Remote sensing and GIS are important tools for studying land use/land cover (LULC) change and integrating the associated driving factors for deriving useful outputs. This study is based on utilization of Earth observation datasets over the highly urbanized Allahabad district in India. Allahabad district has experienced intense change in LULC in the last few decades. To monitor the changes, advanced techniques in remote sensing and GIS, such as Cellular Automata (CA)-Markov Chain Model (CAMCM) were used to identify the spatial and temporal changes that have occurred in LULC in this area. Two images, 1990 and 2000, were used for calibration and optimization of the Markovian algorithm, while 2010 was used for validating the predictions of CA-Markov using the ground based land cover image. After validating the model, plausible future LULC changes for 2020 were predicted using the CAMCM. Analysis of the LULC pattern maps, achieved through classification of multi-temporal satellite datasets, indicated that the socio-economic and biophysical factors have greatly influenced the growth of agricultural lands and settlements in the area. The two urbanization indicators calculated in this study viz. Land Consumption Ratio (LCR) and Land Absorption Coefficient (LAC) were also used, which indicated a drastic change in the area in terms of urbanization. The predicted LULC scenario for year 2020 provides useful inputs to the LULC planners for effective and pragmatic management of the district and a direction for an effective land use policy making. Further suggestions for an effective policy making are also provided which can be used by government officials to protect this important land resource.

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Predicting Spatial and Decadal LULC Changes Through Cellular Automata Markov Chain Models Using Earth Observation Datasets and Geo-information

Land use/land cover (LULC) is a fundamental concept of the Earth's system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology provides an in...

/pdf/land-use-land-cover-in-view-of-earth-observation-data-5esdvpaonv.pdf

Land use/land cover in view of earth observation: data sources, input dimensions, and classifiers—a review of the state of the art

Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing

Floods are common natural disasters worldwide, frequently causing loss of lives and huge economic and environmental damages. A spatial vulnerability mapping approach incorporating multi-criteria at the local scale is essential for deriving detailed vulnerability information for supporting flood mitigation strategies. This study developed a spatial multi-criteria-integrated approach of flood vulnerability mapping by using geospatial techniques at the local scale. The developed approach was applied on Kalapara Upazila in Bangladesh. This study incorporated 16 relevant criteria under three vulnerability components: physical vulnerability, social vulnerability and coping capacity. Criteria were converted into spatial layers, weighted and standardised to support the analytic hierarchy process. Individual vulnerability component maps were created using a weighted overlay technique, and then final vulnerability maps were produced from them. The spatial extents and levels of vulnerability were successfully identified from the produced maps. Results showed that the areas located within the eastern and south-western portions of the study area are highly vulnerable to floods due to low elevation, closeness to the active channel and more social components than other parts. However, with the integrated coping capacity, western and south-western parts are highly vulnerable because the eastern part demonstrated particularly high coping capacity compared with other parts. The approach provided was validated by qualitative judgement acquired from the field. The findings suggested the capability of this approach to assess the spatial vulnerability of flood effects in flood-affected areas for developing effective mitigation plans and strategies.

https://www.mdpi.com/1424-8220/19/6/1302/pdf

Assessing Spatial Flood Vulnerability at Kalapara Upazila in Bangladesh Using an Analytic Hierarchy Process.

Tropical cyclone risk assessment using geospatial techniques for the eastern coastal region of Bangladesh

Land-use and land-cover (LU/LC) studies help in assessing and monitoring the status of the natural resources, detecting the changes in spatial and temporal scale and predict them for the future. Due to changing environments and increasing anthropogenic pressures, the demand for a LU/LC database at the global level is increasing. Therefore, a comprehensive understanding of LU/LC at both local and regional scales is important since it plays a pivotal role in socioeconomic development and global environmental changes. There are many approaches for LU/LC analysis such as supervised classification, unsupervised classification and onscreen digitization but simplest and most popular approach on IRS LISS-III and ${\rm Landsat\hbox{-}ETM}{+}$ satellite data revealed a serious problem in some semidesert areas caused by spectral confusion because of the similar radiometric response like scrub land with harvested land, built-up with bare hills and many other. Present study suggests hybrid classification approach for LU/LC classification, which is found highly useful in achieving high accuracy for areas where spectral classes of images are inseparable.

An Efficient Hybrid Classification Approach for Land Use/Land Cover Analysis in a Semi-Desert Area Using ${\rm ETM}{+}$ and LISS-III Sensor

Tropical forest is one of the great biodiversity repositories of the world ecosystem. Biodiversity is depleting very fast due to conversion of forest region into agricultural or other land use. Here comes the role of biodiversity assessment and evaluation of spatial data of species to prioritize the conservation purposes. Traditionally, ground-based plots were used to assess different biodiversity. Later on, remote sensing approaches were also incorporated along with field-based studies to quantify the results accurately. Assessment of biodiversity constitutes estimation of various indices that were obtained using ground-based plot or survey. With the advancement of the remote sensing technology, spatial information about tree species was collected using field sample and satellite data and field sample plots within the Sariska Tiger Reserve. Different diversity indices were calculated like α, β, diversity, and others, i.e., Pilot's index (J), Shannon-Wiener index (SR), Margalef index (E
 w
), and Whittaker's index (H'). The multistage statistical techniques, which integrate high spatial resolution and spectral characteristics of satellite data (LISS IV), will help in providing precise information about tree species. Regression analysis provides better results to identify forest species among different bands. A positive correlation has been found in the infrared band even negative correlation has been found in other bands. This paper incorporates field-based surveys along with remote sensing technologies using a regression model (r
 2
 = 0.636) to estimate and recognize different species diversity in Sariska Tiger Reserve.

Efficient Recognition of Forest Species Biodiversity by Inventory-Based Geospatial Approach Using LISS IV Sensor

Jaipur, Rajasthan in India is facing scarcity of the ground water and its quality has special magnitude on crop yield that has special significance as it is the major alternate source of domestic, industrial, and irrigation water supply. This paper focuses on how to assess the water quality for irrigation practices. It is then related to the crop yield estimation. The inverse-distance weighted and modified Shepard's method of interpolation has been used for demonstrating the quality wise spatial distribution of chemical parameters using LISS-III imagery in GIS frame. The attribute database for this paper consists of detailed physiochemical analysis of ground water and soil samples collected from predetermined well locations and agricultural plots. The results depict mathematical computation of water quality in terms of suitability index and demonstrate the impact of water quality on regional crop yield.

Binay Kumar Singh

Papers

An Efficient Hybrid Classification Approach for Land Use/Land Cover Analysis in a Semi-Desert Area Using ${\rm ETM}{+}$ and LISS-III Sensor

Efficient Recognition of Forest Species Biodiversity by Inventory-Based Geospatial Approach Using LISS IV Sensor

An Associative Relational Impact of Water Quality on Crop Yield: A Comprehensive Index Analysis Using LISS-III Sensor