Estimating the Growing Stem Volume of Coniferous Plantations Based on Random Forest Using an Optimized Variable Selection Method.

Forest growing stem volume (GSV) reflects the richness of forest resources as well as the quality of forest ecosystems. Remote sensing technology enables robust and efficient GSV estimation as it greatly reduces the survey time and cost while facilitating periodic monitoring. Given its red edge bands and a short revisit time period, Sentinel-2 images were selected for the GSV estimation in Wangyedian forest farm, Inner Mongolia, China. The variable combination was shown to significantly affect the accuracy of the estimation model. After extracting spectral variables, texture features, and topographic factors, a stepwise random forest (SRF) method was proposed to select variable combinations and establish random forest regressions (RFR) for GSV estimation. The linear stepwise regression (LSR), Boruta, Variable Selection Using Random Forests (VSURF), and random forest (RF) methods were then used as references for comparison with the proposed SRF for selection of predictors and GSV estimation. Combined with the observed GSV data and the Sentinel-2 images, the distributions of GSV were generated by the RFR models with the variable combinations determined by the LSR, RF, Boruta, VSURF, and SRF. The results show that the texture features of Sentinel-2's red edge bands can significantly improve the accuracy of GSV estimation. The SRF method can effectively select the optimal variable combination, and the SRF-based model results in the highest estimation accuracy with the decreases of relative root mean square error by 16.4%, 14.4%, 16.3%, and 10.6% compared with those from the LSR-, RF-, Boruta-, and VSURF-based models, respectively. The GSV distribution generated by the SRF-based model matched that of the field observations well. The results of this study are expected to provide a reference for GSV estimation of coniferous plantations.

Inland water bodies in China: Features discovered in the long-term satellite data.

Water bodies (WBs), such as lakes, ponds, and impoundments, provide essential ecosystem services for human society, yet their characteristics and changes over large areas remain elusive. Here we used unprecedented data layers derived from all Landsat images available between 1984 and 2015 to understand the overall characteristics and changes of WBs between 2 epochs (i.e., 1984 to 1999 and 2000 to 2015) in China. Results show that the abundance estimate of WBs greater than 1 km2 and the total WB surface area were 0.3 to 1.5 times and 0.2 to 0.5 times more than the previous estimates, respectively. The size-abundance and shoreline-area relationships of WBs in China conformed to the classic power scaling law, in contradiction to most previous studies. WB changes with various occurrence probabilities show widespread coexistence of disappearance of existent and emergence of new WBs across China driven primarily by human activities and climate change. Our results highlight the importance of using appropriate long-term satellite data to reveal the true properties and dynamics of WBs over large areas, which is essential for developing scaling theories and understanding the relative impacts of human activities and climate change on water resources in the world.

Using stakeholder preferences to select native tree species for reforestation in Lebanon.

Using a diverse assemblage of suitable species for reforestation is necessary to enhance biodiversity and ensure resilient forest ecosystems. However, selection of diverse native species for reforestation is difficult, requiring consideration of the preferences of different stakeholders. In this study we identify species to be included in reforestation of an ecologically important watershed in North Lebanon based on ratings produced by stakeholders from Lebanon's public, private and academic sectors. Twenty-two tree species being produced in Lebanese nurseries were identified as ecologically suitable by experts. Stakeholders (n = 34) were asked to rate these 22 species according to conservation priority and ecological suitability in an online survey. Although there was a high degree of variability in ratings among respondents, those who identified as biodiversity-focused did not differ from those who identified as forestry-focused. Looking within the two foci, we found significant variability among forestry-focused respondents but not among biodiversity-focused respondents. Although there was no significant difference in ratings between biodiversity- and forestry-focussed respondents, the resultant rankings differed considerably. We also found significant variability in preferences within forestry-focussed but not biodiversity-focussed respondents. Weighting by respondents' knowledge of species had little effect on rankings. The variability in preferences between stakeholders, including the considerable within-group variability we found among forestry-focused respondents, highlights the importance of soliciting preferences from multiple stakeholders when selecting species to be used in reforestation efforts.