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This study aims to assess Nilgai (Boselaphus tragocamelus Pallas) habitat in Asola-Bhatti Wildlife Sanctuary (WLS), Delhi, using Multi-Criteria Analysis (MCA). Nilgai is endemic to the Peninsular Indian and Indus divisions of the Indian sub-region in the Asian Indo-Malayan Region. It is a species of least conservation concern in its native range. The study was carried out in the Asola-Bhatti WLS, Delhi, located in the south-eastern part of the southern ridge, the south-east boundary spreading up to the Delhi-Haryana border. The Sanctuary covers an area of about 32.71 km². The vegetation is described as semi-arid thorny scrub, while it is designated as Northern Tropical Desert Thorn Forest (6BC1) and scrub as Ziziphus scrub (6BDS1). Nilgai habitat suitability in the study area was evaluated using MCA and a linear additive model using Cartosat-1, IRS P6 LISS-IV, and GoogleEarth data. MCA of Nilgai habitat was done using Analytical Hierarchy Process (AHP) through empirical judgement of preferences and an acceptable result was obtained, i.e., 0.019, less than 0.1 which is considered as a standard consistency ratio. A model was processed on the reclassified thematic layers by assigning them weightage according to the preferred habitat of nilgai. Thus, 53% (17.28 km²) area of the entire Sanctuary was demonstrated as highly suitable followed by 27% (8.98 km²) as suitable, 8% (2.50 km²) as moderately suitable, and 12% (3.95 km²) as least suitable area for nilgai. From the results of the study, it can be concluded that all the predictor variables derived from satellite imagery demonstrated the usefulness of geospatial technology for habitat modelling. [ABSTRACT FROM AUTHOR]

The recent innovations in remote sensing technologies have given rise to the efficient mapping and monitoring of forests. The developments in the sensor implementation have mainly focused on optimizing the payload of the UAV system and allowed the users to acquire the data simultaneously with a range of active and passive sensors like high-resolution RGB cameras and multispectral cameras LiDAR (Laser Imaging Detection and Ranging). The main objective of this research contribution is to combine the Digital Elevation Model (DEMs) from quadcopter Unmanned Aerial Vehicles (UAVs), Fixed Wing UAV-based cameras, and iPhone datasets for the forest plots. The datasets from two vegetation seasons, namely leaf-off and leaf-on, were used to combine the Digital Elevation Models from different data acquisition platforms. This internship research work aims to create and experiment with new methods, techniques, and technologies for the applications of UAV photogrammetry and iPhone LiDAR in forest napping and inventory management. CHMs are also generated in this work which helps assess the conditions of the forests in the recreational areas, and the possibility of solutions like iPhone LiDAR and UAV photogrammetry would be highly efficient and economical. The leaf-off and leaf-on datasets were processed in Agisoft Metashape Professional software to generate dense point clouds for the forest plots. The point cloud from the leaf-on dataset was rasterized to generate a DSM whereas the leaf-off point cloud generated a DSM of the forest plots after ground filtering with Cloth Simulation Filter (CSF) plugin. The iPhone LiDAR point was also rasterized to a DTM product after pre-processing steps and noise removal. The Canopy Height Models (CHMs) were generated by subtracting UAV and iPhone LiDAR based DTMs from the UAV leaf on DSM. Finally, the accuracy assessment of CHMs from UAB datasets and their integration with iPhone LiDAR has been assessed using the accurate tree heights measured during the forest field visits. The proposed methodology can be used for forest mapping purposes where a moderate accuracy is requested. [ABSTRACT FROM AUTHOR]

The role of agriculture in food security is critical by the rapid increase of the global population. Food production should be strongly increased to secure and provide life necessities. Remote sensing provides a fruitful tool in agriculture management, and it provides advantages in terms of saving time and effort. This study aims to examine the feasibility of deep learning edge detection algorithm in order to automatically extract the agricultural parcel boundaries from the open-source Google Earth data. The potential of using Normalized Difference Vegetation Index (NDVI) in crop classification crop was also presented after analyzing the pattern of Rabi major crops (Wheat and Sugarcane) in Haridwar district, Uttarakhand, India. The advantage of Google Earth Engine cloud-based platform was exploited in generating NDVI data from Sentinel-2 satellite between October 2022 and February 2023 in order to save time and effort. To check the accuracy of the deep learning model, the value of the Mean Intersection of Union (mIoU) was tested and reached 0.79.To examine the results, ground truth data were collected in the study area using Unmanned Aerial Vehicles. The overall accuracy of the rule set-based classification reached 91.17%, and the kappa coefficient value was 0.82. [ABSTRACT FROM AUTHOR]