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North American ash species (Fraxinus spp.) are under dire threat from the invasive pest, emerald ash borer (Agrilus planipennis, EAB), and mapping ash trees is of utmost significance for conservation and prevention efforts. We developed remote sensing techniques to identify ash trees at the individual tree level in a mixed-species forest in Maine, USA, using hyperspectral bands combined with derived spectral vegetation indices (SVIs), texture metrics using Random Forest (RF) and Support Vector Machine (SVM) algorithms. The pixel-based SVM-Optimized model proved to be the most accurate classification method with the lowest number of input variables including six Minimum Noise Fraction (MNF)-reduced bands, four SVIs and two texture variables, achieving 78.2% overall accuracy and 81.0% ash Producer's Accuracy. The technique presented in this study could be used to map ash trees throughout Maine and other states with similar forest types for future preservation efforts. [ABSTRACT FROM AUTHOR]

Assessment of vegetation biochemical and biophysical variables is useful when developing indicators for biodiversity monitoring and climate change studies. Here, we compared a radiative transfer model (RTM) inversion by merit function and five machine learning algorithms trained on an RTM simulated dataset predicting the three plant traits leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), and leaf area index (LAI), in a mixed temperate forest. The accuracy of the retrieval methods in predicting these three plant traits with spectral data from Sentinel-2 acquired on 13 July 2017 over Bavarian Forest National Park, Germany, was evaluated using in situ measurements collected contemporaneously. The RTM inversion using merit function resulted in estimations of LCC (R2 = 0.26, RMSE = 3.9 µg/cm2), CCC (R2 = 0.65, RMSE = 0.33 g/m2), and LAI (R2 = 0.47, RMSE = 0.73 m2/m2), comparable to the estimations based on the machine learning method Random forest regression of LCC (R2 = 0.34, RMSE = 4.06 µg/cm2), CCC (R2 = 0.65, RMSE = 0.34 g/m2), and LAI (R2 = 0.47, RMSE = 0.75 m2/m2). Several of the machine learning algorithms also yielded accuracies and robustness similar to the RTM inversion using merit function. The performance of regression methods trained on synthetic datasets showed promise for fast and accurate mapping of plant traits accross different plant functional types from remote sensing data. [ABSTRACT FROM AUTHOR]

Accurate and up-to-date information on forest dendrometric traits, such as above ground biomass is important in understanding the contribution of terrestrial ecosystems to the regulation of atmsopheric carbon, especially in the face of global environmental change. Besides, dendrometric traits information is critical in assessing the healthy and the spatial planning of fragile ecosystems, such as the savanna dry forests. The aim of this work was to test whether red-edge spectral data derived from WorldView-2 multispectral imagery improve biomass estimation in savanna dry forests. The results of this study have shown that biomass estimation using all Worldview-2 raw spectral bands without the red-edge band yielded low estimation accuracies (R2of 0.67 and a RMSE-CV of 2.2 t ha−1) when compared to when the red-edge band was included as a co-variate (R2of 0.73 and a RMSE-CV of 2.04 t ha−1). Also, similar results were obseved when all WorldView-2 vegetation indices(without the red-edge computed ones), producing slightly low accuracies (R2of about 0.67 and a RMSE-CV of 2.20 t ha−1), when compared to those obtained using all indices and RE-computed indices(R2of 0.76 and a RMSE-CV of 1.88 t ha−1). Overall, the findings of this work have demontrated the potential and importance of strategically positioned bands, such as the red-edge band in the optimal estimation of indigeonus forest biomass. These results underscores the need to shift towards embracing sensors with unique and strategeically positioned bands, such as the forthcoming Sentinel 2 MSI and HysPIRI which have a global footprint. [ABSTRACT FROM PUBLISHER]

Understanding factors affecting the behaviour and movement patterns of the African elephant is important for wildlife conservation, especially in increasingly human-dominated savanna landscapes. Currently, knowledge on how landscape fragmentation and vegetation productivity affect elephant speed of movement remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explains elephant speed of movement in the Amboseli ecosystem in Kenya. We used GPS collar data from five elephants to quantify elephant speed of movement for three seasons (wet, dry and transitional). We then used multiple regression to model the relationship between speed of movement and landscape fragmentation, as well as vegetation productivity for each season. Results of this study demonstrate that landscape fragmentation and vegetation productivity predicted elephant speed of movement poorly (R² < 0.4) when used as solitary covariates. However, a combination of the covariates significantly (p < 0.05) explained variance in elephant speed of movement with improved R² values of 0.69, 0.45, 0.47 for wet, transition and dry seasons, respectively. [ABSTRACT FROM AUTHOR]

Estimation and mapping of forest dendrometric characteristics such as carbon stocks using remote sensing techniques is fundamental for improved understanding of the role of forests in the carbon cycle and climate change. In this study, we tested whether and to what extent spectral transforms, i.e. vegetation indices derived from new generation high-spatial-resolution multispectral sensors (WorldView-2 and GeoEye-1), estimate carbon stocks when compared with medium-spatial-resolution broadband sensors (Landsat 5 Thematic Mapper) based on two savanna woodland types in Zimbabwe. Subsequently, the best ordinary least squares regression model relating carbon stocks and vegetation indices was applied in mapping carbon stocks in two study sites. Based onk-fold cross-validated regression models, vegetation indices computed from new generation high-spatial-resolution multispectral sensors yielded highR 2 values ranging between 82% and 73% (RMSEcv: 5.55–6.87%) for Mukuvisi and 62–73% (RMSEcv: 11.5–13.6) for Malipati compared with Landsat 5 Thematic Mapper derived vegetation indices, which yieldedR 2 values between 47% and 49% (RMSEcv: 9.6–10.1%) for Mukuvisi and 22–41% (RMSEcv: 11.5–19.1%) for Malipati. The findings demonstrated that medium-spatial-resolution sensors are less sensitive to attributes of sparsely distributed trees, especially in savanna woodlands, where the size of trees are often less that the spatial resolution of the mediumspatial-resolution sensors. These findings emphasise the importance of new generation high-spatial-resolution multispectral sensors in estimating forest structural attributes, such as carbon stocks in open woodlands. [ABSTRACT FROM PUBLISHER]

In this study we tested the predictive ability of canopy area in estimating wood volume in deciduous woodlands of Zimbabwe. The study was carried out in four sites of different climatic conditions. We used regression analysis to statistically quantify the prediction of wood volume from canopy area at species and stand level using field data. Our results revealed that canopy area significantly (P< 0.05) predicted wood volume at both levels. Furthermore, the results show that at the species-specific level, canopy area explained 54–81% of the variance in wood volume with standard error of estimate (SEE) ranging from 0.056 to 0.71 m3. At stand level, canopy area significantly (P< 0.05) explained 58–84% of the variance in total wood volume with SEE ranging from 0.15 to 3.99 m3ha−1. Across all study sites, the relationship between canopy area and wood volume at stand level was best described by a logistic regression function, with aR2value of 0.65 and SEE of 0.7 m3. We concluded that canopy area significantly (P< 0.05) predicted wood volume of dominant tree species in Zimbabwean deciduous woodlands. The relationship between wood volume and canopy area provides an opportunity of estimating wood volume using remote sensing as canopy area can be viewed and measured from aerial, as well as satellite-borne sensors. [ABSTRACT FROM AUTHOR]