Soil Chemical Analysis

Beginning with Emlen (1966) and MacArthur and Pianka (1966) and extending through the last ten years, several authors have sought to predict the foraging behavior of animals by means of mathematical models. These models are very similar,in that they all assume that the fitness of a foraging animal is a function of the efficiency of foraging measured in terms of some "currency" (Schoener, 1971) -usually energy- and that natural selection has resulted in animals that forage so as to maximize this fitness. As a result of these similarities, the models have become known as "optimal foraging models"; and the theory that embodies them, "optimal foraging theory." The situations to which optimal foraging theory has been applied, with the exception of a few recent studies, can be divided into the following four categories: (1) choice by an animal of which food types to eat (i.e., optimal diet); (2) choice of which patch type to feed in (i.e., optimal patch choice); (3) optimal allocation of time to different patches; and (4) optimal patterns and speed of movements. In this review we discuss each of these categories separately, dealing with both the theoretical developments and the data that permit tests of the predictions. The review is selective in the sense that we emphasize studies that either develop testable predictions or that attempt to test predictions in a precise quantitative manner. We also discuss what we see to be some of the future developments in the area of optimal foraging theory and how this theory can be related to other areas of biology. Our general conclusion is that the simple models so far formulated are supported are supported reasonably well by available data and that we are optimistic about the value both now and in the future of optimal foraging theory. We argue, however, that these simple models will requre much modification, espicially to deal with situations that either cannot easily be put into one or another of the above four categories or entail currencies more complicated that just energy.

Citation classic - optimal foraging - a selective review of theory and tests

With the availability of multisensor, multitemporal, multiresolution and multifrequency image data from operational Earth observation satellites the fusion of digital image data has become a valuable tool in remote sensing image evaluation. Digital image fusion is a relatively new research field at the leading edge of available technology. It forms a rapidly developing area of research in remote sensing. This review paper describes and explains mainly pixel based image fusion of Earth observation satellite data as a contribution to multisensor integration oriented data processing.

Multisensor image fusion in remote sensing: Concepts, methods and applications

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Remote Sensing And Image Interpretation

Global Biodiversity Assessment

Remote sensing for integrated survey of urban environment

Corbett National Park (CNP), Uttarakhand, India is currently facing pressure of encroachments from human settlements around the buffer zone leading to increasing human and animal conflicts. In the present study, we have analysed the extent of land-use/landcover (LULC) changes between 1976 and 2011 using medium-spatial-resolution satellite data in and around CNP. Future projections have also been generated using CA-Markov and agent-based SaarS model. The study highlights that unlike in the past decades, the projected LULC may undergo restricted growth in built-up area agriculture.

Land-Use Dynamics in Corbett National Park, Uttarakhand, India Using CA-Markov and Agent-Based LULC-SaarS Model

The increasing frequency of heat waves (HW) in many parts of the world is emerging as one of the climatic vulnerabilities across the world resulting in elevated thermal stress and high mortality. With increase in HW intensity, frequency and duration at global level, India has seen several major HW events in the last decade. HW conditions have mostly been studied by analysing ground-based observations; however, this approach lacks information on spatial variability at the local scale, which is not adequate to identify HW risk and vulnerability hotspots. In this study, gridded analysis of spatio-temporal variability of HW indices has been carried out by utilising freely available Moderate Resolution Imaging Spectrometer (MODIS) Land Surface Temperature (LST) data on Google Earth Engine (GEE) platform in the Indo-Gangetic Plains of India. HW indices to analyse duration, frequency and intensity of HW have been identified and further computed on a grid size of 10 km*10 km area. HW risk and vulnerability hotspot in the study region have been identified by spatial modelling of HW indices, LULC change and population density. The HW risk and vulnerability hotspot layer identified NCT Delhi and its surrounding region at the highest risk of HW with high vulnerability. A strong positive correlation of variability of HW indicators with increasing built-up shows that built-up surfaces affect strongly the HW conditions.

Spatio-temporal analysis of land surface temperature for identification of heat wave risk and vulnerability hotspots in Indo-Gangetic Plains of India

The parallel processing and distributed GIServices provide an efficient approach to address the geocomputation challenges in biodiversity modeling. Using the widely applied Spatial Biodiversity Mod...

Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape

Forest fires are the most frequently occurred phenomenon during summer seasons in the state Madhya Pradesh. Monitoring and assessment of forest fires are the crucial steps in effective forest fire management. Forest fire danger estimation helps the disaster management authorities to take necessary mitigation measures for minimizing the losses and to evacuate the local people. Fire danger rating systems predict the fire danger based on the meteorological station parameters and ground datasets. McArthur Forest Fire Danger Index (FFDI) is the most popularly used fire danger rating systems using in the country Australia. This index requires large amount of ground datasets for the computation of drought parameter. In India, it is very difficult to compute the drought parameter due to the unavailability of instruments and man power. In the present research, McArthur Fire Danger Index was modified by inducing Normalized multiband drought index (NMDI) that was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA surface reflectance product MOD09GA as a substitute for fuel availability parameter. To test the robustness of modified FFDI, the research was carried out on Madhya Pradesh state for the assessment of forest fire danger. The results obtained from modified McArthur fire danger index were validated by using MODIS active fire hot spot location data (MOD14) and achieved an overall accuracy of 82%. The research concludes that modified FFDI can be used for assessing the forest fire danger in case of unavailability of fuel availability data for a particular forest.

Arijit Roy

Papers

Remote sensing for integrated survey of urban environment

Land-Use Dynamics in Corbett National Park, Uttarakhand, India Using CA-Markov and Agent-Based LULC-SaarS Model

Spatio-temporal analysis of land surface temperature for identification of heat wave risk and vulnerability hotspots in Indo-Gangetic Plains of India

Spatial biodiversity modeling using high-performance computing cluster: A case study to access biological richness in Indian landscape

Assessment of forest fire danger using automatic weather stations and MODIS TERRA satellite datasets for the state Madhya Pradesh, India