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African elephants (Loxodonta africana) utilise corridors to access limited resources, that is forage and water scattered across heterogeneous habitats they roam. The existence of small elephant metapopulations depend on the intactness of these corridors to access the scarce resources. Due to the sedentarisation of the previously nomadic Maasai people, elephant corridors have been exposed to increased fragmentation from human‐induced activities across the Amboseli ecosystem in Kenya. In this study, we sought to compare the scale of fragmentation between corridors and their immediate landscapes (noncorridors) in the Amboseli ecosystem, Kenya. We used a Brownian Bridge Movement Model (BBMM) to identify corridors used by elephants from global positioning system (GPS) collar data. The scale of fragmentation between corridors and noncorridors was determined using the effective mesh size fragmentation metric (meff). Our results showed that elephant corridors were significantly less fragmented (Wilcoxon sum rank test: W = 6,121.5, p < 0.05) when compared to the noncorridors. The presence of fragmentation geometries in the corridors remains a major cause of concern for wildlife managers as they have the potential to invade and constrict the existing corridors. Our results underscore the need to extend management of elephant habitats to migration corridors outside protected areas. [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 the vertical pattern of leaf traits across plant canopies provide critical information on plant physiology, ecosystem functioning and structure and vegetation response to climate change. However, the impact of vertical canopy position on leaf spectral properties and subsequently leaf traits across the entire spectrum for multiple species is poorly understood. In this study, we examined the ability of leaf optical properties to track variability in leaf traits across the vertical canopy profile using Partial Least Square Discriminatory Analysis (PLS-DA). Leaf spectral measurements together with leaf traits (nitrogen, carbon, chlorophyll, equivalent water thickness and specific leaf area) were studied at three vertical canopy positions along the plant stem: lower, middle and upper. We observed that foliar nitrogen (N), chlorophyll (Cab), carbon (C), and equivalent water thickness (EWT) were higher in the upper canopy leaves compared with lower shaded leaves, while specific leaf area (SLA) increased from upper to lower canopy leaves. We found that leaf spectral reflectance significantly (P ≤ 0.05) shifted to longer wavelengths in the 'red edge' spectrum (685-701 nm) in the order of lower > middle > upper for the pooled dataset. We report that spectral bands that are influential in the discrimination of leaf samples into the three groups of canopy position, based on the PLS-DA variable importance projection (VIP) score, match with wavelength regions of foliar traits observed to vary across the canopy vertical profile. This observation demonstrated that both leaf traits and leaf reflectance co-vary across the vertical canopy profile in multiple species. We conclude that canopy vertical position has a significant impact on leaf spectral properties of an individual plant's traits, and this finding holds for multiple species. These findings have important implications on field sampling protocols, upscaling leaf traits to canopy level, canopy reflectance modelling, and subsequent leaf trait retrieval, especially for studies that aimed to integrate hyperspectral measurements and LiDAR data. [ABSTRACT FROM AUTHOR]

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]

Understanding factors affecting the distribution of the African elephant is important for its conservation in increasingly human-dominated savannah landscapes. However, understanding how landscape fragmentation and vegetation productivity affect elephant habitat utilization remains poorly understood. In this study, we tested whether landscape fragmentation and vegetation productivity explain elephant habitat utilization in the Amboseli ecosystem in Kenya. We used GPS (Global Positioning System) telemetry data from five elephants to quantify elephant habitat utilization. Habitat utilization was determined by calculating the time elephants spent within a unit area. We then used generalized additive models (GAMs) to model the relationship between time density and landscape fragmentation, as well as vegetation productivity. Results show that landscape fragmentation and vegetation productivity significantly ( P < 0.05) explain elephant habitat utilization. A significant ( P < 0.05) unimodal relationship between vegetation productivity and habitat utilization was observed. Results suggest that elephants spend much of their time in less fragmented landscapes of intermediate productivity. [ABSTRACT FROM AUTHOR]

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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]

The Sentinel satellite fleet of the Copernicus Programme offers new potential to map and monitor plant traits at fine spatial and temporal resolutions. Among these traits, leaf area index (LAI) is a crucial indicator of vegetation growth and an essential variable in biodiversity studies. Numerous studies have shown that the radiative transfer approach has been a successful method to retrieve LAI from remote-sensing data. However, the suitability and adaptability of this approach largely depend on the type of remote-sensing data, vegetation cover and the ecosystem studied. Saltmarshes are important wetland ecosystems threatened by sea level rise among other human- and animal-induced changes. Therefore, monitoring their vegetation status is crucial for their conservation, yet few LAI assessments exist for these ecosystems. In this study, the retrieval of LAI in a saltmarsh ecosystem is examined using Sentinel-2 and RapidEye data through inversion of the PROSAIL radiative transfer model. Field measurements of LAI and some other plant traits were obtained during two succeeding field campaigns in July 2015 and 2016 on the saltmarsh of Schiermonnikoog, a barrier island of the Netherlands. RapidEye (2015) and Sentinel-2 (2016) data were acquired concurrent to the time of the field campaigns. The broadly employed PROSAIL model was inverted using two look-up tables (LUTs) generated in the spectral band's settings of the two sensors and in which each contained 500,000 records. Different solutions from the LUTs, as well as, different Sentinel-2 spectral subsets were considered to examine the LAI retrieval. Our results showed that generally the LAI retrieved from Sentinel-2 had higher accuracy compared to RapidEye-retrieved LAI. Utilising the mean of the first 10 best solutions from the LUTs resulted in higher R2 (0.51 and 0.59) and lower normalised root means square error (NRMSE) (0.24 and 0.16) for both RapidEye and Sentinel-2 data respectively. Among different Sentinel-2 spectral subsets, the one comprised of the four near-infrared (NIR) and shortwave infrared (SWIR) spectral bands resulted in higher estimation accuracy (R2 = 0.44, NRMSE = 0.21) in comparison to using other studied spectral subsets. The results demonstrated the feasibility of broadband multispectral sensors, particularly Sentinel-2 for retrieval of LAI in the saltmarsh ecosystem via inversion of PROSAIL. Our results highlight the importance of proper parameterisation of radiative transfer models and capacity of Sentinel-2 spectral range and resolution, with impending high-quality global observation aptitude, for retrieval of plant traits at a global scale. [ABSTRACT FROM AUTHOR]