Spatio-Temporal Analysis of Ecological Vulnerability and Driving Factor Analysis in the Dongjiang River Basin, China, in the Recent 20 Years

Wu, Jiao; Zhang, Zhijun; He, Qinjie; Ma, Guorui

doi:10.3390/rs13224636

Cited by 11 publications

(5 citation statements)

References 51 publications

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“…In addition, natural factors play an important role in landscape ecological risk [59]. This study indicates that the relative humidity and annual average temperature are the main natural factors that cause ecological risk; this is consistent with the conclusion drawn by the research of Wu et al [35], who noted that the relative humidity and annual average temperature are the main factors affecting evapotranspiration in most regions of China. These factors affect vegetation growth, thereby affecting vegetation vulnerability.…”

Section: Driving Forces Of Ecological Risksupporting

confidence: 90%

“…Geodetector is a statistical method used to detect spatial differentiation and reveal the factors that affect it [35], including factor detection, interaction detection, risk zone detection, and ecological detection. Based on the results of previous studies [36], and combined with the actual circumstances of the nature reserve, this study selected 11 impact factors from both natural and human perspectives.…”

Section: Geodetectormentioning

confidence: 99%

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Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China

Rao,

Ouyang,

Pan

et al. 2024

Forests

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It is highly valuable to analyze and assess the landscape ecological risk of nature reserves to prevent and resolve ecological risks, as well as to effectively protect and maintain the sustainable development of nature reserves. Taking the forest landscape of the Lushan National Nature Reserve as its study object, this study performed grid processing for the nature reserve and classified forest landscape types using the Forest Resource Inventory Database in 2019. A landscape ecological index model was constructed to evaluate the ecological risk. Global and local Moran index values were used to reveal the autocorrelations for ecological risk. The geodetector method was used to comprehensively analyze the effects of natural and human factors on ecological risk. The results showed that, in general, the ecological risk level of the nature reserve was relatively low, as the proportion of the lowest-, lower-, and medium-risk areas to the total forestry land area accounted for 91.03%. The ecological risk ranking of each functional zone, from high to low, was in the order of the experimental zone, the buffer zone, and the core zone. The ecological risk levels of different forest landscape types were closely related to their area, spatial distribution, and succession stage, as well as human factors, such as the proximity to roads and settlements, etc. The forest landscape with the highest ecological risk was the Cunninghamia lanceolata (Lamb.) Hook. forest, and the forest landscape with the lowest ecological risk was other forestry land. Ecological risk had a positive spatial correlation and tended to be aggregated in space, demonstrating coupling with the proximity to roads and settlements. The ecological risk was affected by both human and natural factors, among which human factors played a dominant role. The proximity to roads and settlements, the relative humidity, and the temperature were the main driving factors. The interaction of pairwise factors had a stronger influence than that of single factors. Therefore, controlling the intensity of human activities and enhancing the coordination between humans and nature are beneficial for alleviating the ecological risks in the forest landscapes of nature reserves.

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Section: Driving Forces Of Ecological Risksupporting

confidence: 90%

Section: Geodetectormentioning

confidence: 99%

Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China

Rao,

Ouyang,

Pan

et al. 2024

Forests

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“…Of particular interest is the approach of applying multitemporal series of RSD with the use of the BSS spectral characteristics [37,45]. In other areas of knowledge, multitemporal series are used more widely [46][47][48][49][50][51][52][53][54]. We believe that the use of a multitemporal series of BSS spectral characteristics opens up great prospects for studying the spatial heterogeneity of agricultural landscapes.…”

Section: Review Of Similar Studiesmentioning

confidence: 99%

“…Multitemporal RSD series are increasingly being used in various areas of economic activity (restoration after fires, snowmelt monitoring, forest dynamics, tundra classification, environmental vulnerability, etc.) [50][51][52][53][54]. When using multitemporal series, traditional VIs are changed by more complex models-LSMM [55] or PARAFAC [49].…”

Section: Introductionmentioning

confidence: 99%

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

Rukhovich,

Koroleva,

Rukhovich

et al. 2023

Remote Sensing

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For most of the arable land in Russia (132–137 million ha), the dominant and accurate soil information is stored in the form of map archives on paper without coordinate reference. The last traditional soil map(s) (TSM, TSMs) were created over 30 years ago. Traditional and/or archival soil map(s) (ASM, ASMs) are outdated in terms of storage formats, dates, and methods of production. The technology of constructing a multitemporal soil line (MSL) makes it possible to update ASMs and TSMs based on the processing of big remote-sensing data (RSD). To construct an MSL, the spectral characteristics of the bare soil surface (BSS) are used. The BSS on RSD is distinguished within the framework of the conceptual apparatus of the spectral neighborhood of the soil line. The filtering of big RSD is based on deep machine learning. In the course of the work, a vector georeferenced version of the ASM and an updated soil map were created based on the coefficient “C” of the MSL. The maps were verified based on field surveys (76 soil pits). The updated map is called the map of soil interpretation of the coefficient “C” (SIC “C”). The SIC “C” map has a more detailed legend compared to the ASM (7 sections/chapters instead of 5), greater accuracy (smaller errors of the first and second kind), and potential suitability for calculating soil organic matter/carbon (SOM/SOC) reserves (soil types/areals in the SIC “C” map are statistically significant are divided according to the thickness of the organomineral horizon and the content of SOM in the plowed layer). When updating, a systematic underestimation of the numbers of contours and areas of soils with manifestations of negative/degradation soil processes (slitization and erosion) on the TSM was established. In the process of updating, all three shortcomings of the ASMs/TSMs (archaic storage, dates, and methods of creation) were eliminated. The SIC “C” map is digital (thematic raster), modern, and created based on big data processing methods. For the first time, the actualization of the soil map was carried out based on the MSL characteristics (coefficient “C”).

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“…The research on ecological vulnerability in western countries started earlier. In 1905, the American ecologist Clements introduced the concept of an ecological transition zone into the field of ecology [10], which was regarded as the beginning of ecological vulnerability research [11]. In the 1980s and 1990s, some scholars introduced the concept of vulnerability into the study of global environmental change [12], and more focused on linking vulnerability with climate change [13].…”

Section: Introductionmentioning

confidence: 99%

Temporal and Spatial Distribution Changes, Driving Force Analysis and Simulation Prediction of Ecological Vulnerability in Liaoning Province, China

Liu

Huan

Yang

et al. 2022

Land

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As China’s main grain-producing region, the ecological security pattern of Liaoning Province has an extremely important impact on the ecological security of Northeast China and even the whole country. Furthermore, the construction of the ecological security pattern is restricted by the ecological vulnerability assessment in order to explore the ecological vulnerability pattern of spatial distribution and the trend of future vulnerability development in Liaoning Province and guide how to formulate ecological protection policies scientifically. Based on the sensitivity–resilience–pressure (SRP) conceptual model which is combined with natural and socio-economic factors, the ecological vulnerability evaluation index system of Liaoning Province is established in this paper. This paper also evaluates the ecological vulnerability of Liaoning Province from 2010 to 2020 and analyzes the driving factors by using a geographic detector and the CA-Markov model. Moreover, the study forecasts the growing tendency of vulnerability in 2025. The results show that (1) the ecological vulnerability of Liaoning Province is mainly light and has medium vulnerability, which gradually decreases from northwest to southeast; (2) the spatial heterogeneity of the ecological vulnerability index (EVI) is very significant in the southeast and northwest region but not significant in the middle; (3) from the past decade to the next five years, the ecological vulnerability of Liaoning Province has been improving, and the overall distribution pattern of ecological vulnerability is relatively stable; (4) the analysis of driving factors indicates that the impact of natural environmental factors such as land-use type and habitat quality on EVI is more significant than socio-economic factors such as population density. The research results implicate that it is necessary to formulate an ecological protection and restoration plan in Liaoning Province to prevent further ecological degradation in high-value areas of northwest Liaoning, and to balance the relationship between human development and ecological protection and restoration in the metropolitan district.

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Spatio-Temporal Analysis of Ecological Vulnerability and Driving Factor Analysis in the Dongjiang River Basin, China, in the Recent 20 Years

Cited by 11 publications

References 51 publications

Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China

Ecological Risk Assessment of Forest Landscapes in Lushan National Nature Reserve in Jiangxi Province, China

Updating of the Archival Large-Scale Soil Map Based on the Multitemporal Spectral Characteristics of the Bare Soil Surface Landsat Scenes

Temporal and Spatial Distribution Changes, Driving Force Analysis and Simulation Prediction of Ecological Vulnerability in Liaoning Province, China

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