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Remote Sensing And Image Interpretation

Precision agriculture (PA) is the application of geospatial techniques and sensors (e.g., geographic information systems, remote sensing, GPS) to identify variations in the field and to deal with them using alternative strategies. In particular, high-resolution satellite imagery is now more commonly used to study these variations for crop and soil conditions. However, the availability and the often prohibitive costs of such imagery would suggest an alternative product for this particular application in PA. Specifically, images taken by low altitude remote sensing platforms, or small unmanned aerial systems (UAS), are shown to be a potential alternative given their low cost of operation in environmental monitoring, high spatial and temporal resolution, and their high flexibility in image acquisition programming. Not surprisingly, there have been several recent studies in the application of UAS imagery for PA. The results of these studies would indicate that, to provide a reliable end product to farmers, advances in platform design, production, standardization of image georeferencing and mosaicing, and information extraction workflow are required. Moreover, it is suggested that such endeavors should involve the farmer, particularly in the process of field design, image acquisition, image interpretation and analysis.

The application of small unmanned aerial systems for precision agriculture: a review

Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization

Remote sensing for conservation monitoring: Assessing protected areas, habitat extent, habitat condition, species diversity, and threats

This book provides a comprehensive discussion on urban growth and sprawl, and how they can be analyzed using remote sensing imageries. It compiles views of numerous researchers that help in understanding the urban growth and sprawl; their patterns, process, causes, consequences, and countermeasures; how remote sensing data and geographic information system techniques can be used in mapping, monitoring, measuring, analyzing, and simulating the urban growth and sprawl; and what are the merits and demerits of available methods and models. This book will be of value for the scientists and researchers engaged in urban geographic research, especially using remote sensing imageries. This book will serve as a rigours literature review for them. Post graduate students of urban geography or urban/regional planning may refer this book as additional studies. This book may help the academicians for preparing lecture notes and delivering lectures. Industry professionals may also be benefited from the discussed methods and models along with numerous citations.

https://www.arabgeographers.net/up/uploads/14159802841.pdf

Analysis of Urban Growth and Sprawl from Remote Sensing Data

Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

Remote Sensing (RS) and Geographical Information System (GIS) play a crucial role to understand the division of groundwater, an important resource of water supply all over the world. In this present study, groundwater potential and recharge zone maps, are delineated for Loni and Morahi watersheds, Unnao and Rae Bareli district, Uttar Pradesh, India using RS, GIS and Multi-Criteria Decision Making (MCDM) techniques. The Satty’s Analytical Hierarchical Process (AHP) is used as a MCDM technique to normalise the weights of various thematic layers and their classes for delineating the groundwater potential and recharge zone maps. Thematic layers were integrated using weighted overlay in a GIS environment to generate groundwater potential and recharge zone maps. The output potential map is further classified into five zones on the basis of their histograms, viz., ‘very poor’, ‘poor’, ‘good’, ‘very good’ and ‘excellent’. The area falling in the excellent zone is about 150.93 km2 (7.06 % of the total study area), which covers a major portion of the Ganga river. It discriminates the areas where the terrain is suitable for groundwater storage. However, the area falling in the very poor zone is about 372.03 km2 (17.42 % of the total study area) and covers the Loni river south-eastern portion and some areas in north-eastern sides. Groundwater recharge map is classified into four zones namely; ‘most suitable’, ‘moderately suitable’, ‘poorly suitable’ and ‘not suitable’. Yield data of the 40 pumping wells are used to verify the groundwater potential zone map, and the results were found to be good.

Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique

The sustainable development and management of groundwater resource requires precise quantitative assessment based on scientific principle and modern techniques. In the present study, groundwater potential zone are delineated using remote sensing, geographical information system (GIS) and multi-criteria decision making (MCDM) techniques in Unnao district, Uttar Pradesh. The analytical network process (ANP) is a method that makes it possible for one to deal systematically, and includes the analytical hierarchy process (AHP) as a special case. The AHP and ANP are used to determine the weights of various themes and their classes for identifying the groundwater potential zone. These weights are applied in a linear combination to obtain five different groundwater potential zone in the study area, namely ‘very poor’, ‘poor’, ‘moderate’, ‘good’ and ‘very good’. It has been concluded that about 153.39 km2 area has very good groundwater potential which is only 3.37% of the total study area. However, the area having very poor groundwater potential is about 850 km2 which is about 19.63% of the study area. The area having good, moderate and poor groundwater potential is about 540.25, 1135.5, 1868.6 km2, respectively. The groundwater potential zone map was finally verified using the well yield data of 37 pumping wells, and the result was found satisfactory.

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Delineation of groundwater potential zone: An AHP/ANP approach

The concentration of people in densely populated urban areas, especially in developing countries like India and China, calls for the use of sophisticated monitoring systems, like remote sensing and Geographical Information Systems (GIS). Time series of land use/cover changes can easily be generated using sequential satellite images, which are required for the prediction of urban growth, verification of growth model outputs, estimation of impervious area, parameterization of various hydrological models, water resources planning and management and environmental studies. In the present work, urban growth of Ajmer city (India) in the last 29 years has been studied at mid-scale level (5-25 m). Remote sensing and GIS have been used to extract the information related to urban growth, impervious area and its spatial and temporal variation. Statistical classification approaches have been used to derive the land use information from satellite images of eight years (1977-2005). The Shannon's entropy and landscape metrics (patchiness and map density) are computed in order to quantify the urban form (impervious area) in terms of spatial phenomena. Further, multivariate statistical techniques have been used to establish the relationship between the urban growth and its causative factors. Results reveal that land development (200%) in Ajmer is more than three times the population growth (59%). Shannon's entropy and landscape metrics has revealed the spatial distribution of the sprawl.

Pradeep Garg

Papers

Monitoring and modelling of urban sprawl using remote sensing and GIS techniques

Remote Sensing and GIS Based Groundwater Potential & Recharge Zones Mapping Using Multi-Criteria Decision Making Technique

Delineation of groundwater potential zone: An AHP/ANP approach

Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)

Statistical independence test and validation of CA Markov land use land cover (LULC) prediction results