Abstract:Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable dev… Show more
“…From 2000 to 2017, the development degree of rocky desertification and changes in habitat quality in buffers at the same distance from the road networks were different under different development models in the same regions of the study area. On the spatial-temporal scale, the area of rocky desertification have increased in tourist areas with better economic development in the south of the study area, but the landscape with a relatively high level of habitat quality tended to be complete with no fragmentation and an inapparent degree of degradation under the influence of multiple roads [ 45 ]. This phenomenon of rocky desertification was due to the significant increase in the area and the significant aggravation degree in the study area from 2000 to 2005, which has gradually become an obstacle to the development of agriculture and forestry in Shilin County.…”
Frequent cross-regional communication makes road networks increasingly dense and has generated prominent human interference, thus resulting in the destruction of the landscape’s integrity and leading to changes in the functional processes of the habitat. In order to discuss the impacts of intense human activity brought by the road networks on the rocky desertification landscape and habitat quality in karst ecologically fragile areas, taking the road networks as the humans activity intensity factor, a quantitative analysis was conducted to analyze the impacts of road networks on the spatial evolution of the rocky desertification landscape and changes in regional habitat quality characteristics under different development modes in the study area based on a landscape pattern gradient method, spatial analysis, and INVEST model. The results showed that: (1) in the study area, due to the destruction of landscape integrity caused by the development of the road networks over the past 17 years, the landscape pattern of rocky desertification tended to be fragmented and complex, first showing an inclination for rapid fragmentation and then gradual recovery later. (2) The land-use intensity and degree of rocky desertification in the industrial areas and in the tourist areas of the study area have increased to varying degrees over the past 17 years, as is seen mainly via the expansion of construction land, cultivated land enclaves in the urban expansion areas, and new development areas. (3) Unders different regional models, the fragmentation of the rocky desertification landscape in the industrial areas was higher than that in the tourist areas, resulting in a significantly lower habitat quality and obvious degrees of degradation. The research findings provide the basis for further deepening our understanding how human activity intensity affects the evolution of the regional landscape, including the development of rocky desertification, the supply of services, and supporting habitat conservation in karst ecologically fragile areas.
“…From 2000 to 2017, the development degree of rocky desertification and changes in habitat quality in buffers at the same distance from the road networks were different under different development models in the same regions of the study area. On the spatial-temporal scale, the area of rocky desertification have increased in tourist areas with better economic development in the south of the study area, but the landscape with a relatively high level of habitat quality tended to be complete with no fragmentation and an inapparent degree of degradation under the influence of multiple roads [ 45 ]. This phenomenon of rocky desertification was due to the significant increase in the area and the significant aggravation degree in the study area from 2000 to 2005, which has gradually become an obstacle to the development of agriculture and forestry in Shilin County.…”
Frequent cross-regional communication makes road networks increasingly dense and has generated prominent human interference, thus resulting in the destruction of the landscape’s integrity and leading to changes in the functional processes of the habitat. In order to discuss the impacts of intense human activity brought by the road networks on the rocky desertification landscape and habitat quality in karst ecologically fragile areas, taking the road networks as the humans activity intensity factor, a quantitative analysis was conducted to analyze the impacts of road networks on the spatial evolution of the rocky desertification landscape and changes in regional habitat quality characteristics under different development modes in the study area based on a landscape pattern gradient method, spatial analysis, and INVEST model. The results showed that: (1) in the study area, due to the destruction of landscape integrity caused by the development of the road networks over the past 17 years, the landscape pattern of rocky desertification tended to be fragmented and complex, first showing an inclination for rapid fragmentation and then gradual recovery later. (2) The land-use intensity and degree of rocky desertification in the industrial areas and in the tourist areas of the study area have increased to varying degrees over the past 17 years, as is seen mainly via the expansion of construction land, cultivated land enclaves in the urban expansion areas, and new development areas. (3) Unders different regional models, the fragmentation of the rocky desertification landscape in the industrial areas was higher than that in the tourist areas, resulting in a significantly lower habitat quality and obvious degrees of degradation. The research findings provide the basis for further deepening our understanding how human activity intensity affects the evolution of the regional landscape, including the development of rocky desertification, the supply of services, and supporting habitat conservation in karst ecologically fragile areas.
“…Conversely, the lowest values were distributed in the western and central parts of the province, characterized by limited forestland and a high degree of land use fragmen- Moreover, the vigor in the western regions (i.e., Bijie, Liupanshui, and Anshun) increased from low to high from 2010 to 2020. Historically, the ecological environment in these regions was extraordinarily fragile, especially with significant rocky desertification [101,102]. The destruction of the ecological environment due to economic development accelerated the degradation of ecological health.…”
Healthy ecosystems are crucial for sustainable regional development. The lack of spatial distribution patterns and driving factors of ecosystem health limited ecosystem management and urban planning. Understanding the spatiotemporal variation characteristics of ecosystem health and its driving factors can contribute to ecosystem management. Based on the “vigor–organization–resilience” (VOR) framework, this paper focuses on increasing ESs and forming an improved “vigor–organization–resilience–ecosystem services (VORS)” framework to evaluate the ecosystem health of Guizhou Province in 2010 and 2020. At the same time, we used the geographic detector model to investigate the driving factors of ecosystem health in the region. The results revealed the following: (1) The areas of forest land accounted for more than 52%. Simultaneously, farmland and forest land decreased, while construction land increased from 2010 to 2020. Construction land was mainly converted from forest land, grassland and farmland. (2) The level of ecosystem health in Guizhou Province spatially increased from northwest to southeast, with the central part exhibiting the lowest health level. The ecosystem health index (EHI) was mainly moderate, accounting for 78.32% and 83.80% in 2010 and 2020, respectively. (3) Among the 11 selected driving factors, the gross domestic product (GDP), general public budget revenue, annual average temperature, average annual precipitation, and night light index significantly affected ecosystem health. Our research refines ecosystem health research and the results will contribute to effective and precise decision-making in ecosystem management and the implementation of land use policies.
“…Rocky desertification has become a major environmental issue that disturbs ecological construction and seriously restricts the development of the economy and society (Zhang et al, 2015). It is influenced by the combined circumstances of geology, geomorphology, soil, warm and wet climate conditions, vegetation, bare rock rate (BRR), and human overexploitation of natural resources (Bai et al, 2023; Gou et al, 2022; Guo et al, 2023; Qian et al, 2022; Wan et al, 2022; Xiong et al, 2022). The BRR is an important indicator for rocky desertification evaluation.…”
Rocky desertification is a land degradation process. The bare rock rate (BRR) is an important index. Satellite‐based methods for BRR estimation can generate BRR datasets, but the spatial resolution and accuracy are low. Unmanned aerial vehicle (UAV) data can be used for BRR extraction with much higher resolution and higher accuracy, but the data are laborious and costly to collect. This study proposed a stepwise upscaling method employing the UAV‐extracted BRR as the ground truth to establish a multiband regression model and calibrated the BRR derived from 30‐m resolution LANDSAT 8 data, which was then used to calibrate 500‐m resolution moderate resolution imaging spectroradiometer (MODIS) data. We compared the results with the satellite‐based BRR and the results of UAV‐MODIS upscaling methods. The results showed that (1) the UAV provides high‐resolution data, and exposed rocks can be efficiently extracted from UAV images. This method could replace ground surveys. (2) The accuracy of the stepwise upscaling method was higher than that of the nonstepwise upscaling method in large areas. R2 increased by 64.9%, and the root mean square error (RMSE) decreased by 82.9%. (3) Compared with that of the satellite‐based BRR, the accuracy of the multiband regression model‐extracted BRR was higher. R2 increased by 21.5%, and the RMSE decreased by 62.2%. This study demonstrated that the stepwise upscaling method and multiband regression can be combined to extract high‐precision and large‐scale BRR data, which can be used to serve ecological engineering monitoring and ecological governance.
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