Current Trends in Forest Ecological Applications of Three-Dimensional Remote Sensing: Transition from Experimental to Operational Solutions?

The alarming increase in the magnitude and spatiotemporal patterns of changes in composition, structure and function of forest ecosystems during recent years calls for enhanced cross-border mitigation and adaption measures, which strongly entail intensified research to understand the underlying processes in the ecosystems as well as their dynamics. Remote sensing data and methods are nowadays the main complementary sources of synoptic, up-to-date and objective information to support field observations in forest ecology. In particular, analysis of three-dimensional (3D) remote sensing data is regarded as an appropriate complement, since they are hypothesized to resemble the 3D character of most forest attributes. Following their use in various small-scale forest structural analyses over the past two decades, these sources of data are now on their way to be integrated in novel applications in fields like citizen science, environmental impact assessment, forest fire analysis, and biodiversity assessment in remote areas. These and a number of other novel applications provide valuable material for the Forests special issue “3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function”, which shows the promising future of these technologies and improves our understanding of the potentials and challenges of 3D remote sensing in practical forest ecology worldwide

3D Remote Sensing Applications in Forest Ecology: Composition, Structure and Function

Dear Colleagues, The composition, structure and function of forest ecosystems are the key features characterizing their ecological properties, and can thus be crucially shaped and changed by various biotic and abiotic factors on multiple spatial scales. The magnitude and extent of these changes in recent decades calls for enhanced mitigation and adaption measures. Remote sensing data and methods are the main complementary sources of up-to-date synoptic and objective information of forest ecology. Due to the inherent 3D nature of forest ecosystems, the analysis of 3D sources of remote sensing data is considered to be most appropriate for recreating the forest’s compositional, structural and functional dynamics. In this Special Issue of Forests, we published a set of state-of-the-art scientific works including experimental studies, methodological developments and model validations, all dealing with the general topic of 3D remote sensing-assisted applications in forest ecology. We showed applications in forest ecology from a broad collection of method and sensor combinations, including fusion schemes. All in all, the studies and their focuses are as broad as a forest’s ecology or the field of remote sensing and, thus, reflect the very diverse usages and directions toward which future research and practice will be directed

Mapping fractional woody cover in an extensive semi-arid woodland area at different spatial grains with Sentinel-2 and very high-resolution data

Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km2. The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area

Detecting semi-arid forest decline using time series of Landsat data

Detecting forest decline is crucial for effective forest management in arid and semi-arid regions. Remote sensing using satellite image time series is useful for identifying reduced photosynthetic activity caused by defoliation. However, current studies face limitations in detecting forest decline in sparse semi-arid forests. In this study, three Landsat time-series-based approaches were used to distinguish non-declining and declining forest patches in the Zagros forests. The random forest was the most accurate approach, followed by anomaly detection and the Sen’s slope approach, with an overall accuracy of 0.75 (kappa = 0.50), 0.65 (kappa = 0.30), and 0.64 (kappa = 0.30), respectively. The classification results were unaffected by the Landsat acquisition times, indicating that rather, environmental variables may have contributed to the separation of declining and non-declining areas and not the remotely sensed spectral signal of the trees. We conclude that identifying declining forest patches in semi-arid regions using Landsat data is challenging. This difficulty arises from weak vegetation signals caused by limited canopy cover before a bright soil background, which makes it challenging to detect modest degradation signals. Additional environmental variables may be necessary to compensate for these limitations

A 15-year spatio-temporal analysis of plant β-diversity using Landsat time series derived Rao’s Q index

Understanding temporal dynamics of plant biodiversity is crucial for conservation strategies at regional and local levels. The mostly applied hitherto methods are based on field observations of the plant communities and the related taxa. Satellite earth observation time series offer continuous and wider coverage for the assessment of plant diversity, especially in remote areas. Theoretical basis and large-scale solutions for assessing beta-diversity have been recently presented. Yet landscape-scale and context-based analysis are missing. We assessed temporal β-diversity using Raós Q diversity derived from Landsat-based vegetation indices by considering the effect of ERA-5 monthly aggregates environmental factors (temperature and precipitation) extracted using Google Earth Engine (GEE), land use classes, and two common vegetation indices. We derived 15-year Rao’s Q diversity using Landsat-7 based normalized difference vegetation index (NDVI) and modified soil-adjusted vegetation index (MSAVI). We evaluated the temporal turnover in Rao’s Q on multiple land use classes, including agriculture, intact forest and areas affected by and invasive species. Vegetation index and Rao’s Q diverged between pre- and post- monsoon seasons. Rao’s Q had higher temporal turnover with NDVI than MSAVI for all vegetation classes, however the latter showed higher sensitivity towards temperature and precipitation. Moreover, agriculture generally showed higher variability than forest and invasive species. The temporal turnover was correlated between NDVI and MSAVI for all vegetation classes, which indicated that the variability among vegetation types was directly related to spectral heterogeneity. Furthermore, MSAVI was less sensitive to the effect of soil in assessing the vegetation indices, which resulted in higher global sensitivity of QMSAVI. Near infrared and red spectra used in vegetation indices are able to capture a small variation in leaf traits reflectance for vegetation types. Here, the β-diversities and their temporal dynamics derived from the vegetation indices differed based on their sensitivity to soil, vegetation density and seasonality. This approach and its open source implementation can be tested for different forest ecosystems at varying spatial scales

Forest beta-diversity analysis by remote sensing: How scale and sensors affect the Rao’s Q index

Space-borne remote sensing missions provide robust, timely and continuous data to assess biodiversity in remote or protected areas, where direct field observations can be prohibited by difficult accessibility. The objective of this study was to extend the concept of remote sensing based assessment of beta-diversity to multi-scale domain by multi-resolution optical satellite data. This study was conducted in a reserved forest of western Himalaya, India; a region affected by the invasive Lantana camara L (lantana). We calculated and compared Rao’s Q and Shannon indices at different spatial resolutions (0.5, 5, and 30 m) and scales (window sizes) by using imageries from Pléiades 1A, RapidEye, and Landsat-8 acquired in April 2013, the pre-monsoon season. Rao’s Q index explained diversity more accurately than Shannon index for the three analyzed stand densities. Diversity was better approximated by Rao’s Q index calculated by Pléiades 1A at a resolution of 0.5 m at low stand density. We observed higher correlations of the average coefficient of variation (CV) with Rao’s Q and Shannon indices for areas associated with mixed spectral reflectance caused by overstory and understory vegetation. Furthermore, CV was lower in open areas dominated by lantana. These results indicated a strong scale and spatial resolution dependence of Rao’s Q index on remote sensing-derived spectral heterogeneity information. When applied in heterogeneous forest environments, Rao’s Q index could represent a better remote sensing proxy to estimate beta-diversity than the conventional Shannon index

Combination of UAV Photogrammetry and Field Inventories Enables Description of Height-Diameter Relationship within Semi-Arid Silvopastoral Systems

Pollarding oak trees is a traditional silvopastoral technique practiced across wide areas of the northern Zagros mountains, a unique and vast semi-arid forest area with a strong cultural and ecological significance. So far, the effects of pollarding on tree structure in terms of DBH (diameter at breast height)~H (height) relationships within the typical pollarding cycle, which often lasts 4 years, has not been scientifically described. Here, we combine field inventories of DBH with H obtained from photogrammetric UAV flights for the first time to assess DBH~H relationships within this system. We conducted the research at six pollarded forest sites throughout the Northern Zagros. The sampling encompassed all three main species of coppice oak trees. In the case of multi-stem trees, we used the maximum DBH of each tree that formed a unique crown. A linear relationship between UAV and extracted H and the maximum DBH of pollarded trees explained a notable part of the variation in maximum DBH (R2 = 0.56), and more complex and well-known nonlinear allometries were also evaluated, for which the accuracies were in the same range as the linear model. This relationship proved to be stable across oak species, and the pollarding stage had a notable effect on the DBH~H relationship. This finding is relevant for future attempts to inventory biomass using remote sensing approaches across larger areas in northern Zagros, as well as for general DBH estimations within stands dominated by pollarded, multi-stem coppice structures

Comparing time-Lapse PhenoCams with satellite observations across the Boreal forest of Quebec, Canada

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather