Remote sensing applications: an overview

Remote Sensing (RS) refers to the science of identification of earth surface features and estimation of their geo-biophysical properties using electromagnetic radiation as a medium of interaction. Spectral, spatial, temporal and polarization signatures are major characteristics of the sensor/target, which facilitate target discrimination. Earth surface data as seen by the sensors in different wavelengths (reflected, scattered and/or emitted) is radiometrically and geometrically corrected before extraction of spectral information. RS data, with its ability for a synoptic view, repetitive coverage with calibrated sensors to detect changes, observations at different resolutions, provides a better alternative for natural resources management as compared to traditional methods. Indian Earth Observation (EO) programme has been applications-driven and national development has been its prime motivation. From Bhaskara to Cartosat, India's EO capability has increased manifold. Improvements are not only in spatial, spectral, temporal and radiometric resolutions, but also in their coverage and value-added products. Some of the major operational application themes, in which India has extensively used remote sensing data are agriculture, forestry, water resources, land use, urban sprawl, geology, environment, coastal zone, marine resources, snow and glacier, disaster monitoring and mitigation, infrastructure development, etc. The paper reviews RS techniques and applications carried out using both optical and microwave sensors. It also analyses the gap areas and discusses the future perspectives

Continuity of Landsat Obersvations: Short Term Considerations

As of writing in mid-2010, both Landsat-5 and -7 continue to function, with sufficient fuel to enable data collection until the launch of the Landsat Data Continuity Mission (LDCM) scheduled for December of 2012. Failure of one or both of Landsat-5 or -7 may result in a lack of Landsat data for a period of time until the 2012 launch. Although the potential risk of a component failure increases the longer the sensor\u27s design life is exceeded, the possible gap in Landsat data acquisition is reduced with each passing day and the risk of Landsat imagery being unavailable diminishes for all except a handful of applications that are particularly data demanding. Advances in Landsat data compositing and fusion are providing opportunities to address issues associated with Landsat-7 SLC-off imagery and to mitigate a potential acquisition gap through the integration of imagery from different sensors. The latter will likely also provide short-term, regional solutions to application-specific needs for the continuity of Landsat-like observations. Our goal in this communication is not to minimize the community\u27s concerns regarding a gap in Landsat observations, but rather to clarify how the current situation has evolved and provide an up-to-date understanding of the circumstances, implications, and mitigation options related to a potential gap in the Landsat data record

Copernicus high-resolution layers for land cover classification in Italy

The high-resolution layers (HRLs) are land cover maps produced for the entire Italian territory (approximately 30 million hectares) in 2012 by the European Environment Agency, aimed at monitoring soil imperviousness and natural cover, such as forest, grassland, wetland, and water surface, with a high spatial resolution of 20 m. This study presents the methodologies developed for the production, verification, and enhancement of the HRLs in Italy. The innovative approach is mainly based on (a) the use of available reference data for the enhancement process, (b) the reduction of the manual work of operators by using a semi-automatic approach, and (c) the overall increase in the cost-efficiency in relation to the production and updating of land cover maps. The results show the reliability of these methodologies in assessing and enhancing the quality of the HRLs. Finally, an integration of the individual layers, represented by the HRLs, was performed in order to produce a National High-Resolution Land Cover ma

AN AUTOMATED SOFTWARE FRAMEWORK FOR EVALUATION AND IMPROVEMENT OF ABSOLUTE GEOMETRIC LOCATION ACCURACY OF MULTISPECTRAL REMOTE SENSING SATELLITE IMAGERY

Geometric accuracy is an important parameter for quality assessment of a data product and is vital for certain applications aiming on improving and bringing precision in data products. Automatic geometric accuracy evaluation of the satellite image is attempted by matching a known, surveyed location typically a ground control point (GCP) calibrated using a differential global positioning system (DGPS), verified using Google Maps, to the corresponding identifiable feature in an image product. The requirement for this development is to address the non-uniformity in the available data products in terms of coordinates reference system, resolution and available bands, which the software overcomes successfully by benefiting from the classes and functions available in openly available GIS libraries. RMSE of 0.8 pixels is found in analysis for the chosen data. Further, an algorithm is worked up to rectify the image for this geometric shift

A GENERALIZED APPROACH TO WHEAT YIELD FORECASTING USING EARTH OBSERVATIONS: DATA CONSIDERATIONS, APPLICATION, AND RELEVANCE.

In recent years there has been a dramatic increase in the demand for timely, comprehensive global agricultural intelligence. The issue of food security has rapidly risen to the top of government agendas around the world as the recent lack of food access led to unprecedented food prices, hunger, poverty, and civil conflict. Timely information on global crop production is indispensable for combating the growing stress on the world's crop production, for stabilizing food prices, developing effective agricultural policies, and for coordinating responses to regional food shortages. Earth Observations (EO) data offer a practical means for generating such information as they provide global, timely, cost-effective, and synoptic information on crop condition and distribution. Their utility for crop production forecasting has long been recognized and demonstrated across a wide range of scales and geographic regions. Nevertheless it is widely acknowledged that EO data could be better utilized within the operational monitoring systems and thus there is a critical need for research focused on developing practical robust methods for agricultural monitoring. Within this context this dissertation focused on advancing EO-based methods for crop yield forecasting and on demonstrating the potential relevance for adopting EO-based crop forecasts for providing timely reliable agricultural intelligence. This thesis made contributions to this field by developing and testing a robust EO-based method for wheat production forecasting at state to national scales using available and easily accessible data. The model was developed in Kansas (KS) using coarse resolution normalized difference vegetation index (NDVI) time series data in conjunction with out-of-season wheat masks and was directly applied in Ukraine to assess its transferability. The model estimated yields within 7% in KS and 10% in Ukraine of final estimates 6 weeks prior to harvest. The relevance of adopting such methods to provide timely reliable information to crop commodity markets is demonstrated through a 2010 case study

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discusse

A Landsat-based analysis of tropical forest dynamics in the Central Ecuadorian Amazon : Patterns and causes of deforestation and reforestation

Tropical deforestation constitutes a major threat to the Amazon rainforest. Monitoring forest dynamics is therefore necessary for sustainable management of forest resources in this region. However, cloudiness results in scarce good quality satellite observations, and is therefore a major challenge for monitoring deforestation and for detecting subtle processes such as reforestation. Furthermore, varying human pressure highlights the importance of understanding the underlying forces behind these processes at multiple scales but also from an interand transdisciplinary perspective. Against this background, this study analyzes and recommends different methodologies for accomplishing these goals, exemplifying their use with Landsat timeseries and socioeconomic data. The study cases were located in the Central Ecuadorian Amazon (CEA), an area characterized by different deforestation and reforestation processes and socioeconomic and landscape settings. Three objectives guided this research. First, processing and timeseries analysis algorithms for forest dynamics monitoring in areas with limited Landsat data were evaluated, using an innovative approach based in genetic algorithms. Second, a methodology based in image compositing, multisensor data fusion and postclassification change detection is proposed to address the limitations observed in forest dynamics monitoring with timeseries analysis algorithms. Third, the evaluation of the underlying driving forces of deforestation and reforestation in the CEA are conducted using a novel modelling technique called geographically weight ridge regression for improving processing and analysis of socioeconomic data. The methodology for forest dynamics monitoring demonstrates that despite abundant data gaps in the Landsat archive for the CEA, historical patterns of deforestation and reforestation can still be reported biennially with overall accuracies above 70%. Furthermore, the improved methodology for analyzing underlying driving forces of forest dynamics identified local drivers and specific socioeconomic settings that improved the explanations for the high deforestation and reforestation rates in the CEA. The results indicate that the proposed methodologies are an alternative for monitoring and analyzing forest dynamics, particularly in areas where data scarcity and landscape complexity require approaches that are more specialized.Landsat-basierte Analyse der Dynamik tropischer Wälder im Zentral-Ecuadorianischen Amazonasgebiet: Muster und Ursachen von Abholzung und Wiederaufforstung Die tropische Entwaldung stellt eine große Bedrohung für den AmazonasRegenwald dar. Daher ist die Überwachung von Walddynamiken eine notwendige Maßnahme, um eine nachhaltige Bewirtschaftung der Waldressourcen in dieser Region zu gewährleisten. Jedoch verschlechtert Bewölkung die Qualität der Satellitenaufnahmen und stellt die hauptsächliche Herausforderung für die Überwachung der Entwaldung sowie die Detektierung einhergehender Prozesse, wie der Wiederaufforstung, dar. Darüber hinaus zeigt der unterschiedliche menschliche Nutzungsdruck, wie wichtig es ist, die zugrundeliegenden Kräfte hinter diesen Prozessen auf mehreren Ebenen, aber auch interund transdisziplinär, zu verstehen. Variierender anthropogener Einfluss unterstreicht die Notwendigkeit, unterschwellige Prozesse (oder "Driver") auf multiplen Skalen aus interund transdisziplinärer Sicht zu verstehen. Darauf basierend analysiert und empfiehlt die vorliegende Studie unterschiedliche Methoden, welche unter Verwendung von LandsatZeitreihen und sozioökonomischen Daten zur Erreichung dieser Ziele beitragen. Die Untersuchungsgebiete befinden sich im ZentralEcuadorianischen Amazonasgebiet (CEA). Einem Gebiet, das einerseits durch differenzierte Entwaldungsund Aufforstungsprozesse, andererseits durch seine sozioökonomischen und landschaftlichen Gegebenheiten geprägt ist. Das Forschungsprojekt hat drei Zielvorgaben. Erstens werden auf genetischen Algorithmen basierten Verfahren zur Verarbeitung der Zeitreihenanalyse für die Überwachung der Walddynamik in Gebieten, für die nur begrenzte LandsatDaten vorhanden waren, bewertet. Zweitens soll eine Methode in Anlehnung an Satellitenbildkompositen, Datenfusion von mehreren Satellitenbildern und Veränderungsdetektion gefunden werden, die Einschränkungen der Walddynamik durch Entwaldung mithilfe von ZeitreihenAlgorithmen thematisiert. Drittens werden die Ursachen der Entwaldung/Abholzung im CEA anhand der geographischen gewichteten RidgeRegression, die zur einen verbesserten Analyse der sozioökonomischen Information beiträgt, bewertet. Die Methodik für das WalddynamikMonitoring zeigt, dass trotz umfangreicher Datenlücken im LandsatArchiv für das CEA alle zwei Jahre die historischen Entwaldungsund Wiederaufforstungsmuster mit einer Genauigkeit von über 70% gemeldet werden können. Eine verbesserte Analysemethode trägt außerdem dazu bei, die für die Walddynamik verantwortlichen treibenden Kräfte zu identifizieren, sowie lokale Treiber und spezifische sozioökonomische Rahmenbedingungen auszumachen, die eine bessere Erklärung für die hohen Entwaldungsund Wiederaufforstungsraten im CEA aufzeigen. Die erzielten Ergebnisse machen deutlich, dass die vorgeschlagenen Methoden eine Alternative zum Monitoring und zur Analyse der Walddynamik darstellen; Insbesondere in Gebieten, in denen Datenknappheit und Landschaftskomplexität spezialisierte Ansätze erforderlich machen

Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications

The use of satellites to monitor crops and support their management is gathering increasing attention. The improved temporal, spatial, and spectral resolution of the European Space Agency (ESA) launched Sentinel-2 A + B twin platform is paving the way to their popularization in precision agriculture. Besides the Sentinel-2 A + B constellation technical features the open-access nature of the information they generate, and the available support software are a significant improvement for agricultural monitoring. This paper was motivated by the challenges faced by researchers and agrarian institutions entering this field; it aims to frame remote sensing principles and Sentinel-2 applications in agriculture. Thus, we reviewed the features and uses of Sentinel-2 in precision agriculture, including abiotic and biotic stress detection, and agricultural management. We also compared the panoply of satellites currently in use for land remote sensing that are relevant for agriculture to the Sentinel-2 A + B constellation features. Contrasted with previous satellite image systems, the Sentinel-2 A + B twin platform has dramatically increased the capabilities for agricultural monitoring and crop management worldwide. Regarding crop stress monitoring, Sentinel-2 capacities for abiotic and biotic stresses detection represent a great step forward in many ways though not without its limitations; therefore, combinations of field data and different remote sensing techniques may still be needed. We conclude that Sentinel-2 has a wide range of useful applications in agriculture, yet still with room for further improvements. Current and future ways that Sentinel-2 can be utilized are also discussed.This research was funded by the Spanish projects AGL2016-76527-R and IRUEC PCIN-2017-063 from the Ministerio de Economía y Competividad (MINECO, Spain) and by the support of Catalan Institution for Research and Advanced Studies (ICREA, Generalitat de Catalunya, Spain), through the ICREA Academia Program

Glacier area change (1993–2019) and its relationship to debris cover, proglacial lakes, and morphological parameters in the Chandra-Bhaga Basin, Western Himalaya, India

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