scholarly journals Evaluation of decadal land degradation dynamics in old coal-mines of Central India

2022 ◽  
Author(s):  
Tarun Thakur ◽  
JOYSTU DUTTA ◽  
Arvind Bijalwan ◽  
S Swamy
Keyword(s):  
Land Use ◽  
Land Degradation ◽  
Vegetation Indices ◽  
Coal Mines ◽  
Central India ◽  
Vulnerability Index ◽  
Landsat 8 ◽  
Sparse Vegetation ◽  
Mining Areas ◽  
Detection Analysis

The present study attempts to understand land use dynamics in an area subjected to opencast and underground coal mining for the last few decades in Kotma Coalmines of Anuppur district in Madhya Pradesh, India through geospatial techniques. Land Use and Land Cover (LULC) change detection analysis was performed digitally classifying Landsat 5 (2001) as well as Landsat 8 (2020) satellite data using maximum likelihood algorithm. Results revealed that area under Dense native vegetation decreased drastically (13.74 sq. km) with the gradual and consistent expansion in the activities of coal mines which showed the highest increase in area over time (15.84 sq. km). Bivariate regression analysis showed the positive empirical relationships between vegetation indices and soil physico-chemical parameters. Studies suggested soil and vegetation is degraded over the large mining areas consistently over a long time period. Despite the continuous reforestation activities on mined areas, the decline area under dense vegetation and sparse vegetation over the twenty-year time-scale indicates that the reclamation activities are still in its’ infancy. Land Degradation Vulnerability Index (LDVI) map was generated to understand the extent of decadal land degradation trends and it shows that 8.60 % of the area is highly vulnerable to degradation. The LDI inputs will help the planners to develop alternate strategies to tackle vulnerability zones for safe mining. Monthly estimation of various meteorological parameters was also recorded to generate heat plots for the period 2001-2020. The study concludes that monitoring and assessment of fragile ecosystems are indispensable for holistic environmental management.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850 ◽  
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Land use and land cover changes in the Lower Neretva Region from 1990 to 2020

2021 ◽  
Vol 83 (2) ◽  
pp. 7-31
Author(s):  
Josip Šetka ◽  
◽  
Petra Radeljak Kaufmann ◽  
Luka Valožić ◽  
◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Agricultural Land ◽  
Confusion Matrix ◽  
Hybrid Approach ◽  
Land Cover Changes ◽  
Landsat 8 ◽  
Economic Changes ◽  
Sparse Vegetation ◽  
Landsat 7

Changes in land use and land cover are the result of complex interactions between humans and their environment. This study examines land use and land cover changes in the Lower Neretva Region between 1990 and 2020. Political and economic changes in the early 1990s resulted in changes in the landscape, both directly and indirectly. Multispectral image processing was used to create thematic maps of land use and land cover for 1990, 2005, and 2020. Satellite images from Landsat 5, Landsat 7 and Landsat 8 were the main source of data. Land use and land cover structure was assessed using a hybrid approach, combining unsupervised and manual (visual) classification methods. An assessment of classification accuracy was carried out using a confusion matrix and kappa coefficient. According to the results of the study, the percentage of built-up areas increased by almost 33%. Agricultural land and forests and grasslands also increased, while the proportion of swamps and sparse vegetation areas decreased.

scholarly journals Remote sensing inversion and spatial variation of land surface temperature over mining areas of Jixi, Heilongjiang, China

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10257
Author(s):  
Jia-shuo Cao ◽  
Zheng-yu Deng ◽  
Wen Li ◽  
Yuan-dong Hu
Keyword(s):  
Land Use ◽  
Surface Temperature ◽  
Coal Mining ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Vegetation Coverage ◽  
Landsat 8 ◽  
Mining Activities ◽  
Patch Area ◽  
Mining Areas

Background Jixi is a typical mining city in China that has undergone dramatic changes in its land-use pattern of mining areas over the development of its coal resources. The impacts of coal mining activities have greatly affected the regional land surface temperature and ecological system. Methods The Landsat 8 Operational Land Imager (OLI) data from 2015 and 2019 were used from the Jiguan, Didao, and Chengzihe District of Jixi in Heilongjiang, China as the study area. The calculations to determine the land-use classification, vegetation coverage, and land surface temperature (LST) were performed using ArcGIS10.5 and ENVI 5.3 software packages. A correlation analysis revealed the impact of land-use type, vegetation coverage, and coal mining activities on LSTs. Results The results show significant spatial differentiation in the LSTs of Jixi City. The LSTs for various land-use types were ranked from high to low as follows: mining land > construction land > grassland > cultivated land > forest land > water area. The LST was lower in areas with high vegetation coverage than in other areas. For every 0.1 increase in vegetation coverage, the LST is expected to drop by approximately 0.75 °C. An analysis of mining land patches indicates that the patch area of mining lands has a significant positive correlation with both the average and maximum patch temperatures. The average patch temperature shows a logarithmic increase with the growth of the patch area, and within 200,000 m2, the average patch temperature increases significantly. The maximum patch temperature shows a linear increase with the patch area growth, and for every 100,000 m2 increase in the patch area of mining lands, the maximum patch temperature increases by approximately 0.81 °C. The higher the average patch temperature of mining land, the higher the temperature in its buffer zone, and the greater its influence scope. This study provides a useful reference for exploring the warming effects caused by coal mining activities and the definition of its influence scope.

Assessment of land degradation and restoration in coal mines of central India: A time series analysis

2022 ◽  
Vol 175 ◽  
pp. 106493
Author(s):  
Tarun Kumar Thakur ◽  
Joystu Dutta ◽  
Prachi Upadhyay ◽  
Digvesh Kumar Patel ◽  
Anita Thakur ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Land Degradation ◽  
Coal Mines ◽  
Central India ◽  
Series Analysis

scholarly journals SPATIOTEMPORAL CHANGE DETECTION USING LANDSAT IMAGERY: THE CASE STUDY OF KARACABEY FLOODED FOREST, BURSA, TURKEY

2017 ◽  
Vol IV-4/W4 ◽  
pp. 31-35 ◽  
Author(s):  
A. E. Akay ◽  
B. Gencal ◽  
İ. Taş
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Change Detection ◽  
Short Paper ◽  
Landsat 8 ◽  
Land Use Land Cover ◽  
Spatiotemporal Change ◽  
Change Detection Analysis ◽  
Flooded Forest ◽  
Detection Analysis

This short paper aims to detect spatiotemporal detection of land use/land cover change within Karacabey Flooded Forest region. Change detection analysis applied to Landsat 5 TM images representing July 2000 and a Landsat 8 OLI representing June 2017. Various image processing tools were implemented using ERDAS 9.2, ArcGIS 10.4.1, and ENVI programs to conduct spatiotemporal change detection over these two images such as band selection, corrections, subset, classification, recoding, accuracy assessment, and change detection analysis. Image classification revealed that there are five significant land use/land cover types, including forest, flooded forest, swamp, water, and other lands (i.e. agriculture, sand, roads, settlement, and open areas). The results indicated that there was increase in flooded forest, water, and other lands, while the cover of forest and swamp decreased.

scholarly journals USING LANDSAT 8 IMAGE TIME SERIES FOR CROP MAPPING IN A REGION OF CERRADO, BRAZIL

2016 ◽  
Vol XLI-B8 ◽  
pp. 845-850
Author(s):  
H. Bendini ◽  
I. D. Sanches ◽  
T. S. Körting ◽  
L. M. G. Fonseca ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Land Use ◽  
Time Series ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Vegetation Indices ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Use Classification ◽  
Crop Mapping ◽  
Phenological Metrics

The objective of this research is to classify agricultural land use in a region of the Cerrado (Brazilian Savanna) biome using a time series of Enhanced Vegetation Index (EVI) from Landsat 8 OLI. Phenological metrics extracted from EVI time series, a Random Forest algorithm and data mining techniques are used in the process of classification. The area of study is a region in the Cerrado in a region of the municipality of Casa Branca, São Paulo state, Brazil. The results are encouraging and demonstrate the potential of phenological parameters obtained from time series of OLI vegetation indices for agricultural land use classification.

scholarly journals Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

2020 ◽  
Vol 12 (2) ◽  
pp. 291 ◽  
Author(s):  
Giuseppe Mancino ◽  
Agostino Ferrara ◽  
Antonietta Padula ◽  
Angelo Nolè
Keyword(s):  
Land Use ◽  
Land Surface ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Earth Observation ◽  
Landsat 8 ◽  
Mediterranean Environment ◽  
Water Index ◽  
Land Use Types ◽  
Landsat 7

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

scholarly journals Land use and land cover change-induced landscape dynamics: a geospatial study of Durgapur Sub-Division, West Bengal (India).

2021 ◽  
Author(s):  
Sribas Patra ◽  
Kapil Kumar Gavsker
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Supervised Classification ◽  
West Bengal ◽  
Google Earth ◽  
Landscape Dynamics ◽  
Landsat 8 ◽  
Process Of Change ◽  
Detection Analysis

Abstract This article examines the factors and process of change in the land use and land cover change-induced landscape dynamics in the Durgapur Sub-Division region of West Bengal in 1989, 2003, and 2018 by employing the satellite imageries of Landsat 5 (1989 and 2003) and Landsat 8 (2018). The images of the study area were categorized into seven specific land use classes with the help of Google Earth Pro. Based on the supervised classification methodology, the change detection analysis identified a significant increase in built-up land from 11% to 23% between 1989 and 2003 and from 23% to 29% in 2003 and 2018. The areas under fallow land and vegetation cover have mainly decreased, while the areas of industrial activities and urbanization expanded during the study period.

scholarly journals Categories of mining areas of closed mines due to limitations of land use for building purposes on selected examples

2019 ◽  
Vol 106 ◽  
pp. 01010
Author(s):  
Piotr Strzałkowski ◽  
Roman Ścigała ◽  
Katarzyna Szafulera
Keyword(s):  
Land Use ◽  
Deformation Process ◽  
Coal Mines ◽  
Land Development ◽  
Final Phase ◽  
Mining Areas

The paper presents the issue of categorization of mining areas of liquidated coal mines in terms of land development limitations. The authors have presented their observations regarding the determination of hazard zones resulting from the presence of linear and surface discontinuous deformations, excessive subsidence and tilt zones as well as the duration of the final phase of deformation process.

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