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.

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.