Modelling of land use land cover change using earth observation data-sets of Tons River Basin, Madhya Pradesh, India

Landscape Pattern and Ecological Security Assessment and Prediction Using Remote Sensing Approach

“…The CAMarkov model has a capability to simulate and to predict LULC changing status in future with remotely sensed data [19]. It is helpful to the research area in establishing and optimizing urban development decisions at different spatial-temporal dimensions [14,[19][20][21], when simulating and prediction urban landscape changes comprehensively in quantity and spatial aspects. As given in [19] by Singh et al, the Markov chain process can be described as…”

“…Simulation with CA-Markov Model. The CA-Markov is a combination model of Markov model which describes the probability of land cover changes between start and end period by developing a transition probability matrix between them and CA model which allows the transition probabilities of one pixel to be function of adjacent pixel [14]. The CAMarkov model has a capability to simulate and to predict LULC changing status in future with remotely sensed data [19].…”

“…One of previous study suggests the combining use of Spearman's correlation coefficient (SCC) and principal component analysis (PCA) method to reduce redundancies [22]. The method proposed in [22] used SCC to reduce high correlation between two landscape indexes by checking SCC whether greater than 0.9 or less than −0.9 and used PCA [14,[22][23][24] to reduce the number of landscape indexes to a manageable level. In this research, the selection of appropriate landscape indexes was based on experience of previous research as well as the knowledge of the study area when the spatial distribution status and data quality of research area were considered.…”

“…The mathematical representation of RBF is K x i , Journal of Sensors In this research, the overall and comprehensive indicator include OA and KC, which can be calculated using expressions OA = 1/N∑ r i=1 n ii and KC = N∑ r n n ii − ∑ r n n icol n irow /N 2 − ∑ r i=1 n icol n irow , respectively, are selected. Where n ii is number of pixels correctly classified, N is the total number of pixels in the confusion matrix, r is the number of rows, and n icol and n irow are the column and row total, respectively [14]. There are total of 15,538, 13,829, 15,991 and 13,937 pixels selected for training in remotely sensed data captured on 12 August 1986, 15 April 1996, 9 April 2006, and 23 April 2016, respectively.…”

Morphometric based prioritization of watershed for groundwater potential of Mula river basin, Maharashtra, India

“…Afterwards many geomorphologists have further developed the methods of watershed morphometry (Gregory, 1966;Schumm, 1956;Strahler, 1957Strahler, , 1964. In India, many authors (Sreedevi, Subrahmanyam, & Ahmed, 2005;Yadav, Dubey, Szilard, & Singh, 2016;Yadav, Singh, Gupta, & Srivastava, 2014) used remote sensing and GIS tools in morphometric analysis and other (Balázs, Bíró, Gareth, Singh, & Szabó, 2018;Narsimlu, Gosain, Chahar, Singh, & Srivastava, 2015;Paudel, Thakur, Singh, & Srivastava, 2014;Rawat & Singh, 2017;Singh, Basommi, Mustak, Srivastava, & Szabo, 2017;Singh, Mustak, Srivastava, Szabó, & Islam, 2015).…”