Machine learning classifiers are being increasingly used nowadays for Land Use and Land Cover (LULC) mapping from remote sensing images. However, arriving at the right choice of classifier requires understanding the main factors influencing their performance. The present study investigated firstly the effect of training sampling design on the classification results obtained by Random Forest (RF) classifier and, secondly, it compared its performance with other machine learning classifiers for LULC mapping using multi-temporal satellite remote sensing data and the Google Earth Engine (GEE) platform. We evaluated the impact of three sampling methods, namely Stratified Equal Random Sampling (SRS(Eq)), Stratified Proportional Random Sampling (SRS(Prop)), and Stratified Systematic Sampling (SSS) upon the classification results obtained by the RF trained LULC model. Our results showed that the SRS(Prop) method favors major classes while achieving good overall accuracy. The SRS(Eq) method provides good class-level accuracies, even for minority classes, whereas the SSS method performs well for areas with large intra-class variability. Toward evaluating the performance of machine learning classifiers, RF outperformed Classification and Regression Trees (CART), Support Vector Machine (SVM), and Relevance Vector Machine (RVM) with a >95% confidence level. The performance of CART and SVM classifiers were found to be similar. RVM achieved good classification results with a limited number of training samples.

The satellite-based nighttime lights (NTL) data from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS), available in the public domain from 1992 to 2013, are extensively used for socio-economic studies. The improved NTL products from the Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS-DNB), on-board the Suomi National Polar-Orbiting Partnership spacecraft and National Oceanic and Atmospheric Administration – 20 (NOAA-20) spacecraft’s, are now available since April 2012. This study investigates the potential of machine-learning algorithms for inter-calibrating them (i.e., DMSP-OLS and VIIRS-DNB) to produce time-series annual VIIRS-DNB-like NTL datasets for the time when VIIRS-DNB data did not exist, for long-term studies. Uttar Pradesh, one of the most populous and largest States of India, is selected as the study area. Two machine-learning algorithms are utilized: (1) Multi-Layer Perceptron (MLP), having deep neural networks (DNN) architecture, and (2) Random Forest (RF), a widely used method. The DMSP-OLS and VIIRS-DNB data of 2013 (common year of data availability) and ancillary data pertaining to land cover, topography, and road network are used to train the models. The qualitative and quantitative analysis of annual VIIRS-DNB-like NTL images simulated from annual DMSP-OLS composites of 2004–2012 indicates that RF captures better spatial details at the local-scale and is able to efficiently handle the saturation problem at urban centers; while MLP is found to be superior at regional-scale. Both MLP and RF models significantly reduce the blooming effect around settlements, a common problem observed in DMSP-OLS data. It is inferred that depending on the research objectives, both RF and MLP algorithms can be appropriately utilized for producing VIIRS-DNB-like NTL images from DMSP-OLS annual NTL composites. The research can be further expanded by using other DNN architecture-based algorithms and improved spatio-temporal ancillary datasets over areas with different socio-economic, physiographic, and climatic settings.

Keywords: hydrology, climate, modeling, impact analysis, remote sensing, GIS Climate change, whether as a natural cycle variability and/or due to anthropogenic reasons, is affecting and likely to further affect the water resources, which is a vital necessity for existence of life form. The predicted intensification of hydrological cycle would change all of its constituents both in time and space domain. This is a long term phenomenon and the necessity is to understand the intensity of the effects on various aspects of water resources by way of scientific studies backed by the available field data. Therefore, in the present study, the impact of climate and land use land cover change on entire India under different assumed plausible hypothetical scenarios has been studied. These scenarios were developed by increasing; temperature by 1, 2 and 3⁰C; rainfall by 5, 10 and 15%; and then the combination of both. To carry out this analysis, variable infiltration capacity (VIC) semi-distributed macroscale hydrological model has been investigated. It was found that slight change in climate may pose huge difference on hydrological cycle and its component.

Assessment of urban growth is considered an important study for urban areas. The growth patterns and dynamics play a pivotal role in defining urban areas. Spatial metrics can prove to be instrumental in analysing these dynamics. The study focuses on categorising urban growth and quantifying the growth compactness and speed of Bengaluru and Karnataka using built-up obtained from Global Human Settlement Layer and Nighttime Lights data respectively. The evolution of Bengaluru and the skewed development in Karnataka are also observed. Landscape Expansion Index (LEI) is used to identify growth categories showing that 37.75% of Bengaluru's growth was as edge expansion. Area-weighted Mean Expansion Index, Urban Expansion Intensity Index and Shannon’s entropy are used to quantify growth compactness, speed and entropy respectively. The city has more growth compactness and speed compared to any other settlement in Karnataka. These urban growth dynamics shall help decision-makers in making data-driven decisions for urbanscape.

Tree counting can be a challenging and time consuming task, especially if done manually. This study proposes and compares three different approaches for automatic detection and counting of trees in different vegetative regions. First approach is to mark extended minima’s, extended maxima’s along with morphological reconstruction operations on an image for delineation and tree crown segmentation. To separate two touching crowns, a marker controlled watershed algorithm is used. For second approach, the color segmentation method for tree identification is used. Starting with the conversion of an RGB image to HSV color space then filtering, enhancing and thresholding to isolate trees from non-trees elements followed by watershed algorithm to separate touching tree crowns. Third approach involves deep learning method for classification of tree and non-tree, using approximately 2268 positive and 1172 negative samples each. Each segment of an image is then classified and sliding window algorithm is used to locate each tree crown. Experimentation shows that the first approach is well suited for classification of trees is dense vegetation, whereas the second approach is more suitable for detecting trees in sparse vegetation. Deep learning classification accuracy lies in between these two approaches and gave an accuracy of 92% on validation data. The study shows that deep learning can be used as a quick and effective tool to ascertain the count of trees from airborne optical imagery.