Urban green spaces are essential for maintaining biodiversity, mitigating climate-related impacts, and enhancing human well-being. Despite growing interest in biodiversity-friendly urban green space management, the socio-ecological factors associated with user support remain insufficiently understood. This study investigates users’ and administrative perspectives on biodiversity-friendly green space management at Justus Liebig University of Gießen, Germany, using an integrated socio-ecological approach. Survey data from 464 respondents were analyzed to examine biodiversity awareness, sense of responsibility, willingness to participate, and management preferences, as well as key barriers and facilitators associated with support for biodiversity-friendly management. Respondents showed high biodiversity awareness (96.5%) and sense of shared responsibility (74.7%), and favored semi-natural, multifunctional urban green spaces such as wildflower meadows (86.2%). These positive attitudes contrast with trends in green space availability, as contextual spatial per capita analysis (2010–2024) revealed a continuous decline in green space availability, underscoring increasing pressure on green infrastructure and the urgent need for management strategies aligned with the Sustainable Development Goal (SDG) 11.7. Exploratory structural equation modeling suggested that concern about climate change, recognition of habitat function, and social responsibility were positively associated with stated support for biodiversity-friendly management, whereas preferences for tidy lawns and perceived health risks showed negative associations with acceptance. This study contributes to broader discussions on socio-ecological urban transitions and suggests that participatory planning, best-practice examples, and communication strategies emphasizing ecological, climatic, and well-being benefits may help strengthen support for biodiversity-friendly management while aligning green space governance with biodiversity and sustainability objectives.

Territorial sustainability requires assessing whether LULC change remains within a context-specific safe and just space shaped by ecological potential, planning rules, and governance conditions. Doughnut-inspired frameworks offer an intuitive visualization of this space, but they are rarely operationalized for subnational rural planning or explicitly linked to land-use vocation. This article proposes the Territorial Sustainability Doughnut Model (TSDM), a multiscale interpretive framework that integrates land-use vocation, ecosystem-service trajectories, and field-based social evidence across vereda, municipal, and regional scales. The model is applied in Montes de María, Colombia, using multitemporal LULC analysis, agrological and zoning-based conflict diagnostics, and thirty-five semi-structured interviews with community and institutional actors. Results show localized ecosystem-service gains and stronger social conditions in some veredas, but these signals do not scale linearly upward. At the municipal and subregional levels, sustainability remains constrained by land tenure insecurity, institutional fragmentation, and persistent mismatches between observed land use and territorial vocation. Rather than statistically extrapolating social conditions, the TSDM synthesizes convergent spatial and qualitative evidence to interpret how land-use transitions reshape ecosystem-service bundles and social foundations under governance asymmetry. Spatial inputs were evaluated using class-wise classification, with F1-scores ranging from 0.73 to 0.87. Trustworthiness was strengthened through triangulation among LULC maps, zoning overlays, field observations, interviews, official documents, and participatory feedback. The case shows the value of a multiscale territorial approach and identifies political-institutional processes and temporal feedback as priorities for future applications.

Advances in deep learning (DL) and structure from motion (SfM) photogrammetry combined with off-the-shelf unoccupied aerial vehicles (UAVs) and high-resolution cameras enable unprecedented plant species mapping accuracy. While these tools have been mainly applied to flat terrain or upper forest canopies, the forest understorey remains largely unexplored. Here, we present a method combining DL with multi-view UAV imagery to map the invasive tree-of-heaven (Ailanthus altissima) in the understorey of a drought-affected Central European forest. The raw UAV photographs were segmented with convolutional neural networks (CNNs). Resulting predictions were projected onto georeferenced point clouds using SfM. This novel approach revealed that more than 40% of the invasion was hidden beneath the canopy and would have been missed by conventional orthomosaic-based methods. To assess CNN generalization abilities, we altered training and prediction domains: lower-processing-level aerial images vs. higher-processing-level orthomosaic, both originating from a small training extent (420 m2 or 0.25% of the study area). For intra-domain predictions, aerial-trained models (F1 = 0.880) outperformed ortho-trained models (F1 = 0.805). When applied cross-domain, aerial models retained superior performance (F1 = 0.843) over ortho-trained ones (F1 = 0.750). Expanding the training extent eightfold raised the accuracy of the ortho-trained models to F1 = 0.836 within-domain and F1 = 0.846 cross-domain. In summary, integrating photogrammetry with DL is a promising route for utilizing overlapping aerial imagery efficiently and leveraging information that conventional orthomosaic-based analyses discard. The resulting richer 3D information and improved transferability may support decision-making and retrofitting this novel technique to upcoming and existing datasets opens new avenues in vegetation studies and beyond.

Dendroecology offers insights into plant species responses to abrupt and long-term shifts in environmental conditions, but studies are predominantly focused on trees in forest ecosystems. This systematic literature review examines the prevalence and patterns of dendroecological studies conducted outside forest habitats. Utilizing the PRISMA framework, we systematically searched the Web of Science and identified 59 relevant publications encompassing 217 sampling sites across 26 countries. The studies showed a strong geographic bias towards temperate and cold regions, primarily in the Northern Hemisphere, and a scarce research cover from arid regions, where TOFs are highly present. We found a focus on urban and agricultural settings, and a predominant emphasis on Pinaceae, revealing a lack of knowledge on a broader set of TOF species. Tree-ring width was identified as the primary method for assessing climate-growth relationships in the studies, while wood anatomy, wood density, and stable isotopes were used less frequently. Our findings indicate a strong positive correlation between precipitation and tree growth across various climates, while temperature exhibited mixed effects. The underrepresentation of studies in the Tropics and the Southern Hemisphere, the taxonomic composition of TOF dendroecology, and the predominance from ring width as a parameter to study growth highlight a critical bias in current knowledge. To advance TOF dendroecology, future research must prioritize sampling sites where TOFs are highly present, obtaining metadata related to management and stand origin, and adopting a multi-parameter approach to understand climate-growth relationships to create a more comprehensive framework for understanding global tree-growth dynamics.

Excessive tree mortality is a global concern and remains poorly understood as it is a complex phenomenon. We lack global and temporally continuous coverage on tree mortality data. Ground-based observations on tree mortality, e.g., derived from national inventories, are very sparse, and may not be standardized or spatially explicit. Earth observation data, combined with supervised machine learning, offer a promising approach to map overstory tree mortality in a consistent manner over space and time. However, global-scale machine learning requires broad training data covering a wide range of environmental settings and forest types. Low altitude observation platforms (e.g., drones or airplanes) provide a cost-effective source of training data by capturing high-resolution orthophotos of overstory tree mortality events at centimeter-scale resolution. Here, we introduce deadtrees.earth, an open-access platform hosting more than two thousand centimeter-resolution orthophotos, covering more than 1,000,000 ha, of which more than 58,000 ha are manually annotated with live/dead tree classifications. This community-sourced and rigorously curated dataset can serve as a comprehensive reference dataset to uncover tree mortality patterns from local to global scales using space-based Earth observation data and machine learning models. This will provide the basis to attribute tree mortality patterns to environmental changes or project tree mortality dynamics to the future. The open nature of deadtrees.earth, together with its curation of high-quality, spatially representative, and ecologically diverse data will continuously increase our capacity to uncover and understand tree mortality dynamics.

Remote sensing technology is increasingly applied to map the occurrence of invasive plant species, yet its use to map their ecological impact remains limited. Furthermore, invader-induced changes beyond the canopy, as well as the environmental context, are rarely considered. This study aimed to assess the impacts of an invasive tree on ecosystem functioning at the landscape scale using remote sensing, taking into account both spatial effects and environmental heterogeneity. Specifically, we investigated a coastal Mediterranean dune ecosystem invaded by the N-fixing tree Acacia longifolia (Andrews) Willd. ('Acacia'). Four vegetation indices were calculated as proxies of ecosystem functions, and these indices were used to compute functional diversity in terms of spectral Rao's Q for assessing impacts by Acacia based on airborne hyperspectral data. Vegetation cover and topographic indices derived from airborne LiDAR (Light Detection and Ranging) were used to account for spatial heterogeneity. For seven sites, we employed Generalized Linear Mixed Models to model the effects of environmental variables and Acacia-related variables on proxies of ecosystem functions. Significant impact of the invader was found beyond the invaded area augmenting to 50 % total impact on ecosystem functions. These spatial impacts are particularly prevalent at rather early stages of invasion (similar to 20 % invader cover at landscape level). Consequently, the impact of invaders is underestimated when spatial effects are ignored, but it is overestimated when environmental heterogeneity is neglected. Furthermore, functional diversity decreases due to invasion, though it reaches its maximum at the edges of invader stands, where Rao's Q index captures spectral effects of both the invader and the native vegetation. Thus, we highlight that both 2D and 3D remote sensing data complement each other in remote sensing-driven impact assessments. We envision that advancements in remote sensing of ecosystem structure and functioning in terms of increasing availability of high spectral, spatial and temporal data as well as enhanced methods for data analysis will facilitate tracing the context-dependent and function-specific spatial effects of invasive species especially at early stages of invasion to enable timely management.

Understanding the niche dynamics of invasive non-native aquatic plants is limited by the insufficiency of freshwater-specific climatic environmental information. Specifically, non-native species are frequently not in equilibrium with environmental conditions in the introduced areas, allowing them to colonize environments absent from their native range. We investigated the niche dynamics of five South American aquatic non-native plants (Egeria densa, Myriophyllum aquaticum, Pistia stratiotes, Pontederia crassipes, and Salvinia molesta) considered invasive worldwide. The differences between the native and invaded niches were assessed using niche overlap, equivalence, and similarity indices. The dynamics of invasive niches were quantified by indices describing unfilled, stable, and expansion of niches. In general, the plants showed a moderate overlap, except for S. molesta (low overlap). North America showed the highest overlap values, while niche stability was high (mean = 0.726), but with potential expansion and unfilled, especially in Europe and Africa. Niche equivalency was mostly rejected, indicating significant climatic differences between native and invaded niches. This study showed species and continent-specific niche shifts, while niche expansion and unfilling were evident, most shifts occurred within the boundaries of the native niche. The findings highlight limited niche conservatism, suggesting that dispersal events and introduction pathways facilitate invasions into novel conditions.

Urban green space management is shifting from intensive maintenance toward sustainable practices in response to climate change and biodiversity loss. While reduced management intensity can enhance ecosystem services such as soil carbon storage, its impacts on overall soil health have been studied less extensively. We evaluated soil health using a composite index of key physical and chemical soil properties across six urban green space types (woody stands, single trees, hedges, wildflower meadows, shrubs, and short-cut lawns) in Giessen, Germany. High-resolution digital orthophotos were used to map Land Use/Land Cover. Soil samples were collected from 120 sites at three depths (0-1, 1-10, and 10-30 cm), totaling 360 samples. Significant differences were observed among green space types in soil health indicators, including soil organic carbon, nitrogen stocks, SOM/ clay, C/N, Soil Stability Index, and macronutrient levels. Less intensively managed areas, such as woody stands and wildflower meadows, showed higher soil health status than intensively managed lawns (43.1, 32.5, and 21.7, respectively). Converting 100 m2 of lawn to wildflower meadows could increase carbon storage, the key driver of the composite index, by approximately 160 kg. Structural Equation Modeling (SEM) (CFI = 0.94) showed that both current and historical green space types like woody stands ((3 = 0.51), hedges ((3= 0.22), wildflower meadows ((3= 0.05), and past woody stands ((3= 0.31) influence soil health. These findings support the development of urban soil health maps and underscore the value of sustainable green space management in enhancing urban soil health and long-term carbon storage.

The increasing need for precise and detailed geospatial products in sustainability and land-use change research, essential for effective territory management, drives the exploration of alternative methods to harness the potential of geospatial data, particularly satellite imagery. Image fusion techniques have emerged as a promising approach to integrating multispectral satellite images into single images that encapsulate the best available data. This strategy has demonstrated success within single-image spectral bands and, to some extent, in multisensor contexts, producing accurate results that merit collaborative investigation. This study examines the efficacy of an enhanced image fusion method applied to 10m Sentinel-2 and the latest 3m PlanetScope generation for land use and land cover (LULC) mapping in Northern Colombia. The evaluation involves traditional fusion metrics and thematic accuracy indicators, explicitly focusing on optimizing band pairs for SWIR bands. The findings reveal that these methods significantly improve LULC mapping, particularly for dominant classes, and demonstrate that (i) the proposed method enhances image fusion quality over conventional techniques and (ii) the assessment of image fusion quality is more effective when using thematic accuracy metrics in addition to conventional image fusion indicators.

Invasive species can alter the structure and functioning of the invaded ecosystem, but predictions of the impact of invasive species on ecosystem functioning are weak. Invasion is determined by the interplay of invasive species traits, the recipient community, and the environmental context. However, efficient approaches to assess the spatial dimension of functional changes in heterogeneous environments and altered plant-plant interactions are lacking. Based on recent technological progress, we posit a way forward to i) quantify the fine-scale heterogeneity of the environmental context, ii) map the structure and function of the invaded system, iii) trace changes induced by the invader with functional tracers, and iv) integrate the different spatio-temporal information from different scales using (artificial intelligence-based) modelling approaches to better predict invasion impacts. An animated 3-D model visualisation demonstrates how maps of functional tracers reveal spatio-temporal dynamics of invader impacts. Merging fine- to coarse-scale spatially explicit information of functional changes with remotely sensed metrics will open new avenues for detecting invader impacts on ecosystem functioning.

Soil conditions of croplands are a frequent topic of scientific research. In contrast, less is known about large-scale commercial plantations of perennial crops such as oil palm. Oil palm is a globally important tropical commodity crop which contributes to both food and energy security due to its exceptional productivity. However, oil palm crops are associated with short lifecycles and high nutrient demands, which may disproportionately affect soil health. With the goal of exploring baseline soil properties in commercial oil palm plantations, we evaluated data from two large-scale soil surveys carried out in 2014/2015 and 2018/2019 across more than 400 fields located throughout Peninsular Malaysia. We examined variation in field-measured soil quality indicators with a focus on soil organic carbon content at three depths (0–15 cm, 15–30 cm, 30–45 cm) and investigated links with spatial covariates, including plantation age. We found SOC contents to be low (1.6–2

Traditional orchards are distinctive features of cultural landscapes in Central Europe. Despite their high level of ecological importance, they are in decline, and comprehensive spatial data over broad extents, which could enable a trend analysis, are lacking. We analysed traditional orchard maps from 1952 to 1967 and a map from 2010, generated via aerial image interpretation, for the state of Hesse (ca. 21,115 km2), which has the second largest share of traditional orchards in Germany. We aimed to (1) quantify long-term orchard dynamics, (2) compare orchard characteristics in terms of topographical, ecological, and socioeconomic factors, and (3) identify key drivers of orchard loss. We found that the number and area of orchards have clearly decreased across Hesse, with varying local and regional patterns. Further, historically old orchards tended to have a larger area, higher shape complexity, and were located closer to settlements, highways, and neighbouring orchards. In contrast, newly established orchards were often found at higher elevations and on steeper slopes. Finally, the three historical orchard hotspots also experienced the most notable losses driven by different factors, namely the expansion of Artificial Surfaces, Residential Buildings, and Agricultural Land. We highlight the importance of such multitemporal spatial data for a wide range of ecological applications, and we encourage the use of novel technologies to support geospatial analyses in the future.

Terrestrial ecosystems such as coniferous forests in Central Europe are experiencing changes in health status following extreme droughts compounding with severe heat waves. The increasing temporal resolution and spatial coverage of earth observation data offer new opportunities to assess these dynamics. Dense time-series of optical satellite data allow for computing Dynamic Habitat Indices (DHIs), which have been predominantly used in biodiversity studies. However, DHIs cover three aspects of vegetation changes that could be affected by drought: annual productivity, minimum cover, and seasonality. Here, we evaluate the health status of coniferous forests in the federal state of Hesse in Germany over the period 2017-2020 including the severe drought year of 2018 using DHIs based on the Normalized Difference Vegetation Index (NDVI) for drought assessment. To identify the most important variables affecting coniferous forest die-off, a series of environmental variables together with the three DHIs components were used in a logistic regression (LR) model. Each DHI component changed significantly across non-damaged and damaged sites in all years (p-value 0.05). When comparing 2017 to 2019, DHI-based annual productivity decreased and seasonality increased. Most importantly, none of the DHI components had reached pre-drought conditions, which likely indicates a change in ecosystem functioning. We also identified spatially explicit areas highly affected by drought. The LR model revealed that in addition to common environmental parameters related to temperature, precipitation, and elevation, DHI components were the most important factors explaining the health status. Our analysis demonstrates the potential of DHIs to capture the effect of drought events on Central European coniferous forest ecosystems. Since the spaceborne data are available at the global level, this approach can be applied to track the dynamics of ecosystem conditions in other regions, at larger spatial scales, and for other Land Use/Land Cover types.

Agroforestry is a land-use system that combines arable and/or livestock management with tree cultivation, which has been shown to provide a wide range of socio-economic and ecological benefits. It is considered a promising strategy for enhancing resilience of agricultural systems that must remain productive despite increasing environmental and societal pressures. However, agroforestry systems pose a number of challenges for experimental research and scientific hypothesis testing because of their inherent spatiotemporal complexity. We reviewed current approaches to data analysis and sampling strategies of bio-physico-chemical indicators, including crop yield, in European temperate agroforestry systems to examine the existing statistical methods used in agroforestry experiments. We found multilevel models, which are commonly employed in ecology, to be underused and under-described in agroforestry system analysis. This Short Communication together with a companion R script are designed to act as an introduction to multilevel models and to promote their use in agroforestry research.

Processes that drive plant invasions play out across multiple spatial and temporal scales. Understanding individual steps along the introduction-naturalization-invasion continuum and its drivers is crucial for management. This review, targeting the broad audience of invasion scientists, field ecologists and land managers, summarizes the state-of-the-art and potential of remote sensing (RS) in plant invasion science and management. It identifies challenges and research gaps, discusses the discrepancies between technology, science and practice, and suggests ways of addressing some of these issues. Mapping, modelling and predicting invasion processes across scales is a major challenge since they are dynamic and highly complex. Integration of RS data collected at different spatial and temporal scales (“rocking” across scales) has the potential to elucidate the dynamics of invasions and to reveal its drivers, thereby improving the efficiency of control measures. Increasing spatial/temporal resolution of imagery from satellites and drones has much potential to (i) precisely identify even less conspicuous invasive species; (ii) map invasion dynamics; and (iii) provide information on environmental variables and landscape structure at scales fine enough to capture underlying ecological processes. Until now, RS research has focussed primarily on spatio-temporal patterns of plant invasions. Other more challenging topics, such as early monitoring, and revealing the invasion mechanisms and impacts have received less attention. Despite the power of RS technology and recent developments, large discrepancies remain between possibilities and actual implications in research and practical management of invasions. Although recent technological advances, such as powerful algorithms, cloud solutions, and data streams from citizen science, might overcome some limitations, the mutual dialog among field ecologists, managers, invasion scientists and RS specialists remains crucial; our review contributes to such communication.

This study evaluates the sensitivity of the Dynamic Habitat Index (DHI), utilizing multitemporal Normalized Difference Vegetation Index (NDVI) data, to changing environmental conditions across Land Use/Land Cover (LULC) types in a central European landscape (2017-2020). We observed distinct DHI characteristics for all LULC types, and the DHI responded to an extreme drought year in 2018 with no return to pre-drought conditions except for deciduous forests. The DHI also effectively captured spatio-temporal variability of pedo-climatic conditions. Thus, integrated with ancillary geodata, the DHI enhances traditional categorical LULC maps, offering applications in biodiversity and ecosystem research. Such integrated products could serve as valuable tools for decision makers to formulate sustainable land management strategies and contribute to Sustainable Develop Goal indicators related to land degradation, e.g. by identifying deviations from typical, context-specific DHI profiles as a response to disturbance and environmental stress.

Remote sensing is a rapidly advancing technology with a wide range of applications in ecosystem management. This chapter presents a literature review focusing on ecological applications of remote sensing in the context of invasions of Australian Acacia species ('wattles') at the global level. Of ten studied species worldwide, only half, namely A. cyclops, A. dealbata, A. longifolia, A. mearnsii and A. saligna, were studied more than once. Research hotspots are South Africa and Portugal, while large gaps exist elsewhere. The most common study objective is mapping the distribution of invasive wattles using machine learning. Novel approaches using deep learning and citizen science are still largely untapped resources, and comparative approaches to test the transferability of these novel techniques are rare. Coastal dunes and forests are frequently studied, while agroforestry systems, for example, are neglected despite a high interest in using wattles in these habitats. Beyond mapping, remote sensing is used for impact assessments, for example to map effects on nitrogen cycling and water balance, and suggestions have been made on how to include environmental heterogeneity in impact models. However, research in this field is scarce, and further studies as well as conceptual work are required. Other applications include monitoring of invasion after (bio)control, analysing the importance of land use/land cover in the invasion process and modelling invasion dynamics. Phenological information has high potential for mapping wattles, but this possibility needs to be explored further, particularly in combination with environmental impact assessments. The global nature of wattle invasions and recent technological advancements in remote sensing analyses enable both local-scale studies as well as worldwide comparisons to assess context dependency from both a (technical) remote sensing angle and an ecological perspective. We envision that the increased popularity of remote sensing studies on invasive wattles can be projected into the future to fill these research gaps and to inspire remote sensing-based monitoring systems as the backbone of invasion management.

Context Combining field-based assessments with remote-sensing proxies of landscape patterns provides the opportunity to monitor terrestrial ecosystem health status in support of sustainable development goals (SDG). Objectives Linking qualitative field data with quantitative remote-sensing imagery to map terrestrial ecosystem health (SDG15.3.1 “land degradation neutrality”). Methods A field-based approach using the Interpreting Indicators of Rangeland-Health (IIRH) protocol was applied to classify terrestrial ecosystem health status at the watershed level as “healthy”, “at-risk”, and “unhealthy”. Quantitative complex landscape metrics derived from Landsat spaceborne data were used to explore whether similar health statuses can be retrieved on a broader scale. The assignment of terrestrial ecosystem health classes based on field and the remotely sensed metrics were tested using multivariate and cluster analysis methods. Results According to the IIRH assessments, soil surface loss, plant mortality, and invasive species were identified as important indicators of health. According to the quantitative landscape metrics, “healthy” sites had lower amounts of spectral heterogeneity, edge density, and resource leakage. We found a high agreement between health clusters based on field and remote-sensing data (NMI = 0.91) when using a combined approach of DBSCAN and k -means clustering together with non-metric multi-dimensional scaling (NMDS). Conclusions We provide an exemplary workflow on how to combine qualitative field data and quantitative remote-sensing data to assess SDGs indicators related to terrestrial ecosystem health. As we used a standardized method for field assessments together with publicly available satellite data, there is potential to test the generalizability and context-dependency of our approach in other arid and semi-arid rangelands.

Climate change, increasing environmental pollution, continuous loss of biodiversity, and a growing human population with increasing food demand, threaten the functioning of agro-ecosystems and their contribution to people and society. Agroforestry systems promise a number of benefits to enhance nature’s contributions to people. There are a wide range of agroforestry systems implemented representing different levels of establishment across the globe. This range and the long time periods for the establishment of these systems make empirical assessments of impacts on ecosystem functions difficult. In this study we investigate how simulation models can help to assess and predict the role of agroforestry in nature’s contributions. The review of existing models to simulate agroforestry systems reveals that most models predict mainly biomass production and yield. Regulating ecosystem services are mostly considered as a means for the assessment of yield only. Generic agroecosystem models with agroforestry extensions provide a broader scope, but the interaction between trees and crops is often addressed in a simplistic way. The application of existing models for agroforestry systems is particularly hindered by issues related to code structure, licences or availability. Therefore, we call for a community effort to connect existing agroforestry models with ecosystem effect models towards an open-source, multi-effect agroforestry modelling framework.