Emerging hot spot analysis and the spatial–temporal trends of NDVI in the Jing River Basin of China

Bin, Xu; Qi, Bin; Ji, Kai; Liu, Zhao; Deng, Lin; Jiang, Ling

doi:10.1007/s12665-022-10175-5

Cited by 30 publications

(11 citation statements)

References 32 publications

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“…We have applied a combination of complementary spatiotemporal analysis methods in GIS, temporal hot spot and combinatorial change detection, to obtain a comprehensive portrait of vegetation condition. The hot spot analysis has been applied so far in large scale studies, e.g., river basins or drought areas [66,[70][71][72]. We found it to be an effective method for assessment of vegetation cover condition in a differentiated and complex medium scale post mining area.…”

Section: Discussion and Commentsmentioning

confidence: 95%

“…The Getis-Ord Gi* spatial statistic algorithm available in GIS software is widely used for hot spot analysis [10,66]. By calculating the Gi* statistic of spatial features (spatial variable), it is possible to identify the clustering of zones with high (hot spot) and with low (cold spot) values of a spatial variable in the context of their neighbourhood.…”

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confidence: 99%

“…When the analyzed data are a timeseries, the hot spot analysis can identify the changing trend of the data [66,69]. The emerging hot spot analysis has been successfully applied in studies of vegetation condition based on the NDVI satellite derived spectral index in a river basin in China [66], impact assessment of watershed development in India [70], temporal and spatial variation of vegetation in an economic development belt in China [71], drought area analysis in Iran [72] with one of the first applications by Fraser et al [73] for identification of forest burned area in Canada.…”

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confidence: 99%

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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: 95%

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confidence: 99%

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confidence: 99%

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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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“…This represents a new method for spatiotemporal analysis, meets the requirements of retention time and spatial continuity, and has the advantage of visualizing the analysis results [ 24 , 29 ]. In recent years, the emerging hot spot analysis based on STCs has gradually been applied to different scientific fields [ 30 , 31 , 32 ]. The results of the emerging hotspot analysis based on STCs offer important information on the locations and times of WVCs, allowing decision-makers to observe spatiotemporal clustering patterns.…”

Section: Discussionmentioning

confidence: 99%

Identification of Emerging Roadkill Hotspots on Korean Expressways Using Space–Time Cubes

Kim

Lee

2023

IJERPH

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Collisions with wild animals on high-speed expressways not only lead to roadkill but can also cause accidents that incur considerable human and economic costs. Based on roadkill data from 2004–2019 for four common wildlife species involved in collisions with vehicles on expressways in Korea (water deer, common raccoon dog, Korean hare, and wild boar), the present study conducted optimized hotspot analysis and identified spatiotemporal patterns using a space–time cube (STC) approach. Temporal and spatial differences in the roadkill data were observed between species. Water deer were the most common roadkill species of the four studied, with hotspots in the southern region of the capital area, in the Chungnam region, and in the western Chungbuk and Gangwon-do regions. However, the instances of water deer roadkill over time differed between each region. In addition, it was found that the number of cases of wild boar roadkill has increased recently. In particular, a number of new hotspot areas were observed centered on the metropolitan area Gyeonggi-do, which contains a high population and significant infrastructure. Overall, the emerging hotspot analysis based on STCs was able to determine cold spot and hotspot trends over time, allowing for a more intuitive understanding of spatiotemporal clustering patterns and associated changes than cumulative density-oriented hotspot analysis. As a result, it becomes easier to analyze the causes of roadkill and to establish reduction measures according to priority.

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“…Spatial analysis has been used in Land 2022, 11, 2211 2 of 20 many ways in several fields, such as soil sciences [21], groundwater studies [22,23], medical sciences [24][25][26], transportation [27,28], e-commerce, and business [29,30]. The emerging hotspot analysis is an innovative spatial analysis tool that can detect spatial-temporal trends and varying patterns of different phases in each period; it has been progressively utilized in multiple scientific disciplines [31][32][33]. This geospatial technique uses a combination of two statistical measures, the Getis-Ord Gi statistic to identify the location and degree of spatial clustering and the Mann-Kendall trend test to evaluate temporal trends across the time series [34].…”

Section: Introductionmentioning

confidence: 99%

Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis

Younas

Khan

Qasim

et al. 2022

Land

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Land subsidence is an ongoing problem negatively affecting Victoria County along the Gulf Coast. Groundwater withdrawal and hydrocarbon extraction in the County are some of the known factors behind this geological hazard. In this study, we have used geospatial analysis and a conceptual model to evaluate land subsidence. A significant decline in the groundwater level in this area was noted from 2006 to 2016. The decline in the water level correlates with the major drought events along the Gulf Coast reported in earlier studies. These results are further corroborated by the emerging hotspot analysis performed on the groundwater data. This analysis divides the study area into intensifying, sporadic, and persistent hotspots in the northwest region and intensifying, persistent coldspots in the southeast region of Victoria County. Hydrocarbon production data show high oil and gas extraction from 2017 to 2021. There are a higher number of hydrocarbon production wells in the central and southern regions of the County than elsewhere. The conceptual models relate these events and suggest the existence of subsidence in the County, through which the water and hydrocarbon reservoirs in the study area may lose their reservoir characteristics due to sediment compaction.

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Emerging hot spot analysis and the spatial–temporal trends of NDVI in the Jing River Basin of China

Cited by 30 publications

References 32 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

Identification of Emerging Roadkill Hotspots on Korean Expressways Using Space–Time Cubes

Assessing Impacts of Land Subsidence in Victoria County, Texas, Using Geospatial Analysis

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