This paper proposes a method to classify individual tree species groups based on individual tree segmentation and crown-level spectrum extraction (“crown-based ITC” for abbr.) in a natural mixed forest of Northeast China, and compares with the pixel-based classification and segment summarization results (“pixel-based ITC” for abbr.). Tree species is a basic factor in forest management, and it is traditionally identified by field survey. This paper aims to explore the potential of individual tree classification in a natural, needle-leaved and broadleaved mixed forest. First, individual trees were isolated, and the spectra of individual trees were then extracted. The support vector machine (SVM) and spectrum angle mapper (SAM) classifiers were applied to classify the trees species. The pixel-based classification results from hyperspectral data and LiDAR derived individual tree isolation were compared. The results showed that the crown-based ITC classified broadleaved trees better than pixel-based ITC, while the classes distribution of the crown-based ITC was closer to the survey data. This indicated that crown-based ITC performed better than pixel-based ITC. Crown-based ITC efficiently identified the classes of the dominant and sub-dominant species. Regardless of whether SVM or SAM was used, the identification consistency relative to the field observations for the class of the dominant species was greater than 90%. In contrast, the consistencies of the classes of the sub-dominant species were approximately 60%, and the overall consistency of both the SVM and SAM was greater than 70%.

Topography affects forest canopy height retrieval based on airborne Light Detection and Ranging (LiDAR) data a lot. This paper proposes a method for correcting deviations caused by topography based on individual tree crown segmentation. The point cloud of an individual tree was extracted according to crown boundaries of isolated individual trees from digital orthophoto maps (DOMs). Normalized canopy height was calculated by subtracting the elevation of centres of gravity from the elevation of point cloud. First, individual tree crown boundaries are obtained by carrying out segmentation on the DOM. Second, point clouds of the individual trees are extracted based on the boundaries. Third, precise DEM is derived from the point cloud which is classified by a multi-scale curvature classification algorithm. Finally, a height weighted correction method is applied to correct the topological effects. The method is applied to LiDAR data acquired in South China, and its effectiveness is tested using 41 field survey plots. The results show that the terrain impacts the canopy height of individual trees in that the downslope side of the tree trunk is elevated and the upslope side is depressed. This further affects the extraction of the location and crown of individual trees. A strong correlation was detected between the slope gradient and the proportions of returns with height differences more than 0.3, 0.5 and 0.8 m in the total returns, with coefficient of determination R² of 0.83, 0.76, and 0.60 (n = 41), respectively.

Remote sensing provides a consistent form of observation for biodiversity monitoring across space and time. However, the regional mapping of forest species diversity is still difficult because of the complexity of species distribution and overlapping tree crowns. A new method called “spectranomics” that maps forest species richness based on leaf chemical and spectroscopic traits using imaging spectroscopy was developed by Asner and Martin. In this paper, we use this method to detect the relationships among the spectral, biochemical and taxonomic diversity of tree species, based on 20 dominant canopy species collected in a subtropical forest study site in China. Eight biochemical components (chlorophyll, carotenoid, specific leaf area, equivalent water thickness, nitrogen, phosphorus, cellulose and lignin) are quantified by spectral signatures (R² = 0.57–0.85, p < 0.01). We also find that the simulated maximum species number based on the eight optimal biochemical components is approximately 15, which is suitable for most 30 m × 30 m forest sites within this study area. This research may support future work on regional species diversity mapping using airborne imaging spectroscopy.

The effects of forest restoration on ecosystem services and their trade-offs are increasingly discussed by environmental managers and ecologists, but few demonstrations have analyzed ecosystem service trade-offs with a view to informing afforestation choices. Here, we examined how the Grain for Green Program (GGP), an ambitious reforestation program in China, affected ecosystem services. We quantified regulating services and provisioning service in the potential scenarios, which were developed to improve ecosystem services better. The results indicated the GGP drove 14.5% of land-use/land-cover from 2000 to 2015, and all the regulating services increased. Prioritizing reforestations in steep-sloped and riparian farmlands can promote flood mitigation, water purification, and soil retention services by 62.7%, 25.5%, and 216.1% as compared with 2015 levels, respectively, suggesting that the improvements strongly depend on afforestation locations. Driven by the new GGP policy, a high proportion of economic forest increased provisioning service (272.2%), but at the expense of decreases in soil retention (−25.1%), flood mitigation (−11.4%), water purification (−36.6%), and carbon storage (−48.5%). We identified a suitable scenario that would reduce the trade-offs, which associated with afforestation types and their spatial allocation. Identifying priority areas of afforestation types can inform the GGP policy to assure sustainable and broader benefits.

Grassland as a distinct landscape type sustains biological and cultural diversity. However, its use at an unprecedentedly high intensity has had a disastrous impact on grassland ecosystems, with degeneration occurring nationwide. Traditional management policy, drafted according to the stocking rate from livestock statistics and/or in situ assessment, seems to have played no role in alleviating this trend. A major cause is the failure of decision making to consider spatial heterogeneity in the impact of grazing on grassland. To address this issue, high temporal resolution moderate-resolution imaging spectroradiometer data were used to develop a new indicator, named the grazing intensity index (GII), for quantifying grazing intensity (GI). The new index was compared with two traditional approaches and the results demonstrated that GII could substitute for both of them to quantify GI.

With rapid urbanization in China, the phenomenon of urban sprawl has become prominent and has severely challenged sustainable urbanization and ecological civilization. Aiming to understand the impact of urban sprawl on the urban environment, this study calculates the eco-efficiency of 264 prefecture-level cities in China from 2003 to 2016 by using a super-efficiency data envelopment analysis model. Then, we establish a panel Tobit model and threshold regression model to empirically test the impact of urban sprawl on eco-efficiency and the threshold effect of the urban scale. The results show that urban sprawl hinders the improvement of urban eco-efficiency, especially in Eastern China, but relatively weak or even insignificant effects are observed in Central and Western China. Additionally, a threshold effect of urban sprawl on eco-efficiency can be found. When the city scale is small, urban sprawl seriously hinders the improvement of eco-efficiency. As the city scale gradually expands, the negative effect of urban sprawl on eco-efficiency first decreases, then the restraining effect is gradually strengthened. Our research findings can aid urban development in cities with different scales to reduce the negative effect of urban sprawl on the urban environment.