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Land degradation and desertification in arid and semi-arid areas is of great concern. Accurately mapping percentage vegetation cover (PVC) of the areas is critical but challenging because the areas are often remote, sparsely vegetated, and rarely populated, and it is difficult to collect field observations of PVC. Traditional methods such as regression modeling cannot provide accurate predictions of PVC in the areas. Nonparametric constant k-nearest neighbors (Cons_kNN) has been widely used in estimation of forest parameters and is a good alternative because of its flexibility. However, using a globally constant k value in Cons_kNN limits its ability of increasing prediction accuracy because the spatial variability of PVC in the areas leads to spatially variable k values. In this study, a novel method that spatially optimizes determining the spatially variable k values of Cons_kNN, denoted with Opt_kNN, was proposed to map the PVC in both Duolun and Kangbao County located in Inner Mongolia and Hebei Province of China, respectively, using Landsat 8 images and sample plot data. The Opt_kNN was compared with Cons_kNN, a linear stepwise regression (LSR), a geographically weighted regression (GWR), and random forests (RF) to improve the mapping for the study areas. The results showed that (1) most of the red and near infrared band relevant vegetation indices derived from the Landsat 8 images had significant contributions to improving the mapping accuracy; (2) compared with LSR, GWR, RF and Cons_kNN, Opt_kNN resulted in consistently higher prediction accuracies of PVC and decreased relative root mean square errors by 5%, 11%, 5%, and 3%, respectively, for Duolun, and 12%, 1%, 23%, and 9%, respectively, for Kangbao. The Opt_kNN also led to spatially variable and locally optimal k values, which made it possible to automatically and locally optimize k values; and (3) the RF that has become very popular in recent years did not perform the predictions better than the Opt_kNN for the both areas. Thus, the proposed method is very promising to improve mapping the PVC in the arid and semi-arid areas. IntroductionLand degradation and desertification is a serious ecological and environmental problems and has received worldwide attention [1][2][3]. Percentage vegetation cover (PVC) represented with the range of values from 0.0 to 1.0 in this study is one of the effective indicators for assessing land degradation and desertification in arid and semi-arid areas and has been widely used. However, collecting field measurements of PVC in remote and sparsely populated arid and semi-arid areas is labor-intensive and time-consuming [4]. This method works for small areas only, which cannot provide the detailed information of spatial characteristics and temporal trend of PVC at a regional or global scale. Compared with the traditional method, remote sensing technologies can repeatedly offer images that cover a same region and quantify the spatial variability and temporal dynamics of PVC. Moreover, mapping PVC using remotely sensed images al...

Combining forest inventory plot and Landsat Thematic Mapper (TM) data has been widely used for mapping forest carbon. However, uncertainty analysis is a great challenge. This study investigated the uncertainties of mapping and scaling up aboveground forest carbon (AGFC) due to plot location errors in Wu-Yuan of East China. Plot location errors were simulated by randomly perturbing the location of each plot with eleven different distances that varied from 5 to 8000 m. Given a perturbed distance (PD) such as 100 m, a forest carbon map was created by combining and scaling up the plot and TM data from a spatial resolution of 28.5 m × 28.5 m to 969 m × 969 m using a sequential Gaussian block cosimulation algorithm. The maps obtained from the perturbed plot locations were compared with that from the true plot locations. The results showed that, as the plot location PD increased, the accuracy of predicted AGFC values decreased, but their spatial patterns (clustering of high and low values) remained until the PD of 800 m, slightly changed at the PD of 1600 m, looked more different at the PDs of 3000 and 5000 m, and became totally random at the PD of 8000 m. More importantly, it was found that scaling up the spatial data mitigated the impacts of plot location errors on the map accuracy compared to those without the up-scaling.Index Terms-Aboveground forest carbon, plot location error, remote sensing, spatial simulation, uncertainty analysis.

A suitable self‐thinning model is fundamental to effective density control and management. Using data from 265 plot measurements in oak mixed forests in central China, we demonstrated how to estimate a suitable self‐thinning line for oak mixed forests from three aspects, i.e., self‐thinning models (Reineke's model and the variable density model), statistical methods (quantile regression and stochastic frontier analysis), and the variables affecting stands (topography and stand structure factors). The proposed variable density model, which is based on the quadratic mean diameter and dominant height, exhibited a better goodness of fit and biological relevance than Reineke's model for modeling the self‐thinning line for mixed oak forests. In addition, the normal‐truncated normal stochastic frontier model was superior to quantile regression for modeling the self‐thinning line. The altitude, Simpson index, and dominant height–diameter ratio (Hd$$ {H}_{\mathrm{d}} $$/D) also had significant effects on the density of mixed forests. Overall, a variable density self‐thinning model may be constructed using stochastic frontier analysis for oak mixed forests while considering the effects of site quality and stand structure on density. The findings may contribute to a more accurate density management map for mixed forests.