Forest Aboveground Biomass (AGB) is a key parameter for assessing forest productivity and global carbon content. In previous studies, AGB has been estimated using various prediction methods and types of remote sensing data. Increasingly, there is a trend towards integrating various data sources such as Light Detection and Ranging (LiDAR) and optical data. In this study, we constructed and compared the accuracies of five models for estimating AGB of forests in the upper Heihe River Basin in Northwest China. The five models were constructed using field and remotely-sensed data (optical and LiDAR) and algorithms including Random Forest (RF), Support Vector Machines (SVM), Back Propagation Neural Networks (BPNN), K-Nearest Neighbor (KNN) and the Generalized Linear Mixed Model (GLMM). Models based on the RF algorithm emerged as being the best among the five algorithms irrespective of the datasets used. The Random Forest AGB model, using only LiDAR data (R² = 0.899, RMSE = 14.0 t/ha) as the input data, was more effective than the one using optical data (R² = 0.835, RMSE = 22.724 t/ha). Compared to LiDAR or optical data alone, the AGB model (R² = 0.913, RMSE = 13.352 t/ha) that used the RF algorithm and integrated LiDAR and optical data was found to be optimal. Incorporation of terrain variables with optical data resulted in only slight improvements in accuracy. The models developed in this study could be useful for using integrated airborne LiDAR and passive optical data to accurately estimate forest biomass.

Studying land use changes and ecological risk assessment in Yongjiang River Basin in Zhejiang Province, China, provides theoretical references for optimal configuration of land resources and maintaining stability of ecosystems. Given impacts of land use changes on landscape patterns in the Yongjiang River Basin, ecological risk assessment indexes were constructed and used to analyze temporal and spatial variation characteristics of ecological risk within different periods. Results show that (1) the construction land area was increased quickly, while the cultivated area decreased sharply. A prominent characteristic of land use changes was manifested by transforming cultivated area and forestland into construction land. The utilized degree of the land increased continuously. Spatially, the land utilized degree in northern regions was higher than that in southern regions and the degree in eastern regions was higher than that in western regions. (2) The ecological risk in the Yongjiang River Basin was intensified and the area of high ecological risk was expanded by 893.96 km². Regions with low and relatively low ecological risks concentrated in western and southern regions of the Basin, whereas regions with high ecological risks were mainly in northern and eastern regions. Landscapes in cities and towns at a high economic development level are highly sensitive to human activities. (3) Transformation of ecological risk is complicated. Land area with the ecological risk changing from a low level to a high level was 4.15 times that with the ecological risk changing from a high level to a low level. There were 15 transformation directions among different ecological risk regions.

The landscape grain effect reflects the spatial heterogeneity of a landscape and it is used as a research core of landscape ecology. The landscape grain effect can be used to not only explore spatiotemporal variation characteristics of a landscape pattern, but also to disclose variation laws of ecological structures and functions of landscapes. In this study, the sensitivity of landscape pattern indexes to grain sizes 50–1000 m was studied based on landscape data in Yancheng Coastal Wetland acquired in 1991, 2000, 2008, and 2017. Response of the grain effect to landscape changes was analyzed and an optimal grain size for analysis in the study area was determined. Results indicated that: (1) among 27 indexes (12 in a class level and 15 in a landscape level), eight indexes were highly sensitive to grains, ten indexes presented moderate sensitivity, eight indexes presented low sensitivity, and one was unresponsive. It was shown that the area-margin index and the shape index were more sensitive to the different grain sizes. The aggregation index had some differences in the grain size change, and the diversity index had a low response degree to the grain size. (2) Landscape indexes showed six different responses to different grains, including slow reduced response, fast reduced and then slow reduced response, monotonically increased response, fluctuating reduced response, up-down responses, and stable response, which indicated that the landscape index was closely related to the spatial grain. (3) From 1991 to 2017, variation curves of the landscape grain size of different landscape types could be divided into four types: fluctuation rising type, fluctuation type, monotonous decreasing type, and monotonous rising type. Different grain size curves had different interpretations of landscape changes, but in general, Yancheng Coastal Wetland’s landscape tended to be fragmented and complicated, internal connectivity was weakened, and dominant landscape area was reduced. Natural wetlands were more sensitive to grain size effects than artificial wetlands. (4) The landscape index at the 50 m grain size had a strong response to different grain size changes, and the loss of landscape information was the smallest. Therefore, it was determined that the optimal landscape grain size in the study area was 50 m.

Gains and losses in ecosystem service values (ESV) in coastal zones in Zhejiang Province during rapid urbanization were analyzed in terms of land-use changes. Decision-making on coastal development based on ESV estimation is significant for the sustainable utilization of coastal resource. In this study, coastal land-use changes in Zhejiang Province during rapid urbanization were discussed based on remote-sensing derived land-use maps created in the years 1990, 2000 and 2010. The ESV changes in coastal zones in Zhejiang Province from 1990 to 2010 were estimated by using the established ESV estimation model. The analysis results demonstrate the following: (1) with the continuous acceleration of urbanization, land-use types in coastal zones in Zhejiang Province changed significantly from 1990 to 2010, demonstrated by considerable growth of urban construction land and reduction of forest land and farmland; (2) in the study period, the total ESV in coastal zones in Zhejiang Province continuously decreased in value from RMB 35.278 billion to 29.964 billion, a reduction of 15.06%; (3) in terms of the spatial distribution of ESV, the ESVs in coastal zones in Zhejiang Province were generally converted from a higher ESV to a lower ESV; (4) estimates of ESV for the three years 1990, 2000 and 2010 appear to be relatively stable; and (5) land-use intensity in coastal zones in Zhejiang Province continuously increased during the 20 years. The spatial distribution of land-use intensity was consistent with that of the ESV change rate. Disordered land-use changes from forestland and farmland to urban construction land was a major cause of ESV loss.

Carrying out coastal wetland landscape simulations and current and future ecological risk assessments is conducive to formulating policies for coastal wetland landscape planning and promoting the coordinated development of the social economy and ecological environment. This study used the Cellular Automaton (CA)-Markov model to simulate the landscape data of the study area under different scenarios in 2021 and 2025, and built an ecological risk assessment (ERS) index model to analyze the differences of spatio-temporal characteristics of ecological risks. The results showed that: (1) The test accuracy of the CA–Markov model was 0.9562 after passing through the consistency test. The spatial distribution data of landscapes under current utilization scenarios (CUSs), natural development scenarios (NDSs), and ecological protection scenarios (EPSs) were gained through simulations. (2) During 1991–2025, the landscape types of Yancheng coastal wetlands undertake complicated transfers and have vast transfer regions. Under CUSs and NDSs, a large number of natural wetlands are transferred to artificial wetlands. Under EPSs, the area of artificial wetlands declines and artificial wetlands are mainly transferred to natural wetlands. (3) The ecological risk of Yancheng Coastal Wetland increases, accompanied with significant spatial heterogeneity, which is manifested as low in the north area and high in the south area, and there exist some differences between sea areas and land areas. Ecological risk levels transfer violently.

Quantitatively exploring a relationship between coastal wetland landscape pattern and water quality (WQ) is conducive to providing scientific guidance for the improvement of regional WQ, the prevention of water pollution, and the formulation of regional landscape protection and planning. With the 2019 landscape data and WQ sampling data from the Yancheng Coastal Wetland, five-level WQ sampling points at different scales were established. By applying ecological and statistical analysis methods, correlations between the landscape patterns and WQ indicators in different buffer zones were analyzed. Analysis results showed that: (1) The area of aquafarm, farmland and dry ponds were widely distributed at different scales, and the landscape level index and type level index in different buffer zones were significantly different. (2) There were significant correlations between coastal wetland landscape types and WQ indicators. Total nitrogen, total phosphorus, ammonia nitrogen (NH4 +-N), WQ indicators showed significant positive correlations with farmland landscapes in 0.5 km, 1 km, 2 km, and 2.5 km buffer zones, and significant negative correlations with tidal flats in 0.5 km-2.5 km buffer zones. The Chemical Oxygen Demand index was significantly correlated with various landscapes in the area. (3) At the landscape level, the landscape level index, which represents the fragmentation and complexity of coastal wetland landscapes, had a significant correlation with water pollution indicators, such as mean patch areas, fractal dimension, landscape shape index, plaque cohesion, contagion index and other indexes. At the level of landscape class, farmland, aquafarm, dry ponds, construction land in artificial wetlands, and various types of level indexes of Suaeda salsa, Phragmites communis, and Spartina alterniflora in natural wetlands were highly correlated with WQ indicators.