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 km ² 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.

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.

Satellite imagery of 25–30 m spatial resolution has been recognized as an effective tool for monitoring the spatial and temporal dynamics of forest cover at different scales. However, the precise mapping of forest cover over fragmented landscapes is complicated and requires special consideration. We have evaluated the performance of four global forest products of 25–30 m spatial resolution within three flatland subregions of Ukraine that have different forest cover patterns. We have explored the relationship between tree cover extracted from the global forest change (GFC) and relative stocking density of forest stands and justified the use of a 40% tree cover threshold for mapping forest in flatland Ukraine. In contrast, the canopy cover threshold for the analogous product Landsat tree cover continuous fields (LTCCF) is found to be 25%. Analysis of the global forest products, including discrete forest masks Global PALSAR-2/PALSAR Forest/Non-Forest Map (JAXA FNF) and GlobeLand30, has revealed a major misclassification of forested areas under severe fragmentation patterns of landscapes. The study also examined the effectiveness of forest mapping over fragmented landscapes using dense time series of Landsat images. We collected 1548 scenes of Landsat 8 Operational Land Imager (OLI) for the period 2014–2016 and composited them into cloudless mosaics for the following four seasons: yearly, summer, autumn, and April–October. The classification of images was performed in Google Earth Engine (GEE) Application Programming Interface (API) using random forest (RF) classifier. As a result, 30 m spatial resolution forest mask for flatland of Ukraine was created. The user’s and producer’s accuracy were estimated to be 0.910 ± 0.015 and 0.880 ± 0.018, respectively. The total forest area for the flatland Ukraine is 9440.5 ± 239.4 thousand hectares, which is 3% higher than official data. In general, we conclude that the Landsat-derived forest mask performs well over fragmented landscapes if forest cover of the territory is higher than 10–15%.

Aim of study: Incentivising landowners to supply ecosystem services remains challenging, especially when this requires long-term investments such as reforestation. We investigated how landowners perceive, and would respond to, distinct types of incentives for planting diverse native trees on private lands in Lebanon. Our aim was to understand landowners’ attitudes towards hypothetical Payments for Ecosystem Services (PES) contracts options; their likely participation; and the potential additionality they would provide. Area of study: Highland villages situated within eight of Lebanon’s 20 Important Plant Areas Materials and methods: Mixed-methods surveys were conducted with 34 landowners to determine past, present and future land-use strategies. Study participants were presented with three differently structured reforestation contract options (or schemes). The three schemes (results-based loan, action-based grant, and results-based payments) differed in their expected risks and benefits to landowners. Qualitative debriefing questions followed each of the schemes presented. Main results: Although the results-based loan did deter uptake relative to the lower risk action-based grant, results-based payments did not significantly increase uptake or planting area, suggesting asymmetric attitudes to risk. Qualitative probing revealed economic, social (e.g. trust) and institutional factors (e.g. legal implications of planting forest trees on private land) that limited willingness to participate in the results-based contract option. Research highlights: This study demonstrates the importance of combining qualitative and quantitative methods to better understand landowner perceptions of incentives and risks, particularly in challenging socio-political contexts.

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.