Peer reviewed versionCyswllt i'r cyhoeddiad / Link to publication Dyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA):

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 longterm 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. Material and methods:Mixed-methods surveys were conducted with 34 landowners to determine past, present and future landuse 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.Additional keywords: agro-ecosystems; biodiversity; conditionality; displacement; mixed-methods; participation; PES.Correspondence: should be addressed Arbi J. Sarkissian: arbi.sarkissian@outlook.com

Potentially toxic elements (PTEs) pollution has become a serious environmental threat, particularly in developing countries such as China. In response, there is a growing interest in phytoremediation studies to identify plant species as designated hyperaccumulators of PTEs in polluted soils. Poinsettia was selected as a candidate species for phytoremediation of six PTEs (Zn, Pb, Hg, Cr, As, Cu) in this study. A pot cultivation experiment (randomized incomplete block experimental design with 5 treatments and 4 blocks) was conducted using contaminated soils gathered from an industrial area in southcentral China. The bioaccumulation factor (BAF), translocation factor (TF), and bioconcentration factor were analyzed to determine the phytoremediation potential of poinsettia potted in different ratios of polluted soils. One-way ANOVA with post-hoc Tukey’s test showed that poinsettia had significant uptake of Zn, Pb, Cu (BAF < 1 and TF < 1, p < 0.05) and Hg (BAF < 1 and TF > 1, p < 0.05). Poinsettias can therefore effectively accumulate Zn, Pb, and Cu in their lateral roots while extracting and transferring Hg into their leaves. Moreover, poinsettia exhibited tolerance towards As and Cr. Interestingly, it was also observed that PTEs can inhibit the height of potted poinsettia at a certain concentration.

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.Electronic supplementary materialThe online version of this article (10.1007/s11056-018-9648-2) contains supplementary material, which is available to authorized users.

The United Nations predicts that by 2050, 64.1% of the developing world and 85.9% of the developed world will be urbanized. This has resulted in a rapid change in land use and land cover types in the areas surrounding cities in all countries, particularly in China, which determines the relevance of this article. The aim of the study was to evaluate the dynamics of land cover change in Changsha City, Hunan Province, China, between 2005 and 2020, using Landsat time series satellite images and the Random Forest classification algorithm. The data acquisition, pre-processing, and analysis were conducted on the Google Earth Engine (GEE) publicly available online platform. Land cover thematic continuous raster maps were produced using ESRI ArcGIS 10.5.1 software. The overall classification accuracy was obtained by more than 83% for every produced map and the Kappa coefficient was 0.84 and higher, which approves the reliable classification results that are close to similar recent studies in terms of obtained accuracy. The study shows that from 2005 to 2020, the area of settlement in Changsha City, China, increased significantly, with an exponential increase in urban area from 3.23% to 15.95%. The proportion of forest cover gradually decreased from 2005 to 2015 but increased from 2015 to 2020. Cropland was the second most dominant land cover type, with a peak of almost 50% in 2010. Water bodies remained stable at around 3%. The proportion of open soil and bare land cover fluctuated between 180 and 400 km2 (1.5-3%). The study suggests that the offered monitoring approach provides reliable results, and the research findings can be used for sustainable urban planning and management, as well as conservation and development initiatives. The remote sensing data and advanced GIS technologies can provide decision-makers with the accurate data to ensure sustainable development in this area

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