http://hdl.handle.net/10174/666 Mon, 01 Jun 2026 13:25:53 GMT 2026-06-01T13:25:53Z http://hdl.handle.net/10174/42064 Title: Using GEDI‑derived vegetation structural metrics to evaluate avian biodiversity patterns in Mediterranean habitats Authors: Valerio, Francesco; Pereira, Pedro; Pedro, Salgueiro; Godinho, Carlos; Lourenço, Rui; Silva, João Paulo; Gameiro, João; Santana, Joana; Leitão, Pedro; Moreira, Francisco; Lousa, Hugo; Catry, Inês; Reino, Luis; Safara, Jorge; Pedroso, Rui; Venâncio, Luis; Martins, Ricardo; Morgado, Rui; Marques, Ana; Burfin, Thomas; Guise, Inês; Oliveira, André; Neiva, Sara; Faria, João; Crispim-Mendes, Tiago; Corado, Leonel; Basile, Marco; Godinho, Sérgio Editors: Gonzálvez, Pablo; Guerra-Hernández, Juan; Ferreiro, Eduardo Abstract: Avian communities are highly sensitive to variations in vegetation structure, which in turn is strongly influenced by fine-scale habitat heterogeneity. Traditional categorical land-cover maps often fail to capture this heterogeneity, limiting our ability to monitor avian biodiversity across broad extents. To address this, we present a remote-sensing framework that integrates spaceborne light detection and ranging (LiDAR) metrics from the Global Ecosystem Dynamics Investigation (GEDI) with Sentinel‑1 radar and Sentinel‑2 multispectral data to generate vertical and horizontal vegetation structure encompassing biodiversity-rich environments. Our study covers three areas with contrasting Mediterranean habitats in southern Portugal—woodland, open woodland, and grasslands—where breeding birds were surveyed between 2020 and 2024. We used random forests models to evaluate the ability of GEDI‑derived standard metrics (RH75, RH95, PAI, FHD, AGBD) and structural heterogeneity metrics (Shannon entropy, Rao’s Q), to predict avian species richness and abundance along the woodland-grassland habitat gradient. We then developed a targeted model for an intermediate open woodland landscape (montado), using the tawny owl (Strix aluco) as a model species, to evaluate how those same predictors explain local abundance patterns. Finally, we included common aggregation methods (e.g., mean, maximum) for each metric in the analysis, as well as the effect of scale (75 and 225 m) at the plot level where bird surveys were conducted. This study demonstrated that GEDI-derived upper canopy heterogeneity (Rao’s Q of RH75 at 225 m), aboveground biomass, and canopy density together explained over 70% of the variation in avian species richness and total abundance. Grasslands, despite the lower overall densities, supported key specialists such as the little bustard (Tetrax tetrax), underscoring their essential role alongside structurally rich wood pastures. Although the random forests model for the tawny owl accounted for a smaller share of variance, it revealed a significant positive response to canopy height and a bimodal relationship with foliage height diversity (FHD). Together, these findings emphasize that Mediterranean bird communities depend upon a mosaic of habitat structures, such as layered woodlands with canopy gaps and understory clusters providing nesting, roosting, and foraging niches, while open terrains sustain species adapted to sparse cover. By integrating spaceborne LiDAR from GEDI with Sentinel‑1 radar and Sentinel‑2 optical data, our framework offers a scalable, fine‑grained approach for biodiversity monitoring across Mediterranean landscapes that are overlooked for such applications. We recommend that conservation strategies maintain both three-dimensional woodland complexity and retain extensive grassland habitats to support flagship species. Future work linking GEDI metrics with detailed ground-based microhabitat surveys and avifaunal monitoring will be crucial for pinpointing the structural drivers of species distributions and refining management practices to maximize both richness and abundance. Fri, 05 Dec 2025 00:00:00 GMT http://hdl.handle.net/10174/42064 2025-12-05T00:00:00Z http://hdl.handle.net/10174/42063 Title: Land Cover mapping by combining GEDI-derived vertical metrics with SAR and multispectral data Authors: Godinho, Sérgio; Corado, Leonel; Benevides, Pedro; Costa, Hugo; Caetano, Mário Editors: Gonzálvez, Pablo; Guerra-Hernández, Juan; Ferreiro, Eduardo Abstract: Accurate land cover mapping is essential for understanding ecological processes and informing sustainable land management. This study presents a two-stage remote sensing framework that first generates wall-to-wall maps of four key vegetation structure variables [canopy height (RH95), foliage height diversity (FHD), plant area index, and aboveground biomass (AGB)] using random forest models trained with Global Ecosystem Dynamics Investigation (GEDI) footprints and satellite-derived predictors. The second stage integrates these maps with multispectral (Sentinel-2) and synthetic aperture radar (SAR) (Sentinel-1C-band and ALOS-2/PALSAR-2 L-band) data to improve land cover classification across two contrasting landscapes in mainland Portugal. From the first stage, the resulting canopy height maps were independently validated using airborne light detection and ranging (LiDAR), showing strong agreement (R2 = 0.64–0.63; RMSE = 2.6–5.3 m). In the second stage, these structural layers were combined with seasonal and spectral-temporal metrics from Sentinel-2, as well as with texture and backscatter data from Sentinel-1 and PALSAR-2, totalling 230 predictor variables. Land cover classification achieved high accuracy (overall accuracy (OA): 96.0% and 91.7% in the two study areas), with GEDI-derived structural metrics significantly improving class separability, particularly among forest and shrubland types. Variable importance analyses highlighted the synergistic contribution of LiDAR, SAR, and spectral data, with FHD, vegetation canopy height (VCH), and AGB ranking among the most influential predictors. This study demonstrates the value of integrating spaceborne LiDAR with complementary remote sensing data for both vegetation structural modelling and high-resolution land cover mapping. The results reinforce the potential of GEDI in operational land monitoring frameworks, particularly in complex or structurally diverse ecosystems. Fri, 05 Dec 2025 00:00:00 GMT http://hdl.handle.net/10174/42063 2025-12-05T00:00:00Z http://hdl.handle.net/10174/41935 Title: 10: Enhancing rural prosperity through social capital Authors: Noll, Dominik; Rivera, Maria Abstract: The link between social capital and economic development has received much attention in recent decades. While economic development is proven to have positive effects on social inclusion and quality of life, this happens mostly at the expense of environmental sustainability. Research should thus not only focus on the link between social capital and economic development but also prosperity, understood as ecological sustainability, social inclusion, and quality of life at large. Typically, the term prosperity has been associated almost exclusively with economic growth, but evidence has shown that this could only be achieved at the expense of environmental sustainability and that GDP as an indicator falls short in accounting for the general well-being of all humans. Prosperity should include factors such as social cohesion and engagement, achieved through cooperation and trust, environmental sustainability, and knowledge, which increases the ability people have to increase their resilience, and quality of life. All these factors are supported by, powered by, and geared towards social capital, which is one of the key building blocks of the “social web”. With our contribution, we aim at expanding the focus from the link of social capital and economic development to the impact of social capital on prosperity. We do so by providing theoretical and practical information about eight illustrative case studies from Austria, Portugal, Spain and Türkiye that serve as best practice examples for increasing the prosperity of rural regions through the building of social capital. The empirical analysis of these best practice examples shows that in all cases, social, economic, and environmental sustainability are core elements. Thus, future research must look beyond the impact of social capital on economic development, by integrating indicators that show if social capital is a valuable tool to reach this new form of prosperity, incorporating all three dimensions of sustainability. Wed, 14 May 2025 23:00:00 GMT http://hdl.handle.net/10174/41935 2025-05-14T23:00:00Z http://hdl.handle.net/10174/41878 Title: Using GEDI‑derived vegetation structural metrics to evaluate avian biodiversity patterns in Mediterranean habitats Authors: Valerio, Francesco; Pereira, Pedro F; Salgueiro, Pedro; Godinho, Carlos; Silva, João Paulo; Guise, Inês; Godinho, Sérgio Abstract: Avian communities are highly sensitive to variations in vegetation structure, which in turn is strongly influenced by fine-scale habitat heterogeneity. Traditional categorical land-cover maps often fail to capture this heterogeneity, limiting our ability to monitor avian biodiversity across broad extents. To address this, we present a remote-sensing framework that integrates spaceborne light detection and ranging (LiDAR) metrics from the Global Ecosystem Dynamics Investigation (GEDI) with Sentinel‑1 radar and Sentinel‑2 multispectral data to generate vertical and horizontal vegetation structure encompassing biodiversity-rich environments. Our study covers three areas with contrasting Mediterranean habitats in southern Portugal—woodland, open woodland, and grasslands—where breeding birds were surveyed between 2020 and 2024. We used random forests models to evaluate the ability of GEDI‑derived standard metrics (RH75, RH95, PAI, FHD, AGBD) and structural heterogeneity metrics (Shannon entropy, Rao’s Q), to predict avian species richness and abundance along the woodland-grassland habitat gradient. We then developed a targeted model for an intermediate open woodland landscape (montado), using the tawny owl (Strix aluco) as a model species, to evaluate how those same predictors explain local abundance patterns. Finally, we included common aggregation methods (e.g., mean, maximum) for each metric in the analysis, as well as the effect of scale (75 and 225 m) at the plot level where bird surveys were conducted. This study demonstrated that GEDI-derived upper canopy heterogeneity (Rao’s Q of RH75 at 225 m), aboveground biomass, and canopy density together explained over 70% of the variation in avian species richness and total abundance. Grasslands, despite the lower overall densities, supported key specialists such as the little bustard (Tetrax tetrax), underscoring their essential role alongside structurally rich wood pastures. Although the random forests model for the tawny owl accounted for a smaller share of variance, it revealed a significant positive response to canopy height and a bimodal relationship with foliage height diversity (FHD). Together, these findings emphasize that Mediterranean bird communities depend upon a mosaic of habitat structures, such as layered woodlands with canopy gaps and understory clusters providing nesting, roosting, and foraging niches, while open terrains sustain species adapted to sparse cover. By integrating spaceborne LiDAR from GEDI with Sentinel‑1 radar and Sentinel‑2 optical data, our framework offers a scalable, fine‑grained approach for biodiversity monitoring across Mediterranean landscapes that are overlooked for such applications. We recommend that conservation strategies maintain both three-dimensional woodland complexity and retain extensive grassland habitats to support flagship species. Future work linking GEDI metrics with detailed ground-based microhabitat surveys and avifaunal monitoring will be crucial for pinpointing the structural drivers of species distributions and refining management practices to maximize both richness and abundance. Wed, 01 Jan 2025 00:00:00 GMT http://hdl.handle.net/10174/41878 2025-01-01T00:00:00Z