A GIS-based approach to monitor and assess historical forest landscape evolution

In order to assess landscape dynamics, as well as the effectiveness of relevant management strategies, it is necessary to develop monitoring systems based on qualitative and quantitative tools for its conservation, valorisation and restoration. This approach is particularly important for historical rural landscapes having a recognized ecological and cultural value. To do this, it is first necessary to apply a chronological methodology since, by definition, landscapes result from an interaction of natural and anthropogenic factors over time. Thanks to the constant evolution of Geographic Information Systems and of different geodata available, the monitoring of historical landscapes is increasingly effective and inclusive. Using as a case study an historical forest landscape recognized at Italian level for its high value (Lucanian Apennines’s beech forest - Basilicata Region), a diachronic analysis was applied to evaluate its multi-temporal evolution. Starting from historical cartographies up to Sentinel-2 satellite imagery, a GIS-based approach was implemented to evaluate the spatial variations of forest cover in this landscape. The techniques applied have allowed to reconstruct the original structure of the beech forests, useful for a possible restoration in some areas, but also to monitor the processes in place by using vegetation indices derived from remote sensing

Prediction of forest aboveground biomass using multitemporal multispectral remote sensing data

Forest aboveground biomass (AGB) is a prime forest parameter that requires global level estimates to study the global carbon cycle. Light detection and ranging (LiDAR) is the state-of-the-art technology for AGB prediction but it is expensive, and its coverage is restricted to small areas. On the contrary, spaceborne Earth observation data are effective and economical information sources to estimate and monitor AGB at a large scale. In this paper, we present a study on the use of different spaceborne multispectral remote sensing data for the prediction of forest AGB. The objective is to evaluate the effects of temporal, spectral, and spatial capacities of multispectral satellite data for AGB prediction. The study was performed on multispectral data acquired by Sentinel-2, RapidEye, and Dove satellites which are characterized by different spatial resolutions, temporal availability, and number of spectral bands. A systematic process of least absolute shrinkage and selection operator (lasso) variable selection generalized linear modeling, leave-one-out cross-validation, and analysis was accomplished on each satellite dataset for AGB prediction. Results point out that the multitemporal data based AGB models were more effective in prediction than the single-time models. In addition, red-edge and short wave infrared (SWIR) channel dependent variables showed significant improvement in the modeling results and contributed to more than 50% of the selected variables. Results also suggest that high spatial resolution plays a smaller role than spectral and temporal information in the prediction of AGB. The overall analysis emphasizes a good potential of spaceborne multispectral data for developing sophisticated methods for AGB prediction especially with specific spectral channels and temporal informatio

Detection of European Aspen (Populus tremula L.) Based on an Unmanned Aerial Vehicle Approach in Boreal Forests

European aspen (Populus tremula L.) is a keystone species for biodiversity of boreal forests.Large-diameter aspens maintain the diversity of hundreds of species, many of which are threatened in Fennoscandia. Due to a low economic value and relatively sparse and scattered occurrence of aspen in boreal forests, there is a lack of information of the spatial and temporal distribution of aspen, which hampers efficient planning and implementation of sustainable forest management practices and conservation efforts. Our objective was to assess identification of European aspen at the individual tree level in a southern boreal forest using high-resolution photogrammetric point cloud (PPC) and multispectral (MSP) orthomosaics acquired with an unmanned aerial vehicle (UAV). The structure-from-motion approach was applied to generate RGB imagery-based PPC to be used for individual tree-crown delineation. Multispectral data were collected using two UAV cameras:Parrot Sequoia and MicaSense RedEdge-M. Tree-crown outlines were obtained from watershed segmentation of PPC data and intersected with multispectral mosaics to extract and calculate spectral metrics for individual trees. We assessed the role of spectral data features extracted from PPC and multispectral mosaics and a combination of it, using a machine learning classifier—Support Vector Machine (SVM) to perform two different classifications: discrimination of aspen from the other species combined into one class and classification of all four species (aspen, birch, pine, spruce) simultaneously. In the first scenario, the highest classification accuracy of 84% (F1-score) for aspen and overall accuracy of 90.1% was achieved using only RGB features from PPC, whereas in the second scenario, the highest classification accuracy of 86 % (F1-score) for aspen and overall accuracy of 83.3% was achieved using the combination of RGB and MSP features. The proposed method provides a new possibility for the rapid assessment of aspen occurrence to enable more efficient forest management as well as contribute to biodiversity monitoring and conservation efforts in boreal forests.peerReviewe

Predicting Species and Structural Diversity of Temperate Forests with Satellite Remote Sensing and Deep Learning

Anthropogenically-driven climate change, land-use changes, and related biodiversity losses are threatening the capability of forests to provide a variety of valuable ecosystem services. The magnitude and diversity of these services are governed by tree species richness and structural complexity as essential regulators of forest biodiversity. Sound conservation and sustainable management strategies rely on information from biodiversity indicators that is conventionally derived by field-based, periodical inventory campaigns. However, these data are usually site-specific and not spatially explicit, hampering their use for large-scale monitoring applications. Therefore, the main objective of our study was to build a robust method for spatially explicit modeling of biodiversity variables across temperate forest types using open-access satellite data and deep learning models. Field data were obtained from the Biodiversity Exploratories, a research infrastructure platform that supports ecological research in Germany. A total of 150 forest plots were sampled between 2014 and 2018, covering a broad range of environmental and forest management gradients across Germany. From field data, we derived key indicators of tree species diversity (Shannon Wiener Index) and structural heterogeneity (standard deviation of tree diameter) as proxies of forest biodiversity. Deep neural networks were used to predict the selected biodiversity variables based on Sentinel-1 and Sentinel-2 images from 2017. Predictions of tree diameter variation achieved good accuracy (r2 = 0.51) using Sentinel-1 winter-based backscatter data. The best models of species diversity used a set of Sentinel-1 and Sentinel-2 features but achieved lower accuracies (r2 = 0.25). Our results demonstrate the potential of deep learning and satellite remote sensing to predict forest parameters across a broad range of environmental and management gradients at the landscape scale, in contrast to most studies that focus on very homogeneous settings. These highly generalizable and spatially continuous models can be used for monitoring ecosystem status and functions, contributing to sustainable management practices, and answering complex ecological questions.publishedVersio

Towards mapping biodiversity from above: Can fusing lidar and hyperspectral remote sensing predict taxonomic, functional, and phylogenetic tree diversity in temperate forests?

Aim: Rapid global change is impacting the diversity of tree species and essential ecosystem functions and services of forests. It is therefore critical to understand and predict how the diversity of tree species is spatially distributed within and among forest biomes. Satellite remote sensing platforms have been used for decades to map forest structure and function but are limited in their capacity to monitor change by their relatively coarse spatial resolution and the complexity of scales at which different dimensions of biodiversity are observed in the field. Recently, airborne remote sensing platforms making use of passive high spectral resolution (i.e., hyperspectral) and active lidar data have been operationalized, providing an opportunity to disentangle how biodiversity patterns vary across space and time from field observations to larger scales. Most studies to date have focused on single sites and/or one sensor type; here we ask how multiple sensor types from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) perform across multiple sites in a single biome at the NEON field plot scale (i.e., 40 m × 40 m).Location: Eastern USA.Time period: 2017– 2018.Taxa studied: Trees.Methods: With a fusion of hyperspectral and lidar data from the NEON AOP, we as-sess the ability of high resolution remotely sensed metrics to measure biodiversity variation across eastern US temperate forests. We examine how taxonomic, functional, and phylogenetic measures of alpha diversity vary spatially and assess to what degree remotely sensed metrics correlate with in situ biodiversity metrics.Results: Models using estimates of forest function, canopy structure, and topographic diversity performed better than models containing each category alone. Our results show that canopy structural diversity, and not just spectral reflectance, is critical to predicting biodiversity.Main conclusions: We found that an approach that jointly leverages spectral properties related to leaf and canopy functional traits and forest health, lidar derived estimates of forest structure, fine-resolution topographic diversity, and careful consideration of biogeographical differences within and among biomes is needed to accurately map biodiversity variation from above

Mapping Succession in Non-Forest Habitats by Means of Remote Sensing: Is the Data Acquisition Time Critical for Species Discrimination?

The process of secondary succession is one of themost significant threats to non-forest (natural and semi-natural open) Natura 2000 habitats in Poland; shrub and tree encroachment taking place on abandoned, low productive agricultural areas, historically used as pastures or meadows, leads to changes to the composition of species and biodiversity loss, and results in landscape transformations. There is a perceived need to create amethodology for themonitoring of vegetation succession by airborne remote sensing, both from quantitative (area, volume) and qualitative (plant species) perspectives. This is likely to become a very important issue for the effective protection of natural and semi-natural habitats and to advance conservation planning. A key variable to be established when implementing a qualitative approach is the remote sensing data acquisition date, which determines the developmental stage of trees and shrubs forming the succession process. It is essential to choose the optimal date on which the spectral and geometrical characteristics of the species are as different from each other as possible. As part of the research presented here, we compare classifications based on remote sensing data acquired during three different parts of the growing season (spring, summer and autumn) for five study areas. The remote sensing data used include high-resolution hyperspectral imagery and LiDAR (Light Detection and Ranging) data acquired simultaneously from a common aerial platform. Classifications are done using the random forest algorithm, and the set of features to be classified is determined by a recursive feature elimination procedure. The results show that the time of remote sensing data acquisition influences the possibility of differentiating succession species. This was demonstrated by significant differences in the spatial extent of species, which ranged from 33.2% to 56.2% when comparing pairs of maps, and differences in classification accuracies, which when expressed in values of Cohen’s Kappa reached ~0.2. For most of the analysed species, the spring and autumn dates turned out to be slightly more favourable than the summer one. However, the final recommendation for the data acquisition time should take into consideration th

Spruce Budworm Defoliation Detection and Host Species Mapping Using Sentinel Satellite Imagery

Insects are one of the most significant agents causing landscape level disturbances in North American forests, and among them, spruce budworm (Choristoneura fumiferana; SBW) is the most destructive forest pest of northeastern Canada and U.S. The SBW occurrence, its damage extent and severity are highly dependent on characteristics of the forests and availability of the host species (spruce (Picea spp.) and balsam fir (Abies balsamea (L.) Mill.)). This study developed novel methodologies to detect and classify SBW defoliation and to map SBW host species using remote sensing techniques. Optical multispectral remote sensing satellite imagery presents a valuable data source for regional-scale mapping of forest composition as well as defoliation severity and can be effectively used for monitoring insect outbreaks. This study developed two separate models to map both the distribution and abundance of SBW host species as well as the severity of defoliation at 20 m spatial resolution utilizing Sentinel imagery. The two models were integrated to effectively monitor the SBW defoliation. For the detection and severity classification of SBW defoliation, we used Sentinel-2 imagery and site variables (elevation, aspect, and slope) and compared the capabilities of various spectral vegetation indices (SVIs), in particular red-edge SVIs, to detect and classify SBW defoliation using Support Vector Machine (SVM) and Random Forest (RF) models. The study was carried out in the Northern part of New Brunswick, Canada. Results showed the superiority of RF in model building for defoliation detection and classification into three classes (non-defoliated, light and moderate) with overall errors of 17% and 32%, respectively. The most important Sentinel-2 based variables for the best model were Inverted Red Edge Chlorophyll Index (IRECI), Enhanced Vegetation Index 7 (EVI7), Normalized Difference Infrared Index 11 (NDII11), Modified Chlorophyll Absorption in Reflectance Index (MCARI), and Modified Simple Ratio (MSR). Elevation was the only site variable significant in the final model. The study concluded that red-edge SVIs were more effective variables for light defoliation detection compared to the traditional SVIs such as Normalized Difference Vegetation Index (NDVI) and EVI8. These findings can help improve the current remote-sensing based SBW defoliation detection techniques. For SBW host species classification, Sentinel-1 synthetic aperture radar (SAR) and multi-spectral Sentinel-2 imagery were used in combination with several site variables (elevation, slope, aspect, topographic wetness index, soil types, projected climate site index for year 2030, and improved Biomass Growth Index (iBGI)). The study was carried out in the same location where the first study was conducted but extended to a larger area (northern parts of New Brunswick, Canada) using a total of 191 variables. We found Sentinel-2 time series in combination with single spectral bands and spectral vegetation indices (SVIs) promising to map SBW host species using a RF algorithm, with an overall accuracy (OA) of 71.34% and kappa coefficient (K) of 0.64. The use of Sentinel-1 SAR data alone with elevation showed a decent result (OA: 57.5 and K: 0.47). Furthermore, the combination of Sentinel-1, Sentinel-2 and elevation provided us with an OA of 72.3% and K of 0.65. The most important Sentinel-2 variables for the best model were from the images of late spring and fall seasons including three single spectral bands and seven SVIs mostly from near-infrared, red-edge and shortwave-infrared regions. Prediction of spatially explicit SBW host species data is essential for identifying vulnerable forests, tracking the SBW defoliation and minimizing the forest loss as well as serving as a vital input for modelling and managing insect impacts at the landscape and regional scales