Analyzing evolution characteristics of landscape ecological risk patterns would help establish ecological risk early warning mechanism, reduce the ecological risk probability and promote coastal landscape pattern optimization. In this study, landscape pattern indices were first calculated by using multitemporal Landsat TM images acquired in years 1990, 2000 and 2010, then landscape ecological risk pattern evolution models and the spatiotemporal evolutions of ecological risk patterns of coastal zones in Zhejiang province, China were constructed and analyzed. The results demonstrate that (1) greater changes have taken place in landscape structures during the two decades, and (2) the areas of cropland, sea, woodland and water surface have decreased significantly, while the areas of built-up land, unused land and aquaculture land have increased. In 1990, coastal landscapes were mainly occupied by extremely low and low ecological risk areas. However, by 2010, the low ecological risk area apparently decreased compared to that in 1990, while extremely high and high ecological risk areas increased significantly, occupying spatial regions where originally the extremely low and low ecological risk zones located. Transition rates of ecological risk grades in the second ten years (2000–2010) were significantly lower than in the first ten years (1990–2000). The result suggests that people who originally urged to accelerate coastal landscape exploitation and development have started to concern the ecological environment protection and to pursue a mode that economic development and ecological protection are synchronously conducted.

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.

Spatiotemporal analysis of coastline land utilization is important for strengthening coastline resource management and promoting sustainable development in coastal zones. In this study, basic spatiotemporal characteristics of coastline land utilization in Zhejiang Province, China, were analyzed using remote sensing (RS) and GIS techniques. For this case, Landsat Thematic Mapper and Operational Land Imager remote-sensing image data acquired from 1990 to 2015 were collected and processed. Several indices and metrics used for quantifying and analyzing the coastline utilization in Zhejiang Province were defined and calculated through processing remote sensing data and using GIS tools. They included the intensity index of coastline change, fractal dimension of the coastline, subjectivity of coastline utilization, and the integrated coastline utilization index. The analysis results demonstrated that (1) significant coastline changes took place in Zhejiang Province, and the coastline moved seaward, while the total coastline length decreased continuously; (2) the average fractal dimension of the coastlines was 1.0922, and it was relatively stable before 2000, but declined continuously after 2000, reduced to 1.086 in 2015; (3) the artificialization index of the coastlines kept rising; (4) there were single, binary, and other multi-land use types in different coastline sections in the study area; and (5) the coastline land use intensity increased continuously from 0.25 to 0.38, and the coastline in the Xiangshan Harbour had the highest land use intensity index, reaching 0.53 in 2015, throughout the study period, and in the study area.