Using NASA Techniques to Atmospherically Correct AWiFS Data for Carbon Sequestration Studies

Carbon dioxide is a greenhouse gas emitted in a number of ways, including the burning of fossil fuels and the conversion of forest to agriculture. Research has begun to quantify the ability of vegetative land cover and oceans to absorb and store carbon dioxide. The USDA (U.S. Department of Agriculture) Forest Service is currently evaluating a DSS (decision support system) developed by researchers at the NASA Ames Research Center called CASA-CQUEST (Carnegie-Ames-Stanford Approach-Carbon Query and Evaluation Support Tools). CASA-CQUEST is capable of estimating levels of carbon sequestration based on different land cover types and of predicting the effects of land use change on atmospheric carbon amounts to assist land use management decisions. The CASA-CQUEST DSS currently uses land cover data acquired from MODIS (the Moderate Resolution Imaging Spectroradiometer), and the CASA-CQUEST project team is involved in several projects that use moderate-resolution land cover data derived from Landsat surface reflectance. Landsat offers higher spatial resolution than MODIS, allowing for increased ability to detect land use changes and forest disturbance. However, because of the rate at which changes occur and the fact that disturbances can be hidden by regrowth, updated land cover classifications may be required before the launch of the Landsat Data Continuity Mission, and consistent classifications will be needed after that time. This candidate solution investigates the potential of using NASA atmospheric correction techniques to produce science-quality surface reflectance data from the Indian Remote Sensing Advanced Wide-Field Sensor on the RESOURCESAT-1 mission to produce land cover classification maps for the CASA-CQUEST DSS

Geoinformation Perspectives for Managing Change in Ecological Economy of Rainfed Regions

Not AvailableGeoinformation and ecological economy share a strong attribute viz., scale. Scale of entities acting therewith and their role as key development influences. Ecological economy as a process, dealing with products realised at various scales of aggregation of stakes, can have a strong parallel with geoinformation systems capable of representing range of natural and manmade entities as well as their juxtapositions. Agricultural economy generally pertains to generation of assets based on performance of cropped lands and excludes often, the asset generation accomplished using other associated natural resources like forest , fish and recreation. Asset generation based on these latter activities using ecologically non invasive approaches may be considered along with agricultural economy, as a true context of ecological economy or a strongly ecology based economy. Farm economy in most of the rainfed contexts retains for better part, remains at times, a conventional approach of selecting crops and modest level of external inputs due to individuals’ investment capacity. Global level changes either physical or fiscal in nature are increasingly influencing the cropping pattern and related land use initiatives and is pushing near subsistence systems to change. Possible attributes inherent to the system may explain why it is important to perceive the phenomeon as strongly ecology oriented i. Autecological and synecological processes of a crop strongly determines the asset creation for an average farmer. ii. Asset generation as influenced by biotic, climatic and other locality factors acquire stronger ecological connotations. Since any intervention of innovation does not gain significance unless it crosses threshold of either a spatial or temporal scale, in terms of asset generation/ecological amelioration, it is essential to retain intrinsic scale reference. iii. As trends in rainfed farming either with regard to man or his biota related actions have huge impact on the regional market/policy, by virtue of a success or a failure otherwise, it would be naïve to consider the case as a fit candidate under ecological economyNot Availabl

Geoinformation Perspectives for Managing Change in Ecological Economy of Rainfed Regions

Not AvailableGeoinformation and ecological economy share a strong attribute viz., scale. Scale of entities acting therewith and their role as key development influences. Ecological economy as a process, dealing with products realised at various scales of aggregation of stakes, can have a strong parallel with geoinformation systems capable of representing range of natural and manmade entities as well as their juxtapositions. Agricultural economy generally pertains to generation of assets based on performance of cropped lands and excludes often, the asset generation accomplished using other associated natural resources like forest , fish and recreation. Asset generation based on these latter activities using ecologically non invasive approaches may be considered along with agricultural economy, as a true context of ecological economy or a strongly ecology based economy. Farm economy in most of the rainfed contexts retains for better part, remains at times, a conventional approach of selecting crops and modest level of external inputs due to individuals’ investment capacity. Global level changes either physical or fiscal in nature are increasingly influencing the cropping pattern and related land use initiatives and is pushing near subsistence systems to change. Possible attributes inherent to the system may explain why it is important to perceive the phenomeon as strongly ecology oriented i. Autecological and synecological processes of a crop strongly determines the asset creation for an average farmer. ii. Asset generation as influenced by biotic, climatic and other locality factors acquire stronger ecological connotations. Since any intervention of innovation does not gain significance unless it crosses threshold of either a spatial or temporal scale, in terms of asset generation/ecological amelioration, it is essential to retain intrinsic scale reference. iii. As trends in rainfed farming either with regard to man or his biota related actions have huge impact on the regional market/policy, by virtue of a success or a failure otherwise, it would be naïve to consider the case as a fit candidate under ecological economy.Not Availabl

CID Survey Report Satellite Imagery and Associated Services used by the JRC. Current Status and Future Needs

The Agriculture and Fisheries Unit (IPSC) together with the Informatics, Networks and Library Unit (ISD) has performed this inventory called the Community Image Data portal Survey (the CID Survey); 20 Actions from 4 different Institutes (ISD, IPSC, IES, and IHCP) were interviewed. The objectives of the survey were to make an inventory of existing satellite data and future requirements; to obtain an overview of how data is acquired, used and stored; to quantify human and financial resources engaged in this process; to quantify storage needs and to query the staff involved in image acquisition and management on their needs and ideas for improvements in view of defining a single JRC portal through which imaging requests could be addressed. Within the JRC there are (including 2006) more than 700 000 low resolution (LR) and 50 000 medium resolution (MR) images, with time series as far back as 1981 for the LR data. There are more than 10 000 high resolution (HR) images and over 500 000 km2 of very high resolution (VHR) images. For the LR and MR data, cyclic global or continental coverage dominates, while the majority of HR and VHR data is acquired over Europe. The expected data purchase in the future (2007, 2008) known which enables good planning. Most purchases of VHR and HR data are made using the established FCs with common licensing terms. Otherwise multiple types of licensing govern data usage which emphasizes the need for CID to establish adequate means of data access. The total amount of image data stored (2006 inclusive) is 55 TB, with an expected increase of 80% in 2 years. Most of the image data is stored on internal network storage inside the corporate network which implies that the data is accessible from JRC, but difficulties arise when access is to be made by external users via Internet. In principle current storage capacity in the JRC could be enough, but available space is fragmented between Actions which therefore implies that a deficit in storage could arise. One solution to this issue is the sharing of a central storage service. Data reception is dominated by FTP data transfer which therefore requires reliable and fast Internet transfer bandwidth. High total volume for backup requires thorough definition of backup strategy. The user groups at JRC are heterogeneous which places requirements on CID to provide flexible authentication mechanisms. There is a requirement for a detailed analysis of all metadata standards needed for reference in a catalogue. There is a priority interest for such Catalogue Service and also for a centralized storage. The services to implement for data hosted on central storage should be WCS, WMS, file system access. During the analysis of the results mentioned above, some major areas could be identified as a base for common services to be provided to interested Actions, such as: provision of a centralized data storage facility with file serving functionality including authentication service, image catalogue services, data visualization and dissemination services. Specialized data services that require highly customized functionality with respect to certain properties of the different image types will usually remain the sole responsibility of the individual Actions. An orthorectification service for semi-automated orthorectification of HR and VHR data will be provided to certain Actions. At the end of the report some priorities and an implementation schedule for the Community Image Data portal (CID) are given.JRC.G.3-Agricultur

Comparison of optical sensors discrimination ability using spectral libraries

In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensors’ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)

Optical remote sensing of glacier characteristics::A review with focus on the Himalaya

The increased availability of remote sensing platforms with appropriate spatial and temporal resolution, global coverage and low financial costs allows for fast, semi-automated, and cost-effective estimates of changes in glacier parameters over large areas. Remote sensing approaches allow for regular monitoring of the properties of alpine glaciers such as ice extent, terminus position, volume and surface elevation, from which glacier mass balance can be inferred. Such methods are particularly useful in remote areas with limited field-based glaciological measurements. This paper reviews advances in the use of visible and infrared remote sensing combined with field methods for estimating glacier parameters, with emphasis on volume/area changes and glacier mass balance. The focus is on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor and its applicability for monitoring Himalayan glaciers. The methods reviewed are: volumetric changes inferred from digital elevation models (DEMs), glacier delineation algorithms from multi-spectral analysis, changes in glacier area at decadal time scales, and AAR/ELA methods used to calculate yearly mass balances. The current limitations and on-going challenges in using remote sensing for mapping characteristics of mountain glaciers also discussed, specifically in the context of the Himalaya

Fused LISS IV Image Classification using Deep Convolution Neural Networks

These days, earth observation frameworks give a large number of heterogeneous remote sensing information. The most effective method to oversee such fulsomeness in utilizing its reciprocity is a vital test in current remote sensing investigation. Considering optical Very High Spatial Resolution (VHSR) images, satellites acquire both Multi Spectral (MS) and panchromatic (PAN) images at various spatial goals. Information fusion procedures manage this by proposing a technique to consolidate reciprocity among the various information sensors. Classification of remote sensing image by Deep learning techniques using Convolutional Neural Networks (CNN) is increasing a solid decent footing because of promising outcomes. The most significant attribute of CNN-based strategies is that earlier element extraction is not required which prompts great speculation capacities. In this article, we are proposing a novel Deep learning based SMDTR-CNN (Same Model with Different Training Round with Convolution Neural Network) approach for classifying fused (LISS IV + PAN) image next to image fusion. The fusion of remote sensing images from CARTOSAT-1 (PAN image) and IRS P6 (LISS IV image) sensor is obtained by Quantization Index Modulation with Discrete Contourlet Transform (QIM-DCT). For enhancing the image fusion execution, we remove specific commotions utilizing Bayesian channel by Adaptive Type-2 Fuzzy System. The outcomes of the proposed procedures are evaluated with respect to precision, classification accuracy and kappa coefficient. The results revealed that SMDTR-CNN with Deep Learning got the best all-around precision and kappa coefficient. Likewise, the accuracy of each class of fused images in LISS IV + PAN dataset is improved by 2% and 5%, respectively

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda

Procjena utjecaja atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla

Remote sensing technology effectively determines and evaluates salinity-affected areas\u27 spatial and temporal distribution. Soil salinity maps for large areas can be obtained with low cost and low effort using remote sensing methods and techniques. Remote sensing data are delivered raw as Level-1 data, and they can be further atmospherically corrected to surface reflectance values, Level-2 data. This study evaluates the atmospheric correction impact on Landsat 8 and Sentinel-2 data for soil salinity determination. The study has been supported with in-situ measurements in Alpu, Eskisehir, Turkey, where samples were collected from various agricultural fields simultaneously with the overpass of the satellites. Two different analysis cases have been used to determine the effect of atmospheric correction. The first is to examine the relationship between the measurements taken from the areas with mixed product groups and the salinity indices for both data types. The other is to investigate the relationship between the measurement values taken only from the wheat and beet groups and the salinity index values. The results show that atmospheric correction has a high effect on the relationship between spectral indices and in situ salinity measurement values. Especially in all cases examined in Landsat, it was observed that atmospheric correction led to an improvement of over 140%, while nearly 50% was observed in Sentinel on a product basis.Uz pomoć tehnologije daljinskih istraživanja učinkovito se određuje i procjenjuje prostorna i vremenska rasprostranjenost područja zahvaćenih salinitetom. Karte saliniteta tla za velika područja mogu se izraditi uz niske troškove i malo truda koristeći metode i tehnike daljinskih istraživanja. Podaci dobiveni daljinskim istraživanjima isporučuju se neobrađeni kao podaci Level-1 te se zatim mogu atmosferski korigirati na vrijednosti površinske refleksije, podaci Level-2. Ova studija procjenjuje utjecaje atmosferske korekcije na podatke Landsat 8 i Sentinel-2 za određivanje saliniteta tla. Studija je potkrijepljena mjerenjima in situ u Alpu, Eskisehir, Turska, gdje su uzorci bili prikupljeni na različitim poljoprivrednim poljima istovremeno s preletima satelita. Upotrijebljene su dvije različite analize kako bi se odredio učinak atmosferske korekcije. Prva je analiza primijenjena kako bi se ispitao odnos između mjerenja provedenih na područjima s miješanim skupinama proizvoda i indeksima saliniteta za obje vrste podataka. Druga je analiza primijenjena kako bi se istražio odnos između vrijednosti mjerenja dobivenih samo iz skupina pšenice i repe te vrijednosti indeksa saliniteta. Rezultati pokazuju da atmosferska korekcija ima visok učinak na odnos između spektralnih indeksa i vrijednosti mjerenja saliniteta in situ. Posebno se u svim slučajevima ispitivanja putem Landsata moglo primijetiti da je atmosferska korekcija dovela do poboljšanja za više od 140%, dok je gotovo 50% primijećeno za Sentinel na temelju proizvoda