the results of preliminary work on the analysis of historical AVHRR and TM data acquired during El Nino and nonfatal Nino years to characterize ecological conditions in Peru on a monthly basis. This information will then be combined with disease data to determine the relationship between changes in ecological conditions and disease incidence. Our goal is to produce a sequence of remotely sensed images which can be used to show the ecological and disease patterns associated with long term El Nino events and predictions.

Optical remote sensing for forest area estimation

Treesearch

Randolph H. Wynne; Richard G. Oderwald; Gregory A. Reams; John A. Scrivani

2000-01-01

The air photo dot-count method is now widely and successfully used for estimating operational forest area in the USDA Forest Inventory and Analysis (FIA) program. Possible alternatives that would provide for more frequent updates, spectral change detection, and maps of forest area include the AVHRR calibration center technique and various Landsat TM classification...

Shallow sea-floor reflectance and water depth derived by unmixing multispectral imagery

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

Bierwirth, P.N.; Lee, T.J.; Burne, R.V.

1993-03-01

A major problem for mapping shallow water zones by the analysis of remotely sensed data is that contrast effects due to water depth obscure and distort the special nature of the substrate. This paper outlines a new method which unmixes the exponential influence of depth in each pixel by employing a mathematical constraint. This leaves a multispectral residual which represents relative substrate reflectance. Input to the process are the raw multispectral data and water attenuation coefficients derived by the co-analysis of known bathymetry and remotely sensed data. Outputs are substrate-reflectance images corresponding to the input bands and a greyscale depthmore » image. The method has been applied in the analysis of Landsat TM data at Hamelin Pool in Shark Bay, Western Australia. Algorithm derived substrate reflectance images for Landsat TM bands 1, 2, and 3 combined in color represent the optimum enhancement for mapping or classifying substrate types. As a result, this color image successfully delineated features, which were obscured in the raw data, such as the distributions of sea-grasses, microbial mats, and sandy area. 19 refs.« less

Prediction of villages at risk for filariasis transmission in the Nile Delta using remote sensing and geographic information system technologies.

PubMed

Hassan, A N; Beck, L R; Dister, S

1998-04-01

Remote sensing and geographic information system (GIS) technologies were used to discriminate between 130 villages, in the Nile Delta, at high and low risk for filariasis, as defined by microfilarial prevalence. Landsat Thematic Mapper (TM) data were digitally processed to generate a map of landcover as well as spectral indices such as NDVI and moisture index. A Tasseled Cap transformation was also carried out on the TM data which produced three more indices: brightness, greenness and wetness. GIS functions were used to extract information on landcover and spectral indices within one km buffers around the study villages. The relationship between satellite data and prevalence was investigated using discriminant analysis. The analysis indicated that the most important landscape elements associated with prevalence were water and marginal vegetation, while wetness and moisture index were the most important indices. Discriminant functions generated for these variables were able to correctly predict 80% and 74% of high and low prevalence villages, respectively, with an overall accuracy of 77%. The present approach provides a promising tool for regional filariasis surveillance and helps direct control efforts.

Regional yield predictions of malting barley by remote sensing and ancillary data

NASA Astrophysics Data System (ADS)

Weissteiner, Christof J.; Braun, Matthias; Kuehbauch, Walter

2004-02-01

Yield forecasts are of high interest to the malting and brewing industry in order to allow the most convenient purchasing policy of raw materials. Within this investigation, malting barley yield forecasts (Hordeum vulgare L.) were performed for typical growing regions in South-Western Germany. Multisensoral and multitemporal Remote Sensing data on one hand and ancillary meteorological, agrostatistical, topographical and pedological data on the other hand were used as input data for prediction models, which were based on an empirical-statistical modeling approach. Since spring barley production is depending on acreage and on the yield per area, classification is needed, which was performed by a supervised multitemporal classification algorithm, utilizing optical Remote Sensing data (LANDSAT TM/ETM+). Comparison between a pixel-based and an object-oriented classification algorithm was carried out. The basic version of the yield estimation model was conducted by means of linear correlation of Remote Sensing data (NOAA-AVHRR NDVI), CORINE land cover data and agrostatistical data. In an extended version meteorological data (temperature, precipitation, etc.) and soil data was incorporated. Both, basic and extended prediction systems, led to feasible results, depending on the selection of the time span for NDVI accumulation.

Combining remote sensing image with DEM to identify ancient Minqin Oasis, northwest of China

NASA Astrophysics Data System (ADS)

Xie, Yaowen

2008-10-01

The developing and desertification process of Minqin oasis is representative in the whole arid area of northwest China. Combining Remote Sensing image with Digital Elevation Model (DEM) can produce the three-dimensional image of the research area which can give prominence to the spatial background of historical geography phenomenon's distribution, providing the conditions for extracting and analyzing historical geographical information thoroughly. This research rebuilds the ancient artificial Oasis based on the three-dimensional images produced by the TM digital Remote Sensing image and DEM created using 1:100000 topographic maps. The result indicates that the whole area of the ancient artificial oasis in Minqin Basin over the whole historical period reaches 321km2, in the form of discontinuous sheet, separated on the two banks of ancient Shiyang River and its branches, namely, Xishawo area, west to modern Minqin Basin and Zhongshawo area, in the center of the oasis. Except for a little of the ancient oasis unceasingly used by later people, most of it became desert. The combination of digital Remote Sensing image and DEM can integrate the advantages of both in identifying ancient oasis and improve the interpreting accuracy greatly.

BOREAS RSS-7 Regional LAI and FPAR Images From 10-Day AVHRR-LAC Composites

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Nickeson, Jaime (Editor); Chen, Jing; Cihlar, Josef

2000-01-01

The BOReal Ecosystem-Atmosphere Study Remote Sensing Science (BOREAS RSS-7) team collected various data sets to develop and validate an algorithm to allow the retrieval of the spatial distribution of Leaf Area Index (LAI) from remotely sensed images. Advanced Very High Resolution Radiometer (AVHRR) level-4c 10-day composite Normalized Difference Vegetation Index (NDVI) images produced at CCRS were used to produce images of LAI and the Fraction of Photosynthetically Active Radiation (FPAR) absorbed by plant canopies for the three summer IFCs in 1994 across the BOREAS region. The algorithms were developed based on ground measurements and Landsat Thematic Mapper (TM) images. The data are stored in binary image format files.

Analysis of forest disturbance using TM and AVHRR data

NASA Technical Reports Server (NTRS)

Spanner, Michael A.; Hlavka, Christine A.; Pierce, Lars L.

1989-01-01

A methodology that will be used to determine the proportions of undisturbed, successional vegetation and recently disturbed land cover within coniferous forests using remotely sensed data from the advanced very high resolution radiometer (AVHRR) is presented. The method uses thematic mapper (TM) data to determine the proportions of the three stages of forest disturbance and regrowth for each AVHRR pixel in the sample areas, and is then applied to interpret all AVHRR imagery. Preliminary results indicate that there are predictable relationships between TM spectral response and the disturbance classes. Analysis of ellipse plots from a TM classification of the disturbed forested landscape indicates that the forest classes are separable in the red (0.63-0.69 micron) and near-infrared (0.76-0.90 micron) bands, providing evidence that the proportion of disturbance classes may be determined from AVHRR data.

Landsat TM as a Tool for Locating Habitat for Cerulean Warblers

NASA Technical Reports Server (NTRS)

Kellner, Chris

2000-01-01

I believe that I made significant strides in three areas between fall of 1997 and fall of 2000 when I concluded my participation in the JOVE program. First, I acquired skill in digital remote sensing. This was significant to me because it had been 20 years since I had done any work utilizing remote sensing. I used my new skills in two classroom settings (forest ecology and GIS). In addition, I will participate as an instructor of digital remote sensing in a workshop for secondary educators this coming spring. Second, I received funding from the Arkansas Game and Fish Commission and the U.S. Forest Service to supplement JOVE funds. Third, and most importantly, a students and I developed a technique using LandSAT TM for identifying habitat for cerulean warblers. We developed a habitat model using logistic regression to discriminate between pixels that had a high probability of representing good cerulean warbler habitat and pixels that had a low probability of representing cerulean warbler habitat. Using this model, we located five significant populations of cerulean warblers in the Ozark National Forest of Arkansas. These populations were unknown before the initiation of this research and further represent a significant proportion of the known cerulean warblers in Arkansas. Preliminary findings were presented at the Ornithological Societies of America meeting in August of 1999. I also presented findings at the Arkansas Game and Fish Commission Research Symposium held in June of 2000. Finally, one paper is in press: James, D. A., C.J. Kellner, J. Self, and J. Davis., 'Breeding season distribution of cerulean warblers in Arkansas in the 1990's'. In addition, one paper is under construction: 'Population fluctuation and habitat selection by cerulean warblers in upland forests of Arkansas,' and one paper is under consideration: 'LandSAT TM and Logistic regression for identification of cerulean warbler habitat in upland forests of Arkansas.'

Remote Sensing of Water Quality in Multipurpose Reservoirs: Case Study Applications in Indonesia, Mexico, and Uruguay

NASA Astrophysics Data System (ADS)

Miralles-Wilhelm, F.; Serrat-Capdevila, A.; Rodriguez, D.

2017-12-01

This research is focused on development of remote sensing methods to assess surface water pollution issues, particularly in multipurpose reservoirs. Three case study applications are presented to comparatively analyze remote sensing techniquesforo detection of nutrient related pollution, i.e., Nitrogen, Phosphorus, Chlorophyll, as this is a major water quality issue that has been identified in terms of pollution of major water sources around the country. This assessment will contribute to a better understanding of options for nutrient remote sensing capabilities and needs and assist water agencies in identifying the appropriate remote sensing tools and devise an application strategy to provide information needed to support decision-making regarding the targeting and monitoring of nutrient pollution prevention and mitigation measures. A detailed review of the water quality data available from ground based measurements was conducted in order to determine their suitability for a case study application of remote sensing. In the first case study, the Valle de Bravo reservoir in Mexico City reservoir offers a larger database of water quality which may be used to better calibrate and validate the algorithms required to obtain water quality data from remote sensing raw data. In the second case study application, the relatively data scarce Lake Toba in Indonesia can be useful to illustrate the value added of remote sensing data in locations where water quality data is deficient or inexistent. The third case study in the Paso Severino reservoir in Uruguay offers a combination of data scarcity and persistent development of harmful algae blooms. Landsat-TM data was obteined for the 3 study sites and algorithms for three key water quality parameters that are related to nutrient pollution: Chlorophyll-a, Total Nitrogen, and Total Phosphorus were calibrated and validated at the study sites. The three case study applications were developed into capacity building/training workshops for water resources students, applied scientists, practitioners, reservoir and water quality managers, and other interested stakeholders.

Characterizing fire-related spatial patterns in fire-prone ecosystems using optical and microwave remote sensing

NASA Astrophysics Data System (ADS)

Henry, Mary Catherine

The use of active and passive remote sensing systems for relating forest spatial patterns to fire history was tested over one of the Arizona Sky Islands. Using Landsat Thematic Mapper (TM), Shuttle Imaging Radar (SIR-C), and data fusion I examined the relationship between landscape metrics and a range of fire history characteristics. Each data type (TM, SIR-C, and fused) was processed in the following manner: each band, channel, or derived feature was simplified to a thematic layer and landscape statistics were calculated for plots with known fire history. These landscape metrics were then correlated with fire history characteristics, including number of fire-free years in a given time period, mean fire-free interval, and time since fire. Results from all three case studies showed significant relationships between fire history and forest spatial patterns. Data fusion performed as well or better than Landsat TM alone, and better than SIR-C alone. These comparisons were based on number and strength of significant correlations each method achieved. The landscape metric that was most consistent and obtained the greatest number of significant correlations was Shannon's Diversity Index. Results also agreed with field-based research that has linked higher fire frequency to increased landscape diversity and patchiness. An additional finding was that the fused data seem to detect fire-related spatial patterns over a range of scales.

Absolute calibration accuracy of L4 TM and L5 TM sensor image pairs

USGS Publications Warehouse

Chander, G.; Micijevic, E.

2006-01-01

The Landsat suite of satellites has collected the longest continuous archive of multispectral data of any land-observing space program. From the Landsat program's inception in 1972 to the present, the Earth science user community has benefited from a historical record of remotely sensed data. However, little attention has been paid to ensuring that the data are calibrated and comparable from mission to mission, Launched in 1982 and 1984 respectively, the Landsat 4 (L4) and Landsat 5 (L5) Thematic Mappers (TM) are the backbone of an extensive archive of moderate resolution Earth imagery. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The approach involves comparing image statistics derived from large common areas observed eight days apart by the two sensors. The average percent differences in reflectance estimates obtained from the L4 TM agree with those from the L5 TM to within 15 percent. Additional work to characterize the absolute differences between the two sensors over the entire mission is in progress.

Forest and wildlife habitat analysis using remote sensing and geographic information systems. M.S. Thesis, 26 May 1992 Abstract Only

NASA Technical Reports Server (NTRS)

Fiorella, Maria

1995-01-01

Forest and wildlife habitat analyses were conducted at the H.J. Andrews Experimental Forest in the Central Cascade Mountains of Oregon using remotely sensed data and a geographic information system (GIS). Landsat Thematic Mapper (TM) data were used to determine forest successional stages, and to analyze the structure of both old and young conifer forests. Two successional stage maps were developed. One was developed from six TM spectral bands alone, and the second was developed from six TM spectral bands and a relative sun incidence band. Including the sun incidence band in the classification improved the mapping accuracy in the two youngest successional stages, but did not improve overall accuracy or accuracy of the two oldest successional stages. Mean spectral values for old-growth and mature stands were compared in seven TM bands and seven band transformations. Differences between mature and old-growth successional stages were greatest for the band ratio of TM 4/5 (P = 0.00005) and the multiband transformation of wetness (P = 0.00003). The age of young conifer stands had the highest correlation to TM 4/5 values (r = 0.9559) of any of the TM band or band transformations used. TM 4/5 ratio values of poorly regenerated conifer stands were significantly different from well regenerated conifer stands after age 15 (P = 0.0000). TM 4/5 was named a 'Successional Stage Index' (SSI) because of its ability to distinguish forest successional stages. The forest successional stage map was used as input into a vertebrate richness model using GIS. The three variables of (1) successional stage, (2) elevation, and (3) site moisture were used in the GIS to predict the spatial occurrence of small mammal, amphibian, and reptile species based on primary and secondary habitat requirements. These occurrence or habitat maps were overlayed to tally the predicted number of vertebrate at any given point in the study area. Overall, sixty-three and sixty-seven percent of the model predictions for vertebrate occurrence matched the vertebrates that were trapped in the field in eight forested stands. Of the three model variables, site moisture appeared to have the greatest influence on the pattern of high vertebrate richness in all vertebrate classes.

Evaluating MODIS snow products for modelling snowmelt runoff: case study of the Rio Grande headwaters

USDA-ARS?s Scientific Manuscript database

Snow-covered area (SCA) is a key variable in the Snowmelt-Runoff Model (SRM). Landsat Thematic Mapper (TM) or Operational Land Imager (OLI) provide remotely sensed data at an appropriate spatial resolution for mapping SCA in small headwater basins, but the temporal resolution of the data is low and ...

Neuro-classification of multi-type Landsat Thematic Mapper data

NASA Technical Reports Server (NTRS)

Zhuang, Xin; Engel, Bernard A.; Fernandez, R. N.; Johannsen, Chris J.

1991-01-01

Neural networks have been successful in image classification and have shown potential for classifying remotely sensed data. This paper presents classifications of multitype Landsat Thematic Mapper (TM) data using neural networks. The Landsat TM Image for March 23, 1987 with accompanying ground observation data for a study area In Miami County, Indiana, U.S.A. was utilized to assess recognition of crop residues. Principal components and spectral ratio transformations were performed on the TM data. In addition, a layer of the geographic information system (GIS) for the study site was incorporated to generate GIS-enhanced TM data. This paper discusses (1) the performance of neuro-classification on each type of data, (2) how neural networks recognized each type of data as a new image and (3) comparisons of the results for each type of data obtained using neural networks, maximum likelihood, and minimum distance classifiers.

Revised radiometric calibration technique for LANDSAT-4 Thematic Mapper data by the Canada Centre for Remote Sensing

NASA Technical Reports Server (NTRS)

Murphy, J.; Butlin, T.; Duff, P.; Fitzgerald, A.

1984-01-01

Observations of raw image data, raw radiometric calibration data, and background measurements extracted from the raw data streams on high density tape reveal major shortcomings in a technique proposed by the Canadian Center for Remote Sensing in 1982 for the radiometric correction of TM data. Results are presented which correlate measurements of the DC background with variations in both image data background and calibration samples. The effect on both raw data and data corrected using the earlier proposed technique is explained and the correction required for these factors as a function of individual scan line number for each detector is described. How the revised technique can be incorporated into an operational environment is demonstrated.

BOREAS Level-3p Landsat TM Imagery: Geocoded and Scaled At-sensor Radiance

NASA Technical Reports Server (NTRS)

Nickeson, Jaime; Knapp, David; Newcomer, Jeffrey A.; Hall, Forrest G. (Editor); Cihlar, Josef

2000-01-01

For BOReal Ecosystem-Atmosphere Study (BOREAS), the level-3p Landsat Thematic Mapper (TM) data were used to supplement the level-3s Landsat TM products. Along with the other remotely sensed images, the Landsat TM images were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI). Although very similar to the level-3s Landsat TM products, the level-3p images were processed with ground control information, which improved the accuracy of the geographic coordinates provided. Geographically, the level-3p images cover the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA). Temporally, the four images cover the period of 20-Aug-1988 to 07-Jun-1994. Except for the 07-Jun-1994 image, which contains seven bands, the other three contain only three bands.

Thematic mapper data quality and performance assessment in renewable resource/agricultural remote sensing

NASA Technical Reports Server (NTRS)

Erickson, J. D.; Macdonald, R. B. (Principal Investigator)

1982-01-01

A "quick look" investigation of the initial LANDSAT-4, thematic mapper (TM) scene received from Goddard Space Flight Center was performed to gain early insight into the characteristics of TM data. The initial scene, containing only the first four bands of the seven bands recorded by the TM, was acquired over the Detroit, Michigan, area on July 20, 1982. It yielded abundant information for scientific investigation. A wide variety of studies were conducted to assess all aspects of TM data. They ranged from manual analyses of image products to detect obvious optical, electronic, or mechanical defects to detailed machine analyses of the digital data content for evaluation of spectral separability of vegetative/nonvegetative classes. These studies were applied to several segments extracted from the full scene. No attempt was made to perform end-to-end statistical evaluations. However, the output of these studies do identify a degree of positive performance from the TM and its potential for advancing state-of-the-art crop inventory and condition assessment technology.

Evaluation and interpretation of Thematic Mapper ratios in equations for estimating corn growth parameters

NASA Technical Reports Server (NTRS)

Dardner, B. R.; Blad, B. L.; Thompson, D. R.; Henderson, K. E.

1985-01-01

Reflectance and agronomic Thematic Mapper (TM) data were analyzed to determine possible data transformations for evaluating several plant parameters of corn. Three transformation forms were used: the ratio of two TM bands, logarithms of two-band ratios, and normalized differences of two bands. Normalized differences and logarithms of two-band ratios responsed similarly in the equations for estimating the plant growth parameters evaluated in this study. Two-term equations were required to obtain the maximum predictability of percent ground cover, canopy moisture content, and total wet phytomass. Standard error of estimate values were 15-26 percent lower for two-term estimates of these parameters than for one-term estimates. The terms log(TM4/TM2) and (TM4/TM5) produced the maximum predictability for leaf area and dry green leaf weight, respectively. The middle infrared bands TM5 and TM7 are essential for maximizing predictability for all measured plant parameters except leaf area index. The estimating models were evaluated over bare soil to discriminate between equations which are statistically similar. Qualitative interpretations of the resulting prediction equations are consistent with general agronomic and remote sensing theory.

Downscaling of Aircraft-, Landsat-, and MODIS-based Land Surface Temperature Images with Support Vector Machines

NASA Astrophysics Data System (ADS)

Ha, W.; Gowda, P. H.; Oommen, T.; Howell, T. A.; Hernandez, J. E.

2010-12-01

High spatial resolution Land Surface Temperature (LST) images are required to estimate evapotranspiration (ET) at a field scale for irrigation scheduling purposes. Satellite sensors such as Landsat 5 Thematic Mapper (TM) and Moderate Resolution Imaging Spectroradiometer (MODIS) can offer images at several spectral bandwidths including visible, near-infrared (NIR), shortwave-infrared, and thermal-infrared (TIR). The TIR images usually have coarser spatial resolutions than those from non-thermal infrared bands. Due to this technical constraint of the satellite sensors on these platforms, image downscaling has been proposed in the field of ET remote sensing. This paper explores the potential of the Support Vector Machines (SVM) to perform downscaling of LST images derived from aircraft (4 m spatial resolution), TM (120 m), and MODIS (1000 m) using normalized difference vegetation index images derived from simultaneously acquired high resolution visible and NIR data (1 m for aircraft, 30 m for TM, and 250 m for MODIS). The SVM is a new generation machine learning algorithm that has found a wide application in the field of pattern recognition and time series analysis. The SVM would be ideally suited for downscaling problems due to its generalization ability in capturing non-linear regression relationship between the predictand and the multiple predictors. Remote sensing data acquired over the Texas High Plains during the 2008 summer growing season will be used in this study. Accuracy assessment of the downscaled 1, 30, and 250 m LST images will be made by comparing them with LST data measured with infrared thermometers at a small spatial scale, upscaled 30 m aircraft-based LST images, and upscaled 250 m TM-based LST images, respectively.

Remote sensing research for agricultural applications

NASA Technical Reports Server (NTRS)

Colwell, R. N. (Principal Investigator)

1983-01-01

The thematic mapper simulator (TMS) flown by the U-2/ER-2 aircraft is being used as a surrogate for LANDSAT-4TM data. Progress is reported on spectral data acquisition including TMS, color infrared high altitude aerial photography, and LANDSAT 3 MSS and ground data collection to support classification and testing. A test site in San Joaquin County was selected for analysis.

Seasonal and topographic effects on estimating fire severity from Landsat TM/ETM+data.

Treesearch

D.L. Verbyla; E.S. Kasischke; E.E. Hoy

2008-01-01

The maximum solar elevation is typically less than 50 degrees in the Alaskan boreal region and solar elevation varies substantially during the growing season. Because of the relatively low solar elevation at boreal latitudes, the effect of topography on spectral reflectance can influence fire severity indices derived from remotely sensed data. We used Landsat Thematic...

Satellite Remote Sensing For Aluminum And Nickel Laterites

NASA Astrophysics Data System (ADS)

Henderson, Frederick B.; Penfield, Glen T.; Grubbs, Donald K.

1984-08-01

The new LANDSAT-4,-5/Thematic Mapper (TM) land observational satellite remote sensing systems are providing dramatically new and important short wave infrared (SWIR) data, which combined with Landsat's Multi-Spectral Scanner (MSS) visible (VIS), very near infrared (VNIR), and thermal infrared (TI) data greatly improves regional geological mapping on a global scale. The TM will significantly improve clay, iron oxide, aluminum, and nickel laterite mapping capabilities over large areas of the world. It will also improve the ability to discriminate vegetation stress and species distribution associated with lateritic environments. Nickel laterites on Gag Island, Indonesia are defined by MSS imagery. Satellite imagery of the Cape Bougainville and the Darling Range, Australia bauxite deposits show the potential use of MSS data for exploration and mining applications. Examples of satellite syn-thetic aperture radar (SAR) for Jamaica document the use of this method for bauxite exploration. Thematic Mapper data will be combined with the French SPOT satellite's high spatial resolution and stereoscopic digital data, and U.S., Japanese, European, and Canadian Synthetic Aperture Radar (SAR) data to assist with logistics, mine development, and environ-mental concerns associated with aluminum and nickel lateritic deposits worldwide.

Intelligent image processing for vegetation classification using multispectral LANDSAT data

NASA Astrophysics Data System (ADS)

Santos, Stewart R.; Flores, Jorge L.; Garcia-Torales, G.

2015-09-01

We propose an intelligent computational technique for analysis of vegetation imaging, which are acquired with multispectral scanner (MSS) sensor. This work focuses on intelligent and adaptive artificial neural network (ANN) methodologies that allow segmentation and classification of spectral remote sensing (RS) signatures, in order to obtain a high resolution map, in which we can delimit the wooded areas and quantify the amount of combustible materials present into these areas. This could provide important information to prevent fires and deforestation of wooded areas. The spectral RS input data, acquired by the MSS sensor, are considered in a random propagation remotely sensed scene with unknown statistics for each Thematic Mapper (TM) band. Performing high-resolution reconstruction and adding these spectral values with neighbor pixels information from each TM band, we can include contextual information into an ANN. The biggest challenge in conventional classifiers is how to reduce the number of components in the feature vector, while preserving the major information contained in the data, especially when the dimensionality of the feature space is high. Preliminary results show that the Adaptive Modified Neural Network method is a promising and effective spectral method for segmentation and classification in RS images acquired with MSS sensor.

Modelling submerged coastal environments: Remote sensing technologies, techniques, and comparative analysis

NASA Astrophysics Data System (ADS)

Dillon, Chris

Built upon remote sensing and GIS littoral zone characterization methodologies of the past decade, a series of loosely coupled models aimed to test, compare and synthesize multi-beam SONAR (MBES), Airborne LiDAR Bathymetry (ALB), and satellite based optical data sets in the Gulf of St. Lawrence, Canada, eco-region. Bathymetry and relative intensity metrics for the MBES and ALB data sets were run through a quantitative and qualitative comparison, which included outputs from the Benthic Terrain Modeller (BTM) tool. Substrate classification based on relative intensities of respective data sets and textural indices generated using grey level co-occurrence matrices (GLCM) were investigated. A spatial modelling framework built in ArcGIS(TM) for the derivation of bathymetric data sets from optical satellite imagery was also tested for proof of concept and validation. Where possible, efficiencies and semi-automation for repeatable testing was achieved using ArcGIS(TM) ModelBuilder. The findings from this study could assist future decision makers in the field of coastal management and hydrographic studies. Keywords: Seafloor terrain characterization, Benthic Terrain Modeller (BTM), Multi-beam SONAR, Airborne LiDAR Bathymetry, Satellite Derived Bathymetry, ArcGISTM ModelBuilder, Textural analysis, Substrate classification.

Characterizing the scientific potential of satellite sensors. [San Francisco, California

NASA Technical Reports Server (NTRS)

1984-01-01

Analytical and programming support is to be provided to characterize the potential of the LANDSAT thematic mapper (TM) digital imagery for scientific investigations in the Earth sciences and in terrestrial physics. In addition, technical support to define lower atmospheric and terrestrial surface experiments for the space station and technical support to the Research Optical Sensor (ROS) study scientist for advanced studies in remote sensing are to be provided. Eleven radiometric calibration and correction programs are described. Coherent noise and bright target saturation correction are discussed along with image processing on the LAS/VAX and Hp-300/IDIMS. An image of San Francisco, California from TM band 2 is presented.

Geological analysis of parts of the southern Arabian Shield based on Landsat imagery

NASA Astrophysics Data System (ADS)

Qari, Mohammed Yousef Hedaytullah T.

This thesis examines the capability and applicability of Landsat multispectral remote sensing data for geological analysis in the arid southern Arabian Shield, which is the eastern segment of the Nubian-Arabian Shield surrounding the Red Sea. The major lithologies in the study area are Proterozoic metavolcanics, metasediments, gneisses and granites. Three test-sites within the study area, located within two tectonic assemblages, the Asir Terrane and the Nabitah Mobile Belt, were selected for detailed comparison of remote sensing methods and ground geological studies. Selected digital image processing techniques were applied to full-resolution Landsat TM imagery and the results are interpreted and discussed. Methods included: image contrast improvement, edge enhancement for detecting lineaments and spectral enhancement for geological mapping. The last method was based on two principles, statistical analysis of the data and the use of arithmetical operators. New and detailed lithological and structural maps were constructed and compared with previous maps of these sites. Examples of geological relations identified using TM imagery include: recognition and mapping of migmatites for the first time in the Arabian Shield; location of the contact between the Asir Terrane and the Nabitah Mobile Belt; and mapping of lithologies, some of which were not identified on previous geological maps. These and other geological features were confirmed by field checking. Methods of lineament enhancement implemented in this study revealed structural lineaments, mostly mapped for the first time, which can be related to regional tectonics. Structural analysis showed that the southern Arabian Shield has been affected by at least three successive phases of deformation. The third phase is the most dominant and widespread. A crustal evolutionary model in the vicinity of the study area is presented showing four stages, these are: arc stage, accretion stage, collision stage and post-collision stage. The results of this study demonstrate that Landsat TM data can be used reliably for geological investigations in the Arabian Shield and comparable areas, particularly to generate detailed geological maps over large areas by using quantitative remote sensing methods, providing there is prior knowledge of part of the area.

Multidata remote sensing approach to regional geologic mapping in Venezuela

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

Baker, R.N.

1996-08-01

Remote Sensing played an important role in evaluating the exploration potential of selected lease blocks in Venezuela. Data sets used ranged from regional Landsat and airborne radar (SLAR) surveys to high-quality cloud-free air photos for local but largely inaccessible terrains. The resulting data base provided a framework for the conventional analyses of surface and subsurface information available to the project team. (1) Regional surface geology and major structural elements were interpreted from Landsat MSS imagery supplemented by TM and a regional 1:250,000 airborne radar (SLAR) survey. Evidence of dextral offset, en echelon folds and major thoroughgoing faults suggest a regionalmore » transpressional system modified by local extension and readjustment between small-scale crustal blocks. Surface expression of the major structural elements diminishes to the east, but can often be extended beneath the coastal plain by drainage anomalies and subtle geomorphic trends. (2) Environmental conditions were mapped using the high resolution airborne radar images which were used to relate vegetation types to surface texture and elevation; wetlands, outcrop and cultural features to image brightness. Additional work using multispectral TM or SPOT imagery is planned to more accurately define environmental conditions and provide a baseline for monitoring future trends. (3) Offshore oil seeps were detected using ERS-1 satellite radar (SAR) and known seeps in the Gulf of Paria as analogs. While partially successful, natural surfactants, wind shadow and a surprising variety of other phenomena created {open_quotes}false alarms{close_quotes} which required other supporting data and field sampling to verify the results. Key elements of the remote sensing analyses will be incorporated into a comprehensive geographic information (GIS) which will eventually include all of Venezuela.« less

Remote sensing advances in agricultural inventories

NASA Technical Reports Server (NTRS)

Dragg, J. L.; Bizzell, R. M.; Trichel, M. C.; Hatch, R. E.; Phinney, D. E.; Baker, T. C.

1984-01-01

As the complexity of the world's agricultural industry increases, more timely and more accurate world-wide agricultural information is required to support production and marketing decisions, policy formulation, and technology development. The Inventory Technology Development Project of the AgRISTARS Program has developed new automated technology that uses data sets acquired by spaceborne remote sensors. Research has emphasized the development of multistage, multisensor sampling and estimation techniques for use in global environments where reliable ground observations are not available. This paper presents research results obtained from data sets acquired by four different sensors: Landsat MSS, Landsat TM, Shuttle-Imaging Radar and environmental satellite (AVHRR).

Remote Sensing of Vegetation Recovery from Disturbance in Drylands

NASA Astrophysics Data System (ADS)

Poitras, T. B.; Villarreal, M. L.; Waller, E.; Duniway, M.; Nauman, T.

2016-12-01

Characteristics of dryland ecosystems such as climatic extremes and water limitations render semi-arid regions vulnerable to disturbance and slow to recover. Land surface monitoring over time through the use of remote sensing may have potential for identifying dryland ecosystem recovery after anthropogenic and natural disturbance. However, semi-arid vegetation cover is challenging to measure using remote sensing techniques due to low vegetation cover and confusion between bright and variable soils and non-photosynthetic vegetation (NPV). We therefore evaluated the ability of various multispectral indices to distinguish bare ground from total vegetation cover, in order to determine those that can detect changes over time in heavily disturbed sites. We calculated nine spectral indices from Landsat TM using Google Earth Engine (March through October, 2006 through 2008) and tested relationships between index values and ground measurements from long-term monitoring data collected in and around Canyonlands National Park in Utah. We also tested multivariate models, with some showing improvement under cross-validation. We found that indices that included shortwave infrared bands and soil brightness were important for capturing gradients in bare ground, and vegetation cover was best quantified with near-infrared bands. These results will be used to help assess the landscape-scale impacts of oil and gas development in dryland ecosystems and to measure response to restoration efforts. Keywords: remote sensing, landsat, drylands

[Fluorescence peak shift corresponding to high chlorophyll concentrations in inland water].

PubMed

Duan, Hong-Tao; Ma, Rong-Hua; Zhang, Yuan-Zhi; Zhang, Bai

2009-01-01

Hyperspectral remote sensing offers the potential to detect water quality variables such as Chl-a by using narrow spectral channels of less than 10 nm, which could otherwise be masked by broadband satellites such as Landsat TM. Fluorescence peak of the red region is very important for the remote sensing of inland and coastal waters, which is unique to phytoplankton Chl-a that takes place in this region. Based on in situ water sampling and field spectral measurement from 2004 to 2006 in Nanhu Lake, the features of the spectral reflectance were analyzed in detail with peak position shift. The results showed: An exponential fitting model, peak position = a(Chl-a)b, was developed between chlorophyll-a concentration and fluorescence peak shift, where a varies between 686.11 and 686.29, while b between 0.0062 and 0.0065. It was found that the better the spectral resolution, the higher the precision of the model. Except that, the average of peak shift showed a high correlation with the average of different Chl-a grades, and the determination coefficient (R2) was higher than 0.81. It contributed significantly to the increase in the accuracy of the derivation of chlorophyll values from remote sensing data in Nanhu Lake. There is satisfactory correspondence between hyperspectral models and chl-a concentration, therefore, it is possible to monitor the water quality of Nanhu lake throngh the hyperspetral remote sensing data.

Image Mining in Remote Sensing for Coastal Wetlands Mapping: from Pixel Based to Object Based Approach

NASA Astrophysics Data System (ADS)

Farda, N. M.; Danoedoro, P.; Hartono; Harjoko, A.

2016-11-01

The availably of remote sensing image data is numerous now, and with a large amount of data it makes “knowledge gap” in extraction of selected information, especially coastal wetlands. Coastal wetlands provide ecosystem services essential to people and the environment. The aim of this research is to extract coastal wetlands information from satellite data using pixel based and object based image mining approach. Landsat MSS, Landsat 5 TM, Landsat 7 ETM+, and Landsat 8 OLI images located in Segara Anakan lagoon are selected to represent data at various multi temporal images. The input for image mining are visible and near infrared bands, PCA band, invers PCA bands, mean shift segmentation bands, bare soil index, vegetation index, wetness index, elevation from SRTM and ASTER GDEM, and GLCM (Harralick) or variability texture. There is three methods were applied to extract coastal wetlands using image mining: pixel based - Decision Tree C4.5, pixel based - Back Propagation Neural Network, and object based - Mean Shift segmentation and Decision Tree C4.5. The results show that remote sensing image mining can be used to map coastal wetlands ecosystem. Decision Tree C4.5 can be mapped with highest accuracy (0.75 overall kappa). The availability of remote sensing image mining for mapping coastal wetlands is very important to provide better understanding about their spatiotemporal coastal wetlands dynamics distribution.

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

PubMed

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

2008-12-01

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

A new voxel-based model for the determination of atmospheric weighted mean temperature in GPS atmospheric sounding

NASA Astrophysics Data System (ADS)

He, Changyong; Wu, Suqin; Wang, Xiaoming; Hu, Andong; Wang, Qianxin; Zhang, Kefei

2017-06-01

The Global Positioning System (GPS) is a powerful atmospheric observing system for determining precipitable water vapour (PWV). In the detection of PWV using GPS, the atmospheric weighted mean temperature (Tm) is a crucial parameter for the conversion of zenith tropospheric delay (ZTD) to PWV since the quality of PWV is affected by the accuracy of Tm. In this study, an improved voxel-based Tm model, named GWMT-D, was developed using global reanalysis data over a 4-year period from 2010 to 2013 provided by the United States National Centers for Environmental Prediction (NCEP). The performance of GWMT-D was assessed against three existing empirical Tm models - GTm-III, GWMT-IV, and GTmN - using different data sources in 2014 - the NCEP reanalysis data, surface Tm data provided by Global Geodetic Observing System and radiosonde measurements. The results show that the new GWMT-D model outperforms all the other three models with a root-mean-square error of less than 5.0 K at different altitudes over the globe. The new GWMT-D model can provide a practical alternative Tm determination method in real-time GPS-PWV remote sensing systems.

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments

USGS Publications Warehouse

Ramsey, Elijah W.; Nelson, G.A.; Echols, D.; Sapkota, S.K.

2002-01-01

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LM-NRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

The National Vegetation Classification Standard applied to the remote sensing classification of two semiarid environments.

PubMed

Ramsey, Elijah W; Nelson, Gene A; Echols, Darrell; Sapkota, Sijan K

2002-05-01

The National Vegetation Classification Standard (NVCS) was implemented at two US National Park Service (NPS) sites in Texas, the Padre Island National Seashore (PINS) and the Lake Meredith National Recreation Area (LMNRA), to provide information for NPS oil and gas management plans. Because NVCS landcover classifications did not exist for these two areas prior to this study, we created landcover classes, through intensive ground and aerial reconnaissance, that characterized the general landscape features and at the same time complied with NVCS guidelines. The created landcover classes were useful for the resource management and were conducive to classification with optical remote sensing systems, such as the Landsat Thematic Mapper (TM). In the LMNRA, topographic elevation data were added to the TM data to reduce confusion between cliff, high plains, and forest classes. Classification accuracies (kappa statistics) of 89.9% (0.89) and 88.2% (0.87) in PINS and LMNRA, respectively, verified that the two NPS landholdings were adequately mapped with TM data. Improved sensor systems with higher spectral and spatial resolutions will ultimately refine the broad classes defined in this classification; however, the landcover classifications created in this study have already provided valuable information for the management of both NPS lands. Habitat information provided by the classifications has aided in the placement of inventory and monitoring plots, has assisted oil and gas operators by providing information on sensitive habitats, and has allowed park managers to better use resources when fighting wildland fires and in protecting visitors and the infrastructure of NPS lands.

The application of remotely sensed data to pedologic and geomorphic mapping on alluvial fan and playa surfaces in Saline Valley, California

NASA Technical Reports Server (NTRS)

Miller, D. A.; Petersen, G. W.; Kahle, A. B.

1986-01-01

Arid and semiarid regions yield excellent opportunities for the study of pedologic and geomorphic processes. The dominance of rock and soil exposure over vegetation not only provides the ground observer with observational possibilities but also affords good opportunities for measurement by aircraft and satellite remote sensor devices. Previous studies conducted in the area of pedologic and geomorphic mapping in arid regions with remotely sensed data have utilized information obtained in the visible to near-infrared portion of the spectrum. Thermal Infrared Multispectral Scanner (TIMS) and Thematic Mapping (TM) data collected in 1984 are being used in comjunction with maps compiled during a Bureau of Land Management (BLM) soil survey to aid in a detailed mapping of alluvial fan and playa surfaces within the valley. The results from this study may yield valuable information concerning the application of thermal data and thermal/visible data combinations to the problem of dating pedologic and geomorphic features in arid regions.

Landsat-TM identification of Amblyomma variegatum (Acari: Ixodidae) habitats in Guadeloupe

NASA Technical Reports Server (NTRS)

Hugh-Jones, M.; Barre, N.; Nelson, G.; Wehnes, K.; Warner, J.; Garvin, J.; Garris, G.

1992-01-01

The feasibility of identifying specific habitats of the African bont tick, Amblyomma variegatum, from Landsat-TM images was investigated by comparing remotely sensed images of visible farms in Grande Terre (Guadeloupe) with field observations made in the same period of time (1986-1987). The different tick habitates could be separated using principal component analysis. The analysis clustered the sites by large and small variance of band values, and by vegetation and moisture indexes. It was found that herds in heterogeneous sites with large variances had more ticks than those in homogeneous or low variance sites. Within the heterogeneous sites, those with high vegetation and moisture indexes had more ticks than those with low values.

Statistical properties of measures of association and the Kappa statistic for assessing the accuracy of remotely sensed data using double sampling

Treesearch

Mohammed A. Kalkhan; Robin M. Reich; Raymond L. Czaplewski

1996-01-01

A Monte Carlo simulation was used to evaluate the statistical properties of measures of association and the Kappa statistic under double sampling with replacement. Three error matrices representing three levels of classification accuracy of Landsat TM Data consisting of four forest cover types in North Carolina. The overall accuracy of the five indices ranged from 0.35...

Geological Mapping Uses Landsat 4-5TM Satellite Data in Manlai Soum of Omnogovi Aimag

NASA Astrophysics Data System (ADS)

Norovsuren, B.

2014-12-01

Author: Bayanmonkh N1, Undram.G1, Tsolmon.R2, Ariunzul.Ya1, Bayartungalag B31 Environmental Research Information and Study Center 2NUM-ITC-UNESCO Space Science and Remote Sensing International Laboratory, National University of Mongolia 3Geology and Hydrology School, Korea University KEY WORDS: geology, mineral resources, fracture, structure, lithologyABSTRACTGeologic map is the most important map for mining when it does exploration job. In Mongolia geological map completed by Russian geologists which is done by earlier technology. Those maps doesn't satisfy for present requirements. Thus we want to study improve geological map which includes fracture, structural map and lithology use Landsat TM4-5 satellite data. If we can produce a geological map from satellite data with more specification then geologist can explain or read mineralogy very easily. We searched all methodology and researches of every single element of geological mapping. Then we used 3 different remote sensing methodologies to produce structural and lithology and fracture map based on geographic information system's softwares. There can be found a visible lithology border improvement and understandable structural map and we found fracture of the Russian geological map has a lot of distortion. The result of research geologist can read mineralogy elements very easy and discovered 3 unfound important elements from satellite image.

Landscape dynamics analysis of the Yongding River watershed (Mentougou section) by multi-temporal Landsat imagery

NASA Astrophysics Data System (ADS)

Zhang, Yuhu; Yu, Changqing; Qi, Jiaguo; Zhang, Zili; Shi, Qinshan

2007-11-01

The problem of efficient use of multi-temporal remotely sensed data for land-cover and landscape pattern dynamics has already considerable attention in landscape ecology and some other disciplines. This research develops and tests a methodological approach to monitor and analysis landscape dynamics change of Yongding river watershed (Mentougou section) as study area from 1988 to 2005, The result shows that the OIF is the best method of optimal bands selection in Landsat TM remote sensing data, TM3, 4, 5 bands is optimal band combination ;the Mentougou Reach of Yongding river watershed landscape changed significantly in terms of its composition over the period 1988-2005, The total landscape patches of study area in 2005 are more those in 1988,2001, Mean patch size(MPS)decreased sharply, Number of patches(NP) increased sharply, The landscape pattern takes on the fragmentation trends under the effect on the human activity. The forest (woodland and shrubland)are the main landscape matrix. with a significant decrease in croplands and a increase in built-up (residential, urban land) and industrial minerals mining land(coal, open-pit)over the 17 years, And the underlying socio-economic and other drivers of landscape change in study area are discussed.

An investigation of the distribution of eruptive products on the shield volcanoes of the western Galapagos Islands using remotely sensed data

NASA Technical Reports Server (NTRS)

Munro, Duncan C.; Rowland, Scott K.; Mouginis-Mark, Peter J.; Wilson, Lionel; Oviedo-Perez, Victor-Hugo

1991-01-01

Recent volcanic activity in the Galapagos Islands is concentrated on the two westernmost islands, Isla Isabela and Isla Fernandina. Difficult access has thus far prevented comprehensive geological field studies, so we examine the potential of remotely sensed data as a means of studying volcanic processes in the region. Volcan Wolf is used as an example of the analysis of SPOT HRV-1 data undertaken for each volcano. Landsat TM data are analyzed in an attempt to construct a relative age sequence for the recent eruptive activity on Isla Fernandina. No systematic variation in the surface reflectance of lava flows as a function of age could be detected with these data. Thus it was not possible to complete a study of the temporal distribution of volcanic activity.

Classification of bottom composition and bathymetry of shallow waters by passive remote sensing

NASA Astrophysics Data System (ADS)

Spitzer, D.; Dirks, R. W. J.

The use of remote sensing data in the development of algorithms to remove the influence of the watercolumn on upwelling optical signals when mapping the bottom depth and composition in shallow waters. Calculations relating the reflectance spectra to the parameters of the watercolumn and the diverse bottom types are performed and measurements of the underwater reflection coefficient of sandy, mud, and vegetation-type seabottoms are taken. The two-flow radiative transfer model is used. Reflectances within the spectral bands of the Landsat MSS, the Landsat TM, SPOT HVR, and the TIROS-N series AVHRR were computed in order to develop appropriate algorithms suitable for the bottom depth and type mapping. Bottom depth and features appear to be observable down to 3-20 m depending on the water composition and bottom type.

Biochemical processes in sagebrush ecosystems: Interactions with terrain

NASA Technical Reports Server (NTRS)

Matson, P. (Principal Investigator); Reiners, W.; Strong, L.

1985-01-01

The objectives of a biogeochemical study of sagebrush ecosystems in Wyoming and their interactions with terrain are as follows: to describe the vegetational pattern on the landscape and elucidate controlling variables, to measure the soil properties and chemical cycling properties associated with the vegetation units, to associate soil properties with vegetation properties as measured on the ground, to develop remote sensing capabilities for vegetation and surface characteristics of the sagebrush landscape, to develop a system of sensing snow cover and indexing seasonal soil to moisture; and to develop relationships between temporal Thematic Mapper (TM) data and vegetation phenological state.

Modeling Habitat Suitability of Migratory Birds from Remote Sensing Images Using Convolutional Neural Networks

PubMed Central

Su, Jin-He; Piao, Ying-Chao; Luo, Ze; Yan, Bao-Ping

2018-01-01

Simple Summary The understanding of the spatio-temporal distribution of the species habitats would facilitate wildlife resource management and conservation efforts. Existing methods have poor performance due to the limited availability of training samples. More recently, location-aware sensors have been widely used to track animal movements. The aim of the study was to generate suitability maps of bar-head geese using movement data coupled with environmental parameters, such as remote sensing images and temperature data. Therefore, we modified a deep convolutional neural network for the multi-scale inputs. The results indicate that the proposed method can identify the areas with the dense goose species around Qinghai Lake. In addition, this approach might also be interesting for implementation in other species with different niche factors or in areas where biological survey data are scarce. Abstract With the application of various data acquisition devices, a large number of animal movement data can be used to label presence data in remote sensing images and predict species distribution. In this paper, a two-stage classification approach for combining movement data and moderate-resolution remote sensing images was proposed. First, we introduced a new density-based clustering method to identify stopovers from migratory birds’ movement data and generated classification samples based on the clustering result. We split the remote sensing images into 16 × 16 patches and labeled them as positive samples if they have overlap with stopovers. Second, a multi-convolution neural network model is proposed for extracting the features from temperature data and remote sensing images, respectively. Then a Support Vector Machines (SVM) model was used to combine the features together and predict classification results eventually. The experimental analysis was carried out on public Landsat 5 TM images and a GPS dataset was collected on 29 birds over three years. The results indicated that our proposed method outperforms the existing baseline methods and was able to achieve good performance in habitat suitability prediction. PMID:29701686

Remote sensing monitoring and driving force analysis to forest and greenbelt in Zhuhai

NASA Astrophysics Data System (ADS)

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 for the Zhuhai Government in building National Forest City. Keywords: forest and greenbelt; remote sensing; dynamic monitoring; driving force; vegetation coverage

Application of Remote Sensing in Building Damages Assessment after Moderate and Strong Earthquake

NASA Astrophysics Data System (ADS)

Tian, Y.; Zhang, J.; Dou, A.

2003-04-01

- Earthquake is a main natural disaster in modern society. However, we still cannot predict the time and place of its occurrence accurately. Then it is of much importance to survey the damages information when an earthquake occurs, which can help us to mitigate losses and implement fast damage evaluation. In this paper, we use remote sensing techniques for our purposes. Remotely sensed satellite images often view a large scale of land at a time. There are several kinds of satellite images, which of different spatial and spectral resolutions. Landsat-4/5 TM sensor can view ground at 30m resolution, while Landsat-7 ETM Plus has a resolution of 15m in panchromatic waveband. SPOT satellite can provide images with higher resolutions. Those images obtained pre- and post-earthquake can help us greatly in identifying damages of moderate and large-size buildings. In this paper, we bring forward a method to implement quick damages assessment by analyzing both pre- and post-earthquake satellite images. First, those images are geographically registered together with low RMS (Root Mean Square) error. Then, we clip out residential areas by overlaying images with existing vector layers through Geographic Information System (GIS) software. We present a new change detection algorithm to quantitatively identify damages degree. An empirical or semi-empirical model is then established by analyzing the real damage degree and changes of pixel values of the same ground objects. Experimental result shows that there is a good linear relationship between changes of pixel values and ground damages, which proves the potentials of remote sensing in post-quake fast damage assessment. Keywords: Damages Assessment, Earthquake Hazard, Remote Sensing

Visualizing Airborne and Satellite Imagery

NASA Technical Reports Server (NTRS)

Bierwirth, Victoria A.

2011-01-01

Remote sensing is a process able to provide information about Earth to better understand Earth's processes and assist in monitoring Earth's resources. The Cloud Absorption Radiometer (CAR) is one remote sensing instrument dedicated to the cause of collecting data on anthropogenic influences on Earth as well as assisting scientists in understanding land-surface and atmospheric interactions. Landsat is a satellite program dedicated to collecting repetitive coverage of the continental Earth surfaces in seven regions of the electromagnetic spectrum. Combining these two aircraft and satellite remote sensing instruments will provide a detailed and comprehensive data collection able to provide influential information and improve predictions of changes in the future. This project acquired, interpreted, and created composite images from satellite data acquired from Landsat 4-5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper plus (ETM+). Landsat images were processed for areas covered by CAR during the Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCT AS), Cloud and Land Surface Interaction Campaign (CLASIC), Intercontinental Chemical Transport Experiment-Phase B (INTEXB), and Southern African Regional Science Initiative (SAFARI) 2000 missions. The acquisition of Landsat data will provide supplemental information to assist in visualizing and interpreting airborne and satellite imagery.

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

NASA Astrophysics Data System (ADS)

Kumar, Lalit; Ghosh, Manoj Kumer

2012-01-01

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

Tropical forest plantation biomass estimation using RADARSAT-SAR and TM data of south china

NASA Astrophysics Data System (ADS)

Wang, Chenli; Niu, Zheng; Gu, Xiaoping; Guo, Zhixing; Cong, Pifu

2005-10-01

Forest biomass is one of the most important parameters for global carbon stock model yet can only be estimated with great uncertainties. Remote sensing, especially SAR data can offers the possibility of providing relatively accurate forest biomass estimations at a lower cost than inventory in study tropical forest. The goal of this research was to compare the sensitivity of forest biomass to Landsat TM and RADARSAT-SAR data and to assess the efficiency of NDVI, EVI and other vegetation indices in study forest biomass based on the field survey date and GIS in south china. Based on vegetation indices and factor analysis, multiple regression and neural networks were developed for biomass estimation for each species of the plantation. For each species, the better relationships between the biomass predicted and that measured from field survey was obtained with a neural network developed for the species. The relationship between predicted and measured biomass derived from vegetation indices differed between species. This study concludes that single band and many vegetation indices are weakly correlated with selected forest biomass. RADARSAT-SAR Backscatter coefficient has a relatively good logarithmic correlation with forest biomass, but neither TM spectral bands nor vegetation indices alone are sufficient to establish an efficient model for biomass estimation due to the saturation of bands and vegetation indices, multiple regression models that consist of spectral and environment variables improve biomass estimation performance. Comparing with TM, a relatively well estimation result can be achieved by RADARSAT-SAR, but all had limitations in tropical forest biomass estimation. The estimation results obtained are not accurate enough for forest management purposes at the forest stand level. However, the approximate volume estimates derived by the method can be useful in areas where no other forest information is available. Therefore, this paper provides a better understanding of relationships of remote sensing data and forest stand parameters used in forest parameter estimation models.

Quantitative Analysis of Land Loss in Coastal Louisiana Using Remote Sensing

NASA Astrophysics Data System (ADS)

Wales, P. M.; Kuszmaul, J.; Roberts, C.

2005-12-01

For the past thirty-five years the land loss along the Louisiana Coast has been recognized as a growing problem. One of the clearest indicators of this land loss is that in 2000 smooth cord grass (spartina alterniflora) was turning brown well before its normal hibernation period. Over 100,000 acres of marsh were affected by the 2000 browning. In 2001 data were collected using low altitude helicopter based transects of the coast, with 7,400 data points being collected by researchers at the USGS, National Wetlands Research Center, and Louisiana Department of Natural Resources. The surveys contained data describing the characteristics of the marsh, including latitude, longitude, marsh condition, marsh color, percent vegetated, and marsh die-back. Creating a model that combines remote sensing images, field data, and statistical analysis to develop a methodology for estimating the margin of error in measurements of coastal land loss (erosion) is the ultimate goal of the study. A model was successfully created using a series of band combinations (used as predictive variables). The most successful band combinations or predictive variables were the braud value [(Sum Visible TM Bands - Sum Infrared TM Bands)/(Sum Visible TM Bands + Sum Infrared TM Bands)], TM band 7/ TM band 2, brightness, NDVI, wetness, vegetation index, and a 7x7 autocovariate nearest neighbor floating window. The model values were used to generate the logistic regression model. A new image was created based on the logistic regression probability equation where each pixel represents the probability of finding water or non-water at that location in each image. Pixels within each image that have a high probability of representing water have a value close to 1 and pixels with a low probability of representing water have a value close to 0. A logistic regression model is proposed that uses seven independent variables. This model yields an accurate classification in 86.5% of the locations considered in the 1997 and 2001 survey locations. When the logistic regression was modeled to the satellite imagery of the entire Louisiana Coast study area a statewide loss was estimated to be 358 mi2 to 368 mi2, from 1997 to 2001, using two different methods for estimating land loss.

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

NASA Astrophysics Data System (ADS)

Yu, Le; Zhang, Dengrong; Holden, Eun-Jung

2008-07-01

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.

A review of surface energy balance models for estimating actual evapotranspiration with remote sensing at high spatiotemporal resolution over large extents

USGS Publications Warehouse

McShane, Ryan R.; Driscoll, Katelyn P.; Sando, Roy

2017-09-27

Many approaches have been developed for measuring or estimating actual evapotranspiration (ETa), and research over many years has led to the development of remote sensing methods that are reliably reproducible and effective in estimating ETa. Several remote sensing methods can be used to estimate ETa at the high spatial resolution of agricultural fields and the large extent of river basins. More complex remote sensing methods apply an analytical approach to ETa estimation using physically based models of varied complexity that require a combination of ground-based and remote sensing data, and are grounded in the theory behind the surface energy balance model. This report, funded through cooperation with the International Joint Commission, provides an overview of selected remote sensing methods used for estimating water consumed through ETa and focuses on Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) and Operational Simplified Surface Energy Balance (SSEBop), two energy balance models for estimating ETa that are currently applied successfully in the United States. The METRIC model can produce maps of ETa at high spatial resolution (30 meters using Landsat data) for specific areas smaller than several hundred square kilometers in extent, an improvement in practice over methods used more generally at larger scales. Many studies validating METRIC estimates of ETa against measurements from lysimeters have shown model accuracies on daily to seasonal time scales ranging from 85 to 95 percent. The METRIC model is accurate, but the greater complexity of METRIC results in greater data requirements, and the internalized calibration of METRIC leads to greater skill required for implementation. In contrast, SSEBop is a simpler model, having reduced data requirements and greater ease of implementation without a substantial loss of accuracy in estimating ETa. The SSEBop model has been used to produce maps of ETa over very large extents (the conterminous United States) using lower spatial resolution (1 kilometer) Moderate Resolution Imaging Spectroradiometer (MODIS) data. Model accuracies ranging from 80 to 95 percent on daily to annual time scales have been shown in numerous studies that validated ETa estimates from SSEBop against eddy covariance measurements. The METRIC and SSEBop models can incorporate low and high spatial resolution data from MODIS and Landsat, but the high spatiotemporal resolution of ETa estimates using Landsat data over large extents takes immense computing power. Cloud computing is providing an opportunity for processing an increasing amount of geospatial “big data” in a decreasing period of time. For example, Google Earth EngineTM has been used to implement METRIC with automated calibration for regional-scale estimates of ETa using Landsat data. The U.S. Geological Survey also is using Google Earth EngineTM to implement SSEBop for estimating ETa in the United States at a continental scale using Landsat data.

Satellite data in aquatic area research - Some ideas for future studies

NASA Technical Reports Server (NTRS)

Raitala, Jouko T.

1986-01-01

Attempts to apply aquatic remote sensing to the preparation of parametric map-like presentations, quantitative evaluations and time-related investigations in various water areas in Finland are presented. The potential use of Landsat MSS data in aquatic area studies, including limology, aquatic botany, geomorphology and engineering is evaluated using computer-aided digital remote sensing techniques. MSS data may provide information about depth, Secchi disc values, humus content in water, and productivity. Aquatic vegetation classification using MSS is possible only where vegetation units are large enough in respect to the 0.5 hectares ground resolution. Multitemporal satellite imagery has been used to evaluate alterations in the littoral areas of some Finnish water reservoirs between successive periods of high water. It is concluded that although MSS data can be of use in aquatic studies, it should be used in connection with field data and/or TM and SPOT data.

[Object-oriented remote sensing image classification in epidemiological studies of visceral leishmaniasis in urban areas].

PubMed

Almeida, Andréa Sobral de; Werneck, Guilherme Loureiro; Resendes, Ana Paula da Costa

2014-08-01

This study explored the use of object-oriented classification of remote sensing imagery in epidemiological studies of visceral leishmaniasis (VL) in urban areas. To obtain temperature and environmental information, an object-oriented classification approach was applied to Landsat 5 TM scenes from the city of Teresina, Piauí State, Brazil. For 1993-1996, VL incidence rates correlated positively with census tracts covered by dense vegetation, grass/pasture, and bare soil and negatively with areas covered by water and densely populated areas. In 2001-2006, positive correlations were found with dense vegetation, grass/pasture, bare soil, and densely populated areas and negative correlations with occupied urban areas with some vegetation. Land surface temperature correlated negatively with VL incidence in both periods. Object-oriented classification can be useful to characterize landscape features associated with VL in urban areas and to help identify risk areas in order to prioritize interventions.

Fusion of AIRSAR and TM Data for Parameter Classification and Estimation in Dense and Hilly Forests

NASA Technical Reports Server (NTRS)

Moghaddam, Mahta; Dungan, J. L.; Coughlan, J. C.

2000-01-01

The expanded remotely sensed data space consisting of coincident radar backscatter and optical reflectance data provides for a more complete description of the Earth surface. This is especially useful where many parameters are needed to describe a certain scene, such as in the presence of dense and complex-structured vegetation or where there is considerable underlying topography. The goal of this paper is to use a combination of radar and optical data to develop a methodology for parameter classification for dense and hilly forests, and further, class-specific parameter estimation. The area to be used in this study is the H. J. Andrews Forest in Oregon, one of the Long-Term Ecological Research (LTER) sites in the US. This area consists of various dense old-growth conifer stands, and contains significant topographic relief. The Andrews forest has been the subject of many ecological studies over several decades, resulting in an abundance of ground measurements. Recently, biomass and leaf-area index (LAI) values for approximately 30 reference stands have also become available which span a large range of those parameters. The remote sensing data types to be used are the C-, L-, and P-band polarimetric radar data from the JPL airborne SAR (AIRSAR), the C-band single-polarization data from the JPL topographic SAR (TOPSAR), and the Thematic Mapper (TM) data from Landsat, all acquired in late April 1998. The total number of useful independent data channels from the AIRSAR is 15 (three frequencies, each with three unique polarizations and amplitude and phase of the like-polarized correlation), from the TOPSAR is 2 (amplitude and phase of the interferometric correlation), and from the TM is 6 (the thermal band is not used). The range pixel spacing of the AIRSAR is 3.3m for C- and L-bands and 6.6m for P-band. The TOPSAR pixel spacing is 10m, and the TM pixel size is 30m. To achieve parameter classification, first a number of parameters are defined which are of interest to ecologists for forest process modeling. These parameters include total biomass, leaf biomass, LAI, and tree height. The remote sensing data from radar and TM are used to formulate a multivariate analysis problem given the ground measurements of the parameters. Each class of each parameter is defined by a probability density function (pdf), the spread of which defines the range of that class. High classification accuracy results from situations in which little overlap occurs between pdfs. Classification results provide the basis for the future work of class-specific parameter estimation using radar and optical data. This work was performed in part by the Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, and in part by the NASA Ames Research Center, Moffett Field, CA, both under contract from the National Aeronautics and Space Administration.

Utilizing remote sensing of Thematic Mapper data to improve our understanding of estuarine processes and their influence on the productivity of estuarine-dependent fisheries

NASA Technical Reports Server (NTRS)

Browder, J. A.; May, L. N., Jr.; Rosenthal, A.; Baumann, R. H.; Gosselink, J. G.

1986-01-01

LANDSAT thematic mapper (TM) data are being used to refine and validate a stochastic spatial computer model to be applied to coastal resource management problems in Louisiana. Two major aspects of the research are: (1) the measurement of area of land (or emergent vegetation) and water and the length of the interface between land and water in TM imagery of selected coastal wetlands (sample marshes); and (2) the comparison of spatial patterns of land and water in the sample marshes of the imagery to that in marshes simulated by a computer model. In addition to activities in these two areas, the potential use of a published autocorrelation statistic is analyzed.

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 20-Aug-1988 was used to derive this classification. A standard supervised maximum likelihood classification approach was used to produce this classification. The data are provided in a binary image format file. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

The study of hydrothermal alteration zones in Kahang exploration area (north eastern of Isfahan, central of Iran) using microscopy studies and TM and Aster satellite data

NASA Astrophysics Data System (ADS)

Zahra Afshooni, Seyedeh; Esmaeily, Dariush

2010-05-01

Kahang ore deposit located in 73 km to the northeast of Isfahan city and 10 km to the east of Zefreh town, covering an area about 18.6 km2. This ore deposit is a part of Uromieh-Dokhtar volcanopolotonic belt. The rocks of the area included Andesite, Porphyritic Andesite, Dacite, Porphyritic, Rhyodacite, Diorite, Quartz Monzonite and Porphyry Micro Granite. In plutons, there is a trend from basic to acid features along with decreasing of age from margin to center of massive. Kahang region is an alteration and breccia zone. The occurrence of alteration zones and iron oxides were confirmed by satellite images processing. Generally, more than 90% of rocks of this region have been affected by hydrothermal fluids. Remote sensing refers to detection and measurement from a distance. For the first time, this exploration area was studied using satellite images processing (TM) and primary results showed that is suitable place for resources of Copper (Cu) and Molybdenum (Mo). Hydrothermal alteration commonly occurs in geothermal areas in association with ore deposits producing alteration assemblages typically dominated by silicates, sulfides, sulfates and carbonates. In the alteration zones studies the subject discussed is the study of existing minerals in such zones and study of chemical specifications of altering fluids. Four alteration zones Based on observations derived from the study of thin sections, XRD analysis and deep remote sensing using TM and Aster satellite images studies could be identified in this area: propylitic alteration zone with chlorite, epidot, calcite; argillic alteration zone with clay minerals; phyllic (qartz-sericite) alteration zone with quartz, sericite and pyrite and silicic alteration zone with abundant quartz.

Climate Change and Algal Blooms =

NASA Astrophysics Data System (ADS)

Lin, Shengpan

Algal blooms are new emerging hazards that have had important social impacts in recent years. However, it was not very clear whether future climate change causing warming waters and stronger storm events would exacerbate the algal bloom problem. The goal of this dissertation was to evaluate the sensitivity of algal biomass to climate change in the continental United States. Long-term large-scale observations of algal biomass in inland lakes are challenging, but are necessary to relate climate change to algal blooms. To get observations at this scale, this dissertation applied machine-learning algorithms including boosted regression trees (BRT) in remote sensing of chlorophyll-a with Landsat TM/ETM+. The results show that the BRT algorithm improved model accuracy by 15%, compared to traditional linear regression. The remote sensing model explained 46% of the total variance of the ground-measured chlorophyll- a in the first National Lake Assessment conducted by the US Environmental Protection Agency. That accuracy was ecologically meaningful to study climate change impacts on algal blooms. Moreover, the BRT algorithm for chlorophyll- a would not have systematic bias that is introduced by sediments and colored dissolved organic matter, both of which might change concurrently with climate change and algal blooms. This dissertation shows that the existing atmospheric corrections for Landsat TM/ETM+ imagery might not be good enough to improve the remote sensing of chlorophyll-a in inland lakes. After deriving long-term algal biomass estimates from Landsat TM/ETM+, time series analysis was used to study the relations of climate change and algal biomass in four Missouri reservoirs. The results show that neither temperature nor precipitation was the only factor that controlled temporal variation of algal biomass. Different reservoirs, even different zones within the same reservoir, responded differently to temperature and precipitation changes. These findings were further tested in 1157 lakes across the continental United States. The results show that mean annual algal biomass generally increased with annual temperature. Greater increase was found in lakes with more nutrients. Mean annual algal biomass generally decreased with annual total precipitation. In both the "low" and the "high" greenhouse-gas emission scenarios, mean annual algal biomass in lakes generally increased with climate change, and greater increases are predicted from the high emission scenario.

Retrieval of land cover information under thin fog in Landsat TM image

NASA Astrophysics Data System (ADS)

Wei, Yuchun

2008-04-01

Thin fog, which often appears in remote sensing image of subtropical climate region, has resulted in the low image quantity and bad image mapping. Therefore, it is necessary to develop the image processing method to retrieve land cover information under thin fog. In this paper, the Landsat TM image near the Taihu Lake that is in the subtropical climate zone of China was used as an example, and the workflow and method used to retrieve the land cover information under thin fog have been built based on ENVI software and a single TM image. The basic step covers three parts: 1) isolating the thin fog area in image according to the spectral difference of different bands; 2) retrieving the visible band information of different land cover types under thin fog from the near-infrared bands according to the relationships between near-infrared bands and visible bands of different land cover types in the area without fog; 3) image post-process. The result showed that the method in the paper is easy and suitable, and can be used to improve the quantity of TM image mapping more effectively.

Monitoring the lake area changes of the Qinghai-Tibet Plateau using coarse-resolution time series remote sensing data

NASA Astrophysics Data System (ADS)

Ma, M.

2015-12-01

The Qinghai-Tibet Plateau (QTP) is the world's highest and largest plateau and is occasionally referred to as "the roof of the world". As the important "water tower", there are 1,091 lakes of more than 1.0 km2 in the QTP areas, which account for 49.4% of the total area of lakes in China. Some studies focus on the lake area changes of the QTP areas, which mainly use the middle-resolution remote sensing data (e.g. Landsat TM). In this study, the coarse-resolution time series remote sensing data, MODIS data at a spatial resolution of 250m, was used to monitor the lake area changes of the QTP areas during the last 15 years. The dataset is the MOD13Q1 and the Normal Difference Vegetation Index (NDVI) is used to identify the lake area when the NDVI is less than 0. The results show the obvious inner-annual changes of most of the lakes. Therefore the annually average and maximum lake areas are calculated based on the time series remote data, which can better quantify the change characteristics than the single scene of image data from the middle-resolution data. The results indicate that there are big spatial variances of the lake area changes in the QTB. The natural driving factors are analyzed for revealing the causes of changes.

Application of remote sensing in tropical forests

NASA Technical Reports Server (NTRS)

Joyce, Armond T.; Luvall, J. C.; Sever, T.

1990-01-01

Cloud cover in tropical humid forests can pose serious operational constraints on Landsat TM and SPOT HRV instrumentation, given their respective orbital frequencies of 16 and 26 days. SAR data intrinsically precludes such problems; the increase of data acquisition frequency to daily rates, as with the NOAA AVHRR instrument, also bears consideration. It is deemed essential that SAR data-related research be expedited, in order to ascertain inherent SAR information for tropical forests in a timely and cost-effective manner.

Extending airborne electromagnetic surveys for regional active layer and permafrost mapping with remote sensing and ancillary data, Yukon Flats ecoregion, central Alaska

USGS Publications Warehouse

Pastick, Neal J.; Jorgenson, M. Torre; Wylie, Bruce K.; Minsley, Burke J.; Ji, Lei; Walvoord, Michelle Ann; Smith, Bruce D.; Abraham, Jared D.; Rose, Joshua R.

2013-01-01

Machine-learning regression tree models were used to extrapolate airborne electromagnetic resistivity data collected along flight lines in the Yukon Flats Ecoregion, central Alaska, for regional mapping of permafrost. This method of extrapolation (r = 0.86) used subsurface resistivity, Landsat Thematic Mapper (TM) at-sensor reflectance, thermal, TM-derived spectral indices, digital elevation models and other relevant spatial data to estimate near-surface (0–2.6-m depth) resistivity at 30-m resolution. A piecewise regression model (r = 0.82) and a presence/absence decision tree classification (accuracy of 87%) were used to estimate active-layer thickness (ALT) (< 101 cm) and the probability of near-surface (up to 123-cm depth) permafrost occurrence from field data, modelled near-surface (0–2.6 m) resistivity, and other relevant remote sensing and map data. At site scale, the predicted ALTs were similar to those previously observed for different vegetation types. At the landscape scale, the predicted ALTs tended to be thinner on higher-elevation loess deposits than on low-lying alluvial and sand sheet deposits of the Yukon Flats. The ALT and permafrost maps provide a baseline for future permafrost monitoring, serve as inputs for modelling hydrological and carbon cycles at local to regional scales, and offer insight into the ALT response to fire and thaw processes.

Yield estimation of corn based on multitemporal LANDSAT-TM data as input for an agrometeorological model

NASA Astrophysics Data System (ADS)

Bach, Heike

1998-07-01

In order to test remote sensing data with advanced yield formation models for accuracy and timeliness of yield estimation of corn, a project was conducted for the State Ministry for Rural Environment, Food, and Forestry of Baden-Württemberg (Germany). This project was carried out during the course of the `Special Yield Estimation', a regular procedure conducted for the European Union, to more accurately estimate agricultural yield. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on four LANDSAT-derived estimates (between May and August) and daily meteorological data, the grain yield of corn fields was determined for 1995. The modelled yields were compared with results gathered independently within the Special Yield Estimation for 23 test fields in the upper Rhine valley. The agreement between LANDSAT-based estimates (six weeks before harvest) and Special Yield Estimation (at harvest) shows a relative error of 2.3%. The comparison of the results for single fields shows that six weeks before harvest, the grain yield of corn was estimated with a mean relative accuracy of 13% using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results for yield prediction with remote sensing.

Quantifying Vegetation Composition, Structure and Dynamics in Selected Australian Ecosystems: Science to Management

NASA Astrophysics Data System (ADS)

Phinn, S. R.; Scarth, P.; Armston, J.; Witte, C.; Danaher, T.; Flood, N.; Gill, T.; Lucas, R.

2011-12-01

Management of Australian ecosystems is carried out by state governments using information derived from satellite image data. The state of Queensland covers approximately 1.8 x 10^6 km^2 and uses satellite remote sensing and field survey programs to support legislated environmental monitoring, management and compliance activities.This poster outlines how the Joint Remote Sensing Research Program(JRSRP)delivered satellite image based data sets to address these activities by mapping foliage projective cover, vegetation height and biomass. Foliage projective cover (FPC), the vertically projected percentage cover of photosynthetic foliage of all strata, is produced from Landsat TM/ETM data using 88 scenes and over 1700 field sites. The JRSRP enabled government staff to be seconded to a university research group to work on the project, and the university provided postdoctoral and graduate student support. The JRSRP activities focussed on geometric and topographic corrections, BRDF corrections and time-series based approaches for correcting the archive of field survey and Landsat TM/ETM+ images. This has now progressed to a program using the entire Landsat TM/ETM+ archive on an annual basis and annual state-wide field survey data. The Landsat TM/ETM+ calibrations have been a critical input to the Landsat program's global vicarious calibration activities. Vegetation height is a critical parameter required for a range of state-wide activities and can be mapped accurately from field plots to regional areas using airborne Lidar. To develop statewide height estimates, an approach was developed using Icesat and existing vegetation community maps. By aggregating the spaceborne Icesat full waveform data within the mapped vegetation structure polygons it was possible to retrieve vegetation vertical structure information continuously across the landscape. This was used to derive mean canopy and understorey height, depth and density across Queensland, which was validated using airborne lidar data provided by the JRSRP. Biomass mapping is emerging as a critical environmental parameter for local, state and national agencies in Australia. Staff from JRSRP developed an approach with University of Aberystwyth in Wales, through JAXA's Kyoto and Carbon initiative, for acquiring ALOS PALSAR L-band image data, conducting geometric and radiometric corrections, and normalising for significant scene to scene differences in soil and vegetation moisture content. This pre-processing of 31 image strip time-series generated state-wide mosaics for Queensland that were then used with 1815 field survey sites collected across the state to produce a state-wide biomass estimation model for L-HV data, providing estimates for both remnant and non-remnant forests, with saturation at 263 Mg.Ha^-1 for 20% estimation error. The Joint Remote Sensing Research Program has enabled a sound approach to research and development for validated operational applications.

Estimating actual evapotranspiration from remote sensing imagery using R: the package 'TriangleMethod'.

NASA Astrophysics Data System (ADS)

Gampe, David; Huber García, Verena; Marzahn, Philip; Ludwig, Ralf

2017-04-01

Actual evaporation (Eta) is an essential variable to assess water availability, drought risk and food security, among others. Measurements of Eta are however limited to a small footprint, hampering a spatially explicit analysis and application and are very often not available at all. To overcome the problem of data scarcity, Eta can be assessed by various remote sensing approaches such as the Triangle Method (Jiang & Islam, 1999). Here, Eta is estimated by using the Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST). In this study, the R-package 'TriangleMethod' was compiled to efficiently perform the calculations of NDVI and processing LST to finally derive Eta from the applied data set. The package contains all necessary calculation steps and allows easy processing of a large data base of remote sensing images. By default, the parameterization for the Landsat TM and ETM+ sensors are implemented, however, the algorithms can be easily extended to additional sensors. The auxiliary variables required to estimate Eta with this method, such as elevation, solar radiation and air temperature at the overpassing time, can be processed as gridded information to allow for a better representation of the study area. The package was successfully applied in various studies in Spain, Palestine, Costa Rica and Canada.

Study of Burn Scar Extraction Automatically Based on Level Set Method using Remote Sensing Data

PubMed Central

Liu, Yang; Dai, Qin; Liu, JianBo; Liu, ShiBin; Yang, Jin

2014-01-01

Burn scar extraction using remote sensing data is an efficient way to precisely evaluate burn area and measure vegetation recovery. Traditional burn scar extraction methodologies have no well effect on burn scar image with blurred and irregular edges. To address these issues, this paper proposes an automatic method to extract burn scar based on Level Set Method (LSM). This method utilizes the advantages of the different features in remote sensing images, as well as considers the practical needs of extracting the burn scar rapidly and automatically. This approach integrates Change Vector Analysis (CVA), Normalized Difference Vegetation Index (NDVI) and the Normalized Burn Ratio (NBR) to obtain difference image and modifies conventional Level Set Method Chan-Vese (C-V) model with a new initial curve which results from a binary image applying K-means method on fitting errors of two near-infrared band images. Landsat 5 TM and Landsat 8 OLI data sets are used to validate the proposed method. Comparison with conventional C-V model, OSTU algorithm, Fuzzy C-mean (FCM) algorithm are made to show that the proposed approach can extract the outline curve of fire burn scar effectively and exactly. The method has higher extraction accuracy and less algorithm complexity than that of the conventional C-V model. PMID:24503563

Park Smart

NASA Technical Reports Server (NTRS)

1999-01-01

The Parking Garage Automation System (PGAS) is based on a technology developed by a NASA-sponsored project called Robot sensorSkin(TM). Merritt Systems, Inc., of Orlando, Florida, teamed up with NASA to improve robots working with critical flight hardware at Kennedy Space Center in Florida. The system, containing smart sensor modules and flexible printed circuit board skin, help robots to steer clear of obstacles using a proximity sensing system. Advancements in the sensor designs are being applied to various commercial applications, including the PGAS. The system includes a smartSensor(TM) network installed around and within public parking garages to autonomously guide motorists to open facilities, and once within, to free parking spaces. The sensors use non-invasive reflective-ultrasonic technology for high accuracy, high reliability, and low maintenance. The system is remotely programmable: it can be tuned to site-specific requirements, has variable range capability, and allows remote configuration, monitoring, and diagnostics. The sensors are immune to interference from metallic construction materials, such as rebar and steel beams. Inside the garage, smart routing signs mounted overhead or on poles in front of each row of parking spots guide the motorist precisely to free spaces.

Detecting Uniform Areas for Vicarious Calibration using Landsat TM Imagery: A Study using the Arabian and Saharan Deserts

NASA Technical Reports Server (NTRS)

Hilbert, Kent; Pagnutti, Mary; Ryan, Robert; Zanoni, Vicki

2002-01-01

This paper discusses a method for detecting spatially uniform sites need for radiometric characterization of remote sensing satellites. Such information is critical for scientific research applications of imagery having moderate to high resolutions (<30-m ground sampling distance (GSD)). Previously published literature indicated that areas with the African Saharan and Arabian deserts contained extremely uniform sites with respect to spatial characteristics. We developed an algorithm for detecting site uniformity and applied it to orthorectified Landsat Thematic Mapper (TM) imagery over eight uniform regions of interest. The algorithm's results were assessed using both medium-resolution (30-m GSD) Landsat 7 ETM+ and fine-resolution (<5-m GSD) IKONOS multispectral data collected over sites in Libya and Mali. Fine-resolution imagery over a Libyan site exhibited less than 1 percent nonuniformity. The research shows that Landsat TM products appear highly useful for detecting potential calibration sites for system characterization. In particular, the approach detected spatially uniform regions that frequently occur at multiple scales of observation.

Using the Sonoran Desert test site to monitor the long-term radiometric stability of the Landsat TM/ETM+ and Terra MODIS sensors

USGS Publications Warehouse

Angal, A.; Xiong, X.; Choi, T.; Chander, G.; Wu, A.

2009-01-01

Pseudo-invariant ground targets have been extensively used to monitor the long-term radiometric calibration stability of remote sensing instruments. The NASA MODIS Characterization Support Team (MCST), in collaboration with members from the U.S. Geological Survey (USGS) Earth Resources Observation and Science (EROS) Center, has previously demonstrated the use of pseudo-invariant ground sites for the long-term stability monitoring of Terra MODIS and Landsat 7 ETM+ sensors. This paper focuses on the results derived from observations made over the Sonoran Desert. Additionally, Landsat 5 TM data over the Sonoran Desert site were used to evaluate the temporal stability of this site. Top-ofatmosphere (TOA) reflectances were computed for the closely matched TM, ETM+, and MODIS spectral bands over selected regions of interest. The impacts due to different viewing geometries, or the effect of test site Bi-directional Reflectance Distribution Function (BRDF), are also presented. ?? 2009 SPIE.

Efficient CW diode-pumped Tm, Ho:YLF laser with tunability near 2.067 microns

NASA Technical Reports Server (NTRS)

Mcguckin, B. T.; Menzies, Robert T.

1992-01-01

A conversion efficiency of 42 percent and slope efficiency of approximately 60 percent relative to absorbed pump power are reported from a continuous wave diode-pumped Tm, Ho:YLF laser at 2 microns with output power of 84 mW at sub-ambient temperatures. The emission spectrum is etalon tunable over a range of 16/cm centered on 2.067 microns, with fine tuning capability of the transition frequency with crystal temperature at a measured rate of about -0.03/cm-K. The effective emission cross section is measured to be 5 x 10 exp -21 sq cm. These and other aspects of the laser performance are discussed in the context of calculated atmospheric absorption characteristics in this spectral region and potential use in remote sensing applications.

Compiling and editing agricultural strata boundaries with remotely sensed imagery and map attribute data using graphics workstations

NASA Technical Reports Server (NTRS)

Cheng, Thomas D.; Angelici, Gary L.; Slye, Robert E.; Ma, Matt

1991-01-01

The USDA presently uses labor-intensive photographic interpretation procedures to delineate large geographical areas into manageable size sampling units for the estimation of domestic crop and livestock production. Computer software to automate the boundary delineation procedure, called the computer-assisted stratification and sampling (CASS) system, was developed using a Hewlett Packard color-graphics workstation. The CASS procedures display Thematic Mapper (TM) satellite digital imagery on a graphics display workstation as the backdrop for the onscreen delineation of sampling units. USGS Digital Line Graph (DLG) data for roads and waterways are displayed over the TM imagery to aid in identifying potential sample unit boundaries. Initial analysis conducted with three Missouri counties indicated that CASS was six times faster than the manual techniques in delineating sampling units.

[Quantitative analysis of landscape patterns at the juncture of Shaanxi, Shanxi and Inner Mongolia, based on remote sensing data--taking Yulin sheet TM image as an example].

PubMed

Li, Tuansheng

2004-03-01

Based on the TM image of Yulin sheet and with the help of ERDAS, ARC/INFO and ARC/VIEW software, the landscape of Yulin sheet was classified. Using the spatial pattern analysis software FRAGSTATS of the vector version, a set of landscape indices were calculated at three scale levels of patches, classes and landscape. The results showed that landscape pattern indices could be successfully used in characterizing the spatial pattern of the studied area. However, this study should be further extended to the landscape of the same area in other period to analyze its dynamic change. FRAGSTATS was a good software, but should be improved by adding some indices such as PD2 developed by us.

Preliminary Evaluation of the Radiometric Calibration of LANDSAT-4 Thematic Mapper Data by the Canada Centre for Remote Sensing

NASA Technical Reports Server (NTRS)

Murphy, J.; Park, W.; Fitzgerald, A.

1985-01-01

The radiometric characteristics of the LANDSAT-4 TM sensor are being studied with a view to developing absolute and relative radiometric calibration procedures. Preliminary results from several different approaches to the relative correction of all detectors within each band are reported. Topics covered include: the radiometric correction method; absolute calibration; the relative radiometric calibration algorithm; relative gain and offset calibration; relative gain and offset observations; and residual radiometric stripping.

Thematic Mapper Data Quality and Performance Assessment in Renewable Resources/agriculture Remote Sensing

NASA Technical Reports Server (NTRS)

Bizzell, R. M.; Prior, H. L.

1984-01-01

It is believed that the increased spatial resolution will provide solutions to proportion estimation error due to mixed pixels, and the increased spectral resolution will provide for the identification of important agricultural features such as crop stage, and condition. The results of analyses conducted relative to these hypothesis from sample segments extracted from the 4-band Detroit scene and the 7-band Mississippi County, Arkansas engineering test scene are described. Several studies were conducted to evaluate the geometric and radiometric performance of the TM to determine data viability for the more pertinent investigations of TM utility. In most cases this requirement was more than sufficiently satisfied. This allowed the opportunity to take advantage of detailed ground observations for several of the sample segments to assess class separability and detection of other important features with TM. The results presented regarding these TM characteristics show that not only is the increased definition of the within scene variance captured by the increased spatial and spectral resolution, but that the mid-IR bands (5 and 7) are necessary for optimum crop type classification. Both qualitative and quantitative results are presented that describe the improvements gained with the TM both relative to the MSS and on its own merit.

Surficial geology of the Safsaf region, south-central Egypt, derived from remote-sensing and field data

USGS Publications Warehouse

Davis, P.A.; Breed, C.S.; McCauley, J.F.; Schaber, G.G.

1993-01-01

We used a decorrelation-stretched image of Landsat Thematic Mapper (TM) Bands 1, 4, and 7 and field data to map and describe the main surficial units in the hyperarid Safsaf region in south-central Egypt. We show that the near-infrared bands on Landsat TM, which are sensitive to very subtle changes in mineralogy common to arid regions, significantly improve the geologist's capability to discriminate geologic units in desert regions. These data also provide the spatial and spectral information necessary to determine the migration patterns and provenance of eolian materials. The Safsaf area was the focus of our post flight field studies using Shuttle Imaging Radar (SIR) data following the discovery of buried paleochannels in North Africa. Most of the channels discernible on SIR images are not expressed in TM data, but traces of a few channels are present in both the SIR and the TM data within the Wadi Safsaf area. Here we present a detailed digital examination of the SIR and the TM-band reflectance and reflectance-ratio data at three locations of the more obvious surface expressions of the buried channels. Our results indicate that the TM expressions of the channels are not purely topographic but are more compositional in nature. Two possibilities may account for the TM expressions of the buried channels: 1) concentrations of windblown, iron-rich materials that accumulated along subtle curvilinear topograpohic traps, or 2) curvilinear exposures of an iron-rich underlying unit of the flat sand sheet. ?? 1993.

A remote sensing study of regional variation in sinkhole morphology-Florida karst vs. Minnesota karst

NASA Astrophysics Data System (ADS)

Ernst, C. L.; Hadizadeh, J.; McCarty, J. L.

2010-12-01

In many regions of the United States, database technologies and GIS have facilitated spatial analysis of karst. The purpose of this research was to compare regional latitudinal variation in sinkhole karst morphology via remote sensing techniques. Such comparison may be significant because the development of a karst landscape depends primarily on climate and availability of water as well as lithology. Sinkhole karst, a common karst in the U.S., is morphologically defined as cone-shaped depressions with circular or oval opening to the surface that result from the dissolution of relatively soluble bedrock such as limestone or gypsum. The two regions of interest, north-central Florida and southeastern Minnesota, were selected based on structural and lithological similarity of limestone bedrock and the fact that the bedrock study areas are located in clearly different climate zones. This approach utilized topographic maps, digital elevation models, state karst feature databases, and high resolution 0.6m QuickBirdTM and 0.5m WorldView 1TM satellite images in a GIS environment. Morphological parameters - area, perimeter, minor axis and major axis length - were calculated on a total of 80 sinkholes in the study regions using the zonal geometry function, a tool in the spatial analysis extension provided by ESRITM. Our results show that north-central Florida and southeastern Minnesota karst are statistically different in terms of sinkhole shape and size distribution. Florida has larger sinkholes (2,835 square meter Mean) that are closer to circular shape. Minnesota has smaller (1,213 square meter Mean) and more elliptical sinkholes with a comparatively shorter minor axis. Of the possible explanations, climate appears to be the most likely cause for the observed differences. The higher amount of precipitation in Florida coupled with warmer year round temperatures provides an environment conducive to a more chemically involved hydrological regime, which may be responsible for the greater average sinkhole size in Florida. The integration of high resolution satellite imagery was successfully implemented to delineate sinkhole features. There were several advantages to using QuickBirdTM and WorldView1TM satellite images such as easy integration into GIS, regional coverage and high spatial resolution.

Application of Thematic Mapper data to corn and soybean development stage estimation

NASA Technical Reports Server (NTRS)

Badhwar, G. D.; Henderson, K. E.

1985-01-01

A model, utilizing direct relationship between remotely sensed spectral data and the development stage of both corn and soybeans has been proposed and published previously (Badhwar and Henderson, 1981; and Henderson and Badhwar, 1984). This model was developed using data acquired by instruments mounted on trucks over field plots of corn and soybeans as well as satellite data from Landsat. In all cases, the data was analyzed in the spectral bands equivalent to the four bands of Landsat multispectral scanner (MSS). In this study the same model has been applied to corn and soybeans using Landsat-4 Thematic Mapper (TM) data combined with simulated TM data to provide a multitemporal data set in TM band intervals. All data (five total acquisitions) were acquired over a test site in Webster County, Iowa from June to October 1982. The use of TM data for determining development state is as accurate as with Landsat MSS and field plot data in MSS bands. The maximum deviation of 0.6 development stage for corn and 0.8 development stage for soybeans is well within the uncertainty with which a field can be estimated with procedures used by observers on the ground in 1982.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery.

PubMed

Li, Guiying; Lu, Dengsheng; Moran, Emilio; Hetrick, Scott

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms - maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes.

Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery

PubMed Central

LI, GUIYING; LU, DENGSHENG; MORAN, EMILIO; HETRICK, SCOTT

2011-01-01

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images, and different classification algorithms – maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA), and object-based classification (OBC), were explored. The results indicated that a combination of vegetation indices as extra bands into Landsat TM multispectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multispectral bands improved overall classification accuracy by 5.6% and kappa coefficient by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes having complex stand structures and having relatively large patch sizes. PMID:22368311

REVIEW OF DEVELOPMENTS IN SPACE REMOTE SENSING FOR MONITORING RESOURCES.

USGS Publications Warehouse

Watkins, Allen H.; Lauer, D.T.; Bailey, G.B.; Moore, D.G.; Rohde, W.G.

1984-01-01

Space remote sensing systems are compared for suitability in assessing and monitoring the Earth's renewable resources. Systems reviewed include the Landsat Thematic Mapper (TM), the National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR), the French Systeme Probatoire d'Observation de la Terre (SPOT), the German Shuttle Pallet Satellite (SPAS) Modular Optoelectronic Multispectral Scanner (MOMS), the European Space Agency (ESA) Spacelab Metric Camera, the National Aeronautics and Space Administration (NASA) Large Format Camera (LFC) and Shuttle Imaging Radar (SIR-A and -B), the Russian Meteor satellite BIK-E and fragment experiments and MKF-6M and KATE-140 camera systems, the ESA Earth Resources Satellite (ERS-1), the Japanese Marine Observation Satellite (MOS-1) and Earth Resources Satellite (JERS-1), the Canadian Radarsat, the Indian Resources Satellite (IRS), and systems proposed or planned by China, Brazil, Indonesia, and others. Also reviewed are the concepts for a 6-channel Shuttle Imaging Spectroradiometer, a 128-channel Shuttle Imaging Spectrometer Experiment (SISEX), and the U. S. Mapsat.

A remote sensing analysis of Adelie penguin rookeries

NASA Technical Reports Server (NTRS)

Schwaller, Mathew R.; Olson, Charles E., Jr.; Ma, Zhenqui; Zhu, Zhiliang; Dahmer, Paul

1989-01-01

The Adelie penguin (Pygoscelis adeliae) makes up the vast majority of bird biomass in the Antarctic. As a major consumer of krill, these birds play an important role in the Antarctic food web, and they have been proposed as an indicator species of the vitality of the Southern Ocean ecosystem. This study explores the terrestrial habitat of the Adelie penguin as a target for remote sensing reconnaissance. Laboratory and ground-level reflectance measurements of Antarctic materials found in and around penguin rookeries were examined in detail. These analyses suggested data transformations which helped separate penguin rookeries from surrounding areas in Landsat Thematic Mapper imagery. The physical extent of penguin rookeries on Ross and Beaufort Islands, Antarctic, was estimated from the satellite data and compared to published estimates of penguin populations. The results suggest that TM imagery may be used to identify previously undiscovered penguin rookeries, and the imagery may provide a means of developing new population estimation methods for Antarctic ornithology.

Modelling Rift Valley fever (RVF) disease vector habitats using active and passive remote sensing systems

NASA Technical Reports Server (NTRS)

Ambrosia, Vincent G.; Linthicum, K. G.; Bailey, C. L.; Sebesta, P.

1989-01-01

The NASA Ames Ecosystem Science and Technology Branch and the U.S. Army Medical Research Institute of Infectious Diseases are conducting research to detect Rift Valley fever (RVF) vector habitats in eastern Africa using active and passive remote-sensing. The normalized difference vegetation index (NDVI) calculated from Landsat TM and SPOT data is used to characterize the vegetation common to the Aedes mosquito. Relationships have been found between the highest NDVI and the 'dambo' habitat areas near Riuru, Kenya on both wet and dry data. High NDVI values, when combined with the vegetation classifications, are clearly related to the areas of vector habitats. SAR data have been proposed for use during the rainy season when optical systems are of minimal use and the short frequency and duration of the optimum RVF mosquito habitat conditions necessitate rapid evaluation of the vegetation/moisture conditions; only then can disease potential be stemmed and eradication efforts initiated.

BOREAS Level-3s SPOT Imagery: Scaled At-sensor Radiance in LGSOWG Format

NASA Technical Reports Server (NTRS)

Strub, Richard; Nickeson, Jaime; Newcomer, Jeffrey A.; Hall, Forrest G. (Editor); Cihlar, Josef

2000-01-01

For BOReal Ecosystem-Atmosphere Study (BOREAS), the level-3s Satellite Pour l'Observation de la Terre (SPOT) data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as Fraction of Photosynthetically Active Radiation (FPAR) and Leaf Area Index (LAI). The SPOT images acquired for the BOREAS project were selected primarily to fill temporal gaps in the Landsat Thematic Mapper (TM) image data collection. CCRS collected and supplied the level-3s images to BOREAS Information System (BORIS) for use in the remote sensing research activities. Spatially, the level-3s images cover 60- by 60-km portions of the BOREAS Northern Study Area (NSA) and Southern Study Area (SSA). Temporally, the images cover the period of 17-Apr-1994 to 30-Aug-1996. The images are available in binary image format files. Due to copyright issues, the SPOT images may not be publicly available.

Urban thermal environment and its biophysical parameters derived from satellite remote sensing imagery

NASA Astrophysics Data System (ADS)

Zoran, Maria A.; Savastru, Roxana S.; Savastru, Dan M.; Tautan, Marina N.; Baschir, Laurentiu V.

2013-10-01

In frame of global warming, the field of urbanization and urban thermal environment are important issues among scientists all over the world. This paper investigated the influences of urbanization on urban thermal environment as well as the relationships of thermal characteristics to other biophysical variables in Bucharest metropolitan area of Romania based on satellite remote sensing imagery Landsat TM/ETM+, time series MODIS Terra/Aqua data and IKONOS acquired during 1990 - 2012 period. Vegetation abundances and percent impervious surfaces were derived by means of linear spectral mixture model, and a method for effectively enhancing impervious surface has been developed to accurately examine the urban growth. The land surface temperature (Ts), a key parameter for urban thermal characteristics analysis, was also retrieved from thermal infrared band of Landsat TM/ETM+, from MODIS Terra/Aqua datasets. Based on these parameters, the urban growth, urban heat island effect (UHI) and the relationships of Ts to other biophysical parameters have been analyzed. Results indicated that the metropolitan area ratio of impervious surface in Bucharest increased significantly during two decades investigated period, the intensity of urban heat island and heat wave events being most significant. The correlation analyses revealed that, at the pixel-scale, Ts possessed a strong positive correlation with percent impervious surfaces and negative correlation with vegetation abundances at the regional scale, respectively. This analysis provided an integrated research scheme and the findings can be very useful for urban ecosystem modeling.

Estimation of crop water requirements using remote sensing for operational water resources management

NASA Astrophysics Data System (ADS)

Vasiliades, Lampros; Spiliotopoulos, Marios; Tzabiras, John; Loukas, Athanasios; Mylopoulos, Nikitas

2015-06-01

An integrated modeling system, developed in the framework of "Hydromentor" research project, is applied to evaluate crop water requirements for operational water resources management at Lake Karla watershed, Greece. The framework includes coupled components for operation of hydrotechnical projects (reservoir operation and irrigation works) and estimation of agricultural water demands at several spatial scales using remote sensing. The study area was sub-divided into irrigation zones based on land use maps derived from Landsat 5 TM images for the year 2007. Satellite-based energy balance for mapping evapotranspiration with internalized calibration (METRIC) was used to derive actual evapotranspiration (ET) and crop coefficient (ETrF) values from Landsat TM imagery. Agricultural water needs were estimated using the FAO method for each zone and each control node of the system for a number of water resources management strategies. Two operational strategies of hydro-technical project development (present situation without operation of the reservoir and future situation with the operation of the reservoir) are coupled with three water demand strategies. In total, eight (8) water management strategies are evaluated and compared. The results show that, under the existing operational water resources management strategies, the crop water requirements are quite large. However, the operation of the proposed hydro-technical projects in Lake Karla watershed coupled with water demand management measures, like improvement of existing water distribution systems, change of irrigation methods, and changes of crop cultivation could alleviate the problem and lead to sustainable and ecological use of water resources in the study area.

Evaluating nitrogen removal by vegetation uptake using satellite image time series in riparian catchments.

PubMed

Wang, Xuelei; Wang, Qiao; Yang, Shengtian; Zheng, Donghai; Wu, Chuanqing; Mannaerts, C M

2011-06-01

Nitrogen (N) removal by vegetation uptake is one of the most important functions of riparian buffer zones in preventing non-point source pollution (NSP), and many studies about N uptake at the river reach scale have proven the effectiveness of plants in controlling nutrient pollution. However, at the watershed level, the riparian zones form dendritic networks and, as such, may be the predominant spatially structured feature in catchments and landscapes. Thus, assessing the functions of riparian system at the basin scale is important. In this study, a new method coupling remote sensing and ecological models was used to assess the N removal by riparian vegetation on a large spatial scale. The study site is located around the Guanting reservoir in Beijing, China, which was abandoned as the source water system for Beijing due to serious NSP in 1997. SPOT 5 data was used to map the land cover, and Landsat-5 TM time series images were used to retrieve land surface parameters. A modified forest nutrient cycling and biomass model (ForNBM) was used to simulate N removal, and the modified net primary productivity (NPP) module was driven by remote sensing image time series. Besides the remote sensing data, the necessary database included meteorological data, soil chemical and physical data and plant nutrient data. Pot and plot experiments were used to calibrate and validate the simulations. Our study has proven that, by coupling remote sensing data and parameters retrieval techniques to plant growth process models, catchment scale estimations of nitrogen uptake rates can be improved by spatial pixel-based modelling. Copyright © 2011 Elsevier B.V. All rights reserved.

Multi-scale remote sensing sagebrush characterization with regression trees over Wyoming, USA: laying a foundation for monitoring

USGS Publications Warehouse

Homer, Collin G.; Aldridge, Cameron L.; Meyer, Debra K.; Schell, Spencer J.

2012-01-01

agebrush ecosystems in North America have experienced extensive degradation since European settlement. Further degradation continues from exotic invasive plants, altered fire frequency, intensive grazing practices, oil and gas development, and climate change – adding urgency to the need for ecosystem-wide understanding. Remote sensing is often identified as a key information source to facilitate ecosystem-wide characterization, monitoring, and analysis; however, approaches that characterize sagebrush with sufficient and accurate local detail across large enough areas to support this paradigm are unavailable. We describe the development of a new remote sensing sagebrush characterization approach for the state of Wyoming, U.S.A. This approach integrates 2.4 m QuickBird, 30 m Landsat TM, and 56 m AWiFS imagery into the characterization of four primary continuous field components including percent bare ground, percent herbaceous cover, percent litter, and percent shrub, and four secondary components including percent sagebrush (Artemisia spp.), percent big sagebrush (Artemisia tridentata), percent Wyoming sagebrush (Artemisia tridentata Wyomingensis), and shrub height using a regression tree. According to an independent accuracy assessment, primary component root mean square error (RMSE) values ranged from 4.90 to 10.16 for 2.4 m QuickBird, 6.01 to 15.54 for 30 m Landsat, and 6.97 to 16.14 for 56 m AWiFS. Shrub and herbaceous components outperformed the current data standard called LANDFIRE, with a shrub RMSE value of 6.04 versus 12.64 and a herbaceous component RMSE value of 12.89 versus 14.63. This approach offers new advancements in sagebrush characterization from remote sensing and provides a foundation to quantitatively monitor these components into the future.

Implication of relationship between natural impacts and land use/land cover (LULC) changes of urban area in Mongolia

NASA Astrophysics Data System (ADS)

Gantumur, Byambakhuu; Wu, Falin; Zhao, Yan; Vandansambuu, Battsengel; Dalaibaatar, Enkhjargal; Itiritiphan, Fareda; Shaimurat, Dauryenbyek

2017-10-01

Urban growth can profoundly alter the urban landscape structure, ecosystem processes, and local climates. Timely and accurate information on the status and trends of urban ecosystems is critical to develop strategies for sustainable development and to improve the urban residential environment and living quality. Ulaanbaatar city was urbanized very rapidly caused by herders and farmers, many of them migrating from rural places, have played a big role in this urban expansion (sprawl). Today, 1.3 million residents for about 40% of total population are living in the Ulaanbaatar region. Those human activities influenced stronger to green environments. Therefore, the aim of this study is determined to change detection of land use/land cover (LULC) and estimating their areas for the trend of future by remote sensing and statistical methods. The implications of analysis were provided by change detection methods of LULC, remote sensing spectral indices including normalized difference vegetation index (NDVI), normalized difference water index (NDWI) and normalized difference built-up index (NDBI). In addition, it can relate to urban heat island (UHI) provided by Land surface temperature (LST) with local climate issues. Statistical methods for image processing used to define relations between those spectral indices and change detection images and regression analysis for time series trend in future. Remote sensing data are used by Landsat (TM/ETM+/OLI) satellite images over the period between 1990 and 2016 by 5 years. The advantages of this study are very useful remote sensing approaches with statistical analysis and important to detecting changes of LULC. The experimental results show that the LULC changes can image on the present and after few years and determined relations between impacts of environmental conditions.

Integrating remote sensing with species distribution models; Mapping tamarisk invasions using the Software for Assisted Habitat Modeling (SAHM)

USGS Publications Warehouse

West, Amanda M.; Evangelista, Paul H.; Jarnevich, Catherine S.; Young, Nicholas E.; Stohlgren, Thomas J.; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-01-01

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling (SAHM).

PubMed

West, Amanda M; Evangelista, Paul H; Jarnevich, Catherine S; Young, Nicholas E; Stohlgren, Thomas J; Talbert, Colin; Talbert, Marian; Morisette, Jeffrey; Anderson, Ryan

2016-10-11

Early detection of invasive plant species is vital for the management of natural resources and protection of ecosystem processes. The use of satellite remote sensing for mapping the distribution of invasive plants is becoming more common, however conventional imaging software and classification methods have been shown to be unreliable. In this study, we test and evaluate the use of five species distribution model techniques fit with satellite remote sensing data to map invasive tamarisk (Tamarix spp.) along the Arkansas River in Southeastern Colorado. The models tested included boosted regression trees (BRT), Random Forest (RF), multivariate adaptive regression splines (MARS), generalized linear model (GLM), and Maxent. These analyses were conducted using a newly developed software package called the Software for Assisted Habitat Modeling (SAHM). All models were trained with 499 presence points, 10,000 pseudo-absence points, and predictor variables acquired from the Landsat 5 Thematic Mapper (TM) sensor over an eight-month period to distinguish tamarisk from native riparian vegetation using detection of phenological differences. From the Landsat scenes, we used individual bands and calculated Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), and tasseled capped transformations. All five models identified current tamarisk distribution on the landscape successfully based on threshold independent and threshold dependent evaluation metrics with independent location data. To account for model specific differences, we produced an ensemble of all five models with map output highlighting areas of agreement and areas of uncertainty. Our results demonstrate the usefulness of species distribution models in analyzing remotely sensed data and the utility of ensemble mapping, and showcase the capability of SAHM in pre-processing and executing multiple complex models.

Remote sensing study of the impact of vegetation on thermal environment in different contexts

NASA Astrophysics Data System (ADS)

Xie, Qijiao; Wu, Yingjiao; Zhou, Zhixiang; Wang, Zhengxiang

2018-02-01

Satellite remote sensing technology provides informative data for detecting the land surface temperature (LST) distribution and urban heat island (UHI) effect remotely and regionally. In this study, two Landsat Thematic Mapper (TM) images acquired on September 26, 1987 and September 17, 2013 were used to derive LST and the normalized difference vegetation index (NDVI) values in Wuhan, China. The relationships between NDVI and LST were examined in different contexts, namely built-up area, farmland, grassland and forest. Results showed that negative correlations between the mean NDVI and LST were detected in all observed land covers, which meant that vegetation was efficient in decreasing surface temperatures and mitigating UHI effect. The cooling efficiency of vegetation on thermal environment varied with different contexts. As mean NDVI increased at each 0.1, the decreased LST values in built-up area, farmland, grassland and forest were 1.4 °C, 1.4 °C, 1.1 °C, 1.9 °C in 1987 and 1.4 °C, 1.7 °C, 1.3 °C, 1.8 °C in 2013, respectively. This finding encourages urban planners and greening designers to devote more efforts in protecting urban forests.

Potential of Sentinel Satellites for Schistosomiasis Monitoring

NASA Astrophysics Data System (ADS)

Li, C.-R.; Tang, L.-L.; Niu, H.-B.; Zhou, X.-N.; Liu, Z.-Y.; Ma, L.-L.; Zhou, Y.-S.

2012-04-01

Schistosomiasis is a parasitic disease that menaces human health. In terms of impact this disease is second only to malaria as the most devastating parasitic disease. Oncomelania hupensis is the unique intermediate host of Schistosoma, and hence monitoring and controlling of the number of oncomelania is key to reduce the risk of schistosomiasis transmission. Remote sensing technology can real-timely access the large-scale environmental factors related to oncomelania breeding and reproduction, such as temperature, moisture, vegetation, soil, and rainfall, and can also provide the efficient information to determine the location, area, and spread tendency of oncomelania. Many studies show that the correlation coefficient between oncomelania densities and remote sensing environmental factors depends largely on suitable and high quality remote sensing data used in retrieve environmental factors. Research achievements on retrieving environmental factors (which are related to the living, multiplying and transmission of oncomelania) by multi-source remote data are shown firstly, including: (a) Vegetation information (e.g., Modified Soil-Adjusted Vegetation Index, Normalized Difference Moisture Index, Fractional Vegetation Cover) extracted from optical remote sensing data, such as Landsat TM, HJ-1A/HSI image; (b) Surface temperature retrieval from Thermal Infrared (TIR) and passive-microwave remote sensing data; (c) Water region, soil moisture, forest height retrieval from synthetic aperture radar data, such as Envisat SAR, DLR's ESAR image. Base on which, the requirements of environmental factor accuracy for schistosomiasis monitoring will be analyzed and summarized. Our work on applying remote sensing technique to schistosomiasis monitoring is then presented. The fuzzy information theory is employed to analyze the sensitivity and feasibility relation between oncomelania densities and environmental factors. Then a mechanism model of predicting oncomelania distribution and densities is developed. The new model is validated with field data of Dongting Lake and the dynamic monitoring of schistosomiasis breeding in Dongting Lake region is presented. Finally, emphasis are placed on analyzing the potential of Sentinel satellites for schistosomiasis monitoring. The requirements of optical high resolution data on spectral resolution, spatial resolution, radiometric resolution/accuracy, as well as the requirements of synthetic aperture radar data on operation frequency, spatial resolution, polarization, radiometric accuracy, repeat cycle are presented and then compared with the parameters of Sentinel satellites. The parameters of Sentinel satellites are also compared with those of available remote satellites, such as Envisat, Landsat, whose data are being used for schistosomiasis monitoring. The application potential of Sentinel satellites for the schistosomiasis monitoring will be concluded in the end, which will benefit for the mission operation, model development, etc.

Single Longitudinal Mode, High Repetition Rate, Q-switched Ho:YLF Laser for Remote Sensing

NASA Technical Reports Server (NTRS)

Bai, Yingxin; Yu, Jirong; Petzar, Paul; Petros, M.; Chen, Songsheng; Trieu, Bo; Lee, Nyung; Singh, U.

2009-01-01

Ho:YLF/LuLiF lasers have specific applications for remote sensing such as wind-speed measurement and carbon dioxide (CO2) concentration measurement in the atmosphere because the operating wavelength (around 2 m) is located in the eye-safe range and can be tuned to the characteristic lines of CO2 absorption and there is strong backward scattering signal from aerosol (Mie scattering). Experimentally, a diode pumped Ho:Tm:YLF laser has been successfully used as the transmitter of coherent differential absorption lidar for the measurement of with a repetition rate of 5 Hz and pulse energy of 75 mJ [1]. For highly precise CO2 measurements with coherent detection technique, a laser with high repetition rate is required to averaging out the speckle effect [2]. In addition, laser efficiency is critically important for the air/space borne lidar applications, because of the limited power supply. A diode pumped Ho:Tm:YLF laser is difficult to efficiently operate in high repetition rate due to the large heat loading and up-conversion. However, a Tm:fiber laser pumped Ho:YLF laser with low heat loading can be operated at high repetition rates efficiently [3]. No matter whether wind-speed or carbon dioxide (CO2) concentration measurement is the goal, a Ho:YLF/LuLiF laser as the transmitter should operate in a single longitudinal mode. Injection seeding is a valid technique for a Q-switched laser to obtain single longitudinal mode operation. In this paper, we will report the new results for a single longitudinal mode, high repetition rate, Q-switched Ho:YLF laser. In order to avoid spectral hole burning and make injection seeding easier, a four mirror ring cavity is designed for single longitudinal mode, high repetition rate Q-switched Ho:YLF laser. The ramp-fire technique is chosen for injection seeding.

Ten Years of Forest Cover Change in the Sierra Nevada Detected Using Landsat Satellite Image Analysis

NASA Technical Reports Server (NTRS)

Potter, Christopher S.

2014-01-01

A detailed geographic record of recent vegetation regrowth and disturbance patterns in forests of the Sierra Nevada remains a gap that can be filled with remote sensing data. Landsat (TM) imagery was analyzed to detect 10 years of recent changes (between 2000 and 2009) in forest vegetation cover for areas burned by wildfires between years of 1995 to 1999 in the region. Results confirmed the prevalence of regrowing forest vegetation during the period 2000 and 2009 over 17% of the combined burned areas.

The use of satellite data for monitoring temporal and spatial patterns of fire: a comprehensive review

NASA Astrophysics Data System (ADS)

Lasaponara, R.

2009-04-01

Remotely sensed (RS) data can fruitfully support both research activities and operative monitoring of fire at different temporal and spatial scales with a synoptic view and cost effective technologies. "The contribution of remote sensing (RS) to forest fires may be grouped in three categories, according to the three phases of fire management: (i) risk estimation (before fire), (ii) detection (during fire) and (iii) assessment (after fire)" Chuvieco (2006). Relating each phase, wide research activities have been conducted over the years. (i) Risk estimation (before fire) has been mainly based on the use of RS data for (i) monitoring vegetation stress and assessing variations in vegetation moisture content, (ii) fuel type mapping, at different temporal and spatial scales from global, regional down to a local scale (using AVHRR, MODIS, TM, ASTER, Quickbird images and airborne hyperspectral and LIDAR data). Danger estimation has been mainly based on the use of AVHRR (onborad NOAA), MODIS (onboard TERRA and AQUA), VEGETATION (onboard SPOT) due to the technical characteristics (i.e. spectral, spatial and temporal resolution). Nevertheless microwave data have been also used for vegetation monitoring. (ii) Detection: identification of active fires, estimation of fire radiative energy and fire emission. AVHRR was one of the first satellite sensors used for setting up fire detection algorithms. The availbility of MODIS allowed us to obtain global fire products free downloaded from NASA web site. Sensors onboard geostationary satellite platforms, such as GOES, SEVIRI, have been used for fire detection, to obtain a high temporal resolution (at around 15 minutes) monitoring of active fires. (iii) Post fire damage assessment includes: burnt area mapping, fire emission, fire severity, vegetation recovery, fire resilience estimation, and, more recently, fire regime characterization. Chuvieco E. L. Giglio, C. Justice, 2008 Global charactrerization of fire activity: toward defining fire regimes from Earth observation data Global Change Biology vo. 14. doi: 10.1111/j.1365-2486.2008.01585.x 1-15, Chuvieco E., P. Englefield, Alexander P. Trishchenko, Yi Luo Generation of long time series of burn area maps of the boreal forest from NOAA-AVHRR composite data. Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2381-2396 Chuvieco Emilio 2006, Remote Sensing of Forest Fires: Current limitations and future prospects in Observing Land from Space: Science, Customers and Technology, Advances in Global Change Research Vol. 4 pp 47-51 De Santis A., E. Chuvieco Burn severity estimation from remotely sensed data: Performance of simulation versus empirical models, Remote Sensing of Environment, Volume 108, Issue 4, 29 June 2007, Pages 422-435. De Santis A., E. Chuvieco, Patrick J. Vaughan, Short-term assessment of burn severity using the inversion of PROSPECT and GeoSail models, Remote Sensing of Environment, Volume 113, Issue 1, 15 January 2009, Pages 126-136 García M., E. Chuvieco, H. Nieto, I. Aguado Combining AVHRR and meteorological data for estimating live fuel moisture content Remote Sensing of Environment, Volume 112, Issue 9, 15 September 2008, Pages 3618-3627 Ichoku C., L. Giglio, M. J. Wooster, L. A. Remer Global characterization of biomass-burning patterns using satellite measurements of fire radiative energy. Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2950-2962. Lasaponara R. and Lanorte, On the capability of satellite VHR QuickBird data for fuel type characterization in fragmented landscape Ecological Modelling Volume 204, Issues 1-2, 24 May 2007, Pages 79-84 Lasaponara R., A. Lanorte, S. Pignatti,2006 Multiscale fuel type mapping in fragmented ecosystems: preliminary results from Hyperspectral MIVIS and Multispectral Landsat TM data, Int. J. Remote Sens., vol. 27 (3) pp. 587-593. Lasaponara R., V. Cuomo, M. F. Macchiato, and T. Simoniello, 2003 .A self-adaptive algorithm based on AVHRR multitemporal data analysis for small active fire detection.n International Journal of Remote Sensing, vol. 24, No 8, 1723-1749. Minchella A., F. Del Frate, F. Capogna, S. Anselmi, F. Manes Use of multitemporal SAR data for monitoring vegetation recovery of Mediterranean burned areas Remote Sensing of Environment, In Press Næsset E., T. Gobakken Estimation of above- and below-ground biomass across regions of the boreal forest zone using airborne laser Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 3079-3090 Peterson S. H, Dar A. Roberts, Philip E. Dennison Mapping live fuel moisture with MODIS data: A multiple regression approach, Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4272-4284. Schroeder Wilfrid, Elaine Prins, Louis Giglio, Ivan Csiszar, Christopher Schmidt, Jeffrey Morisette, Douglas Morton Validation of GOES and MODIS active fire detection products using ASTER and ETM+ data Remote Sensing of Environment, Volume 112, Issue 5, 15 May 2008, Pages 2711-2726 Shi J., T. Jackson, J. Tao, J. Du, R. Bindlish, L. Lu, K.S. Chen Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E Remote Sensing of Environment, Volume 112, Issue 12, 15 December 2008, Pages 4285-4300 Tansey, K., Grégoire, J-M., Defourny, P., Leigh, R., Pekel, J-F., van Bogaert, E. and Bartholomé, E., 2008 A New, Global, Multi-Annual (2000-2007) Burnt Area Product at 1 km Resolution and Daily Intervals Geophysical Research Letters, VOL. 35, L01401, doi:10.1029/2007GL031567, 2008. Telesca L. and Lasaponara R., 2006; "Pre-and Post- fire Behaviural trends revealed in satellite NDVI time series" Geophysical Research Letters,., 33, L14401, doi:10.1029/2006GL026630 Telesca L. and Lasaponara R 2005 Discriminating Dynamical Patterns in Burned and Unburned Vegetational Covers by Using SPOT-VGT NDVI Data. Geophysical Research Letters,, 32, L21401, doi:10.1029/2005GL024391. Telesca L. and Lasaponara R. Investigating fire-induced behavioural trends in vegetation covers , Communications in Nonlinear Science and Numerical Simulation, 13, 2018-2023, 2008 Telesca L., A. Lanorte and R. Lasaponara, 2007. Investigating dynamical trends in burned and unburned vegetation covers by using SPOT-VGT NDVI data. Journal of Geophysics and Engineering, Vol. 4, pp. 128-138, 2007 Telesca L., R. Lasaponara, and A. Lanorte, Intra-annual dynamical persistent mechanisms in Mediterranean ecosystems revealed SPOT-VEGETATION Time Series, Ecological Complexity, 5, 151-156, 2008 Verbesselt, J., Somers, B., Lhermitte, S., Jonckheere, I., van Aardt, J., and Coppin, P. (2007) Monitoring herbaceous fuel moisture content with SPOT VEGETATION time-series for fire risk prediction in savanna ecosystems. Remote Sensing of Environment 108: 357-368. Zhang X., S. Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897 Zhang X., Shobha Kondragunta Temporal and spatial variability in biomass burned areas across the USA derived from the GOES fire product Remote Sensing of Environment, Volume 112, Issue 6, 16 June 2008, Pages 2886-2897

Seasonal LAI in slash pine estimated with LANDSAT TM

NASA Technical Reports Server (NTRS)

Curran, Paul J.; Dungan, Jennifer L.; Gholz, Henry L.

1990-01-01

The leaf area index (LAI, total area of leaves per unit area of ground) of most forest canopies varies throughout the year, yet for logistical reasons it is difficult to estimate anything more detailed than a seasonal maximum LAI. To determine if remotely sensed data can be used to estimate LAI seasonally, field measurements of LAI were compared to normalized difference vegetation index (NDVI) values derived using LANDSAT Thematic Mapper (TM) data, for 16 fertilized and control slash pine plots on 3 dates. Linear relationships existed between NDVI and LAI with R(sup 2) values of 0.35, 0.75, and 0.86 for February 1988, September 1988, and March, 1989, respectively. This is the first reported study in which NDVI is related to forest LAI recorded during the month of sensor overpass. Predictive relationships based on data from eight of the plots were used to estimate the LAI of the other eight plots with a root-mean-square error of 0.74 LAI, which is 15.6 percent of the mean LAI. This demonstrates the potential use of LANDSAT TM data for studying seasonal dynamics in forest canopies.

Characterization of Forested Landscapes From Remotely Sensed Data Using Fractals and Spatial Autocorrelation

NASA Technical Reports Server (NTRS)

Al-Hamdan, Mohammad Z.; Cruise, James F.; Rickman, Douglas L.; Quattrochi, Dale A.

2007-01-01

The characterization of forested areas is frequently required in resource management practice. Passive remotely sensed data, which are much more accessible and cost effective than are active data, have rarely, if ever, been used to characterize forest structure directly, but rather they usually focus on the estimation of indirect measurement of biomass or canopy coverage. In this study, some spatial analysis techniques are presented that might be employed with Landsat TM data to analyze forest structure characteristics. A case study is presented wherein fractal dimensions, along with a simple spatial autocorrelation technique (Moran s I), were related to stand density parameters of the Oakmulgee National Forest located in the southeastern United States (Alabama). The results of the case study presented herein have shown that as the percentage of smaller diameter trees becomes greater, and particularly if it exceeds 50%, then the canopy image obtained from Landsat TM data becomes sufficiently homogeneous so that the spatial indices reach their lower limits and thus are no longer determinative. It also appears, at least for the Oakmulgee forest, that the relationships between the spatial indices and forest class percentages within the boundaries can reasonably be considered linear. The linear relationship is much more pronounced in the sawtimber and saplings cases than in samples dominated by medium sized trees (poletimber). In addition, it also appears that, at least for the Oakmulgee forest, the relationships between the spatial indices and forest species groups (Hardwood and Softwood) percentages can reasonably be considered linear. The linear relationship is more pronounced in the forest species groups cases than in the forest classes cases. These results appear to indicate that both fractal dimensions and spatial autocorrelation indices hold promise as means of estimating forest stand characteristics from remotely sensed images. However, additional work is needed to confirm that the boundaries identified for Oakmulgee forest and the linear nature of the relationship between image complexity indices and forest characteristics are generally evident in other forests. In addition, the effects of other parameters such ,as topographic relief and image distortion due to sun angle and cloud cover, for example, need to be examined.

Integrating remote sensing and terrain data in forest fire modeling

NASA Astrophysics Data System (ADS)

Medler, Michael Johns

Forest fire policies are changing. Managers now face conflicting imperatives to re-establish pre-suppression fire regimes, while simultaneously preventing resource destruction. They must, therefore, understand the spatial patterns of fires. Geographers can facilitate this understanding by developing new techniques for mapping fire behavior. This dissertation develops such techniques for mapping recent fires and using these maps to calibrate models of potential fire hazards. In so doing, it features techniques that strive to address the inherent complexity of modeling the combinations of variables found in most ecological systems. Image processing techniques were used to stratify the elements of terrain, slope, elevation, and aspect. These stratification images were used to assure sample placement considered the role of terrain in fire behavior. Examination of multiple stratification images indicated samples were placed representatively across a controlled range of scales. The incorporation of terrain data also improved preliminary fire hazard classification accuracy by 40%, compared with remotely sensed data alone. A Kauth-Thomas transformation (KT) of pre-fire and post-fire Thematic Mapper (TM) remotely sensed data produced brightness, greenness, and wetness images. Image subtraction indicated fire induced change in brightness, greenness, and wetness. Field data guided a fuzzy classification of these change images. Because fuzzy classification can characterize a continuum of a phenomena where discrete classification may produce artificial borders, fuzzy classification was found to offer a range of fire severity information unavailable with discrete classification. These mapped fire patterns were used to calibrate a model of fire hazards for the entire mountain range. Pre-fire TM, and a digital elevation model produced a set of co-registered images. Training statistics were developed from 30 polygons associated with the previously mapped fire severity. Fuzzy classifications of potential burn patterns were produced from these images. Observed field data values were displayed over the hazard imagery to indicate the effectiveness of the model. Areas that burned without suppression during maximum fire severity are predicted best. Areas with widely spaced trees and grassy understory appear to be misrepresented, perhaps as a consequence of inaccuracies in the initial fire mapping.

Prospects for phenological monitoring in an arid southwestern U.S. rangeland using field observations with hyperspatial and moderate resolution imagery

NASA Astrophysics Data System (ADS)

Browning, D. M.; Laliberte, A. S.; Rango, A.; Herrick, J. E.

2009-12-01

Relating field observations of plant phenological events to remotely sensed depictions of land surface phenology remains a challenge to the vertical integration of data from disparate sources. This research conducted at the Jornada Basin Long-Term Ecological Research site in southern New Mexico capitalizes on legacy datasets pertaining to reproductive phenology and biomass and hyperspatial imagery. Large amounts of exposed bare soil and modest cover from shrubs and grasses in these arid and semi-arid ecosystems challenge the integration of field observations of phenology and remotely sensed data to monitor changes in land surface phenology. Drawing on established field protocols for reproductive phenology, hyperspatial imagery (4 cm), and object-based image analysis, we explore the utility of two approaches to scale detailed observations (i.e., field and 4 cm imagery) to the extent of long-term field plots (50 x 50m) and moderate resolution Landsat Thematic Mapper (TM) imagery (30 x 30m). Very high resolution color-infrared imagery was collected June 2007 across 15 LTER study sites that transect five distinct vegetation communities along a continuum of grass to shrub dominance. We examined two methods for scaling spectral vegetation indices (SVI) at 4 cm resolution: pixel averaging and object-based integration. Pixel averaging yields the mean SVI value for all pixels within the plot or TM pixel. Alternatively, the object-based method is based on a weighted average of SVI values that correspond to discrete image objects (e.g., individual shrubs or grass patches). Object-based image analysis of 4 cm imagery provides a detailed depiction of ground cover and allows us to extract species-specific contributions to upscaled SVI values. The ability to discern species- or functional-group contributions to remotely sensed signals of vegetation greenness can greatly enhance the design of field sampling protocols for phenological research. Furthermore, imagery from unmanned aerial vehicles (UAV) is a cost-effective and increasingly available resource and generation of UAV mosaics has been accomplished so that larger study areas can be addressed. This technology can provide a robust basis for scaling relationships for phenology-based research applications.

Remote sensing of land degradation: experiences from Latin America and the Caribbean.

PubMed

Metternicht, G; Zinck, J A; Blanco, P D; del Valle, H F

2010-01-01

Land degradation caused by deforestation, overgrazing, and inappropriate irrigation practices affects about 16% of Latin America and the Caribbean (LAC). This paper addresses issues related to the application of remote sensing technologies for the identification and mapping of land degradation features, with special attention to the LAC region. The contribution of remote sensing to mapping land degradation is analyzed from the compilation of a large set of research papers published between the 1980s and 2009, dealing with water and wind erosion, salinization, and changes of vegetation cover. The analysis undertaken found that Landsat series (MSS, TM, ETM+) are the most commonly used data source (49% of the papers report their use), followed by aerial photographs (39%), and microwave sensing (ERS, JERS-1, Radarsat) (27%). About 43% of the works analyzed use multi-scale, multi-sensor, multi-spectral approaches for mapping degraded areas, with a combination of visual interpretation and advanced image processing techniques. The use of more expensive hyperspectral and/or very high spatial resolution sensors like AVIRIS, Hyperion, SPOT-5, and IKONOS tends to be limited to small surface areas. The key issue of indicators that can directly or indirectly help recognize land degradation features in the visible, infrared, and microwave regions of the electromagnetic spectrum are discussed. Factors considered when selecting indicators for establishing land degradation baselines include, among others, the mapping scale, the spectral characteristics of the sensors, and the time of image acquisition. The validation methods used to assess the accuracy of maps produced with satellite data are discussed as well.

Nature and origin of mineral coatings on volcanic rocks of the Black Mountain, Stonewall Mountain and Kane Springs Wash volcanic centers, southern Nevada

NASA Technical Reports Server (NTRS)

Taranik, J. V.; Noble, D. C.; Hsu, L. C.; Hutsinpiller, A.; Spatz, D.

1986-01-01

Surface coatings on volcanic rock assemblages that occur at select tertiary volcanic centers in southern Nevada were investigated using LANDSAT 5 Thematic Mapper imagery. Three project sites comprise the subject of this study: the Kane Springs Wash, Black Mountain, and Stonewall Mountain volcanic centers. LANDSAT 5 TM work scenes selected for each area are outlined along with local area geology. The nature and composition of surface coatings on the rock types within the subproject areas are determined, along with the origin of the coatings and their genetic link to host rocks, geologic interpretations are related to remote sensing units discriminated on TM imagery. Image processing was done using an ESL VAX/IDIMS image processing system, field sampling, and observation. Aerial photographs were acquired to facilitate location on the ground and to aid stratigraphic differentiation.

Tunable CW diode-pumped Tm,Ho:YLiF4 laser operating at or near room temperature

NASA Technical Reports Server (NTRS)

Mcguckin, Brendan T. (Inventor); Menzies, Robert T. (Inventor)

1995-01-01

A conversion efficiency of 42% and slope efficiency of 60% relative to absorbed pump power are obtained from a continuous wave diode-pumped Tm,Ho:YLiF4 laser at 2 microns with output power of 84 mW at a crystal temperature of 275 K. The emission spectrum is etalon tunable over a range of7 nm (16.3/cm) centered on 2.067 microns with fine tuning capability of the transition frequency with crystal temperature at a measured rate of -0.03/(cm)K. The effective emission cross-section is measured to be 5 x 10(exp -21) cm squared. These and other aspects of the laser performance are disclosed in the context of calculated atmospheric absorption characteristics in this spectral region and potential use in remote sensing applications. Single frequency output and frequency stabilization are achieved using an intracavity etalon in conjunction with an external reference etalon.

Simple models for complex natural surfaces - A strategy for the hyperspectral era of remote sensing

NASA Technical Reports Server (NTRS)

Adams, John B.; Smith, Milton O.; Gillespie, Alan R.

1989-01-01

A two-step strategy for analyzing multispectral images is described. In the first step, the analyst decomposes the signal from each pixel (as expressed by the radiance or reflectance values in each channel) into components that are contributed by spectrally distinct materials on the ground, and those that are due to atmospheric effects, instrumental effects, and other factors, such as illumination. In the second step, the isolated signals from the materials on the ground are selectively edited, and recombined to form various unit maps that are interpretable within the framework of field units. The approach has been tested on multispectral images of a variety of natural land surfaces ranging from hyperarid deserts to tropical rain forests. Data were analyzed from Landsat MSS (multispectral scanner) and TM (Thematic Mapper), the airborne NS001 TM simulator, Viking Lander and Orbiter, AIS, and AVRIS (Airborne Visible and Infrared Imaging Spectrometer).

Identification of central Kenyan Rift Valley Fever virus vector habitats with Landsat TM and evaluation of their flooding status with airborne imaging radar

NASA Technical Reports Server (NTRS)

Pope, K. O.; Sheffner, E. J.; Linthicum, K. J.; Bailey, C. L.; Logan, T. M.; Kasischke, E. S.; Birney, K.; Njogu, A. R.; Roberts, C. R.

1992-01-01

Rift Valley Fever (RVF) is a mosquito-borne virus that affects livestock and humans in Africa. Landsat TM data are shown to be effective in identifying dambos, intermittently flooded areas that are potential mosquite breeding sites, in an area north of Nairobi, Kenya. Positive results were obtained from a limited test of flood detection in dambos with airborne high resolution L, C, and X band multipolarization SAR imagery. L and C bands were effective in detecting flooded dambos, but LHH was by far the best channel for discrimination between flooded and nonflooded sites in both sedge and short-grass environments. This study demonstrates the feasibility of a combined passive and active remote sensing program for monitoring the location and condition of RVF vector habitats, thus making future control of the disease more promising.

BOREAS TE-18 Landsat TM Physical Classification Image of the NSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the NSA. A Landsat-5 TM image from 21-Jun-1995 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used in a way that is similar to training data to classify the image into the different land cover classes. The data are provided in a binary, image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Archive Center (DAAC).

BOREAS TE-18 Landsat TM Physical Classification Image of the SSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep-1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Activity Archive Center (DAAC).

Landsat-5 TM reflective-band absolute radiometric calibration

USGS Publications Warehouse

Chander, G.; Helder, D.L.; Markham, B.L.; Dewald, J.D.; Kaita, E.; Thome, K.J.; Micijevic, E.; Ruggles, T.A.

2004-01-01

The Landsat-5 Thematic Mapper (TM) sensor provides the longest running continuous dataset of moderate spatial resolution remote sensing imagery, dating back to its launch in March 1984. Historically, the radiometric calibration procedure for this imagery used the instrument's response to the Internal Calibrator (IC) on a scene-by-scene basis to determine the gain and offset of each detector. Due to observed degradations in the IC, a new procedure was implemented for U.S.-processed data in May 2003. This new calibration procedure is based on a lifetime radiometric calibration model for the instrument's reflective bands (1-5 and 7) and is derived, in part, from the IC response without the related degradation effects and is tied to the cross calibration with the Landsat-7 Enhanced Thematic Mapper Plus. Reflective-band absolute radiometric accuracy of the instrument tends to be on the order of 7% to 10%, based on a variety of calibration methods.

Novel solid state lasers for Lidar applications at 2 μm

NASA Astrophysics Data System (ADS)

Della Valle, G.; Galzerano, G.; Toncelli, A.; Tonelli, M.; Laporta, P.

2005-09-01

A review on the results achieved by our group in the development of novel solid-state lasers for Lidar applications at 2 μm is presented. These lasers, based on fluoride crystals (YLF4, BaY2F8, and KYF4) doped with Tm and Ho ions, are characterized by high-efficiency and wide wavelength tunability around 2 μm. Single crystals of LiYF4, BaY2F8, and KYF4 codoped with the same Tm3+ and Ho3+ concentrations were successfully grown by the Czochralski method. The full spectroscopic characterization of the different laser crystals and the comparison between the laser performance are presented. Continuous wave operation was efficiently demonstrated by means of a CW diode-pumping. These oscillators find interesting applications in the field of remote sensing (Lidar and Dial systems) as well as in high-resolution molecular spectroscopy, frequency metrology, and biomedical applications.

BOREAS TE-18 Landsat TM Maximum Likelihood Classification Image of the SSA

NASA Technical Reports Server (NTRS)

Hall, Forrest G. (Editor); Knapp, David

2000-01-01

The BOREAS TE-18 team focused its efforts on using remotely sensed data to characterize the successional and disturbance dynamics of the boreal forest for use in carbon modeling. The objective of this classification is to provide the BOREAS investigators with a data product that characterizes the land cover of the SSA. A Landsat-5 TM image from 02-Sep- 1994 was used to derive the classification. A technique was implemented that uses reflectances of various land cover types along with a geometric optical canopy model to produce spectral trajectories. These trajectories are used as training data to classify the image into the different land cover classes. These data are provided in a binary image file format. The data files are available on a CD-ROM (see document number 20010000884), or from the Oak Ridge National Laboratory (ORNL) Distributed Active Center (DAAC).

Characterization and analysis of pasture degradation in Rondonia using remote sensing

NASA Astrophysics Data System (ADS)

Numata, Izaya

2006-04-01

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. This dissertation evaluated pasture degradation as it varied due to environmental and human factors across different scales by combining field measures, ancillary data, and remote sensing. To better understand the link between pasture nutrients and soil chemistry, samples were analyzed in the laboratory demonstrating that pasture soil fertility and grass nutrients varied significantly according to soil order. Pastures established on Alfisols, nutrient-rich soils, had higher levels of Phosphorus in soil and grass compared to pastures established on Oxisols and Ultisols. To evaluate remote sensing measures of pasture biophysical properties related to pasture degradation, remote sensing analysis focused on a variety of sensors that provide a range in spatial, spectral and temporal scales, including Landsat Thematic Mapper (TM), a field spectrometer, Hyperion, and the Moderate Resolution Imaging Spectroradiometer (MODIS). Of the measures derived from Landsat, degraded pastures were best characterized by high non-photosynthetic vegetation (NPV) and low shade fractions, while pastures with high biomass were characterized by high green vegetation and low NPV fractions. Absorption features calculated from hyperspectral spectra collected in the field, including water and ligno-cellulose absorption depth and area, provided the best estimates of field grass measures. Temporal MODIS Normalized Difference Vegetation Index (NDVI) data were used to characterize changes in pasture quality across the region and through time. Degraded pastures were characterized by low temporal NDVI variation and occurred in dry or very wet climate conditions and on nutrient poor soils. Productive pastures were characterized by high temporal NDVI variation, were predominantly found more in the central part of the state, and were located in areas with milder climate conditions and relatively more fertile soils. As a general trend of regional pasture change in Rondonia, the proportions of productive pastures decreased and degraded pastures increased as pastures aged. The results obtained in this dissertation will contribute to understanding pasture sustainability needs for the future of Rondonia and provide the first step in monitoring pasture degradation in the Amazon using remote sensing.

Research activity of the greenhouse gas measurements using optical remote sensing in Japan (Invited)

NASA Astrophysics Data System (ADS)

Asai, K.

2009-12-01

Japan might be one of the most active countries dedicating themselves to studying the greenhouse gas (GHG) measurements using optical remote sensing not only on the ground but also from space. There are two reasons; one of them ascends to the Kyoto Protocol, agreed in December 1997 in Kyoto, an ancient city of Japan until 19th centuries, was designed to address the international response to serious climate change due to greenhouse gases. The other reason is due to a revision of the Basic Environment Law of Japan in order to meet the Kyoto Protocol in 1998. The State makes efforts to ensure international collaboration so as to effectively promote the monitoring, observation and measurement of the environmental situation with regard to global warming. Main activities are listed in a Table1. They are divided into two categories, i.e. the Greenhouse gases Observing SATellite (GOSAT), launched on Jan.23, 2009 and active remote sensing using lidar technology. In case of GOSAT, an initial analysis of carbon dioxide and methane concentrations was obtained for clear-sky scenes over land. In the future, after further calibration and validation of the data, observation data and corresponding analyzed products will be made available. On the other hand, studies of the laser remote sensing for measuring GHG have been actively carrying out to achieve reliable data with a higher accuracy at wavelengths of 1.6micron meter (Tokyo Metropolitan University, JAXA, Mitsubishi Electric Co.) and 2 micron meter (National Institute of Information and Communications Technology). As well-known, one of the most interests regarding atmospheric CO2 measurements is that carbon dioxide molecule measured are due to anthropological emission from fossil fuel burning or due to natural one from forest fires etc. We proposed a newly advanced CO2/CO DIAL using a hybrid of pulsed Tm,Ho:YLF and pulsed OPO pumped by it for better understanding them. Now, our effort is directed to find out the most suitable wavelength pairs to be selected.Activities of optical remote sensing for GHG in Japan

Quantifying suspended sediment dynamics in mega deltas using remote sensing data: A case study of the Mekong floodplains

NASA Astrophysics Data System (ADS)

Dang, Thanh Duc; Cochrane, Thomas A.; Arias, Mauricio E.

2018-06-01

Temporal and spatial concentrations of suspended sediment in floodplains are difficult to quantify because in situ measurements can be logistically complex, time consuming and costly. In this research, satellite imagery with long temporal and large spatial coverage (Landsat TM/ETM+) was used to complement in situ suspended sediment measurements to reflect sediment dynamics in a large (70,000 km2) floodplain. Instead of using a single spectral band from Landsat, a Principal Component Analysis was applied to obtain uncorrelated reflectance values for five bands of Landsat TM/ETM+. Significant correlations between the scores of the 1st principal component and the values of continuously gauged suspended sediment concentration, shown via high coefficients of determination of sediment rating curves (R2 ranging from 0.66 to 0.92), permit the application of satellite images to quantify spatial and temporal sediment variation in the Mekong floodplains. Estimated suspended sediment maps show that hydraulic regimes at Chaktomuk (Cambodia), where the Mekong, Bassac, and Tonle Sap rivers diverge, determine the amount of seasonal sediment supplies to the Mekong Delta. The development of flood prevention systems to allow for three rice crops a year in the Vietnam Mekong Delta significantly reduces localized flooding, but also prevents sediment (source of nutrients) from entering fields. A direct consequence of this is the need to apply more artificial fertilizers to boost agricultural productivity, which may trigger environmental problems. Overall, remote sensing is shown to be an effective tool to understand temporal and spatial sediment dynamics in large floodplains.

Spatio-temporal pattern of eco-environmental parameters in Jharia coalfield, India

NASA Astrophysics Data System (ADS)

Saini, V.; Gupta, R. P.; Arora, M. K.

2015-10-01

Jharia coal-field holds unequivocal importance in the Indian context as it is the only source of prime coking coal in the country. The coalfield is also known for its infamous coal mine fires which have been burning since last more than a century. Haphazard mining over a century has led to eco-environmental changes to a large extent such as changes in vegetation distribution and widespread development of surface and subsurface fires. This article includes the spatiotemporal study of remote sensing derived eco-environmental parameters like vegetation index (NDVI), tasseled cap transformation (TCT) and temperature distribution in fire areas. In order to have an estimate of the temporal variations of NDVI over the years, a study has been carried out on two subsets of the Jharia coalfield using Landsat images of 1972 (MSS), 1992 (TM), 1999 (ETM+) and 2013 (OLI). To assess the changes in brightness and greenness over the year s, difference images have been calculated using the 1992 (TM) and 2013 (OLI) images. Radiance images derived from thermal bands have been used to calculate at-sensor brightness temperature over a 23 year period from 1991 to 2013. It has been observed that during the years 1972 to 2013, moderate to dense vegetation has decreased drastically due to the intense mining going on in the area. TCT images show the areas that have undergone changes in both brightness and greenness from 1992 to 2013. Surface temperature data obtained shows a constant increase from 1991 to 2013 apparently due to coal fires. The utility of remote sensing data in such EIA studies has been emphasized.

Comparison of remote sensing indices for monitoring of desert cienegas

USGS Publications Warehouse

Wilson, Natalie R.; Norman, Laura M.; Villarreal, Miguel; Gass, Leila; Tiller, Ron; Salywon, Andrew

2016-01-01

This research considers the applicability of different vegetation indices at 30 m resolution for mapping and monitoring desert wetland (cienega) health and spatial extent through time at Cienega Creek in southeastern Arizona, USA. Multiple stressors including the risk of decadal-scale drought, the effects of current and predicted global warming, and continued anthropogenic pressures threaten aquatic habitats in the southwest and cienegas are recognized as important sites for conservation and restoration efforts. However, cienegas present a challenge to satellite-imagery based analysis due to their small size and mixed surface cover of open water, exposed soils, and vegetation. We created time series of five well-known vegetation indices using annual Landsat Thematic Mapper (TM) images retrieved during the April–June dry season, from 1984 to 2011 to map landscape-level distribution of wetlands and monitor the temporal dynamics of individual sites. Indices included the Normalized Difference Vegetation Index (NDVI), the Soil-Adjusted Vegetation Index (SAVI), the Normalized Difference Water Index (NDWI), and the Normalized Difference Infrared Index (NDII). One topographic index, the Topographic Wetness Index (TWI), was analyzed to examine the utility of topography in mapping distribution of cienegas. Our results indicate that the NDII, calculated using Landsat TM band 5, outperforms the other indices at differentiating cienegas from riparian and upland sites, and was the best means to analyze change. As such, it offers a critical baseline for future studies that seek to extend the analysis of cienegas to other regions and time scales, and has broader applicability to the remote sensing of wetland features in arid landscapes.

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

NASA Astrophysics Data System (ADS)

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

2008-10-01

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

Can Satellite Remote Sensing be Applied in Geological Mapping in Tropics?

NASA Astrophysics Data System (ADS)

Magiera, Janusz

2018-03-01

Remote sensing (RS) techniques are based on spectral data registered by RS scanners as energy reflected from the Earth's surface or emitted by it. In "geological" RS the reflectance (or emittence) should come from rock or sediment. The problem in tropical and subtropical areas is a dense vegetation. Spectral response from the rocks and sediments is gathered only from the gaps among the trees and shrubs. Images of high resolution are appreciated here, therefore. New generation of satellites and scanners (Digital Globe WV2, WV3 and WV4) yield imagery of spatial resolution of 2 m and up to 16 spectral bands (WV3). Images acquired by Landsat (TM, ETM+, OLI) and Sentinel 2 have good spectral resolution too (6-12 bands in visible and infrared) and, despite lower spatial resolution (10-60 m of pixel size) are useful in extracting lithological information too. Lithological RS map may reveal good precision (down to a single rock or outcrop of a meter size). Supplemented with the analysis of Digital Elevation Model and high resolution ortophotomaps (Google Maps, Bing etc.) allows for quick and cheap mapping of unsurveyed areas.

Estuarine Sediment Deposition during Wetland Restoration: A GIS and Remote Sensing Modeling Approach

NASA Technical Reports Server (NTRS)

Newcomer, Michelle; Kuss, Amber; Kentron, Tyler; Remar, Alex; Choksi, Vivek; Skiles, J. W.

2011-01-01

Restoration of the industrial salt flats in the San Francisco Bay, California is an ongoing wetland rehabilitation project. Remote sensing maps of suspended sediment concentration, and other GIS predictor variables were used to model sediment deposition within these recently restored ponds. Suspended sediment concentrations were calibrated to reflectance values from Landsat TM 5 and ASTER using three statistical techniques -- linear regression, multivariate regression, and an Artificial Neural Network (ANN), to map suspended sediment concentrations. Multivariate and ANN regressions using ASTER proved to be the most accurate methods, yielding r2 values of 0.88 and 0.87, respectively. Predictor variables such as sediment grain size and tidal frequency were used in the Marsh Sedimentation (MARSED) model for predicting deposition rates for three years. MARSED results for a fully restored pond show a root mean square deviation (RMSD) of 66.8 mm (<1) between modeled and field observations. This model was further applied to a pond breached in November 2010 and indicated that the recently breached pond will reach equilibrium levels after 60 months of tidal inundation.

Analyzing Red and Gray Stages of Bark Beetle Attack in the San Bernardino National Forest Using Remote Sensing

NASA Astrophysics Data System (ADS)

Morgan, Andy J.

The San Bernardino National Forest (SBNF) has experienced periods of high, concentrated bark beetle epidemics in the late 1990's and into the 2000's. This increased activity has caused huge amounts of forest loss, resulting from disease introduced by bark beetles. Using remote sensing techniques and Landsat Thematic Mapper 5 (TM5) imagery, the spread of bark beetle diseased trees is mapped over a period from 1998 to 2008. Acreage of two attack stages (red and gray) were calculated from a level sliced classification method developed on data training sites. In each image using Normalized Difference Vegetation Index (NDVI) is the driver of forest health classifications. The results of the analysis are classification maps for each year, red acreage estimated for each study year, and gray attack acreage estimated for each study year. Additionally, for the period of 2001-2004, acreage was compared to those reported by the USDA with a thirteen percent lower mortality total in comparison to USDA federal land and a thirty-two percent lower total mortality (federal and non-federal) land in the SBNF.

Estimation of urban runoff and water quality using remote sensing and artificial intelligence.

PubMed

Ha, S R; Park, S Y; Park, D H

2003-01-01

Water quality and quantity of runoff are strongly dependent on the landuse and landcover (LULC) criteria. In this study, we developed a more improved parameter estimation procedure for the environmental model using remote sensing (RS) and artificial intelligence (AI) techniques. Landsat TM multi-band (7bands) and Korea Multi-Purpose Satellite (KOMPSAT) panchromatic data were selected for input data processing. We employed two kinds of artificial intelligence techniques, RBF-NN (radial-basis-function neural network) and ANN (artificial neural network), to classify LULC of the study area. A bootstrap resampling method, a statistical technique, was employed to generate the confidence intervals and distribution of the unit load. SWMM was used to simulate the urban runoff and water quality and applied to the study watershed. The condition of urban flow and non-point contaminations was simulated with rainfall-runoff and measured water quality data. The estimated total runoff, peak time, and pollutant generation varied considerably according to the classification accuracy and percentile unit load applied. The proposed procedure would efficiently be applied to water quality and runoff simulation in a rapidly changing urban area.

Developing hydrological model for water quality in Iraq marshes zone using Landsat-TM

NASA Astrophysics Data System (ADS)

Marghany, Maged; Hasab, Hashim Ali; Mansor, Shattri; Shariff, Abdul Rashid Bin Mohamed

2016-06-01

The Mesopotamia marshlands constitute the largest wetland ecosystem in the Middle East and Western Eurasia. These wetlands are located at the confluence of the Tigris and Euphrates Rivers in southern Iraq. However, there are series reductions in the wetland zones because of neighbor countries, i.e. Turkey, Syria built dams upstream of Tigris and Euphrates Rivers. In addition, the first Gulf war of the 1980s had damaged majority of the marches resources. In fact,the marshes had been reduced in size to less than 7% since 1973 and had deteriorated in water quality parameters. The study integrates Hydrological Model of RMA-2 with Geographic Information System, and remote sensing techniques to map the water quality in the marshlands south of Iraq. This study shows that RMA-2 shows the two dimensional water flow pattern and water quality quantities in the marshlands. It can be said that the integration between Hydrological Model of RMA-2, Geographic Information System, and remote sensing techniques can be used to monitor water quality in the marshlands south of Iraq.

[Study on suitable distribution areas of Grifola umbellate in Sichuan province based on remote sensing and GIS].

PubMed

Zhang, You; Wang, Juan; Zhang, Jie; Peng, Wen-Fu; Xu, Xin-Liang; Fang, Qing-Mao

2016-09-01

Grifola umbellate is the important medicinal materials in China which has a very high medicinal value. This study analyzedthe suitable distribution areasof G. umbellate and provided scientific basis for determining G. umbellate planting regions and planning production distribution reasonably. The suitable distribution areas of G. umbellate in Sichuan province was researched based on TM, ETM+, and DEM data,the key ecological factors that affect the growth of G. umbellate were extracted, including elevation, slope, aspect, average annual temperature,average annual precipitation,forest information,soil information, following remote sensing and GIS techniques, combining field researchdata. The results showed that the G. umbellate resources in Sichuan province were mainly distributed in Pingwu, Beichuan, Licountry, Yanyuan, Xichang, Dechang, Yanbian, Miyi, Huidong, Panzhihua and so on, the suitability distribution areas is 276.214 4 km² approximately and accounting for more than 0.143 3% of the total area.According to the related document information and the field investigation, showed that the suitability distribution based on RS and GIS were corresponded with the actual distribution areas of G. umbellate. Copyright© by the Chinese Pharmaceutical Association.

Forest to agriculture conversion in southern Belize: Implications for migrant land birds

USGS Publications Warehouse

Spruce, J.P.; Dowell, B.A.; Robbins, C.S.; Sader, S.A.; Doyle, Jamie K.; Schelhas, John

1993-01-01

Central America offers a suite of neotropical habitats vital to overwintering migrant land birds. The recent decline of many forest dwelling avian migrants is believed to be related in part to neotropical deforestation and land use change. However, spatio-temporal trends in neotropical habitat availability and avian migrant habitat use are largely unknown. Such information is needed to assess the impact of agriculture conversion on migrant land birds. In response, the USDI Fish and Wildlife Service and the University of Maine began a cooperative study in 1988 which applies remote sensing and field surveys to determine current habitat availability and avian migrant habitat use. Study sites include areas in Belize, Costa Rica, Guatemala and southern Mexico. Visual assessment of Landsat TM imagery indicates southern Belize forests are fragmented by various agricultural systems. Shifting agriculture is predominant in some areas, while permanent agriculture (citrus and mixed animal crops) is the primary system in others. This poster focuses on efforts to monitor forest to agriculture conversion in southern Belize using remote sensing, field surveys and GIS techniques. Procedures and avian migrant use of habitat are summarized.

Satellite-based monitoring of cotton evapotranspiration

NASA Astrophysics Data System (ADS)

Dalezios, Nicolas; Dercas, Nicholas; Tarquis, Ana Maria

2016-04-01

Water for agricultural use represents the largest share among all water uses. Vulnerability in agriculture is influenced, among others, by extended periods of water shortage in regions exposed to droughts. Advanced technological approaches and methodologies, including remote sensing, are increasingly incorporated for the assessment of irrigation water requirements. In this paper, remote sensing techniques are integrated for the estimation and monitoring of crop evapotranspiration ETc. The study area is Thessaly central Greece, which is a drought-prone agricultural region. Cotton fields in a small agricultural sub-catchment in Thessaly are used as an experimental site. Daily meteorological data and weekly field data are recorded throughout seven (2004-2010) growing seasons for the computation of reference evapotranspiration ETo, crop coefficient Kc and cotton crop ETc based on conventional data. Satellite data (Landsat TM) for the corresponding period are processed to estimate cotton crop coefficient Kc and cotton crop ETc and delineate its spatiotemporal variability. The methodology is applied for monitoring Kc and ETc during the growing season in the selected sub-catchment. Several error statistics are used showing very good agreement with ground-truth observations.

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the registration of Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Zavorine, Ilya

1999-01-01

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

An Application of Rotation- and Translation-Invariant Overcomplete Wavelets to the Registration of Remotely Sensed Imagery

NASA Technical Reports Server (NTRS)

LeMoigne, Jacqueline; Zavorine, Ilya

1999-01-01

A wavelet-based image registration approach has previously been proposed by the authors. In this work, wavelet coefficient maxima obtained from an orthogonal wavelet decomposition using Daubechies filters were utilized to register images in a multi-resolution fashion. Tested on several remote sensing datasets, this method gave very encouraging results. Despite the lack of translation-invariance of these filters, we showed that when using cross-correlation as a feature matching technique, features of size larger than twice the size of the filters are correctly registered by using the low-frequency subbands of the Daubechies wavelet decomposition. Nevertheless, high-frequency subbands are still sensitive to translation effects. In this work, we are considering a rotation- and translation-invariant representation developed by E. Simoncelli and integrate it in our image registration scheme. The two types of filters, Daubechies and Simoncelli filters, are then being compared from a registration point of view, utilizing synthetic data as well as data from the Landsat/ Thematic Mapper (TM) and from the NOAA Advanced Very High Resolution Radiometer (AVHRR).

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

PubMed

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

2006-02-16

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

[Monitoring of soil salinization in Northern Tarim Basin, Xinjiang of China in dry and wet seasons based on remote sensing].

PubMed

Yao, Yuan; Ding, Jian-Li; Zhang, Fang; Wang, Gang; Jiang, Hong-Nan

2013-11-01

Soil salinization is one of the most important eco-environment problems in arid area, which can not only induce land degradation, inhibit vegetation growth, but also impede regional agricultural production. To accurately and quickly obtain the information of regional saline soils by using remote sensing data is critical to monitor soil salinization and prevent its further development. Taking the Weigan-Kuqa River Delta Oasis in the northern Tarim River Basin of Xinjiang as test object, and based on the remote sensing data from Landsat-TM images of April 15, 2011 and September 22, 2011, in combining with the measured data from field survey, this paper extracted the characteristic variables modified normalized difference water index (MNDWI), normalized difference vegetation index (NDVI), and the third principal component from K-L transformation (K-L-3). The decision tree method was adopted to establish the extraction models of soil salinization in the two key seasons (dry and wet seasons) of the study area, and the classification maps of soil salinization in the two seasons were drawn. The results showed that the decision tree method had a higher discrimination precision, being 87.2% in dry season and 85.3% in wet season, which was able to be used for effectively monitoring the dynamics of soil salinization and its spatial distribution, and to provide scientific basis for the comprehensive management of saline soils in arid area and the rational utilization of oasis land resources.

An Enhanced Text-Mining Framework for Extracting Disaster Relevant Data through Social Media and Remote Sensing Data Fusion

NASA Astrophysics Data System (ADS)

Scheele, C. J.; Huang, Q.

2016-12-01

In the past decade, the rise in social media has led to the development of a vast number of social media services and applications. Disaster management represents one of such applications leveraging massive data generated for event detection, response, and recovery. In order to find disaster relevant social media data, current approaches utilize natural language processing (NLP) methods based on keywords, or machine learning algorithms relying on text only. However, these approaches cannot be perfectly accurate due to the variability and uncertainty in language used on social media. To improve current methods, the enhanced text-mining framework is proposed to incorporate location information from social media and authoritative remote sensing datasets for detecting disaster relevant social media posts, which are determined by assessing the textual content using common text mining methods and how the post relates spatiotemporally to the disaster event. To assess the framework, geo-tagged Tweets were collected for three different spatial and temporal disaster events: hurricane, flood, and tornado. Remote sensing data and products for each event were then collected using RealEarthTM. Both Naive Bayes and Logistic Regression classifiers were used to compare the accuracy within the enhanced text-mining framework. Finally, the accuracies from the enhanced text-mining framework were compared to the current text-only methods for each of the case study disaster events. The results from this study address the need for more authoritative data when using social media in disaster management applications.

Analysis of Urban Expansion of the Resort City of Al Ain Using Remote Sensing and GIS

NASA Astrophysics Data System (ADS)

Issa, S.; Al Shuwaihi, A.

2009-12-01

The urban growth of AL Ain city has been investigated using remote sensing data for three different dates, 1972, 1990 and 2000. We used three Landsat images together with socio-economic data in a post-classification analysis to map the spatial dynamics of land use/cover changes and identify the urbanization process in Al Ain resort city, United Arab Emirates. Land use/cover statistics, extracted from Landsat Multi-spectral Scanner (MSS). Thematic Mapper (TM) and Enhanced Thematic Mapper plus (ETM +) images for 1972. 1990 and 2000 respectively, revealed that the built-up area has expanded by about 170.53km2. The city was found to have a tendency for major expansion in four different directions: along the Abu Dhabi highway, along Dubai highway, Myziad direction and Hafeet recreational area. Expansion in any direction was found to be governed by the availability of road network, suitability for construction, utilities, economic activities, geographical constraints, and legal factors (boundary with Sultanate of Oman). The road network in particular has influenced the spatial patterns and structure of urban development, so that the expansion of the built-up areas has assumed an accretive as well as linear growth along the major roads. The research concludes that the development is based on conservation of agricultural areas (oases) and reclamation of the desert for farming and agricultural activities. The integration of remote sensing and GIS was found to be effective in monitoring LULC changes and providing valuable information necessary for planning and research.

Single-scattering properties of ice particles in the microwave regime: Temperature effect on the ice refractive index with implications in remote sensing

NASA Astrophysics Data System (ADS)

Ding, Jiachen; Bi, Lei; Yang, Ping; Kattawar, George W.; Weng, Fuzhong; Liu, Quanhua; Greenwald, Thomas

2017-03-01

An ice crystal single-scattering property database is developed in the microwave spectral region (1 to 874 GHz) to provide the scattering, absorption, and polarization properties of 12 ice crystal habits (10-plate aggregate, 5-plate aggregate, 8-column aggregate, solid hexagonal column, hollow hexagonal column, hexagonal plate, solid bullet rosette, hollow bullet rosette, droxtal, oblate spheroid, prolate spheroid, and sphere) with particle maximum dimensions from 2 μm to 10 mm. For each habit, four temperatures (160, 200, 230, and 270 K) are selected to account for temperature dependence of the ice refractive index. The microphysical and scattering properties include projected area, volume, extinction efficiency, single-scattering albedo, asymmetry factor, and six independent nonzero phase matrix elements (i.e. P11, P12, P22, P33, P43 and P44). The scattering properties are computed by the Invariant Imbedding T-Matrix (II-TM) method and the Improved Geometric Optics Method (IGOM). The computation results show that the temperature dependence of the ice single-scattering properties in the microwave region is significant, particularly at high frequencies. Potential active and passive remote sensing applications of the database are illustrated through radar reflectivity and radiative transfer calculations. For cloud radar applications, ignoring temperature dependence has little effect on ice water content measurements. For passive microwave remote sensing, ignoring temperature dependence may lead to brightness temperature biases up to 5 K in the case of a large ice water path.

Mapping wildfire burn severity in the Arctic Tundra from downsampled MODIS data

USGS Publications Warehouse

Kolden, Crystal A.; Rogan, John

2013-01-01

Wildfires are historically infrequent in the arctic tundra, but are projected to increase with climate warming. Fire effects on tundra ecosystems are poorly understood and difficult to quantify in a remote region where a short growing season severely limits ground data collection. Remote sensing has been widely utilized to characterize wildfire regimes, but primarily from the Landsat sensor, which has limited data acquisition in the Arctic. Here, coarse-resolution remotely sensed data are assessed as a means to quantify wildfire burn severity of the 2007 Anaktuvuk River Fire in Alaska, the largest tundra wildfire ever recorded on Alaska's North Slope. Data from Landsat Thematic Mapper (TM) and downsampled Moderate-resolution Imaging Spectroradiometer (MODIS) were processed to spectral indices and correlated to observed metrics of surface, subsurface, and comprehensive burn severity. Spectral indices were strongly correlated to surface severity (maximum R2 = 0.88) and slightly less strongly correlated to substrate severity. Downsampled MODIS data showed a decrease in severity one year post-fire, corroborating rapid vegetation regeneration observed on the burned site. These results indicate that widely-used spectral indices and downsampled coarse-resolution data provide a reasonable supplement to often-limited ground data collection for analysis and long-term monitoring of wildfire effects in arctic ecosystems.

Fast variogram analysis of remotely sensed images in HPC environment

NASA Astrophysics Data System (ADS)

Pesquer, Lluís; Cortés, Anna; Masó, Joan; Pons, Xavier

2013-04-01

Exploring and describing spatial variation of images is one of the main applications of geostatistics to remote sensing. The variogram is a very suitable tool to carry out this spatial pattern analysis. Variogram analysis is composed of two steps: empirical variogram generation and fitting a variogram model. The empirical variogram generation is a very quick procedure for most analyses of irregularly distributed samples, but time consuming increases quite significantly for remotely sensed images, because number of samples (pixels) involved is usually huge (more than 30 million for a Landsat TM scene), basically depending on extension and spatial resolution of images. In several remote sensing applications this type of analysis is repeated for each image, sometimes hundreds of scenes and sometimes for each radiometric band (high number in the case of hyperspectral images) so that there is a need for a fast implementation. In order to reduce this high execution time, we carried out a parallel solution of the variogram analyses. The solution adopted is the master/worker programming paradigm in which the master process distributes and coordinates the tasks executed by the worker processes. The code is written in ANSI-C language, including MPI (Message Passing Interface) as a message-passing library in order to communicate the master with the workers. This solution (ANSI-C + MPI) guarantees portability between different computer platforms. The High Performance Computing (HPC) environment is formed by 32 nodes, each with two Dual Core Intel(R) Xeon (R) 3.0 GHz processors with 12 Gb of RAM, communicated with integrated dual gigabit Ethernet. This IBM cluster is located in the research laboratory of the Computer Architecture and Operating Systems Department of the Universitat Autònoma de Barcelona. The performance results for a 15km x 15km subcene of 198-31 path-row Landsat TM image are shown in table 1. The proximity between empirical speedup behaviour and theoretical linear speedup confirms a suitable parallel design and implementation applied. N Workers Time (s) Speedup 0 2975.03 2 2112.33 1.41 4 1067.45 2.79 8 534.18 5.57 12 357.54 8.32 16 269.00 11.06 20 216.24 13.76 24 186.31 15.97 Furthermore, very similar performance results are obtained for CASI images (hyperspectral and finer spatial resolution than Landsat), showed in table 2, and demonstrating that the distributed load design is not specifically defined and optimized for unique type of images, but it is a flexible design that maintains a good balance and scalability suitable for different range of image dimensions. N Workers Time (s) Speedup 0 5485.03 2 3847.47 1.43 4 1921.62 2.85 8 965.55 5.68 12 644.26 8.51 16 483.40 11.35 20 393.67 13.93 24 347.15 15.80 28 306.33 17.91 32 304.39 18.02 Finally, we conclude that this significant time reduction underlines the utility of distributed environments for processing large amount of data as remotely sensed images.

COVER Project and Earth resources research transition

NASA Technical Reports Server (NTRS)

Botkin, D. B.; Estes, J. E. (Principal Investigator)

1986-01-01

Results of research in the remote sensing of natural boreal forest vegetation (the COVER project) are summarized. The study objectives were to establish a baseline forest test site; develop transforms of LANDSAT MSS and TM data for forest composition, biomass, leaf area index, and net primary productivity; and perform tasks required for testing hypotheses regarding observed spectral responses to changes in leaf area index in aspen. In addition, the transfer and documentation of data collected in the COVER project (removed from the Johnson Space Center following the discontinuation of Earth resources research at that facility) is described.

LANDSAT-4 evaluation program and scientific characterization activities

NASA Technical Reports Server (NTRS)

Barker, J. L.

1983-01-01

The characterization objectives of the LANDSAT 4 Science Office at GSFC are to: (1) determine the accuracy and precision of sensor and spacecraft performance, image data quality, and derived information; (2) recommend LANDSAT 4 system improvements; and (3) communicate results to the research community. In-house activities are directed toward full access and utilization of the prelaunch and in-orbit engineering test data on the sensor and spacecraft. Principle scientists in remote sensing are involved as part of a major scientific characterization effort, and workshops were held for these investigative teams. A symposium is scheduled prior to turnover of the TM to NOAA.

Remotely Sensed Index of Deforestation/Urbanization for use in Climate Models

NASA Technical Reports Server (NTRS)

Carlson, Toby N.

1996-01-01

The purpose of this investigation is to use a new method for deriving land surface parameters from a combination of thermal infrared and vegetation index measurements from satellites (Landsat-TM, and NOAA-AVHRR) and to integrate these parameters with more conventional data bases. We have completed an investigation of urbanization in the State College, PA area and have begun work in Chester County, PA, and Costa Rica. Our basic hypothesis is that changes in land use, including deforestation, exert a profound influence on local microclimates whose effects may greatly exceed in importance those occurring on larger scales.

a New Multi-Spectral Threshold Normalized Difference Water Index Mst-Ndwi Water Extraction Method - a Case Study in Yanhe Watershed

NASA Astrophysics Data System (ADS)

Zhou, Y.; Zhao, H.; Hao, H.; Wang, C.

2018-05-01

Accurate remote sensing water extraction is one of the primary tasks of watershed ecological environment study. Since the Yanhe water system has typical characteristics of a small water volume and narrow river channel, which leads to the difficulty for conventional water extraction methods such as Normalized Difference Water Index (NDWI). A new Multi-Spectral Threshold segmentation of the NDWI (MST-NDWI) water extraction method is proposed to achieve the accurate water extraction in Yanhe watershed. In the MST-NDWI method, the spectral characteristics of water bodies and typical backgrounds on the Landsat/TM images have been evaluated in Yanhe watershed. The multi-spectral thresholds (TM1, TM4, TM5) based on maximum-likelihood have been utilized before NDWI water extraction to realize segmentation for a division of built-up lands and small linear rivers. With the proposed method, a water map is extracted from the Landsat/TM images in 2010 in China. An accuracy assessment is conducted to compare the proposed method with the conventional water indexes such as NDWI, Modified NDWI (MNDWI), Enhanced Water Index (EWI), and Automated Water Extraction Index (AWEI). The result shows that the MST-NDWI method generates better water extraction accuracy in Yanhe watershed and can effectively diminish the confusing background objects compared to the conventional water indexes. The MST-NDWI method integrates NDWI and Multi-Spectral Threshold segmentation algorithms, with richer valuable information and remarkable results in accurate water extraction in Yanhe watershed.

Long-term remote monitoring of salt marsh biomass

NASA Astrophysics Data System (ADS)

Gross, M. F.; Klemas, V.; Hardisky, M. A.

1990-12-01

An objective of NASA's Biospheric Research Program is to understand biogeochemical cycling on a global scale. Being both very biologically productive and anoxic, wetlands are major sites of carbon dioxide, mean, and sulfur gas flux on a per area basis. Biogeochemical cycling in wetlands is intricately linked to vegetation biomass production. We have been monitoring biomass dynamics of the dominant salt marsh grass Spartina alterniflora for over ten years using remote sensing. Live above ground biomass is highly correlated (r = .79) with Laridsat Thematic Mapper ('IN) and SPOT spectral data transformed into normalized difference vegetation indices. Live belowg round biomass is, in turn, highly correlated (r = .86) with live above ground biomass. Therefore, below ground biomass, a source of carbon substrates for microbial gas production, can be measured using remote sensing indirectly. These relationships have been tested over a wide latitudinal range (from Georgia to Nova Scotia). Analysis of TM and SPOT satellite images from several years has revealed substantial interannual variability in mean live aerial biomass of this species in a 580ha Delaware marsh. Additionally, interannual spatial variability in biomass distribution within the marsh is evident and seems to be linked to precipitation. The aerial biomass of high salinity areas least influenced by upland runoff is the most sensitive to precipitation, whereas marsh areas adjacent to large upland areas or freshwater creeks are the least sensitive. In summary, remote sensing is an effective tool for studying aboveground and belowground biomass in salt marshes. Once the relationship between gas flux data and vegetation biomass is better understood, satellite data could be used to estimate biomass arid gas flux over large regions of the world.

Use of vegetation indices to estimate intercepted solar radiation and net carbon dioxide exchange of a grass canopy

NASA Technical Reports Server (NTRS)

Bartlett, David S.; Whiting, Gary J.; Hartman, Jean M.

1989-01-01

Results are presented from field experiments relating spectral reflectance to intercepted photosynthetically active radiation (PAR) and net CO2 exchange in a natural canopy composed of the marsh cordgrass (Spartina alterniflora). Reflectance measurements made by a hand-held radiometer with Landsat TM spectral wavebands are used to compute remote sensing indices such as the normalized difference vegetation index. Consideration is given to the impact of standing dead canopy material on the relationship between intercepted PAR and spectral vegetation indices and the impact of changes in photosynthetic efficiency on the relationship between vegetation indices and CO2 exchange rates. The results suggest that quantitative remote assessment of photosynthesis and net gas exchange in natural vegetation is feasible, especially if the analysis incorporates information on biological responses to environmental variables.

Image Texture Predicts Avian Density and Species Richness

PubMed Central

Wood, Eric M.; Pidgeon, Anna M.; Radeloff, Volker C.; Keuler, Nicholas S.

2013-01-01

For decades, ecologists have measured habitat attributes in the field to understand and predict patterns of animal distribution and abundance. However, the scale of inference possible from field measured data is typically limited because large-scale data collection is rarely feasible. This is problematic given that conservation and management typical require data that are fine grained yet broad in extent. Recent advances in remote sensing methodology offer alternative tools for efficiently characterizing wildlife habitat across broad areas. We explored the use of remotely sensed image texture, which is a surrogate for vegetation structure, calculated from both an air photo and from a Landsat TM satellite image, compared with field-measured vegetation structure, characterized by foliage-height diversity and horizontal vegetation structure, to predict avian density and species richness within grassland, savanna, and woodland habitats at Fort McCoy Military Installation, Wisconsin, USA. Image texture calculated from the air photo best predicted density of a grassland associated species, grasshopper sparrow (Ammodramus savannarum), within grassland habitat (R2 = 0.52, p-value <0.001), and avian species richness among habitats (R2 = 0.54, p-value <0.001). Density of field sparrow (Spizella pusilla), a savanna associated species, was not particularly well captured by either field-measured or remotely sensed vegetation structure variables, but was best predicted by air photo image texture (R2 = 0.13, p-value = 0.002). Density of ovenbird (Seiurus aurocapillus), a woodland associated species, was best predicted by pixel-level satellite data (mean NDVI, R2 = 0.54, p-value <0.001). Surprisingly and interestingly, remotely sensed vegetation structure measures (i.e., image texture) were often better predictors of avian density and species richness than field-measured vegetation structure, and thus show promise as a valuable tool for mapping habitat quality and characterizing biodiversity across broad areas. PMID:23675463

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

NASA Astrophysics Data System (ADS)

Perni, Venkateswarlu

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

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

NASA Astrophysics Data System (ADS)

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

2014-12-01

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

Optical image modulation above the submarine bottom topography: a case study on the Taiwan Banks, China

NASA Astrophysics Data System (ADS)

Li, Yan; Hu, Jianyu; Li, Jing; Fu, Bin; Ma, Liming

2003-05-01

A possible mechanism to explain the correlation between submarine topography and the direct sunlight specially reflected from the sea surface with variable roughness caused by the bottom-current effect was suggested fifteen years ago by Henning et al. in International Journal of Remote Sensing, 9, 45-67, after comparing radar satellite image and Skylab satellite photograph of the North American east coast (Nantucket Shoals) with submarine relief features. A case study is carried out in the famous sand waves field located at the Taiwan banks of Taiwan Strait in August 1998. The TM images, either visible bands (TM1, TM2, TM3) or near infrared bands (TM4, TM5, TM7), shows submarine relief features for sand waves, with wavelength of 300 to 2000 meters, riding on the lager scale sand ridges and channel system. Sea truth data including 660 nm beam attenuation coefficient profiles were conducted in the same period. We compare signals of TM images, attenuation coefficient profiles, and sounding maps of the Taiwan Bands. The subsurface upwelling signals with contributions of the water column and the bottom, either estimated by single or quasi-single-scattering theory or revealed by the TM images after removing the contribution of direct sunlight reflected signals from sea surface, were too weak to distinguish the ridges and troughs of bedforms especially for red and near infrared bands. However, the direct sunlight specially reflected signals from the sea surface, approximately at same level in water-leaving reflectance not only for visible bands (TM1, TM2, TM3) but also for near infrared bands (TM4, TM5, TM7), was the major submarine bottom topography signals especially for those pixels towards the direction of the sun azimuth. Following a physical description for the lee waves appeared on free surface when the current flows round an underwater obstacle, the direct sunlight reflected signals related wave face slope, is dominated by the height and depth of sand waves and sand ridges, and current speed of the flows over those bedforms. The direct sunlight reflected signals from the sea surface could be regarded as a powerful tool to detect bedforms and other underwater obstacles.

Determining crop residue type and class using satellite acquired data. M.S. Thesis Progress Report, Jun. 1990

NASA Technical Reports Server (NTRS)

Zhuang, Xin

1990-01-01

LANDSAT Thematic Mapper (TM) data for March 23, 1987 with accompanying ground truth data for the study area in Miami County, IN were used to determine crop residue type and class. Principle components and spectral ratioing transformations were applied to the LANDSAT TM data. One graphic information system (GIS) layer of land ownership was added to each original image as the eighth band of data in an attempt to improve classification. Maximum likelihood, minimum distance, and neural networks were used to classify the original, transformed, and GIS-enhanced remotely sensed data. Crop residues could be separated from one another and from bare soil and other biomass. Two types of crop residue and four classes were identified from each LANDSAT TM image. The maximum likelihood classifier performed the best classification for each original image without need of any transformation. The neural network classifier was able to improve the classification by incorporating a GIS-layer of land ownership as an eighth band of data. The maximum likelihood classifier was unable to consider this eighth band of data and thus, its results could not be improved by its consideration.

BOREAS Level-3a Landsat TM Imagery: Scaled At-sensor Radiance in BSQ Format

NASA Technical Reports Server (NTRS)

Nickerson, Jaime; Hall, Forrest G. (Editor); Knapp, David; Newcomer, Jeffrey A.; Cihlar, Josef

2000-01-01

For BOREAS, the level-3a Landsat TM data, along with the other remotely sensed images, were collected in order to provide spatially extensive information over the primary study areas. This information includes radiant energy, detailed land cover, and biophysical parameter maps such as FPAR and LAI. Although very similar in content to the level-3s Landsat TM products, the level-3a images were created to provide users with a more usable BSQ format and to provide information that permitted direct determination of per-pixel latitude and longitude coordinates. Geographically, the level-3a images cover the BOREAS NSA and SSA. Temporally, the images cover the period of 22-Jun-1984 to 30-Jul-1996. The images are available in binary, image-format files. With permission from CCRS and RSI, several of the full-resolution images are included on the BOREAS CD-ROM series. Due to copyright issues, the images not included on the CD-ROM may not be publicly available. See Sections 15 and 16 for information about how to acquire the data. Information about the images not on the CD-ROMs is provided in an inventory listing on the CD-ROMs.

Assessment of underground water potential zones using modern geomatics technologies in Jhansi district, Uttar Pradesh, India.

NASA Astrophysics Data System (ADS)

Pandey, N. K.; Shukla, A. K.; Shukla, S.; Pandey, M.

2014-11-01

Ground water is a distinguished component of the hydrologic cycle. Surface water storage and ground water withdrawal are traditional engineering approaches which will continue to be followed in the future. The uncertainty about the occurrence, distribution and quality aspect of the ground water and the energy requirement for its withdrawal impose restriction on exploitation of ground water. The main objective of the study is assessment of underground water potential zones of Jhansi city and surrounding area, by preparing underground water potential zone map using Geographical Information System (GIS), remote sensing, and validation by underground water inventory mapping using GPS field survey done along the parts of National Highway 25 and 26 and some state highway passing through the study area. Study area covers an area of 1401 km2 and its perimeter is approximate 425 km. For this study Landsat TM (0.76-0.90 um) band data were acquired from GLCF website. Sensor spatial resolution is 30 m. Satellite image has become a standard tool aiding in the study of underground water. Extraction of different thematic layers like Land Use Land Cover (LULC), settlement, etc. can be done through unsupervised classification. The modern geometics technologies viz. remote sensing and GIS are used to produce the map that classifies the groundwater potential zone to a number of qualitative zone such as very high, high, moderate, low or very low. Thematic maps are prepared by visual interpretation of Survey of India topo-sheets and linearly enhanced Landsat TM satellite image on 1 : 50,000 scale using AutoCAD, ArcGIS 10.1 and ERDAS 11 software packages.

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

PubMed

Wen, Yuming; Khosrowpanah, Shahram; Heitz, Leroy

2011-08-01

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

An application of remote sensing data in mapping landscape-level forest biomass for monitoring the effectiveness of forest policies in northeastern China.

PubMed

Wang, Xinchuang; Shao, Guofan; Chen, Hua; Lewis, Bernard J; Qi, Guang; Yu, Dapao; Zhou, Li; Dai, Limin

2013-09-01

Monitoring the dynamics of forest biomass at various spatial scales is important for better understanding the terrestrial carbon cycle as well as improving the effectiveness of forest policies and forest management activities. In this article, field data and Landsat image data acquired in 1999 and 2007 were utilized to quantify spatiotemporal changes of forest biomass for Dongsheng Forestry Farm in Changbai Mountain region of northeastern China. We found that Landsat TM band 4 and Difference Vegetation Index with a 3 × 3 window size were the best predictors associated with forest biomass estimations in the study area. The inverse regression model with Landsat TM band 4 predictor was found to be the best model. The total forest biomass in the study area decreased slightly from 2.77 × 10(6) Mg in 1999 to 2.73 × 10(6) Mg in 2007, which agreed closely with field-based model estimates. The area of forested land increased from 17.9 × 10(3) ha in 1999 to 18.1 × 10(3) ha in 2007. The stabilization of forest biomass and the slight increase of forested land occurred in the period following implementations of national forest policies in China in 1999. The pattern of changes in both forest biomass and biomass density was altered due to different management regimes adopted in light of those policies. This study reveals the usefulness of the remote sensing-based approach for detecting and monitoring quantitative changes in forest biomass at a landscape scale.

Monitoring the coastline change of Hatiya Island in Bangladesh using remote sensing techniques

NASA Astrophysics Data System (ADS)

Ghosh, Manoj Kumer; Kumar, Lalit; Roy, Chandan

2015-03-01

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.

Examining in vivo tympanic membrane mobility using smart phone video-otoscopy and phase-based Eulerian video magnification

NASA Astrophysics Data System (ADS)

Janatka, Mirek; Ramdoo, Krishan S.; Tatla, Taran; Pachtrachai, Krittin; Elson, Daniel S.; Stoyanov, Danail

2017-03-01

The tympanic membrane (TM) is the bridging element between the pressure waves of sound in air and the ossicular chain. It allows for sound to be conducted into the inner ear, achieving the human sense of hearing. Otitis media with effusion (OME, commonly referred to as `glue ear') is a typical condition in infants that prevents the vibration of the TM and causes conductive hearing loss, this can lead to stunting early stage development if undiagnosed. Furthermore, OME is hard to identify in this age group; as they cannot respond to typical audiometry tests. Tympanometry allows for the mobility of the TM to be examined without patient response, but requires expensive apparatus and specialist training. By combining a smartphone equipped with a 240 frames per second video recording capability with an otoscopic clip-on accessory, this paper presents a novel application of Eulerian Video Magnification (EVM) to video-otology, that could provide assistance in diagnosing OME. We present preliminary results showing a spatio-temporal slice taken from an exaggerated video visualization of the TM being excited in vivo on a healthy ear. Our preliminary results demonstrate the potential for using such an approach for diagnosing OME under visual inspection as alternative to tympanometry, which could be used remotely and hence help diagnosis in a wider population pool.

Net radiation estimated by remote sensing in Cerrado areas in the Upper Paraguay River Basin

NASA Astrophysics Data System (ADS)

Fausto, Marcos Alves; Machado, Nadja Gomes; de Souza Nogueira, José; Biudes, Marcelo Sacardi

2014-01-01

The Cerrado is a heterogeneous landscape which is shrinking due to deforestation, giving rise to managed ecosystems. The land cover changes alter net radiation (Rn), which determines the quantity of available energy to the energy balance partition. The objectives of this study were (1) to determine the spatial pattern of the vegetation indices, albedo, and land surface temperature (LST) and (2) to evaluate the Rn estimated by Landsat 5 Thematic Mapper (TM) images over Cerrado areas in the Upper Paraguay River Basin. We estimated the vegetation indices, albedo, LST, and Rn of five selected vegetation types. The values estimated by Landsat 5 TM images had seasonal variations with higher values of the vegetation indices and lower values of the albedo and the LST during the wet season. The riparian and Cerrado strictu sensu had higher values of vegetation indices and lower albedo and LST than grasslands. The Rn estimated by Landsat 5 TM images was highly correlated with the measured Rn. The Rn had a seasonal pattern, following the solar radiation, with higher values during the wet season and varied spatially with higher values in the riparian forest and Cerrado strictu sensu and lower in the grasslands. This study showed the applicability of the Landsat 5 TM images to estimate Rn, which can help to understand the heterogeneity in the study area.

Remote sensing and GIS integration: Towards intelligent imagery within a spatial data infrastructure

NASA Astrophysics Data System (ADS)

Abdelrahim, Mohamed Mahmoud Hosny

2001-11-01

In this research, an "Intelligent Imagery System Prototype" (IISP) was developed. IISP is an integration tool that facilitates the environment for active, direct, and on-the-fly usage of high resolution imagery, internally linked to hidden GIS vector layers, to query the real world phenomena and, consequently, to perform exploratory types of spatial analysis based on a clear/undisturbed image scene. The IISP was designed and implemented using the software components approach to verify the hypothesis that a fully rectified, partially rectified, or even unrectified digital image can be internally linked to a variety of different hidden vector databases/layers covering the end user area of interest, and consequently may be reliably used directly as a base for "on-the-fly" querying of real-world phenomena and for performing exploratory types of spatial analysis. Within IISP, differentially rectified, partially rectified (namely, IKONOS GEOCARTERRA(TM)), and unrectified imagery (namely, scanned aerial photographs and captured video frames) were investigated. The system was designed to handle four types of spatial functions, namely, pointing query, polygon/line-based image query, database query, and buffering. The system was developed using ESRI MapObjects 2.0a as the core spatial component within Visual Basic 6.0. When used to perform the pre-defined spatial queries using different combinations of image and vector data, the IISP provided the same results as those obtained by querying pre-processed vector layers even when the image used was not orthorectified and the vector layers had different parameters. In addition, the real-time pixel location orthorectification technique developed and presented within the IKONOS GEOCARTERRA(TM) case provided a horizontal accuracy (RMSE) of +/- 2.75 metres. This accuracy is very close to the accuracy level obtained when purchasing the orthorectified IKONOS PRECISION products (RMSE of +/- 1.9 metre). The latter cost approximately four times as much as the IKONOS GEOCARTERRA(TM) products. The developed IISP is a step closer towards the direct and active involvement of high-resolution remote sensing imagery in querying the real world and performing exploratory types of spatial analysis. (Abstract shortened by UMI.)

The correlation of sedimentation processes and land use through remote sensing: The case study of the Jequia Lagoon, Alagoas, Brazil

NASA Astrophysics Data System (ADS)

Ferreira Gomes, Heliene

This study is focused on mapping the correlation between the land-cover change, sedimentation processes and the spectral radiance patterns along the Jequia estuary using field observations and reference information, laboratory spectral reflectance measurements and LANDSAT Thematic Mapper (TM) data. The Jequia estuary comprises the lagoon and its surrounding and a tidal channel to the Atlantic Ocean. It is inserted in Tertiary and Quaternary deposits covered mainly by sugar-cane crops and rare remains of the moist tropical forest. Bottom sediments along the lagoon were sampled and used to analyze their grain size distribution and mineral content by X-ray diffraction. Those samples were also used in an experiment where several sediment concentrations were simulated and their spectra's reflectance was determined. Silt is the predominating grain size within the lagoon. Quartz and kaolinite are the main minerals present within the silt and clay grain size fractions. Aerial photographs, satellite quick-look recorded in 1989 and a LANDSAT TM digital image recorded in 1990, were analyzed. The satellite data were analyzed through the photo-interpretation and quantitative approaches. Eight land-use units were mapped based on the aerial photographs and six land-use units were defined from the quick-look image. Despite the different scales within the data, we recognized that the most significant change in land-cover from 1968 to 1990 was the clearing of almost 100% of the moist tropical forest and its replacement by sugarcane crops. Overall, the digital results (0,48 to 0,82 mum) using standard classifiers indicate three water classes for the lagoon and ten land-use classes for the land surrounding the lagoon. The laboratory reflectance experimentation for suspended sediments demonstrates an overall increase of reflectance with increasing wavelength range for low and high concentrations. The reflectance for high sediment concentration shows a distinctive increase close to the TM4 range (0,82 mum). Although the correlation between the results was not precisely assessed, the results within this study demonstrate that any multidisciplinary approach is significantly facilitated using remote sensing techniques.

Actual evapotranspiration estimation in a Mediterranean mountain region by means of Landsat-5 TM and TERRA/AQUA MODIS imagery and Sap Flow measurements in Pinus sylvestris forest stands.

NASA Astrophysics Data System (ADS)

Cristóbal, J.; Poyatos, R.; Ninyerola, M.; Pons, X.; Llorens, P.

2009-04-01

Evapotranspiration monitoring has important implications on global and regional climate modelling, as well as in the knowledge of the hydrological cycle and in the assessment of environmental stress that affects forest and agricultural ecosystems. An increase of evapotranspiration while precipitation remains constant, or is reduced, could decrease water availability for natural and agricultural systems and human needs. Consequently, water balance methods, as the evapotranspiration modelling, have been widely used to estimate crop and forest water needs, as well as the global change effects. Nowadays, radiometric measurements provided by Remote Sensing and GIS analysis are the technologies used to compute evapotranspiration at regional scales in a feasible way. Currently, the 38% of Catalonia (NE of the Iberian Peninsula) is covered by forests, and one of the most important forest species is Scots Pine (Pinus sylvestris) which represents the 18.4% of the area occupied by forests. The aim of this work is to model actual evapotranspiration in Pinus sylvestris forest stands, in a Mediterranean mountain region, using remote sensing data, and compare it with stand-scale sap flow measurements measured in the Vallcebre research area (42° 12' N, 1° 49' E), in the Eastern Pyrenees. To perform this study a set of 30 cloud-free TERRA-MODIS images and 10 Landsat-5 TM images of path 198 and rows 31 and 32 from June 2003 to January 2005 have been selected to perform evapotranspiration modelling in Pinus sylvestris forest stands. TERRA/AQUA MODIS images have been downloaded by means of the EOS Gateway. We have selected two different types of products which contain the remote sensing data we have used to model daily evapotranspiration, daily LST product and daily calibrated reflectances product. Landsat-5 TM images have been corrected by means of conventional techniques based on first order polynomials taking into account the effect of land surface relief using a Digital Elevation Model, obtaining an RMS less than 30 m. Radiometric correction of Landsat non-thermal bands has been done following the methodology proposed by Pons and Solé (1994) which allows to reduce the number of undesired artifacts that are due to the effects of the atmosphere or to the differential illumination which is, in turn, due to the time of the day, the location in the Earth and the relief (zones being more illuminated than others, shadows, etc). Atmospheric correction of Landsat thermal band has been carried out by means of a single-channel algorithm improvement developed by Cristóbal et al. (2009). To compute actual evapotranspiration (AET) we have used the B-Method proposed by Jakson et al. (1977) and modified by Carlson et al. (1995) and Caselles et al. (1998), based on the energy budget, that needs as an input variables net radiation (Rn) and the difference between land surface temperature (LST) and air temperature (Ta). Air temperature has been modelled by means of multiple regression analysis and GIS interpolation using ground meteorological stations. Net radiation have been computed following two approaches based on the energy balance equation using albedo, land surface temperature, air temperature and solar radiation. Both air temperature and net radiation have been modelled at a regional scale. We have compared remote sensing daily actual evapotranspiration estimates with measured canopy transpiration. Sap flux density was measured by means of Heat dissipation sensors in 12 trees per stand, sampled according to diametric distribution, corrected to account for radial patter of sap flow using the Heat Field Deformation method and then scaled-up to stand level transpiration using tree sapwood areas. Sap flow measurements are comparable with AETd as in the Scots pine stand understorey evaporation is not significant. Measurements with sap flow technique show a mean, minimum and maximum values of AETd = 2.2, 0.6 and 3.6 mm day -1, respectively (Poyatos et al. 2005). Results show, in the case of MODIS AETd modelling, a RMSE of 1.6 mm compared with sap flow measurements. These results show that computing AETd by means of MODIS data in a heterogeneous area do not offer good results due to its spatial resolution (1 km). In the case of Landsat-5 TM AETd modelling, we have obtained better results with a RMSE of 0.6 mm which are in agreement with other studies that present an estimated error of about ± 30%. Moreover, we have to take into account that Landsat-like spatial resolution seems to be the best option to estimate AETd in this kind of areas. Keywords: Actual evapotranspiration modelling, Sap Flow, Remote Sensing, Pinus sylvestris, Mediterranian region.

Bridging Epidemiology and Remote Sensing: A Case Study of Dengue Fever and Land Use and Land Cover Change in Roatán, Honduras

NASA Astrophysics Data System (ADS)

Tuholske, C.; Brooks, T.

2015-12-01

Dengue fever is one of the fastest spreading infectious diseases in Latin America and the Caribbean. As part of a yearlong epidemiological study of dengue, this paper takes the first step to model the relationship between the urban/built environment and incidents of dengue fever in Roatán, Honduras. Roatán has experienced an 80-fold increase in annual tourists since the 1990s, with over 1.2 million people now visiting the island yearly. In tandem, the Caribbean island's population has exploded from fewer than 13,000 people in the 1970s to over 100,000 people today. Using broadband remote sensed satellite imagery, this paper maps and measures how this massive influx of tourists and population has altered the island's landscape. Results from a decision tree classifying technique applied to a Landsat 5 Thematic Mapper (TM) image from 1985 and Landsat 8 Operational Land Imager (OLI) image from 2014 suggest a rapid pace of urbanization; built and impervious surface has increased over 300% in the last 30 years. Emerging research suggests, similar to other mosquito-borne diseases, a correlation between built environment and risk to dengue because of the increase in stagnate water that serve as disease-host reservoirs. This remote sensing analysis will be integrated with georeferenced household level data of cases of dengue collected during a year-long cross-sectional study of dengue patients in Roatán. The result will be to model the relationship between dengue fever and urban/built environment.

The preservation of LANDSAT data by the National Land Remote Sensing Archive

NASA Technical Reports Server (NTRS)

Boyd, John E.

1992-01-01

Digital data, acquired by the National Landsat Remote Sensing Program, document nearly two decades of global agricultural, environmental, and sociological change. The data were widely applied and continue to be essential to a variety of geologic, hydrologic, agronomic, and strategic programs and studies by governmental, academic, and commercial researchers. Landsat data were acquired by five observatories that use primarily two digital sensor systems. The Multispectral Scanner (MSS) was onboard all five Landsats, which have orbited over 19 years; the higher resolution Thematic Mapper (TM) sensor acquired data for the last 9 years on Landsats 4 and 5 only. The National Land Remote Sensing Archive preserves the 800,000 scenes, which total more than 60 terabytes of data, on master tapes that are steadily deteriorating. Data are stored at two terabytes of data, on master tapes that are steadily deteriorating. Data are stored at two locations (Sioux Falls, South Dakota and Landover, Maryland), in three archive formats. The U.S. Geological Survey's EROS Data Center has initiated a project to consolidate and convert, over the next 4 years, two of the archive formats from antiquated instrumentation tape to rotary-recorded cassette magnetic tape. The third archive format, consisting of 300,000 scenes of MSS data acquired from 1972 through 1978, will not be converted because of budgetary constraints. This data preservation project augments EDC's experience in data archiving and information management, expertise that is critical to EDC's role as a Distributed Active Archive Center for the Earth Observing System, a new and much larger national earth science program.

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

NASA Astrophysics Data System (ADS)

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

2014-10-01

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

Tunable CW diode-pumped Tm,Ho:YLiF4 laser operating at or near room temperature

NASA Technical Reports Server (NTRS)

Mcguckin, Brendan T. (Inventor); Menzies, Robert T. (Inventor)

1993-01-01

A conversion efficiency of 42 percent and slope efficiency of 60 percent relative to absorbed pump power are obtained from a continuous wave diode-pumped Tm,Ho:YLiF4 laser at 2 microns with output power of 84mW at a crystal temperature of 275K. The emission spectrum is etalon tunable over a range of 7nm (16.3 cm(sup -1) centered on 2.067 microns with fine tuning capability of the transition frequency with crystal temperature at a measured rate of -0.03/(cm)K. The effective emission cross-section is measured to be 5 x 10(sup -21) cm squared. These and other aspects of the laser performance are disclosed in the context of calculated atmospheric absorption characteristics in this spectral region and potential use in remote sensing applications. Single frequency output and frequency stabilization are achieved using an intracavity etalon in conjunction with an external reference etalon.

The influence of spectral and spatial resolution in classification approaches: Landsat TM data vs. Hyperspectral data

NASA Astrophysics Data System (ADS)

Rodríguez-Galiano, Víctor; Garcia-Soldado, Maria José; Chica-Olmo, Mario

The importance of accurate and timely information describing the nature and extent of land and natural resources is increasing especially in rapidly growing metropolitan areas. While metropolitan area decision makers are in constant need of current geospatial information on patterns and trends in land cover and land use, relatively little researchers has investigated the influence of the satellite data resolution for monitoring geo-enviromental information. In this research a suite of remote sensing and GIS techniques is applied in a land use mapping study. The main task is to asses the influence of the spatial and spectral resolution in the separability between classes and in the classificatiońs accuracy. This study has been focused in a very dynamical area with respect to land use, located in the province of Granada (SE of Spain). The classifications results of the Airborne Hyperspectral Scanner (AHS, Daedalus Enterprise Inc., WA, EEUU) at different spatial resolutions: 2, 4 and 6 m and Landsat 5 TM data have been compared.

Observation of coral reefs on Ishigaki Island, Japan, using Landsat TM images and aerial photographs

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

Matsunaga, Tsuneo; Kayanne, Hajime

1997-06-01

Ishigaki Island is located at the southwestern end of Japanese Islands and famous for its fringing coral reefs. More than twenty LANDSAT TM images in twelve years and aerial photographs taken on 1977 and 1994 were used to survey two shallow reefs on this island, Shiraho and Kabira. Intensive field surveys were also conducted in 1995. All satellite images of Shiraho were geometrically corrected and overlaid to construct a multi-date satellite data set. The effects of solar elevation and tide on satellite imagery were studied with this data set. The comparison of aerial and satellite images indicated that significant changesmore » occurred between 1977 and 1984 in Kabira: rapid formation in the western part and decrease in the eastern part of dark patches. The field surveys revealed that newly formed dark patches in the west contain young corals. These results suggest that remote sensing is useful for not only mapping but also monitoring of shallow coral reefs.« less

Response of Thematic Mapper bands to plant water stress

NASA Technical Reports Server (NTRS)

Cibula, W. G.; Zetka, E. F.; Rickman, D. L.

1992-01-01

Changes in leaf reflectance as water content decreases have been hypothesized to occur in the 1.55-1.75 and 2.08-2.35 micron wavelength regions. To evaluate this hypothesis, studies were conducted on ryegrass (Lolium multiflorum Lam.) and oats (Avena sativa L.), which were grown in a controlled, outdoor situation. Both fully-watered control beds and water-stressed beds were periodically examined with a spectroradiometer calibrated against a reflectance reference of polytetrafluoroethylene. The observed changes correspond to those predicted by stochastic leaf models employed by other investigators (leaf reflection increases in the 1.55-1.75 micron region as leaf water content decreases). Although the percentage changes in TM bands 1-3 are nearly as great as those found in TM bands 5 and 7, the absolute values of reflectance change are much lower. It is believed that these patterns are probably characteristic of a broad range of vegetation types. In terms of phenomena detection, these patterns should be considered in any practical remote sensing sensor scenario.

Healthy Buddies[TM] Reduces Body Mass Index Z-Score and Waist Circumference in Aboriginal Children Living in Remote Coastal Communities

ERIC Educational Resources Information Center

Ronsley, Rebecca; Lee, Andrew S.; Kuzeljevic, Boris; Panagiotopoulos, Constadina

2013-01-01

Background: Aboriginal children are at increased risk for obesity and type 2 diabetes. Healthy Buddies [TM]-First Nations (HB) is a curriculum-based, peer-led program promoting healthy eating, physical activity, and self-esteem. Methods: Although originally designed as a pilot pre-/post-analysis of 3 remote Aboriginal schools that requested and…

Remote sensing and landslide hazard assessment

NASA Technical Reports Server (NTRS)

Mckean, J.; Buechel, S.; Gaydos, L.

1991-01-01

Remotely acquired multispectral data are used to improve landslide hazard assessments at all scales of investigation. A vegetation map produced from automated interpretation of TM data is used in a GIS context to explore the effect of vegetation type on debris flow occurrence in preparation for inclusion in debris flow hazard modeling. Spectral vegetation indices map spatial patterns of grass senescence which are found to be correlated with soil thickness variations on hillslopes. Grassland senescence is delayed over deeper, wetter soils that are likely debris flow source areas. Prediction of actual soil depths using vegetation indices may be possible up to some limiting depth greater than the grass rooting zone. On forested earthflows, the slow slide movement disrupts the overhead timber canopy, exposes understory vegetation and soils, and alters site spectral characteristics. Both spectral and textural measures from broad band multispectral data are successful at detecting an earthflow within an undisturbed old-growth forest.

New fiber laser for lidar developments in disaster management

NASA Astrophysics Data System (ADS)

Besson, C.; Augere, B.; Canat, G.; Cezard, N.; Dolfi-Bouteyre, A.; Fleury, D.; Goular, D.; Lombard, L.; Planchat, C.; Renard, W.; Valla, M.

2014-10-01

Recent progress in fiber technology has enabled new laser designs along with all fiber lidar architectures. Their asset is to avoid free-space optics, sparing lengthy alignment procedures and yielding compact setups that are well adapted for field operations and on board applications thanks to their intrinsic vibration-resistant architectures. We present results in remote sensing for disaster management recently achieved with fiber laser systems. Field trials of a 3-paths lidar vibrometer for the remote study of modal parameters of buildings has shown that application-related constraints were fulfilled and that the obtained results are consistent with simultaneous in situ seismic sensors measurements. Remote multi-gas detection can be obtained using broadband infrared spectroscopy. Results obtained on methane concentration measurement using an infrared supercontinuum fiber laser and analysis in the 3-4 μm band are reported. For gas flux retrieval, air velocity measurement is also required. Long range scanning all-fiber wind lidars are now available thanks to innovative laser architectures. High peak power highly coherent pulses can be extracted from Er3+:Yb3+ and Tm3+ active fibers using methods described in the paper. The additional laser power provides increased coherent lidar capability in range and scanning of large areas but also better system resistance to adverse weather conditions. Wind sensing at ranges beyond 10 km have been achieved and on-going tests of a scanning system dedicated to airport safety is reported.

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

NASA Technical Reports Server (NTRS)

Kiang, Richard K.

1999-01-01

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

Analgzing long-term changes in vegetation with geographic information system and remotely sensed data

NASA Astrophysics Data System (ADS)

Iverson, Louis R.; Risser, Paul G.

Geographic information systems and remote sensing techniques are powerful tools in the analysis of long-term changes in vegetation and land use, especially because spatial information from two or more time intervals can be compared more readily than by manual methods. A primary restriction is the paucity of data that has been digitized from earlier periods. The Illinois State Geographic Information System has a number of automated data sets containing land-use information, including original land survey plat maps that show the boundaries of forests, prairies, and wetlands as they existed prior to European colonization in the early 1800s. More recent data include the United States Forest Service inventories of 1948, 1962, and 1985; the United States Geological Survey Land Use Data Analysis; National High Altitude Program photographs of vegetation; and Landsat MSS and TM information. These data can be used to compare vegetation patterns and changes in land use over time and to suggest factors that may have caused or influenced these variations. Profound changes have occurred in the Illinois landscape since European settlement, primarily because of conversion to agricultural use; in certain parts of the state, however, urbanization has been the major factor contributing to changes.

A primary study on finding hot groundwater using infrared remote sensing

NASA Astrophysics Data System (ADS)

Qiao, Y.; Wu, Q.

Hot groundwater is a kind of valuable natural resources to be explored utilized. Shanxi Province, located in the eastern Loess Plateau of China, is rich in geothermal resources, most of which was found in irrigation well drilling or geological survey. Basic study is weak. Now new developed Remote Sensing technique provides geothermal study with an advanced way. Air-RS information of thermal infrared and dada from thermal channel of Meteorological Landset AVHRR has been used widely. A thermal infrared channel (TM6) was installed in the U. S. second Landset, Its resolving power of space is as high as 120 m, 10 times more t an one ofh AVHRR. A Landset earth recourses launched by China and Brazil (CBERS-1) in 1999, including a spectrum of thermal infrared. It is paid a great interested and attention to survey geothermal resources using thermal infrared. This article is a brief introduction of finding hot groundwater with on the bases of differences of thermal radiation of objects reflected by thermal infrared in the Landset, and treated with HIS colors changes. This study provides an advanced way widely used to exploit hot groundwater and to promote the development of tourism and geothermal medical in China.

Comparison analysis in growth process in Asian cities by using tandem time series remote sensing of different satellite

NASA Astrophysics Data System (ADS)

Hashiba, Hideki; Nakayama, Yasunori; Sugimura, Toshiro

The growth of major cities in Asia, as a consequence of economic development, is feared to have adverse influences on the natural environment of the surrounding areas. Comparison of land cover changes in major cities from the viewpoints of both spatial and time series is necessary to fully understand the characteristics of urban development in Asia. To accomplish this, multiple satellite remote sensing data were analyzed across a wide range and over a long term in this study. The process of transition of a major Asian city in Tokyo, Osaka, Beijing, Shanghai, and Hong Kong was analyzed from the characteristic changes of the vegetation index value and the land cover over about 40 years, from 1972 to 2010. Image data for LANDSAT/MSS, LAND-SAT/TM, ALOS/AVNIR-2, and ALOS/PRISM were obtained using a tandem time series. The ratio and state of detailed distribution of land cover were clarified by the classification processing. The time series clearly showed different change characteristics for each city and its surrounding natural environment of vegetation and forest etc. as a result of development processes.

[A spatial adaptive algorithm for endmember extraction on multispectral remote sensing image].

PubMed

Zhu, Chang-Ming; Luo, Jian-Cheng; Shen, Zhan-Feng; Li, Jun-Li; Hu, Xiao-Dong

2011-10-01

Due to the problem that the convex cone analysis (CCA) method can only extract limited endmember in multispectral imagery, this paper proposed a new endmember extraction method by spatial adaptive spectral feature analysis in multispectral remote sensing image based on spatial clustering and imagery slice. Firstly, in order to remove spatial and spectral redundancies, the principal component analysis (PCA) algorithm was used for lowering the dimensions of the multispectral data. Secondly, iterative self-organizing data analysis technology algorithm (ISODATA) was used for image cluster through the similarity of the pixel spectral. And then, through clustering post process and litter clusters combination, we divided the whole image data into several blocks (tiles). Lastly, according to the complexity of image blocks' landscape and the feature of the scatter diagrams analysis, the authors can determine the number of endmembers. Then using hourglass algorithm extracts endmembers. Through the endmember extraction experiment on TM multispectral imagery, the experiment result showed that the method can extract endmember spectra form multispectral imagery effectively. What's more, the method resolved the problem of the amount of endmember limitation and improved accuracy of the endmember extraction. The method has provided a new way for multispectral image endmember extraction.

Monitoring urban growth and detection of land use with GIS and remote sensing: a case study of the Kyrenia region

NASA Astrophysics Data System (ADS)

Kara, Can; Akçit, Nuhcan

2016-08-01

Land-cover change is considered one of the central components in current strategies for managing natural resources and monitoring environmental changes. It is important to manage land resources in a sustainable manner which targets at compacting and consolidating urban development. From 2005 to 2015,urban growth in Kyrenia has been quite dramatic, showing a wide and scattered pattern, lacking proper plan. As a result of this unplanned/unorganized expansion, agricultural areas, vegetation and water bodies have been lost in the region. Therefore, it has become a necessity to analyze the results of this urban growth and compare the losses between land-cover changes. With this goal in mind, a case study of Kyrenia region has been carried out using a supervised image classification method and Landsat TM images acquired in 2005 and 2015 to map and extract land-cover changes. This paper tries to assess urban-growth changes detected in the region by using Remote Sensing and GIS. The study monitors the changes between different land cover types. Also, it shows the urban occupation of primary soil loss and the losses in forest areas, open areas, etc.

Propagation Limitations in Remote Sensing.

DTIC Science & Technology

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

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites

PubMed Central

Karl, Jason W.

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral ‘fingerprint’ of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches. PMID:28414731

A hyper-temporal remote sensing protocol for high-resolution mapping of ecological sites.

PubMed

Maynard, Jonathan J; Karl, Jason W

2017-01-01

Ecological site classification has emerged as a highly effective land management framework, but its utility at a regional scale has been limited due to the spatial ambiguity of ecological site locations in the U.S. or the absence of ecological site maps in other regions of the world. In response to these shortcomings, this study evaluated the use of hyper-temporal remote sensing (i.e., hundreds of images) for high spatial resolution mapping of ecological sites. We posit that hyper-temporal remote sensing can provide novel insights into the spatial variability of ecological sites by quantifying the temporal response of land surface spectral properties. This temporal response provides a spectral 'fingerprint' of the soil-vegetation-climate relationship which is central to the concept of ecological sites. Consequently, the main objective of this study was to predict the spatial distribution of ecological sites in a semi-arid rangeland using a 28-year time series of normalized difference vegetation index from Landsat TM 5 data and modeled using support vector machine classification. Results from this study show that support vector machine classification using hyper-temporal remote sensing imagery was effective in modeling ecological site classes, with a 62% correct classification. These results were compared to Gridded Soil Survey Geographic database and expert delineated maps of ecological sites which had a 51 and 89% correct classification, respectively. An analysis of the effects of ecological state on ecological site misclassifications revealed that sites in degraded states (e.g., shrub-dominated/shrubland and bare/annuals) had a higher rate of misclassification due to their close spectral similarity with other ecological sites. This study identified three important factors that need to be addressed to improve future model predictions: 1) sampling designs need to fully represent the range of both within class (i.e., states) and between class (i.e., ecological sites) spectral variability through time, 2) field sampling protocols that accurately characterize key soil properties (e.g., texture, depth) need to be adopted, and 3) additional environmental covariates (e.g. terrain attributes) need to be evaluated that may help further differentiate sites with similar spectral signals. Finally, the proposed hyper-temporal remote sensing framework may provide a standardized approach to evaluate and test our ecological site concepts through examining differences in vegetation dynamics in response to climatic variability and other drivers of land-use change. Results from this study demonstrate the efficacy of the hyper-temporal remote sensing approach for high resolution mapping of ecological sites, and highlights its utility in terms of reduced cost and time investment relative to traditional manual mapping approaches.

Association of American Geographers, Remote Sensing Specialty Group Special Issue of Geocarto International

NASA Technical Reports Server (NTRS)

Allen, Thomas R. (Editor); Emerson, Charles W. (Editor); Quattrochi, Dale A. (Editor); Arnold, James E. (Technical Monitor)

2001-01-01

This special issue continues the precedence of the Association of American Geographers (AAG), Remote Sensing Specialty Group (RSSG) for publishing selected articles in Geocarto International as a by-product from the AAG annual meeting. As editors, we issued earlier this year, a solicitation for papers to be published in a special issue of Geocarto International that were presented in RSSG-sponsored sessions at the 2001 AAG annual meeting held in New York City on February 27-March 3. Although not an absolute requisite for publication, the vast majority of the papers in this special issue were presented at this year's AAG meeting in New York. Other articles in this issue that were not part of a paper or poster session at the 2001 AAG meeting are authored by RSSG members. Under the auspices of the RSSG, this special Geocarto International issue provides even more compelling evidence of the inextricable linkage between remote sensing and geography. The papers in this special issue fall into four general themes: 1) Urban Analysis and Techniques for Urban Analysis; 2) Land Use/Land Cover Analysis; 3) Fire Modeling Assessment; and 4) Techniques. The first four papers herein are concerned with the use of remote sensing for analysis of urban areas, and with use or development of techniques to better characterize urban areas using remote sensing data. As the lead paper in this grouping, Rashed et al., examine the usage of spectral mixture analysis (SMA) for analyzing satellite imagery of urban areas as opposed to more 'standard' methods of classification. Here SMA has been applied to IRS-1C satellite multispectral imagery to extract measures that better describe the 'anatomy' of the greater Cairo, Egypt region. Following this paper, Weng and Lo describe how Landsat TM data have been used to monitor land cover types and to estimate biomass parameters within an urban environment. The research reported in this paper applies an integrated GIS (Geographic Information System) approach for detecting urban growth and assessing its impact on biomass in the Zhujiang Delta, China. The remaining two papers in this first grouping deal with improved techniques for characterizing and analyzing urban areas using remote sensing data. Myint examines the use of texture analysis to better classify urban features. Here wavelet analysis has been employed to assist in deriving a more robust classification of the urban environment from high spatial resolution, multispectral aircraft data. Mesev provides insight on how through the modification of the standard maximum likelihood image analysis technique, population census data can be used enhance the overall robustness of urban image classification through the modification of the standard maximum likelihood image analysis technique.

Potentials and limitations of remote fire monitoring in protected areas.

PubMed

Dos Santos, João Flávio Costa; Romeiro, Joyce Machado Nunes; de Assis, José Batuíra; Torres, Fillipe Tamiozzo Pereira; Gleriani, José Marinaldo

2018-03-01

Protected areas (PAs) play an important role in maintaining the biodiversity and ecological processes of the site. One of the greatest challenges for the PA management in several biomes in the world is wildfires. The objective of this work was to evaluate the potentialities and limitations of the use of data obtained by orbital remote sensing in the monitoring fire occurrence in PAs. Fire Occurrence Records (FORs) were analyzed in Serra do Brigadeiro State Park, Minas Gerais, Brazil, from 2007 to 2015, using photo interpreted data from TM, ETM + and OLI sensors of the Landsat series and the Hot Spot Database (HSD) from the Brazilian Institute of Space Research - INPE. It was also observed the time of permanence of the scar left by fire on the landscape, through the multitemporal analysis of the behavior of NDVI (Normalized Difference Vegetation Index) and NBR (Normalized Burn Ratio) indexes, before and after the occurrence. The greatest limitation found for the orbital remote monitoring was the presence of clouds in the passage of the sensor in dates close to the occurrence of the fires. The burned area identified by photo interpretation was 54.9% less than the area contained in the FOR. Although the HSD reported fire occurrences in the buffer zone (up to 10km from the Park), no FORs were found at a distance greater than 1100m from the boundaries of the PA. As the main potential of remote sensing, the possibility of identifying burned areas throughout the park and surroundings is highlighted, with low costs and greater accuracy. Copyright © 2017 Elsevier B.V. All rights reserved.

Estimating of gross primary production in an Amazon-Cerrado transitional forest using MODIS and Landsat imagery.

PubMed

Danelichen, Victor H M; Biudes, Marcelo S; Velasque, Maísa C S; Machado, Nadja G; Gomes, Raphael S R; Vourlitis, George L; Nogueira, José S

2015-09-01

The acceleration of the anthropogenic activity has increased the atmospheric carbon concentration, which causes changes in regional climate. The Gross Primary Production (GPP) is an important variable in the global carbon cycle studies, since it defines the atmospheric carbon extraction rate from terrestrial ecosystems. The objective of this study was to estimate the GPP of the Amazon-Cerrado Transitional Forest by the Vegetation Photosynthesis Model (VPM) using local meteorological data and remote sensing data from MODIS and Landsat 5 TM reflectance from 2005 to 2008. The GPP was estimated using Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) calculated by MODIS and Landsat 5 TM images. The GPP estimates were compared with measurements in a flux tower by eddy covariance. The GPP measured in the tower was consistent with higher values during the wet season and there was a trend to increase from 2005 to 2008. The GPP estimated by VPM showed the same increasing trend observed in measured GPP and had high correlation and Willmott's coefficient and low error metrics in comparison to measured GPP. These results indicated high potential of the Landsat 5 TM images to estimate the GPP of Amazon-Cerrado Transitional Forest by VPM.

Earth view: A business guide to orbital remote sensing

NASA Technical Reports Server (NTRS)

Bishop, Peter C.

1990-01-01

The following subject areas are covered: Earth view - a guide to orbital remote sensing; current orbital remote sensing systems (LANDSAT, SPOT image, MOS-1, Soviet remote sensing systems); remote sensing satellite; and remote sensing organizations.

Study on the techniques of valuation of ecosystem services based on remote sensing in Anxin County

NASA Astrophysics Data System (ADS)

Wang, Hongyan; Li, Zengyuan; Gao, Zhihai; Wang, Bengyu; Bai, Lina; Wu, Junjun; Sun, Bin; Wang, Zhibo

2014-05-01

The farmland ecosystem is an important component of terrestrial ecosystems and has a fundamental role in the human life. The wetland is an unique and versatile ecological system. It is important for rational development and sustainable utilization of farmland and wetland resources to study on the measurement of valuation of farmland and wetland ecosystem services. It also has important significance for improving productivity. With the rapid development of remote sensing technology, it has become a powerful tool for evaluation of the value of ecosystem services. The land cover types in Anxin County mainly was farmland and wetland, the indicator system for ecosystem services valuation was brought up based on the remote sensing data of high spatial resolution ratio(Landsat-5 TM data and SPOT-5 data), the technology system for measurement of ecosystem services value was established. The study results show that the total ecosystem services value in 2009 in Anxin was 4.216 billion yuan, and the unit area value was between 8489 yuan/hm2 and 329535 yuan/hm2. The value of natural resources, water conservation value in farmland ecosystem and eco-tourism value in wetland ecosystem were higher than the other, total of the three values reached 2.858 billion yuan, and the percentage of the total ecosystem services values in Anxin was 67.79%. Through the statistics in the nine towns and three villages of Anxin County, the juantou town has the highest services value, reached 0.736 billion yuan. Scientific and comprehensive evaluation of the ecosystem services can conducive to promoting the understanding of the importance of the ecosystem. The research results had significance to ensure the sustainable use of wetland resources and the guidance of ecological construction in Anxin County.

Multisensor earth observations to characterize wetlands and malaria epidemiology in Ethiopia

PubMed Central

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

2014-01-01

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

Investigation of coastline changes in three provinces of Thailand using remote sensing

NASA Astrophysics Data System (ADS)

Tochamnanvita, T.; Muttitanon, W.

2014-11-01

The measuring of coastal in the certain short period of time is almost impossible, but applying the remote sensing with the satellite imagery bring mankind to track down and analyze the approximately length of the coastal changes at the Nano technology speed. An attempt has been made to study the length of shoreline changes along three provinces in the upper gulf of Thailand. The significant purpose is to investigate coastline length changes and to evaluate those different coastal changes at different times. Two specialties of chosen areas are the outstanding location at mouth of river in curve pattern and ecological important mangrove forest, as nominated and designated area listed in Ramsar convention, international wetlands treaty. In employing the remote sensing will help to investigate the shoreline erosion, stable or construction shoreline. Rapid and drastic shoreline changes have been compared and measured base on satellite image Landsat 5 TM on 1994, 2002 and 2007 at path129 row 051. There were geometrically co-registered and, in the process were resampled to 25 m. By composing RGB band, fusion, supervised classification. By apply different theories will give different results but the similarly pattern. Training sites were selected by signature editor, area of interest, evaluate by seperabilitly and contingency. Principle component analysis (PCA) was employed as a method of change detection. This is to conclude that these shoreline areas were in erosion from natural processes and manmade activities, for example, aquaculture and agriculture expansion, such as shrimp farm. These coastal line lost were not just losing the land; it's losing the soul of the cycle of marine life, economically, and environmentally. Moreover, this project, in the future, could benefit to set the recovery buffer zone for mangrove restoration also.

Identifying Geographic Areas at Risk of Soil-transmitted Helminthes Infection Using Remote Sensing and Geographical Information Systems: Boaco, Nicaragua as a Case Study

NASA Technical Reports Server (NTRS)

Moreno, Max J.; Al-Hamdan, Mohammad Z.; Parajon, David G.; Rickman, Douglas L.; Luvall, Jeffrey; Estes, Sue; Podest, Erika

2011-01-01

Several types of intestinal nematodes, that can infect humans and specially school-age children living in poverty, develop part of their life cycle in soil. Presence and survival of these parasites in the soil depend on given environmental characteristics like temperature and moisture that can be inferred with remote sensing (RS) technology. Prevalence of diseases caused by these parasitic worms can be controlled and even eradicated with anthelmintic drug treatments and sanitation improvement. Reliable and updated identification of geographic areas at risk is required to implement effective public health programs; to calculate amount of drug required and to distribute funding for sanitation projects. RS technology and geographical information systems (GIS) will be used to analyze for associations between in situ prevalence and remotely sensed data in order to establish RS proxies of environmental parameters that indicate the presence of these parasits. In situ data on helminthisasis will be overlaid over an ecological map derived from RS data using ARC Map 9.3 (ESRI). Temperature, vegetation, and distance to bodies of water will be inferred using data from Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat TM and ETM+. Elevation will be estimated with data from The Shuttle Radar Topography Mission (SRTM). Prevalence and intensity of infections are determined by parasitological survey (Kato Katz) of children enrolled in rural schools in Boaco, Nicaragua, in the communities of El Roblar, Cumaica Norte, Malacatoya 1, and Malacatoya 2). This study will demonstrate the importance of an integrated GIS/RS approach to define clusters and areas at risk. Such information will help to the implementation of time and cost efficient control programs and sanitation efforts.

Spatio-temporal modeling with GIS and remote sensing for schistosomiasis control in Sichuan, China

NASA Astrophysics Data System (ADS)

Xu, Bing

Schistosomiasis is a water-borne parasitic disease endemic in tropical and subtropical areas. Its transmission requires certain kind of snail as the intermediate host. Some efforts have been made to mapping snail habitats with remote sensing and schistosomiasis transmission modeling. However, the modeling is limited to isolated residential groups and does not include spatial interaction among those groups. Remotely sensed data are only used in snail habitat classification, not in estimation of snail abundance that is an important parameter in schistosomiasis transmission modeling. This research overcomes the above two problems using innovative geographic information system (GIS) and remote sensing technology. A mountainous environment near Xichang, China, is chosen as the test site. Environmental and epidemiological data are stored in a GIS to support modeling. Snail abundance is estimated from land-cover and land-use fractions derived from high spatial resolution IKONOS satellite data. Spatial interaction is determined in consideration of neighborhoods, group areas, relative slopes among groups, and natural barriers. Land-cover and land-use information extracted from 4 m high resolution IKONOS data is used as reference in scaling up to the regional level. The scale-up is done with coarser resolution satellite data including Landsat Thematic Mapper (TM), EO-1 Advanced Land Imager (ALI) and Hyperion data all at 30 m resolution. Snail abundance is estimated by regressing snail survey data with land-cover and land-use fractions. An R2 of 0.87 is obtained between the average snail density predicted and that surveyed at the group level. With such a model, a snail density map is generated for all residential groups in the study area. A spatio-temporal model of schistosomiasis transmission is finally built to incorporate the spatial interaction caused by miracidia and cercaria migration. Comparing the model results with and without spatial interaction has revealed a number of advantages of the spatio-temporal model. Particularly, with the inclusion of spatial interaction, more effective control of schistosomiasis transmission over the whole study area can be achieved.

L5 TM radiometric recalibration procedure using the internal calibration trends from the NLAPS trending database

USGS Publications Warehouse

Chander, G.; Haque, Md. O.; Micijevic, E.; Barsi, J.A.

2008-01-01

From the Landsat program's inception in 1972 to the present, the earth science user community has benefited from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for this imagery used the instrument's response to the Internal Calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time causing radiometric calibration errors up to 20 percent. In May 2003 the National Landsat Archive Production System (NLAPS) was updated to use a gain model rather than the scene acquisition specific IC gains to calibrate TM data processed in the United States. Further modification of the gain model was performed in 2007. L5 TM data that were processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing Level-1 products. The best recalibration results are obtained if the work order report that was originally included in the standard data product delivery is available. However, many users may not have the original work order report. In such cases, the IC gain look-up table that was generated using the radiometric gain trends recorded in the NLAPS database can be used for recalibration. This paper discusses the procedure to recalibrate L5 TM data when the work order report originally used in processing is not available. A companion paper discusses the generation of the NLAPS IC gain and bias look-up tables required to perform the recalibration.

Technology study of quantum remote sensing imaging

NASA Astrophysics Data System (ADS)

Bi, Siwen; Lin, Xuling; Yang, Song; Wu, Zhiqiang

2016-02-01

According to remote sensing science and technology development and application requirements, quantum remote sensing is proposed. First on the background of quantum remote sensing, quantum remote sensing theory, information mechanism, imaging experiments and prototype principle prototype research situation, related research at home and abroad are briefly introduced. Then we expounds compress operator of the quantum remote sensing radiation field and the basic principles of single-mode compression operator, quantum quantum light field of remote sensing image compression experiment preparation and optical imaging, the quantum remote sensing imaging principle prototype, Quantum remote sensing spaceborne active imaging technology is brought forward, mainly including quantum remote sensing spaceborne active imaging system composition and working principle, preparation and injection compression light active imaging device and quantum noise amplification device. Finally, the summary of quantum remote sensing research in the past 15 years work and future development are introduced.

Investigating change detection of archaeological sites by multiscale and multitemporal satellite imagery

NASA Astrophysics Data System (ADS)

Lasaponara, R.; Lanorte, A.; Coluzzi, R.; Masini, N.

2009-04-01

The systematic monitoring of cultural and natural heritage is a basic step for its conservation. Monitoring strategies should constitute an integral component of policies relating to land use, development, and planning. To this aim remote sensing technologies can be used profitably. This paper deals with the use of multitemporal, multisensors, and multiscale satellite data for assessing and monitoring changes affecting cultural landscapes and archaeological sites. The discussion is focused on some significant test cases selected in Peru (South America) and Southern Italy . Artifacts, unearthed sites, and marks of buried remains have been investigated by using multitemporal aerial and satellite data, such as Quickbird, ASTER, Landsat MSS and TM.

Classification with spatio-temporal interpixel class dependency contexts

NASA Technical Reports Server (NTRS)

Jeon, Byeungwoo; Landgrebe, David A.

1992-01-01

A contextual classifier which can utilize both spatial and temporal interpixel dependency contexts is investigated. After spatial and temporal neighbors are defined, a general form of maximum a posterior spatiotemporal contextual classifier is derived. This contextual classifier is simplified under several assumptions. Joint prior probabilities of the classes of each pixel and its spatial neighbors are modeled by the Gibbs random field. The classification is performed in a recursive manner to allow a computationally efficient contextual classification. Experimental results with bitemporal TM data show significant improvement of classification accuracy over noncontextual pixelwise classifiers. This spatiotemporal contextual classifier should find use in many applications of remote sensing, especially when the classification accuracy is important.

Revised radiometric calibration technique for LANDSAT-4 Thematic Mapper data

NASA Technical Reports Server (NTRS)

Murphy, J.; Butlin, T.; Duff, P.; Fitzgerald, A.

1984-01-01

Depending on detector number, there are random fluctuations in the background level for spectral band 1 of magnitudes ranging from 2 to 3.5 digital numbers (DN). Similar variability is observed in all the other reflective bands, but with smaller magnitude in the range 0.5 to 2.5 DN. Observations of background reference levels show that line dependent variations in raw TM image data and in the associated calibration data can be measured and corrected within an operational environment by applying simple offset corrections on a line-by-line basis. The radiometric calibration procedure defined by the Canadian Center for Remote Sensing was revised accordingly in order to prevent striping in the output product.

A bibliography of planetary geology principal investigators and their associates, 1982 - 1983

NASA Technical Reports Server (NTRS)

Plescia, J. B.

1984-01-01

This bibliography cites recent publications by principal investigators and their associates, supported through NASA's Office of Space Science and Applications, Earth and Planetary Exploration Division, Planetary Geology Program. It serves as a companion piece to NASA TM-85127, ""Reports of Planetary Programs, 1982". Entries are listed under the following subject areas: solar system, comets, asteroids, meteorites and small bodies; geologic mapping, geomorphology, and stratigraphy; structure, tectonics, and planetary and satellite evolutions; impact craters; volcanism; fluvial, mass wasting, glacial and preglacial studies; Eolian and Arid climate studies; regolith, volatiles, atmosphere, and climate, radar; remote sensing and photometric studies; and cartography, photogrammetry, geodesy, and altimetry. An author index is provided.

Image analysis by integration of disparate information

NASA Technical Reports Server (NTRS)

Lemoigne, Jacqueline

1993-01-01

Image analysis often starts with some preliminary segmentation which provides a representation of the scene needed for further interpretation. Segmentation can be performed in several ways, which are categorized as pixel based, edge-based, and region-based. Each of these approaches are affected differently by various factors, and the final result may be improved by integrating several or all of these methods, thus taking advantage of their complementary nature. In this paper, we propose an approach that integrates pixel-based and edge-based results by utilizing an iterative relaxation technique. This approach has been implemented on a massively parallel computer and tested on some remotely sensed imagery from the Landsat-Thematic Mapper (TM) sensor.

Upper Klamath Basin Landsat Image for September 30, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 18, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for October 29, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for June 23, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for August 29, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for September 21, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 25, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 28, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for October 22, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for November 8, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for September 27, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for August 19, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for August 19, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for October 16, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for August 4, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for September 20, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for October 7, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 9, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for May 6, 2006: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for June 26, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for April 29, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 12, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for July 2, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for April 30, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for May 25, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for June 1, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for June 17, 2004: Path 45 Rows 30 and 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This image is a mosaic of Landsat-5 images of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for June 16, 2006: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

Upper Klamath Basin Landsat Image for April 7, 2004: Path 44 Row 31

USGS Publications Warehouse

Snyder, Daniel T.

2012-01-01

This subset of a Landsat-5 image shows part of the upper Klamath Basin. The original images were obtained from the U.S. Geological Survey Earth Resources Observation and Science Center (EROS). EROS is responsible for archive management and distribution of Landsat data products. The Landsat-5 satellite is part of an ongoing mission to provide quality remote sensing data in support of research and applications activities. The launch of Landsat-5 on March 1, 1984 marks the addition of the fifth satellite to the Landsat series. The Landsat-5 satellite carries the Thematic Mapper (TM) sensor. More information on the Landsat program can be found online at http://landsat.usgs.gov/.

QWIP technology for both military and civilian applications

NASA Astrophysics Data System (ADS)

Gunapala, Sarath D.; Kukkonen, Carl A.; Sirangelo, Mark N.; McQuiston, Barbara K.; Chehayeb, Riad; Kaufmann, M.

2001-10-01

Advanced thermal imaging infrared cameras have been a cost effective and reliable method to obtain the temperature of objects. Quantum Well Infrared Photodetector (QWIP) based thermal imaging systems have advanced the state-of-the-art and are the most sensitive commercially available thermal systems. QWIP Technologies LLC, under exclusive agreement with Caltech University, is currently manufacturing the QWIP-ChipTM, a 320 X 256 element, bound-to-quasibound QWIP FPA. The camera performance falls within the long-wave IR band, spectrally peaked at 8.5 μm. The camera is equipped with a 32-bit floating-point digital signal processor combined with multi- tasking software, delivering a digital acquisition resolution of 12-bits using nominal power consumption of less than 50 Watts. With a variety of video interface options, remote control capability via an RS-232 connection, and an integrated control driver circuit to support motorized zoom and focus- compatible lenses, this camera design has excellent application in both the military and commercial sector. In the area of remote sensing, high-performance QWIP systems can be used for high-resolution, target recognition as part of a new system of airborne platforms (including UAVs). Such systems also have direct application in law enforcement, surveillance, industrial monitoring and road hazard detection systems. This presentation will cover the current performance of the commercial QWIP cameras, conceptual platform systems and advanced image processing for use in both military remote sensing and civilian applications currently being developed in road hazard monitoring.

City of Flagstaff Project: Ground Water Resource Evaluation, Remote Sensing Component

USGS Publications Warehouse

Chavez, Pat S.; Velasco, Miguel G.; Bowell, Jo-Ann; Sides, Stuart C.; Gonzalez, Rosendo R.; Soltesz, Deborah L.

1996-01-01

Many regions, cities, and towns in the Western United States need new or expanded water resources because of both population growth and increased development. Any tools or data that can help in the evaluation of an area's potential water resources must be considered for this increasingly critical need. Remotely sensed satellite images and subsequent digital image processing have been under-utilized in ground water resource evaluation and exploration. Satellite images can be helpful in detecting and mapping an area's regional structural patterns, including major fracture and fault systems, two important geologic settings for an area's surface to ground water relations. Within the United States Geological Survey's (USGS) Flagstaff Field Center, expertise and capabilities in remote sensing and digital image processing have been developed over the past 25 years through various programs. For the City of Flagstaff project, this expertise and these capabilities were combined with traditional geologic field mapping to help evaluate ground water resources in the Flagstaff area. Various enhancement and manipulation procedures were applied to the digital satellite images; the results, in both digital and hardcopy format, were used for field mapping and analyzing the regional structure. Relative to surface sampling, remotely sensed satellite and airborne images have improved spatial coverage that can help study, map, and monitor the earth surface at local and/or regional scales. Advantages offered by remotely sensed satellite image data include: 1. a synoptic/regional view compared to both aerial photographs and ground sampling, 2. cost effectiveness, 3. high spatial resolution and coverage compared to ground sampling, and 4. relatively high temporal coverage on a long term basis. Remotely sensed images contain both spectral and spatial information. The spectral information provides various properties and characteristics about the surface cover at a given location or pixel (that is, vegetation and/or soil type). The spatial information gives the distribution, variation, and topographic relief of the cover types from pixel to pixel. Therefore, the main characteristics that determine a pixel's brightness/reflectance and, consequently, the digital number (DN) assigned to the pixel, are the physical properties of the surface and near surface, the cover type, and the topographic slope. In this application, the ability to detect and map lineaments, especially those related to fractures and faults, is critical. Therefore, the extraction of spatial information from the digital images was of prime interest in this project. The spatial information varies among the different spectral bands available; in particular, a near infrared spectral band is better than a visible band when extracting spatial information in highly vegetated areas. In this study, both visible and near infrared bands were analyzed and used to extract the desired spatial information from the images. The wide swath coverage of remotely sensed satellite digital images makes them ideal for regional analysis and mapping. Since locating and mapping highly fractured and faulted areas is a major requirement for ground water resource evaluation and exploration this aspect of satellite images was considered critical; it allowed us to stand back (actually up about 440 miles), look at, and map the regional structural setting of the area. The main focus of the remote sensing and digital image processing component of this project was to use both remotely sensed digital satellite images and a Digital Elevation Model (DEM) to extract spatial information related to the structural and topographic patterns in the area. The data types used were digital satellite images collected by the United States' Landsat Thematic Mapper (TM) and French Systeme Probatoire d'Observation de laTerre (SPOT) imaging systems, along with a DEM of the Flagstaff region. The USGS Mini Image Processing Sy

Consistency of L4 TM absolute calibration with respect to the L5 TM sensor based on near-simultaneous image acquisition

NASA Astrophysics Data System (ADS)

Chander, Gyanesh; Helder, Dennis L.; Malla, Rimy; Micijevic, Esad; Mettler, Cory J.

2007-09-01

The Landsat archive provides more than 35 years of uninterrupted multispectral remotely sensed data of Earth observations. Since 1972, Landsat missions have carried different types of sensors, from the Return Beam Vidicon (RBV) camera to the Enhanced Thematic Mapper Plus (ETM+). However, the Thematic Mapper (TM) sensors on Landsat 4 (L4) and Landsat 5 (L5), launched in 1982 and 1984 respectively, are the backbone of an extensive archive. Effective April 2, 2007, the radiometric calibration of L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) was updated to use an improved lifetime gain model, based on the instrument's detector response to pseudo-invariant desert site data and cross-calibration with the L7 ETM+. However, no modifications were ever made to the radiometric calibration procedure of the Landsat 4 (L4) TM data. The L4 TM radiometric calibration procedure has continued to use the Internal Calibrator (IC) based calibration algorithms and the post calibration dynamic ranges, as previously defined. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The number of coincident image pairs in the USGS EROS archive is limited, so the scene selection for the cross-calibration studies proved to be a challenge. Additionally, because of the lack of near-simultaneous images available over well-characterized and traditionally used calibration sites, alternate sites that have high reflectance, large dynamic range, high spatial uniformity, high sun elevation, and minimal cloud cover were investigated. The alternate sites were identified in Yuma, Iraq, Egypt, Libya, and Algeria. The cross-calibration approach involved comparing image statistics derived from large common areas observed eight days apart by the two sensors. This paper summarizes the average percent differences in reflectance estimates obtained between the two sensors. The work presented in this paper is a first step in understanding the current performance of L4 TM absolute calibration and potentially serves as a platform to revise and improve the radiometric calibration procedures implemented for the processing of L4 TM data.

Consistency of L4 TM absolute calibration with respect to the L5 TM sensor based on near-simultaneous image acquisition

USGS Publications Warehouse

Chander, G.; Helder, D.L.; Malla, R.; Micijevic, E.; Mettler, C.J.

2007-01-01

The Landsat archive provides more than 35 years of uninterrupted multispectral remotely sensed data of Earth observations. Since 1972, Landsat missions have carried different types of sensors, from the Return Beam Vidicon (RBV) camera to the Enhanced Thematic Mapper Plus (ETM+). However, the Thematic Mapper (TM) sensors on Landsat 4 (L4) and Landsat 5 (L5), launched in 1982 and 1984 respectively, are the backbone of an extensive archive. Effective April 2, 2007, the radiometric calibration of L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) was updated to use an improved lifetime gain model, based on the instrument's detector response to pseudo-invariant desert site data and cross-calibration with the L7 ETM+. However, no modifications were ever made to the radiometric calibration procedure of the Landsat 4 (L4) TM data. The L4 TM radiometric calibration procedure has continued to use the Internal Calibrator (IC) based calibration algorithms and the post calibration dynamic ranges, as previously defined. To evaluate the "current" absolute accuracy of these two sensors, image pairs from the L5 TM and L4 TM sensors were compared. The number of coincident image pairs in the USGS EROS archive is limited, so the scene selection for the cross-calibration studies proved to be a challenge. Additionally, because of the lack of near-simultaneous images available over well-characterized and traditionally used calibration sites, alternate sites that have high reflectance, large dynamic range, high spatial uniformity, high sun elevation, and minimal cloud cover were investigated. The alternate sites were identified in Yuma, Iraq, Egypt, Libya, and Algeria. The cross-calibration approach involved comparing image statistics derived from large common areas observed eight days apart by the two sensors. This paper summarizes the average percent differences in reflectance estimates obtained between the two sensors. The work presented in this paper is a first step in understanding the current performance of L4 TM absolute calibration and potentially serves as a platform to revise and improve the radiometric calibration procedures implemented for the processing of L4 TM data.

A high throughput geocomputing system for remote sensing quantitative retrieval and a case study

NASA Astrophysics Data System (ADS)

Xue, Yong; Chen, Ziqiang; Xu, Hui; Ai, Jianwen; Jiang, Shuzheng; Li, Yingjie; Wang, Ying; Guang, Jie; Mei, Linlu; Jiao, Xijuan; He, Xingwei; Hou, Tingting

2011-12-01

The quality and accuracy of remote sensing instruments have been improved significantly, however, rapid processing of large-scale remote sensing data becomes the bottleneck for remote sensing quantitative retrieval applications. The remote sensing quantitative retrieval is a data-intensive computation application, which is one of the research issues of high throughput computation. The remote sensing quantitative retrieval Grid workflow is a high-level core component of remote sensing Grid, which is used to support the modeling, reconstruction and implementation of large-scale complex applications of remote sensing science. In this paper, we intend to study middleware components of the remote sensing Grid - the dynamic Grid workflow based on the remote sensing quantitative retrieval application on Grid platform. We designed a novel architecture for the remote sensing Grid workflow. According to this architecture, we constructed the Remote Sensing Information Service Grid Node (RSSN) with Condor. We developed a graphic user interface (GUI) tools to compose remote sensing processing Grid workflows, and took the aerosol optical depth (AOD) retrieval as an example. The case study showed that significant improvement in the system performance could be achieved with this implementation. The results also give a perspective on the potential of applying Grid workflow practices to remote sensing quantitative retrieval problems using commodity class PCs.

Association of environmental and climatic factors in the epidemiology of American visceral leishmaniasis in northeast Brazil using remote sensing and geographical information system methods

NASA Astrophysics Data System (ADS)

Thompson, Roland Alexander

This study associated climatic and environmental factors with the incidence of visceral leishmaniasis (calazar) in Northeast Brazil. Remote sensing (RS) techniques permitted evaluation of spatial and temporal landscape features to stratify the region and define the target population for this vector-borne disease. The Municipality of Caninde, Ceara, Brazil was divided into 873-- 2 x 2 km2 squares centered on coordinates from a Universal Transverse Mercator projection (scale 1:100,000, 1994) and geo-referenced with 2 Landsat T.M. (TM) scenes (September 26, 1976 and July 2, 1996). The assignment of squares into foothills, plains or city strata was based on vegetative categories determined from TM scenes (Bands: 4,5,3) with ERDAS Imagine ISODATA classification procedures. Odds Ratios (OR) with 95% Confidence Intervals (CI) were determined for the juveniles less than age 10 based on 17 years of demographic, calazar incidence and rainfall information supplied by: Fundacao Nacional de Saude, Fundacao Cearense do Metorologia e Recurso Hidricos, and Fundacao Instituto de Planejamento do Ceara. The population and number of calazar cases were determined for each 2 x 2 km 2 square. The odds ratio of calazar for a Caninde juvenile in the foothills relative to the city was OR = 4.11 CI (3.2, 5.3). The calazar odds ratio for juveniles living in years with 3-year rainfall average between 60--90 cm was OR = 3.07 CI (1.3, 7.2), the rainfall average between 40--60 cm had OR = 9.12 CI (4.4, 23.3), and with less than 40 cm OR = 9.23 CI (3.9, 25.2) relative to years with an average greater than 90 cm. The logistic regression model for Ceara comprised an ordinal-incidence-density-response variable, a 5-level region explanatory variable, and a 3-level juvenile proportion variable. The odds ratios for calazar in municipalities located in the interior high plains was OR = 1.94 CI (1.6, 2.4) relative to location in the littoral and for a municipality with less than 26% juvenile population was OR = 0.63 CI (0.5, 0.78). Results suggest that RS can classify climatic and environmental factors resulting in increased strata homogeneity and better defined population at risk which reduces dilution of data and increases the probability for detecting statistical associations.

Combining Landsat TM multispectral satellite imagery and different modelling approaches for mapping post-fire erosion changes in a Mediterranean site

NASA Astrophysics Data System (ADS)

Petropoulos, George P.; Kairis, Orestis; Karamesouti, Mina; Papanikolaou, Ioannis D.; Kosmas, Constantinos

2013-04-01

South European countries are naturally vulnerable to wildfires. Their natural resources such as soil, vegetation and water may be severely affected by wildfires, causing an imminent environmental deterioration due to the complex interdependence among biophysical components. Soil surface water erosion is a natural process essential for soil formation that is affected by such interdependences. Accelerated erosion due to wildfires, constitutes a major restrictive factor for ecosystem sustainability. In 2007, South European countries were severely affected by wildfires, with more than 500,000 hectares of land burnt in that year alone, well above the average of the last 30 years. The present work examines the changes in spatial variability of soil erosion rates as a result of a wildfire event that took place in Greece in 2007, one of the most devastating years in terms of wildfire hazards. Regional estimates of soil erosion rates before and after the fire outbreak were derived from the Revised Universal Soil Loss Equation (RUSLE, Renard et al. 1991) and the Pan-European Soil Erosion Risk Assessment model (PESERA, Kirkby, 1999; Kirkby et al., 2000). Inputs for both models included climatic, land-use, soil type, topography and land use management data. Where appropriate, both models were also fed with input data derived from the analysis of LANDSAT TM satellite imagery available in our study area, acquired before and shortly after the fire suppression. Our study was compiled and performed in a GIS environment. In overall, the loss of vegetation from the fire outbreak caused a substantial increase of soil erosion rates in the affected area, particularly towards the steep slopes. Both tested models were compared to each other and noticeable differences were observed in the soil erosion predictions before and after the fire event. These are attributed to the different parameterization requirements of the 2 models. This quantification of sediment supply through the river network provides also important insights regarding both the present-day sedimentation processes in the study area as well as the potential flooding hazard. Our work underpins that valuable contribution of remote sensing technology, combined with modeling approaches for depicting the spatial distribution of changes in erosion rates after the wildfire. KEYWORDS: erosion risk, RUSLE, PESERA, wildland fires, LANDSAT TM, remote sensing, Geographical Information Systems, Greece.

Applications of Remote Sensing to Emergency Management.

DTIC Science & Technology

1980-02-15

Contents: Foundations of Remote Sensing : Data Acquisition and Interpretation; Availability of Remote Sensing Technology for Disaster Response...Imaging Systems, Current and Near Future Satellite and Aircraft Remote Sensing Systems; Utilization of Remote Sensing in Disaster Response: Categories of...Disasters, Phases of Monitoring Activities; Recommendations for Utilization of Remote Sensing Technology in Disaster Response; Selected Reading List.

Cross-calibration of the Landsat-7 ETM+ and Landsat-5 TM with the ResourceSat-1 (IRS-P6) AWiFS and LISS-III sensors

USGS Publications Warehouse

Chander, G.; Scaramuzza, P.L.

2006-01-01

Increasingly, data from multiple sensors are used to gain a more complete understanding of land surface processes at a variety of scales. The Landsat suite of satellites has collected the longest continuous archive of multispectral data. The ResourceSat-1 Satellite (also called as IRS-P6) was launched into the polar sunsynchronous orbit on Oct 17, 2003. It carries three remote sensing sensors: the High Resolution Linear Imaging Self-Scanner (LISS-IV), Medium Resolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide Field Sensor (AWiFS). These three sensors are used together to provide images with different resolution and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to the Landsat-5 TM and Landsat-7 ETM+ sensors. The approach involved the calibration of nearly simultaneous surface observations based on image statistics from areas observed simultaneously by the two sensors.

Evaluation of C-band SAR data from SAREX 1992: Tapajos study site

NASA Technical Reports Server (NTRS)

Shimabukuro, Yosio Edemir; Filho, Pedro Hernandez; Lee, David Chung Liang; Ahern, F. J.; Paivadossantosfilho, Celio; Rolodealmeida, Rionaldo

1993-01-01

As part of the SAREX'92 (South American Radar Experiment), the Tapajos study site, located in Para State, Brazil was imaged by the Canada Center for Remote Sensing (CCRS) Convair 580 SAR system using a C-band frequency in HH and VV polarization and 3 different imaging modes (nadir, narrow, and wide swath). A preliminary analysis of this dataset is presented. The wide swath C-band HH polarized image was enlarged to 1:100,000 in a photographic form for manual interpretation. This was compared with a vegetation map produced primarily from Landsat Thematic Mapper (TM) data and with single-band and color composite images derived from a decomposition analysis of TM data. The Synthetic Aperture Radar (SAR) image shows well the topography and drainage network defining the different geomorphological units, and canopy texture differences which appear to be related to the size and maturity of the forest canopy. Areas of recent clearing of the primary forest can also be identified on the SAR image. The SAR system appears to be a source of information for monitoring tropical forest which is complementary to the Landsat Thematic Mapper.

Variation of River Islands around a Large City along the Yangtze River from Satellite Remote Sensing Images

PubMed Central

Shi, Haiyun; Gao, Chao; Dong, Changming; Xia, Changshui; Xu, Guanglai

2017-01-01

River islands are sandbars formed by scouring and silting. Their evolution is affected by several factors, among which are runoff and sediment discharge. The spatial-temporal evolution of seven river islands in the Nanjing Section of the Yangtze River of China was examined using TM (Thematic Mapper) and ETM (Enhanced Thematic Mapper)+ images from 1985 to 2015 at five year intervals. The following approaches were applied in this study: the threshold value method, binarization model, image registration, image cropping, convolution and cluster analysis. Annual runoff and sediment discharge data as measured at the Datong hydrological station upstream of Nanjing section were also used to determine the roles and impacts of various factors. The results indicated that: (1) TM/ETM+ images met the criteria of information extraction of river islands; (2) generally, the total area of these islands in this section and their changing rate decreased over time; (3) sediment and river discharge were the most significant factors in island evolution. They directly affect river islands through silting or erosion. Additionally, anthropocentric influences could play increasingly important roles. PMID:28953218

Remodeling census population with spatial information from Landsat TM imagery

USGS Publications Warehouse

Yuan, Y.; Smith, R.M.; Limp, W.F.

1997-01-01

In geographic information systems (GIS) studies there has been some difficulty integrating socioeconomic and physiogeographic data. One important type of socioeconomic data, census data, offers a wide range of socioeconomic information, but is aggregated within arbitrary enumeration districts (EDs). Values reflect either raw counts or, when standardized, the mean densities in the EDs. On the other hand, remote sensing imagery, an important type of physiogeographic data, provides large quantities of information with more spatial details than census data. Based on the dasymetric mapping principle, this study applies multivariable regression to examine the correlation between population counts from census and land cover types. The land cover map is classified from LandSat TM imagery. The correlation is high. Census population counts are remodeled to a GIS raster layer based on the discovered correlations coupled with scaling techniques, which offset influences from other than land cover types. The GIS raster layer depicts the population distribution with much more spatial detail than census data offer. The resulting GIS raster layer is ready to be analyzed or integrated with other GIS data. ?? 1998 Elsevier Science Ltd. All rights reserved.

Cross-comparison of the IRS-P6 AWiFS sensor with the L5 TM, L7 ETM+, & Terra MODIS sensors

USGS Publications Warehouse

Chander, G.; Xiong, X.; Angal, A.; Choi, T.; Malla, R.

2009-01-01

As scientists and decision makers increasingly rely on multiple Earth-observing satellites to address urgent global issues, it is imperative that they can rely on the accuracy of Earth-observing data products. This paper focuses on the crosscomparison of the Indian Remote Sensing (IRS-P6) Advanced Wide Field Sensor (AWiFS) with the Landsat 5 (L5) Thematic Mapper (TM), Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+), and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) sensors. The cross-comparison was performed using image statistics based on large common areas observed by the sensors within 30 minutes. Because of the limited availability of simultaneous observations between the AWiFS and the Landsat and MODIS sensors, only a few images were analyzed. These initial results are presented. Regression curves and coefficients of determination for the top-of-atmosphere (TOA) trends from these sensors were generated to quantify the uncertainty in these relationships and to provide an assessment of the calibration differences between these sensors. ?? 2009 SPIE.

Evaluation and comparison of the IRS-P6 and the landsat sensors

USGS Publications Warehouse

Chander, G.; Coan, M.J.; Scaramuzza, P.L.

2008-01-01

The Indian Remote Sensing Satellite (IRS-P6), also called ResourceSat-1, was launched in a polar sun-synchronous orbit on October 17, 2003. It carries three sensors: the highresolution Linear Imaging Self-Scanner (LISS-IV), the mediumresolution Linear Imaging Self-Scanner (LISS-III), and the Advanced Wide-Field Sensor (AWiFS). These three sensors provide images of different resolutions and coverage. To understand the absolute radiometric calibration accuracy of IRS-P6 AWiFS and LISS-III sensors, image pairs from these sensors were compared to images from the Landsat-5 Thematic Mapper (TM) and Landsat-7 Enhanced TM Plus (ETM+) sensors. The approach involves calibration of surface observations based on image statistics from areas observed nearly simultaneously by the two sensors. This paper also evaluated the viability of data from these nextgeneration imagers for use in creating three National Land Cover Dataset (NLCD) products: land cover, percent tree canopy, and percent impervious surface. Individual products were consistent with previous studies but had slightly lower overall accuracies as compared to data from the Landsat sensors.

Remote sensing of submerged aquatic vegetation in lower Chesapeake Bay - A comparison of Landsat MSS to TM imagery

NASA Technical Reports Server (NTRS)

Ackleson, S. G.; Klemas, V.

1987-01-01

Landsat MSS and TM imagery, obtained simultaneously over Guinea Marsh, VA, as analyzed and compares for its ability to detect submerged aquatic vegetation (SAV). An unsupervised clustering algorithm was applied to each image, where the input classification parameters are defined as functions of apparent sensor noise. Class confidence and accuracy were computed for all water areas by comparing the classified images, pixel-by-pixel, to rasterized SAV distributions derived from color aerial photography. To illustrate the effect of water depth on classification error, areas of depth greater than 1.9 m were masked, and class confidence and accuracy recalculated. A single-scattering radiative-transfer model is used to illustrate how percent canopy cover and water depth affect the volume reflectance from a water column containing SAV. For a submerged canopy that is morphologically and optically similar to Zostera marina inhabiting Lower Chesapeake Bay, dense canopies may be isolated by masking optically deep water. For less dense canopies, the effect of increasing water depth is to increase the apparent percent crown cover, which may result in classification error.

A procedure for radiometric recalibration of Landsat 5 TM reflective-band data

USGS Publications Warehouse

Chander, G.; Haque, M.O.; Micijevic, E.; Barsi, J.A.

2010-01-01

From the Landsat program's inception in 1972 to the present, the Earth science user community has been benefiting from a historical record of remotely sensed data. The multispectral data from the Landsat 5 (L5) Thematic Mapper (TM) sensor provide the backbone for this extensive archive. Historically, the radiometric calibration procedure for the L5 TM imagery used the detectors' response to the internal calibrator (IC) on a scene-by-scene basis to determine the gain and offset for each detector. The IC system degraded with time, causing radiometric calibration errors up to 20%. In May 2003, the L5 TM data processed and distributed by the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center through the National Landsat Archive Production System (NLAPS) were updated to use a lifetime lookup-table (LUT) gain model to radiometrically calibrate TM data instead of using scene-specific IC gains. Further modification of the gain model was performed in 2007. The L5 TM data processed using IC prior to the calibration update do not benefit from the recent calibration revisions. A procedure has been developed to give users the ability to recalibrate their existing level-1 products. The best recalibration results are obtained if the work-order report that was included in the original standard data product delivery is available. However, if users do not have the original work-order report, the IC trends can be used for recalibration. The IC trends were generated using the radiometric gain trends recorded in the NLAPS database. This paper provides the details of the recalibration procedure for the following: 1) data processed using IC where users have the work-order file; 2) data processed using IC where users do not have the work-order file; 3) data processed using prelaunch calibration parameters; and 4) data processed using the previous version of the LUT (e.g., LUT03) that was released before April 2, 2007.

Room temperature operation of 2.67 mJ pulse LD end pumped Q-switched Tm:YAG laser

NASA Astrophysics Data System (ADS)

Song, Xuedi; Wu, Chunting; Chen, Xinyu; Yu, Kai; Jin, Guangyong

2014-12-01

Due to 2 μm band in the absorption of water and CO2, the diode pumped solid state lasers with wavelength around 2 μm have important applications in laser medicine and remote sensing, such as it can be used as a scalpe or a light source of Coherent Doppler Wind Lidar and Differential Absorption Lidar. In the recently years, scientists have done much work on the development of such lasers. There're many reports on continuous Tm:YAG laser. However, the study on Q-switched Tm:YAG laser, which is more useful in applications, was very rare. As the light source of Coherent Doppler Wind Lidar, large energy and wide pulse width is desired. Current reports mostly adopted CW pumped source, but it would make a mount of heat. Pulse pumping method could reduce the heat accumulation and improve the heat stability of the laser. How to improve the single pulse energy was the focus of current study. In this paper, a single end bonding Tm:YAG crystal with Tm3+ doping concentration of 3.5at.% was used. Acousto-optic (AO) Q-switched (GOOCH and HOUSEGO QS041-10M-HI8) operation was adopted in our experiment. In the repetition frequency of 100Hz, a maximum single energy of 2.67 mJ (measured by Ophir 30A-BB) and the narrowest pulse width of 149 ns (measured by Vigo PCI-3TE-12 detector) were achieved at room temperature. The M2x was 1.31 and the M2y was 1.35 (measured by Spiricon Pyrocam-III). Tm:YAG laser was developed by using a pulse diode pumped L shape resonant cavity. The transmittance of the curve output mirror was 4% and the curvature radius of which was 300 mm. The output center wavelength of the laser was measured to be 2013.5 nm (measured by YOKOGAWA AQ6375).

Water resources assessment in a poorly gauged mountainous catchment using a geographical information system and remote sensing

NASA Astrophysics Data System (ADS)

Shrestha, Roshan; Takara, Kaoru; Tachikawa, Yasuto; Jha, Raghu N.

2004-11-01

Water resources assessment, which is an essential task in making development plans managing water resources, is considerably difficult to do in a data-poor region. In this study, we attempted to conduct a quantitative water resources assessment in a poorly gauged mountainous catchment, i.e. the River Indrawati catchment (1233 km2) in Nepal. This catchment is facing problems such as dry-season water scarcity and water use conflicts. However, the region lacks the basic data that this study needs. The data needed are supplemented from field surveys and global data (e.g. GTOPO30 DEM data, LandsatTM data and MODIS NDVI data). The global data have significantly helped us to draw out the information needed for a number of water-use scenarios. These data helped us determine that the available water quantity is enough at present to address the dry-season problems. The situation is not much worse for the immediate future; however, the threat of drought is noticed in a future scenario in which resources are consumed extensively. The study uses a geographical information system and remotely sensed data analysis tools extensively. Utilization of modern tools and global data is found effective for investigating practical problems and for detecting important features of water resources, even though the catchment is poorly gauged.

Mapping environmental susceptibility to Saint Louis encephalitis virus, based on a decision tree model of remotely-sensed data.

PubMed

Rotela, Camilo H; Spinsanti, Lorena I; Lamfri, Mario A; Contigiani, Marta S; Almirón, Walter R; Scavuzzo, Carlos M

2011-11-01

In response to the first human outbreak (January May 2005) of Saint Louis encephalitis (SLE) virus in Córdoba province, Argentina, we developed an environmental SLE virus risk map for the capital, i.e. Córdoba city. The aim was to provide a map capable of detecting macro-environmental factors associated with the spatial distribution of SLE cases, based on remotely sensed data and a geographical information system. Vegetation, soil brightness, humidity status, distances to water-bodies and areas covered by vegetation were assessed based on pre-outbreak images provided by the Landsat 5TM satellite. A strong inverse relationship between the number of humans infected by SLEV and distance to high-vigor vegetation was noted. A statistical non-hierarchic decision tree model was constructed, based on environmental variables representing the areas surrounding patient residences. From this point of view, 18% of the city could be classified as being at high risk for SLEV infection, while 34% carried a low risk, or none at all. Taking the whole 2005 epidemic into account, 80% of the cases came from areas classified by the model as medium-high or high risk. Almost 46% of the cases were registered in high-risk areas, while there were no cases (0%) in areas affirmed as risk free.

Use of satellite imagery as environmental impact assessment tool: a case study from the NW Egyptian Red Sea coastal zone.

PubMed

Moufaddal, Wahid M

2005-08-01

Knowledge and detecting impacts of human activities on the coastal ecosystem is an essential management requirement and also very important for future and proper planning of coastal areas. Moreover, documentation of these impacts can help in increasing public awareness about side effects of unsustainable practices. Analysis of multidate remote sensing data can be used as an effective tool in environmental impact assessment (EIA). Being synoptic and frequent in coverage, multidate data from Landsat and other satellites provide a reference record and bird's eye viewing to the environmental situation of the coastal ecosystem and the associated habitats. Furthermore, integration of satellite data with field observations and background information can help in decision if a certain activity has caused deterioration to a specific habitat or not. The present paper is an attempt to utilize remote sensing data for assessment impacts of some human activities on the major sensitive habitats of the NW Egyptian Red Sea coastal zone, definitely between Ras Gemsha and Safaga. Through multidate change analysis of Landsat data (TM & ETM+ sensors), it was possible to depict some of the human infringements in the area and to provide, in some cases, exclusive evidences for the damaging effect of some developmental activities.

Yield estimation of corn with multispectral data and the potential of using imaging spectrometers

NASA Astrophysics Data System (ADS)

Bach, Heike

1997-05-01

In the frame of the special yield estimation, a regular procedure conducted for the European Union to more accurately estimate agricultural yield, a project was conducted for the state minister for Rural Environment, Food and Forestry of Baden-Wuerttemberg, Germany) to test remote sensing data with advanced yield formation models for accuracy and timelines of yield estimation of corn. The methodology employed uses field-based plant parameter estimation from atmospherically corrected multitemporal/multispectral LANDSAT-TM data. An agrometeorological plant-production-model is used for yield prediction. Based solely on 4 LANDSAT-derived estimates and daily meteorological data the grain yield of corn stands was determined for 1995. The modeled yield was compared with results independently gathered within the special yield estimation for 23 test fields in the Upper Rhine Valley. The agrement between LANDSAT-based estimates and Special Yield Estimation shows a relative error of 2.3 percent. The comparison of the results for single fields shows, that six weeks before harvest the grain yield of single corn fields was estimated with a mean relative accuracy of 13 percent using satellite information. The presented methodology can be transferred to other crops and geographical regions. For future applications hyperspectral sensors show great potential to further enhance the results or yield prediction with remote sensing.

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

NASA Astrophysics Data System (ADS)

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

2013-12-01

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

Tm:germanate Fiber Laser: Tuning And Q-switching

NASA Technical Reports Server (NTRS)

Barnes, Norman P.; Walsh, Brian M.; Reichle, Donald J.; DeYoung, R. J.; Jiang, Shibin

2007-01-01

A Tm:germanate fiber laser produced >0.25 mJ/pulse in a 45 ns pulse. It is capable of producing multiple Q-switched pulses from a single p ump pulse. With the addition of a diffraction grating, Tm:germanate f iber lasers produced a wide, but length dependent, tuning range. By s electing the fiber length, the tuning range extends from 1.88 to 2.04 ?m. These traits make Tm:germanate lasers suitable for remote sensin g of water vapor.

The remote sensing image segmentation mean shift algorithm parallel processing based on MapReduce

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhou, Liqing

2015-12-01

With the development of satellite remote sensing technology and the remote sensing image data, traditional remote sensing image segmentation technology cannot meet the massive remote sensing image processing and storage requirements. This article put cloud computing and parallel computing technology in remote sensing image segmentation process, and build a cheap and efficient computer cluster system that uses parallel processing to achieve MeanShift algorithm of remote sensing image segmentation based on the MapReduce model, not only to ensure the quality of remote sensing image segmentation, improved split speed, and better meet the real-time requirements. The remote sensing image segmentation MeanShift algorithm parallel processing algorithm based on MapReduce shows certain significance and a realization of value.

[Satellite remote sensing retrieval of canopy nitrogen nutritional status of apple trees at blossom stage].

PubMed

Wang, Ling; Zhao, Geng-Xing; Zhu, Xi-Cun; Wang, Rui-Yan; Chang, Chun-Yan

2013-10-01

Taking Qixia City of Shandong, China as the study area, and based on the Landsat-5 TM and ALOS AVNIR-2 images, the canopy retrieval reflectance of apple trees at blossom stage was acquired. In combining with the measured reflectance of sample trees, the nitrogen-sensitive spectral indices were constructed and selected. By using the sensitive spectral indices as the independent variables, the nitrogen retrieval models were established, and the model with the best accuracy was used for spatial retrieve. The correlations between the spectral indices and the nitrogen nutritional status were in the order of canopy > leaf > flower. The sensitive indices were mainly composed of green, red, and near infrared bands. The accuracy of the retrieval models was in the order of support vector regression > multi-variable stepwise regression > one-variable regression. The retrieval results based on different images were similar, and showed that the leaf nitrogen content was mainly of grades 3-4 (27-33 g x kg(-1)), and the canopy nitrogen nutrient indices were mainly of grades 2-4 (TM: 38-47 g x kg(-1); ALOS: 32-41 g x kg(-1)). The spatial distribution of the retrieval nitrogen nutritional status based on different images also showed the similar trend, i. e., the nitrogen nutritional status was higher in the north and south than that in the middle part of the study area, and the areas with the high grades of leaf nitrogen and canopy nitrogen were mainly located in Sujiadian Town and Songshan subdistrict in the northwest, Zangjiazhuang Town and Tingkou Town in the northeast, and Shewopo Town in the south, which were consistent with the distribution of the key towns for apple production in Qixia City. This study provided a feasible method for the acquisition of nitrogen nutritional status of apple trees on macroscopic scale, and also, provided reference for other similar remote sensing retrievals.

Monitoring the Mesoamerican Biological Corridor: A NASA/CCAD Cooperative Research Project

NASA Technical Reports Server (NTRS)

Sever, Thomas; Irwin, Daniel; Sader, Steven A.; Saatchi, Sassan

2004-01-01

To foster scientific cooperation under a Memorandum of Understanding between NASA and the Central American countries, the research project developed regional databases to monitor forest condition and environmental change throughout the region. Of particular interest is the Mesoamerican Biological Corridor (MBC), a chain of protected areas and proposed conservation areas that will link segments of natural habitats in Central America from the borders of northern Columbia to southern Mexico. The first and second year of the project focused on the development of regional satellite databases (JERS-IC, MODIS, and Landsat-TM), training of Central American cooperators and forest cover and change analysis. The three regional satellite mosaics were developed and distributed on CD-ROM to cooperators and regional outlets. Four regional remote sensing training courses were conducted in 3 countries including participants from all 7 Central American countries and Mexico. In year 3, regional forest change assessment in reference to Mesoamerican Biological Corridor was completed and land cover maps (from Landsat TM) were developed for 7 Landsat scenes and accuracy assessed. These maps are being used to support validation of MODIS forest/non forest maps and to examine forest fragmentation and forest cover change in selected study sites. A no-cost time extension (2003-2004) allowed the completion of an M.S. thesis by a Costa Rican student and preparation of manuscripts for future submission to peer-reviewed outlets. Proposals initiated at the end of the project have generated external funding from the U.S. Forest Service (to U. Maine), NASA-ESSF (Oregon State U.) and from USAID and EPA (to NASA-MSFC-GHCC) to test MODIS capabilities to detect forest change; conduct literature review on biomass estimation and carbon stocks and develop a regional remote sensing monitoring center in Central America. The success of the project has led to continued cooperation between NASA, other federal agencies, and scientists from all seven Central American Countries (see SERVIR web site for this ongoing work - servir.nsstc.nasa.gov).

Remote Sensing and GIS Methods to Detect Uranium Contamination in Watersheds on the Navajo Nation: A NASA/AIHEC Summer Research Experience

NASA Astrophysics Data System (ADS)

Chaco, E.; Robinson, D. K.; Carlson, M.; Rock, B. N.

2010-12-01

Using ground-based mapping of private drinking water wells contaminated with uranium, we developed Landsat Thematic Mapper (TM) band combinations which indicate possible contamination of extensive areas along the Polacca Wash, the Cottonwood Wash and the Balakai Wash below Black Mesa on the Navajo Nation. The project built on water quality samples taken on unregulated wells by a Field Research Water Quality Team from Dine’ College. The Nevada State Health Laboratory analyzed twenty-six samples, and of those, 12 wells showed uranium in exceedance of 13 μR/hr, the equivalent of 114 mrem per year, greater than the Nuclear Regulatory Commission’s exposure limit of 100 mrem per year. This project hypothesized that point locations of contaminated wells could be compared with US Geologic Survey National Uranium Resource Evaluation (NURE) measures of high uranium levels in soil to identify other possible areas of contamination. We used Cluster Analysis remote sensing methods from MultiSpec© with data acquired by Landsat 5-TM satellite to produce a false color composite band combination, (7 4 2/R G B). Overlaid with a geological map, the Landsat classification correlated sections of sediment with pixilated colored minerals in the NURE data. This map shows possible high levels of uranium in the soil in the watersheds below mine and mill locations. Ground truth studies are needed to confirm the presence of uranium at these suspected sites. The larger goal of this study is to help solve the uranium contamination problem for the Navajo Nation. Chaco was one of 21 TCU (Tribal Colleges and Universities) students who participated in the 2010 NASA/AIHEC (National Aeronautics and Space Administration/American Indian Higher Education Council) Summer Research Experience program. Robinson was his TCU faculty mentor, and Carlson and Rock were Summer Research Experience instructors.

Small-scale open ocean currents have large effects on wind wave heights

NASA Astrophysics Data System (ADS)

Ardhuin, Fabrice; Gille, Sarah T.; Menemenlis, Dimitris; Rocha, Cesar B.; Rascle, Nicolas; Chapron, Bertrand; Gula, Jonathan; Molemaker, Jeroen

2017-06-01

Tidal currents and large-scale oceanic currents are known to modify ocean wave properties, causing extreme sea states that are a hazard to navigation. Recent advances in the understanding and modeling capability of open ocean currents have revealed the ubiquitous presence of eddies, fronts, and filaments at scales 10-100 km. Based on realistic numerical models, we show that these structures can be the main source of variability in significant wave heights at scales less than 200 km, including important variations down to 10 km. Model results are consistent with wave height variations along satellite altimeter tracks, resolved at scales larger than 50 km. The spectrum of significant wave heights is found to be of the order of 70>>2/>(g2>>2>) times the current spectrum, where >> is the spatially averaged significant wave height, >> is the energy-averaged period, and g is the gravity acceleration. This variability induced by currents has been largely overlooked in spite of its relevance for extreme wave heights and remote sensing.