Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass. Full article

The Democratic People’s Republic of Korea (DPR Korea) suffered considerable upland deforestation during the 1990s, yet its consequences remain relatively unknown. This paper examines this deforestation and resulting land-use change patterns by analysis of Landsat satellite images from 1979, 1992, 2001 and 2006 in Suan County, Hwanghae Province, DPR Korea. Results show that there has been significant closed canopy forest loss and a dramatic expansion of agricultural land during this period. Most forestlands were converted to farmland during 1992 and 2001. Food shortages, along with fuelwood and timber extraction, are considered to be the main drivers of deforestation. Landscape analysis also showed that closed canopy forests have been severely fragmented and degraded. These research findings make a contribution to an insufficient body of literature on environmental issues in DPR Korea and helps to establish a baseline for monitoring land-use and land-cover changes in the country. Full article