Wild res are an important disturbance factor in forest ecosystems. Assessing the probability of forest wild res can assist in forest wild re prevention, control, and supervision. The logistic regression model is widely used to forecast the probability, spatial patterns, and drivers of forest wild res. This study used logistic regression to establish a spatial prediction model for forest wild re susceptibility, which was applied to evaluate the risk of forest wild res in Central Yunnan Province (CYP), China. A forest wild re risk classi cation was implemented for CYP using forest burn scar data for 2001 to 2020 and the logistic spatial prediction model for forest wild re susceptibility. Climate, vegetation, topographical, human activities, and location were selected as forest wild re prediction variables. The results showed that: (1) The distributions of temperature, vegetation coverage, distance to water bodies, distance to roads, and precipitation were positively correlated with the occurrence of forest wild res. Elevation, relative humidity, the global vegetation moisture index, wind speed, slope, latitude, and distance to residential areas were negatively correlated with the occurrence of forest wild res. (2) The results of the logistic spatial prediction model for forest wild re susceptibility showed a good t to observed data, with an overall simulation probability of 81.6%. The optimal threshold for spatial prediction for forest wild re susceptibility in CYP was determined to be 0.414. A signi cance level of a selected model variable of < 0.05 resulted in an area under the receiver operating characteristic curve (AUC) of 0.882-0.890. (3) Forest wild re prevention efforts should focus on Southwest Yuxi City and southern Qujing City since they accounted for a high proportion of the areas at high risk of forest wild res. Other localities should adjust forest wild re prevention measures according to local conditions and strengthen existing wild re prevention and emergency resource planning and allocation. (4) Some factors contributing to forest wild res where different among the different areas. Forest wild re risk factors had different degrees of impact under different spatial and temporal scales. The spatial relationships between wild re disasters and in uencing factors should be established in areas with heterogeneous environmental conditions for the selection of relevant factors.
Understanding the influence of landscape patterns on the water quality of agricultural wetlands is critically important for their management and related decision-making. However, the question of how to quantify this objectively remains a challenge in the relevant scientific fields. In this study, the location-weighted landscape index (LWLI), a process-oriented indicator that integrates ecological processes with landscape patterns based on the source and sink theory, was modified into the SLWLI by assigning nutrient-based weights in the Honghe Hani Rice Terraces World Heritage Site (HHRT). The results indicate that the five watersheds are dominated by sink landscapes, representing 64 percent of the total area. Rice terraced fields were a composite “source–sink” landscape, and their areas in the five watersheds ranged from 4.82% to 20.40%. The nutrient retention function of the sink landscapes of total nitrogen (TN) ranged from 0.64 to 0.86, whereas the total phosphorus (TP) ranged from 0.72 to 0.82, showing good retention function in regard to both nutrients. The contribution rates of forest land and rice terraces to TN and TP retention were greater than 47.07% and 17.07%, respectively, which indicates their key regulation of the nutrient retention function, reducing the risk of water eutrophication and leading to optimized conservation. The vertical pattern of the HHRT plays an important role in nutrient retention function. The SLWLI is an effective index that can be used to assess nutrient retention function and to identify sink landscapes for regulating water pollution in agricultural wetlands.
Wildfires are an important disturbance factor in forest ecosystems. Assessing the probability of forest wildfires can assist in forest wildfire prevention, control, and supervision. The logistic regression model is widely used to forecast the probability, spatial patterns, and drivers of forest wildfires. This study used logistic regression to establish a spatial prediction model for forest wildfire susceptibility, which was applied to evaluate the risk of forest wildfires in Central Yunnan Province (CYP), China. A forest wildfire risk classification was implemented for CYP using forest burn scar data for 2001 to 2020 and the logistic spatial prediction model for forest wildfire susceptibility. Climate, vegetation, topographical, human activities, and location were selected as forest wildfire prediction variables. The results showed that: (1) The distributions of temperature, vegetation coverage, distance to water bodies, distance to roads, and precipitation were positively correlated with the occurrence of forest wildfires. Elevation, relative humidity, the global vegetation moisture index, wind speed, slope, latitude, and distance to residential areas were negatively correlated with the occurrence of forest wildfires. (2) The results of the logistic spatial prediction model for forest wildfire susceptibility showed a good fit to observed data, with an overall simulation probability of 81.6%. The optimal threshold for spatial prediction for forest wildfire susceptibility in CYP was determined to be 0.414. A significance level of a selected model variable of < 0.05 resulted in an area under the receiver operating characteristic curve (AUC) of 0.882–0.890. (3) Forest wildfire prevention efforts should focus on Southwest Yuxi City and southern Qujing City since they accounted for a high proportion of the areas at high risk of forest wildfires. Other localities should adjust forest wildfire prevention measures according to local conditions and strengthen existing wildfire prevention and emergency resource planning and allocation. (4) Some factors contributing to forest wildfires where different among the different areas. Forest wildfire risk factors had different degrees of impact under different spatial and temporal scales. The spatial relationships between wildfire disasters and influencing factors should be established in areas with heterogeneous environmental conditions for the selection of relevant factors.
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