Landslides are a common geodynamic phenomenon that cause substantial life and property damage worldwide. In the present study, we developed models to evaluate landslide susceptibility in forest-covered areas in Lin’an, southeastern China using logistic regression (LR), decision tree (DT), and random forest (RF) techniques. In addition to conventional landslide-related natural and human disturbance factors, factors describing forest cover, including forest type (two plantations (hickory and bamboo) and four natural forests (conifer, hardwood, shrub, and moso bamboo) and understory vegetation conditions, were included as predictors. Model performance was evaluated based on true-positive rate, Kappa value, and area under the ROC curve using a 10-fold cross-validation method. All models exhibited good performance with measures of ≥0.70, although the LR model was relatively inferior. The key predictors were forest type, understory vegetation height (UVH), normalized differential vegetation index (NDVI) in summer, distance to road (DTRD), and maximum daily rainfall (MDR). Hickory plantations yielded the highest landslide probability, while conifer and hardwood forests had the lowest values. Bamboo plantations had probability results comparable to those of natural forests. Using the RF model, areas with a shorter UVH (<1.2 m), a lower NDVI (115 mm), or a shorter DTRD (<500 m) were predicted to be landslide-prone. Information on forest cover is essential for predicting landslides in areas with rich forest cover, and conversion from natural forests to plantations could increase landslide risk. Across the study areas, the northwestern part was the most landslide-prone. In terms of landslide prevention, the RF model-based map produced the most accurate predictions for the “very high” category of landslide. These results will help us better understand landslide occurrences in forest-covered areas and provide valuable information for governments in designing disaster mitigation.

Context: Optimizing landscape patterns by considering ecosystem services and human well-being is crucial for landscape sustainability research. Such optimization process shall also consider multiple stakeholders’ perspectives, interests and benefits, which remain less well investigated. Objectives: The study aims to integrate ecosystem services and land-use scenario analysis to design sustainable landscapes. The overall goal of spatial optimization is to explore pathways towards sustainable development. Methods: This study uses the Mulberry-Dyke and Fish-Pond System as a representative agricultural landscape. It analyzes the spatiotemporal pattern of the landscape from 1975 to 2019. The market value method, InVEST model, and MaxEnt model are used to quantify the ecosystem services of the landscape. The analytic hierarchy process is used to set the weight of ecosystem services from the perspective of three stakeholder groups. We compare the outcomes of different scenarios and assessed through emergy yield ratio, environmental loading ratio, and environmental sustainability index. Results: Our results indicated that: (1) The scale of the Mulberry-Dyke and Fish-Pond system decreased significantly from 1975 to 2019. (2) Among the six major ecosystem services, the average value of water purification service was the highest, while average value of mulberry supply service was the lowest. (3) Spatial optimization of the three design objectives, ‘sustainably intensify’, ‘increase landscape multifunctionality’, and ‘restore ecological integrity’, were primarily distributed in the study area’s eastern, central, and western regions, respectively. (4) All three stakeholder-oriented scenarios showed improvements in ecological quality or agricultural output. Conclusions: Our study incorporates viewpoints of critical, diverse stakeholders in the assessment of ecosystem services and scenario analysis to establish sustainable and culturally important agricultural landscape. The framework and methods can help minimize environmental stress on the system, balance agricultural productivity and profitability, and enhance the sustainability of agricultural landscapes.