Exploring the evolution of livelihood patterns of farm households relocated for poverty alleviation in three prefectures of Guizhou Province, China, can provide a theoretical basis and practical value for achieving sustainable livelihoods of relocated farm households in ethnic minority areas. This study took three ethnic minority autonomous prefectures in Guizhou as the research area. The types and evolution pathways of farm households’ livelihood patterns were divided using 355 tracking and monitoring data from the four phases of relocated farm households’ livelihoods. The evolution characteristics of farm households’ livelihood patterns and changes in their evolution pathways from “before relocation” to 2021 were analyzed. An indicator system of influencing factors was established based on the sustainable livelihood framework. The influencing factors of the evolution path selection and transformation of livelihood patterns were identified using the binary and multiple logistic regression models. The main results are as follows: (1) Implementing the poverty alleviation relocation project shifted the livelihood activities of farm households from agricultural production to non-agricultural industries. The transformation trend of livelihood patterns shifted from agro-dominated, agricultural, agricultural-diversified and balanced types to highly diversified, deeply diversified and subsidy-dependent types. (2) The transformation mode of the evolution pathways of farm households’ livelihood patterns mainly included the stable transformation modes of “active$\to$retention$\to$active” and “active$\to$retention$\to$retention” and the fluctuating transformation modes of “active$\to$retention$\to$fallback”, “active$\to$fallback$\to$active” and “retention$\to$fallback$\to$active”. (3) Natural capital promoted farm households to choose fallback pathways, while material and human capital showed an inhibitory impact. In contrary to human capital, social capital promoted farm households to choose retention pathways. Financial and human capital promoted farm households to choose active pathways. (4) Per capita income, the household head age and relocation years positively affected the evolution pathways of the livelihood pattern from the fallback type to the retention type. The number of means of transport, per capita education level, transportation and communication cost, and the presence of civil servants in the household positively affected the evolution pathways of the livelihood pattern from the fallback type to the active type. Policy suggestions are as follows: Expanding local and nearby employment channels, strengthening vocational skills training for farm households, expanding financial access for farm households, improving policy support for ethnic characteristic industries, implementing bottom-line guarantee policies and exploring ethnic interactive activities for resettlement farm households.

Net primary productivity (NPP) is a crucial indicator of ecosystem function and sustainability. Quantifying the response of NPP to phenological dynamics is essential for understanding the impact of climate change on ecosystem processes. In this study, vegetation phenology data for Guizhou Province were extracted from the MCD12Q2 dataset, and NPP was estimated using the Normalized Difference Vegetation Index (NDVI) combined with meteorological data. Linear regression, trend analysis, and structural equation modeling were employed to clarify the spatiotemporal patterns of NPP and phenology as a basis for exploring the role of climatic factors in the NPP’s response to phenological changes. The results indicate that 72.15% of Guizhou Province shows an increasing trend in vegetation NPP (slope = 5.0981, p = 0.002). The start of the growing season (measured as SOS) tends to advance (slope = −0.4004, p = 0.0528), while the end of the growing season (measured as EOS) tends to delay (slope = 0.2747, p = 0.1011), resulting in an overall extension of the increasing length of the season (LOS) (slope = 0.64549, p = 0.0065). The spatiotemporal patterns of SOS, EOS, LOS, and NPP varied with elevation changes. For every 500 m increase in altitude, NPP decreased by 25.3 gC/m², SOS was delayed by 7.1 days, EOS advanced by 1.25 days, and LOS decreased by 8.36 days. These findings suggest that the response of NPP to phenological changes is primarily controlled by local climatic and topographical conditions. Additionally, the indirect effects of climate on NPP through phenological changes were more significant than the direct effects. Climatic factors play varying roles in the NPP response to phenological dynamics, highlighting the profound influence of climate in regulating the mechanisms by which NPP responds to phenological changes.

Guanling-Zhenfeng County, a microcosm of the ecologically fragile karst area in southwest China, experiences rapid population growth and urban expansion which intensifies land use transformation and ecological landscape fragmentation. Exploring the spatiotemporal characteristics of landscape fragmentation and its causes in Guanling-Zhenfeng County is of great significance in maintaining the stability of the ecosystem and ecological protection in karst areas. In this study, a comprehensive landscape fragmentation index (FI), geographic probe, multi-scale geographically weighted regression (MGWR), and PLUS model were used to quantitatively explore the spatiotemporal characteristic heterogeneity, causes, and future scenario projections of landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020. The results showed that: (1) the distribution of each landscape index was characterized by obvious spatial differentiation. Among them, the spatial distribution trends of patch density (PD) and largest patch index (LPI) were opposite and the distribution trends of Shannon diversity index (SHDI) and Shannon evenness index (SHEI) were similar. There were fewer heterogeneous patches in the study area from 2000 to 2020, and the landscape shape was more regular and less fragmented. (2) The overall landscape fragmentation in Guanling-Zhenfeng County from 2000 to 2020 was dominated by moderate fragmentation, with the smallest percentage of extreme fragmentation, and heavy fragmentation was mainly distributed in the north-central part of the study area. (3) Natural and social factors jointly affect the landscape fragmentation in Guanling-Zhenfeng County, and there is a significant interactive enhancement effect among the factors, with population density being the most important influence factor. In addition, the effects of the factors on landscape fragmentation showed significant spatial non-stationarity. (4) The characteristics of landscape fragmentation changes in Guanling-Zhenfeng County under different scenarios varied significantly, with the largest percentage of increase in heavy landscape fragmentation under the business-as-usual scenario (BAU), the next under the land use planning scenario (LUP), and the smallest under the ecological protection scenario (ESP).

Forest restoration landscapes are vital for restoring native habitats and enhancing ecosystem resilience. However, field monitoring (lasting months to years) in areas with complex surface habitats affected by karst rocky desertification is time-consuming. To address this, forest structural parameters were introduced, and training samples were optimized by excluding fragmented samples and those with a positive case ratio below 30%. The U-Net instance segmentation model in ArcGIS Pro was then applied to classify five forest restoration landscape types: intact forest, agroforestry, planted forest, unmanaged, and managed naturally regenerated forests. The optimized model achieved a 2% improvement in overall accuracy, with unmanaged and intact forests showing the highest increases (7%). Incorporating tree height and age improved the model’s accuracy by 3.5% and 1.9%, respectively, while biomass reduced it by 2.9%. RGB imagery combined with forest height datasets was most effective for agroforestry and intact forests, RGB imagery with aboveground biomass was optimal for unmanaged naturally regenerated forests, and RGB imagery with forest age was most suitable for managed naturally regenerated forests. These findings provide a practical and efficient method for monitoring forest restoration and offer a scientific basis for sustainable forest management in regions with complex topography and fragile ecosystems.

This study addresses the challenges posed by the complex topography and forest structure in karst mountainous areas, as well as the difficulties in estimating forest stock using traditional methods. We propose a method that integrates optical remote sensing data from Sentinel-2 into airborne LiDAR data to estimate forest stock in karst areas. First, an Allometric Growth Model correlating tree height and diameter at breast height (DBH) in karst areas was developed based on field measurements. Tree height information extracted from LiDAR data was then combined with the binary wood volume model specific to fir trees in Guizhou Province to calculate the individual tree biomass of fir trees. In addition, this study evaluated the robustness of three machine learning methods, the Random Forest Regression Model, K-Nearest Neighbors Regression Model, and Backpropagation Neural Network Model, in estimating forest stock in karst mountainous areas. The results indicate the following: (1) The Allometric Growth Model based on field data showed strong predictive power for DBH and can be used for large-scale estimation. (2) The distribution characteristics of individual tree biomass and plot biomass under different site conditions revealed the distribution pattern of fir trees in the study area, providing important information for understanding the growth status of forest stock in the region. (3) The Random Forest Regression Model demonstrated exceptional accuracy, generalization capability, and robustness in the estimation of forest stock within karst mountainous regions. This study provides an effective technical tool for estimating forest stock in karst areas and under complex terrain conditions and has significant scientific value and practical implications for the monitoring and management of forest ecosystem carbon sinks.