Biomass is one significant biophysical parameter of a forest ecosystem, and accurate biomass estimation on the regional scale provides important information for carbon-cycle investigation and sustainable forest management. In this study, Landsat satellite imagery data combined with field-based measurements were integrated through comparisons of five regression approaches [stepwise linear regression, K-nearest neighbor, support vector regression, random forest (RF), and stochastic gradient boosting] with two different candidate variable strategies to implement the optimal spatial above-ground biomass (AGB) estimation. The results suggested that RF algorithm exhibited the best performance by 10-fold cross-validation with respect to R2 (0.63) and root-mean-square error (26.44 ton/ha). Consequently, the map of estimated AGB was generated with a mean value of 89.34 ton/ha in northwestern Zhejiang Province, China, with a similar pattern to the distribution mode of local forest species. This research indicates that machine-learning approaches associated with Landsat imagery provide an economical way for biomass estimation. Moreover, ensemble methods using all candidate variables, especially for Landsat images, provide an alternative for regional biomass simulation.
There is an increasing need to understand pattern and growth of impervious surfaces in rural regions. However,
studies using remote sensing of impervious surfaces have often focused on mapping impervious surfaces in urban
regions with less emphasis placed on the rural impervious surfaces. In this paper, we proposed a new index, Rural
Impervious Surface Index (RISI) by taking advantage of narrow spectral bands of Landsat 8 OLI for estimating
impervious surfaces within rural land covers. This index is based on the combination of Normalized Difference Built-up
Index (NDBI), Soil Adjusted Vegetation Index (SAVI) and Soil Index (SI). Respectively, these represent the three major
rural land covers components: impervious surfaces, vegetation, and soil. The index was further used for estimating
fraction of impervious surfaces using fuzzy KNN classifier. The performance of this technique was also compared with
Linear Spectral Mixture Analysis (LSMA). Our results showed that RISI could accurately detect spatial pattern of rural
impervious surfaces due to the suppressing background noise and minimizing spectral confusion. Accuracy assessment
revealed that incorporation of RISI with fuzzy KNN classification generates higher correlation coefficient, lower root
mean square and systematic error compared to the LSMA technique.
The coastal region is an important potential land resource, and reclamation is a valid means to utilize land and expand
human living space. Since the 1970s, large-scale reclamation projects have taken place in eastern coastal regions, China. To
examine the reclamation program around the Hangzhou Bay in Zhejiang Province, China-using a time-series Landsat
dataset in 1976, 1980, 1990, 2000, 2005, 2010 and 2014, a visual interpretation is applied to extract artificial coastline and
reclamation land-use information. The result showed that during the year 1976 to 2014 period, the total reclamation area
around Hangzhou Bay is 1039.84 km<sup>2</sup>, and the project was mainly occurred in south of Hangzhou Bay, particularly in
Ningbo and Shaoxing county. In addition, between 1976 and 1980, the speed of reclamation was higher than any other
period, followed by period from 2006 to 2009. Moreover, the early reclamation lands were mainly used for cropland and
aqua-farm ponds. After the year 1990, industrial warehouse space and land for harbor and wharf first appeared, and both of
them have increased markedly. The land use types tend to be of diversity overall since 21<sup>st </sup>century.