Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Hu, Yongguang; Raza, Ali; Syed, Neyha Rubab; Acharki, Siham; Ray, Ram L.; Hussain, Sajjad; Dehghanisanij, Hossein; Zubair, Muhammad; Elbeltagi, Ahmed

doi:10.3390/su15043572

Cited by 34 publications

(13 citation statements)

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“…We have opted for a manual setting of the number of vegetation cover classes, as well as their boundaries based on the analysis of statistics and ground control points. There are other methods available such as cross-correlation analysis, image differencing, object pixel-based classification, post-classification comparison, or image fusion-based change detection [86][87][88][89] that can be applied.…”

Section: Discussion and Commentsmentioning

confidence: 99%

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

Blachowski,

Dynowski,

Buczyńska

et al. 2023

Remote Sensing

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The motivation for this study arises from the need to monitor the condition of a rehabilitated post-mining areas even decades after the end of the recovery phase. This can be facilitated with satellite derived spectral vegetation indices and Geographic Information System (GIS) based spatiotemporal analysis. The study area described in this work is located in Western Poland and has unique characteristics, as it was subjected to the combined underground and open pit mining of lignite deposits that had been shaped by glaciotectonic processes. The mining ended in early 1970’ties and the area was subjected to reclamation procedures that ended in the 1980’ties. We used the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) spectral indices derived from Sentinel-2 data for the 2015–2022. period. Then, we applied a combination of GIS-based map algebra statistics (local, zonal and combinatorial) and GI* spatial statistics (hot spot and temporal hot spot) for a complex analysis and assessment of the vegetation cover condition in a post-mining area thought to be in the rehabilitated phase. The mean values of NDVI and EVI for the post-mining study area range from 0.48 to 0.64 and 0.24 to 0.31 and are stable in the analyzed 8 year period. This indicates general good condition of the vegetation and post-recovery phase of the area of interest. However, the combination of spatiotemporal analysis allowed us to identify statistically significant clusters of higher and lower values of the vegetation indices and change of vegetation cover classes on 3% of the study area. These clusters signify the occurrence of local processes such as, the encroachment of aquatic vegetation in waterlogged subsidence basins, and growth of low vegetation in old pits filled with waste material, barren earth zones on external waste dumps, as well as present-day forest management activities. We have confirmed that significant vegetation changes related to former mining occur even five decades later. Furthermore, we identified clusters of the highest values that are associated with zones of older, healthy forest and deciduous tree species. The results confirmed applicability of Sentinel-2 derived vegetation indices for studies of post-mining environment and for the detection of local phenomena related to natural landscaping processes still taking place in the study area. The methodology adopted for this study consisting of a combination of GIS-based data mining methods can be used in combination or separately in other areas of interest, as well as aid their sustainable management.

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Section: Discussion and Commentsmentioning

confidence: 99%

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

Blachowski,

Dynowski,

Buczyńska

et al. 2023

Remote Sensing

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“…Finally, use the number of times each point changes to obtain land cover change information [105]. Yongguang Hu used an Iterative Self-Organizing (ISO) clustering method to quantify land cover changes [106].…”

Section: Unsupervised Learning Methodsmentioning

confidence: 99%

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Gu,

Zeng

2023

Sustainability

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The integration of Artificial Intelligence (AI) and Satellite Remote Sensing in Land Cover Change Detection (LCCD) has gained increasing significance in scientific discovery and research. This collaboration accelerates research efforts, aiding in hypothesis generation, experiment design, and large dataset interpretation, providing insights beyond traditional scientific methods. Mapping land cover patterns at global, regional, and local scales is crucial for monitoring the dynamic world, given the significant impact of land cover distribution on climate and environment. Satellite remote sensing is an efficient tool for monitoring land cover across vast spatial extents. Detection of land cover change through satellite remote sensing images is critical in influencing ecological balance, climate change mitigation, and urban development guidance. This paper conducts a comprehensive review of LCCD using remote sensing images, encompassing exhaustive examination of satellite remote sensing data types and contemporary methods, with a specific focus on advanced AI technology applications. Furthermore, the study delves into the challenges and potential solutions in the field of LCCD, providing a comprehensive overview of the state of the art, offering insights for future research and practical applications in this domain.

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“…The change detection is the process of finding differences in the state of an object by observing it at different points in time [ 21 , 42 ]. The goal of change detection is to examine the variability in LULC recorded during a certain time period connected to a specific location.…”

Section: Methodsmentioning

confidence: 99%

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

Seifu,

Woldesenbet,

Alemayehu

et al. 2023

The Scientific World Journal

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Land use and land cover (LULC) change and variability are some of the challenges to present-day water resource management. The purpose of this study was to determine LULC and Normalized Difference Vegetation Index (NDVI) fluctuations in western Ethiopia during the last 20 years. The first part of the study used MODIS LULC data for the change analysis, change detection, and spatial and temporal coverage in the study region. In the second part, the study analyzes the NDVI change and its spatial and temporal coverage. In this study, The Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data were applied to determine LULC and NDVI changes over four different periods. Evergreen broadleaf forests, deciduous broadleaf forests, mixed forests, woody savannas, savannas, grasslands, permanent wetlands, croplands, urban and built-up lands, and water bodies are the LULC in the period of analysis. The overall classification accuracy for the classified image from 2001 to 2020 was 85.4% and the overall kappa statistic was 81.2%. The results indicate a substantial increase in woody savannas, deciduous broadleaf, grasslands, permanent wetlands, and mixed forest areas by 119.6%, 57.7% 45.2%, 37%, and 21.3%, respectively, followed by reductions in croplands, water bodies, savannas, and evergreen broadleaf forest by 90.1%, 19.8%, 13.2%, and 4.8%, respectively, for the catchment between 2001 and 2020. The result also showed that the area’s vegetation cover increased by 64% from 2001 to 2022. This study could provide valuable information for water resource and environmental management as well as policy and decision-making.

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Land Use/Land Cover Change Detection and NDVI Estimation in Pakistan’s Southern Punjab Province

Cited by 34 publications

References 100 publications

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

Integrated Spatiotemporal Analysis of Vegetation Condition in a Complex Post-Mining Area: Lignite Mine Case Study

The Use of Artificial Intelligence and Satellite Remote Sensing in Land Cover Change Detection: Review and Perspectives

Spatio-Temporal Change of Land Use/Land Cover and Vegetation Using Multi-MODIS Satellite Data, Western Ethiopia

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