PEOPLERuth Sonnenschein

de en it

Ruth Sonnenschein

Senior Researcher
Institute for Earth Observation

T +39 0471 055 385

ruth.sonnenschein@eurac.edu

https://orcid.org/0000-0002-9112-0897

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About

Research topics

Earth Observation
Optical data time-series analysis
Vegetation and land cover

Short bio

Ruth Sonnenschein (PhD) is a senior scientist at the Institute for Earth Observation. She graduated in Applied Environmental Sciences and received her PhD from the Humboldt-Universität zu Berlin, Germany, where she analysed land use change and its effects on vegetation trends and fire patterns in Mediterranean rangelands. At Eurac Research her main research activities focus on the drivers and impacts of vegetation and land cover dynamics in mountainous environments using optical satellite data. She has led projects, participated as WP leader in several projects and strongly contributed significantly to project acquisitions in different funding schemes (Horizon Europe, H2020, ESA, ASI, regional funding), many of them in close collaboration with stakeholders.

Scientific Publications

Journal article2025

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

Patriarca E, Stendardi L, Dorigatti E, Sonnenschein R, Ventura B, Claus M, Castelli M, Tufail B,

Ruth Sonnenschein, Bartolomeo Ventura, Claudia Notarnicola, Mariapina Castelli, Michele Claus, Emanuela Patriarca

Mountain grasslands are increasingly threatened by climate change, land abandonment, and overexploitation. Remote sensing is a valuable tool for monitoring these changes through vegetation mapping. However, challenges such as frequent cloud cover, short growing seasons, and limited field data can reduce the accuracy of results. In this study, we evaluated the effectiveness of different remote sensing data for classifying mountain grasslands in the Sciliar-Catinaccio Nature Park, Italy. We compared classification results using a hyperspectral PRISMA image (Sept 29, 2023), multispectral data from a single-date Sentinel-2 image (Sept 25, 2023), and Spectral-Temporal Metrics (STM) derived from a Sentinel-2 time series from 2021 to 2023. Additionally, we assessed the impact on accuracy of combining optical datasets with Synthetic Aperture Radar (SAR) data, including a time series of 2023 Sentinel-1 backscatter and coherence metrics. Using the Recursive Feature Elimination algorithm (RFE), we selected the most relevant features for classification and applied both Random Forest (RF) and Support Vector Machines (SVM) classifiers. SVM outperformed RF, performing better with the limited training data available. SAR data did not significantly enhance classification and was therefore excluded by the RFE algorithm. PRISMA-based classification achieved up to 74 % accuracy, while single-date Sentinel-2 imagery reached 52 %. The use of STM improved classification performance, yielding an overall accuracy of 77 %. The highest accuracy (87 %) was achieved by combining PRISMA and STM features. These findings suggest that while individual optical datasets may not provide optimal classification accuracy, integrating data from multiple optical sensors significantly enhances the mapping of mountain grasslands.

DOI

DOI: 10.1016/j.rsase.2025.101666

Inbook2024

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Notarnicola C, Callegari M, Castelli M, Cuozzo G, De Gregorio L, Greifeneder F, Jacob A, Marin C,

Giovanni Cuozzo, Ruth Sonnenschein, Mattia Callegari, Massimiliano Pittore, Alexander Jacob, Claudia Notarnicola, Mariapina Castelli, Carlo Marin, Marc Zebisch

Mountain regions are biodiversity hotspots, support a great diversity of habitats and species, and provide numerous ecosystem services for lowland areas. At the same time, mountain areas are very sensitive environments because of their peculiarities, and are considered as sentinel of climate changes. In fact, despite large uncertainties (Kohler et al., 2014; Stocker et al., 2013), the temperature increase in mountains doubles the global average (Kohler et al., 2014) and several parameter changes (e.g., albedo, snow cover) intensify with altitude (Notarnicola, 2020; Pepin et al., 2015). The importance of mountains is acknowledged and supported by numerous global initiatives. The United Nations included mountain ecosystem conservation in its Sustainable Development Goals and in 2019 the GEO Mountain called for the identification of Essential Climate Variables (ECVs) and Biodiversity Variables (EBVs) for high altitudes (Adler, 2018a, 2018b; Shahgedanova et al., 2021; Thornton et al., 2021). Moreover, GEO Mountain is aiming at supporting the different UN frameworks and has a relevant role in several policy agendas such as the Intergovernmental SciencePolicy Platform on Biodiversity and Ecosystem Services (IPBES), the Sendai Framework for Disaster Risk Reduction, the Paris Agreement, and assessments of the Intergovernmental Panel on Climate Change (IPCC). With the advent of the Copernicus era, Earth Observation (EO) tools in terms of imagery, platforms, and software offer a unique opportunity to continuously monitor the mountain environment and provide valuable information for its protection and safety. This chapter addresses the current and future challenges of EO observations in the different sectors, going from the cryosphere to water resources and agriculture, to ecosystems, and to gravity-driven natural hazards. Current data and processing platforms are presented to provide an overview of the plethora of information and tools now made available to researchers and practitioners. In this chapter, we selected these sectors based on our experience in several national and international projects, where EO data play a major role. Moreover, the analysis of these sections can provide a connection thread on how the changes in snow and glaciers impact the water resources and then consequently agriculture and ecosystems. It is out of the scope of this chapter to draw a complete picture of all the sectors that can be affected by climate changes.

DOI

DOI: 10.1016/B978-0-12-822095-5.00014-0

Inbook2024

Land Degradation Assessment and Monitoring of Drylands

Stellmes M, Sonnenschein R, Röder A, Udelhoven T, del Barrio G, Hill J

Ruth Sonnenschein

Project: Analysing grassland use intensity in Alpine regions using an EO-based mapping approach

This chapter provides an overview of important Earth observation data and studies to assess land degradation in drylands. Section 2 presents general considerations regarding the assessment and monitoring of land degradation, including suitable indicators as well as sensor systems. The following sections review the state of the art on the assessment of land condition (Section 3), the monitoring of land use/land cover changes to assess land degradation processes (Section 4), and the identification of human-induced drivers of land degradation using integrated concepts (Section 5), whereas Section 6 describes limits and uncertainties regarding dryland observation. This chapter concludes with a summary of land degradation assessment and monitoring by remote sensing techniques (Section 7).

DOI

DOI: 10.1201/9781003541417-6

Journal article2023

Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritage

Sousa JJ, Lin J, Wang Q, Liu G, Fan J, Bai S, Zhao H, Pan H,

Ruth Sonnenschein

Project: Dragon 5: Integration of multi-source Remote Sensing Data to detect and monitoring large and rapid landslides and use of Artificial Intelligence for Cultural Heritage preservation

Remote sensing, particularly satellite-based, can play a valuable role in monitoring areas prone to geohazards. The high spatial and temporal coverage provided by satellite data can be used to reconstruct past events and continuously monitor sensitive areas for potential hazards. This paper presents a range of techniques and methods that were applied for in-depth analysis and utilization of Earth observation data, with a particular emphasis on: (1) detecting mining sub-sidence, where a novel approach is proposed by combining an improved U-Net model and Interferometry Synthetic Aperture Radar (InSAR) technology. The results showed that the Efficient Channel Attention (ECA) U-Net model performed better than the U-Net (baseline) model in terms of Mean Intersection over Union (MIoU) and Intersection over Union (IoU) indicators; (2) monitor-ing water conservancy and hydropower engineering. The Xiaolangdi multipurpose dam complex was monitored using Small BAsline Subsets (SBAS) InSAR method on Sentinel-1 time series data and four small regions with high deformation rates were identified on the slope of the reservoir bank on the north side. The dam body also showed obvious deformation with a velocity exceeding 60 mm/a; (3) the evaluation of the potential of InSAR results to integrate monitoring and warning systems for valuable heritage and architectural preservation. The overall outcome of these methods showed that the use of Artificial Intelligence (AI) techniques in combination with InSAR data leads to more efficient analysis and interpretation, resulting in improved accuracy and prompt identification of potential hazards; and (4) finally, this study also presents a method for detecting landslides in mountainous regions, using optical imagery. The new temporal landslide detection method is evaluated over a 7-year analysis period and unlike conventional bi-temporal change detection methods, this approach does not depend on any prior-knowledge and can potentially detect landslides over extended periods of time such as decades.

DOI Source

DOI: 10.1080/10095020.2023.2234008

Journal article2022

A Globally Relevant Change Taxonomy and Evidence-based Change Framework for Land Monitoring

Lucas MR, German S, Metternicht G, Schmidt RK, Owers C, Prober SM, Richards A, Tetreault-Campbell S,

Ruth Sonnenschein

Project: Improving future ecosystem benefits through earth observations

A globally relevant and standardized taxonomy and framework for consistently describing land cover change based on evidence is presented, which makes use of structured land cover taxonomies and is underpinned by the Driver-Pressure-State-Impact-Response (DPSIR) framework. The Global Change Taxonomy currently lists 246 classes based on the notation ‘impact (pressure)’, with this encompassing the consequence of observed change and associated reason(s), and uses scale-independent terms that factor in time. Evidence for different impacts is gathered through temporal comparison (e.g., days, decades apart) of land cover classes constructed and described from Environmental Descriptors (EDs; state indicators) with pre-defined measurement units (e.g., m, %) or categories (e.g., species type). Evidence for pressures, whether abiotic, biotic or human-influenced, is similarly accumulated, but EDs often differ from those used to determine impacts. Each impact and pressure term is defined separately, allowing flexible combination into ‘impact (pressure)’ categories, and all are listed in an openly accessible glossary to ensure consistent use and common understanding. The taxonomy and framework are globally relevant and can reference EDs quantified on the ground, retrieved/classified remotely (from ground-based, airborne or spaceborne sensors) or predicted through modelling. By providing capacity to more consistently describe change processes – including land degradation, desertification and ecosystem restoration - the overall framework addresses a wide and diverse range of local to international needs including those relevant to policy, socioeconomics and land management. Actions in response to impacts and pressures and monitoring towards targets are also supported to assist future planning, including impact mitigation actions.

DOI Source

DOI: 10.1111/gcb.16346

Journal article2022

Comparing PlanetScope and Sentinel-2 imagery for mapping mountain pines in the Sarntal Alps, Italy

Rösch M, Sonnenschein R, Buchelt S, Ullmann T

Ruth Sonnenschein

The mountain pine (Pinus mugo ssp. Mugo Turra) is an important component of the alpine treeline ecotone and fulfills numerous ecosystem functions. To understand and quantify the impacts of increasing logging activities and climatic changes in the European Alps, accurate information on the occurrence and distribution of mountain pine stands is needed. While Earth observation provides up-to-date information on land cover, space-borne mapping of mountain pines is challenging as different coniferous species are spectrally similar, and small-structured patches may remain undetected due to the sensor’s spatial resolution. This study uses multi-temporal optical imagery from PlanetScope (3 m) and Sentinel-2 (10 m) and combines them with additional features (e.g., textural statistics (homogeneity, contrast, entropy, spatial mean and spatial variance) from gray level co-occurrence matrix (GLCM), topographic features (elevation, slope and aspect) and canopy height information) to overcome the present challenges in mapping mountain pine stands. Specifically, we assessed the influence of spatial resolution and feature space composition including the GLCM window size for textural features. The study site is covering the Sarntal Alps, Italy, a region known for large stands of mountain pine. Our results show that mountain pines can be accurately mapped (PlanetScope (90.96%) and Sentinel-2 (90.65%)) by combining all features. In general, Sentinel-2 can achieve comparable results to PlanetScope independent of the feature set composition, despite the lower spatial resolution. In particular, the inclusion of textural features improved the accuracy by +8% (PlanetScope) and +3% (Sentinel-2), whereas accuracy improvements of topographic features and canopy height were low. The derived map of mountain pines in the Sarntal Alps supports local forest management to monitor and assess recent and ongoing anthropogenic and climatic changes at the treeline. Furthermore, our study highlights the importance of freely available Sentinel-2 data and image-derived textural features to accurately map mountain pines in Alpine environments.

DOI Source

DOI: 10.3390/rs14133190

Journal article2021

Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques

Sousa JJ, Liu G, Fan J, Perski Z, Steger S, Bai S, Wei L, Salvi S,

Ruth Sonnenschein

Geological disasters are responsible for the loss of human lives and for significant economic and financial damage every year. Considering that these disasters may occur anywhere—both in remote and/or in highly populated areas—and anytime, continuously monitoring areas known to be more prone to geohazards can help to determine preventive or alert actions to safeguard human life, property and businesses. Remote sensing technology—especially satellite-based—can be of help due to its high spatial and temporal coverage. Indeed, data acquired from the most recent satellite missions is considered suitable for a detailed reconstruction of past events but also to continuously monitor sensitive areas on the lookout for potential geohazards. This work aims to apply different techniques and methods for extensive exploitation and analysis of remote sensing data, with special emphasis given to landslide hazard, risk management and disaster prevention. Multi-temporal SAR (Synthetic Aperture Radar) interferometry, SAR tomography, high-resolution image matching and data modelling are used to map out landslides and other geohazards and to also monitor possible hazardous geological activity, addressing different study areas: (i) surface deformation of mountain slopes and glaciers; (ii) land surface displacement; and (iii) subsidence, landslides and ground fissure. Results from both the processing and analysis of a dataset of earth observation (EO) multi-source data support the conclusion that geohazards can be identified, studied and monitored in an effective way using new techniques applied to multi-source EO data. As future work, the aim is threefold: extend this study to sensitive areas located in different countries; monitor structures that have strategic, cultural and/or economical relevance; and resort to artificial intelligence (AI) techniques to be able to analyse the huge amount of data generated by satellite missions and extract useful information in due course.

DOI Source

DOI: 10.3390/rs13214269

Journal article2020

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Hauffe HC, Heym M, Pretzsch H, Tonon G

Ruth Sonnenschein, Marc Zebisch

An indirect method for estimating biodiversity from Earth observations is the Spectral Variation Hypothesis (SVH). SVH states that the higher the spatial variability of the spectral response of an optical remotely sensed image, the higher the number of available ecological niches and hence, the higher the diversity of tree species in the considered area. Here for the first time we apply the concept of the SVH to Light Detection and Ranging (LiDAR) data to understand the relationship between the height heterogeneity (HH) of a forest and its tree species diversity, a concept we have named the ‘Height Variation Hypothesis’ (HVH). We tested HVH in two different European forest types: a coniferous mountain forest in the eastern Italian Alps and a mixed temperate forest in southern Germany. We used the heterogeneity index Rao’s Q to estimate HH using a Canopy Height Model (CHM) at different resolutions derived from LiDAR data, and linear regression models and relation analysis to assess the relationships between HH and three species diversity indices derived from in situ collected data: Shannon’s H, Simpson’s S and species richness. The relationships were calculated for all plots in both study areas, and separately for plots with a defined Canopy Closure (CC > 70%, CC > 80%, CC > 90%) to understand the effect of forest density on the relationship between HH and tree species diversity. Our results showed that HH is related to the tree species diversity of the forest ecosystems reaching (in the case of Shannon’s H) values of R² = 0.63 for the coniferous mountain forest and R² = 0.56 for the mixed temperate forest, particularly when calculated with a CHM resolution of 2.5 m. The associations also increased with increasing canopy closure suggesting that HVH is scale and forest density dependent. Our results also underlined that the abundance-based diversity measures are more highly correlated with HH than with species richness. Finally, our findings suggest that the HVH is a valuable tool for assessing tree species diversity in forest ecosystems, and could also be useful for overall biodiversity estimates.

DOI

DOI: 10.1016/j.ecolind.2020.106520

Journal article2020

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Dittrich A, Roilo S, Sonnenschein R, Cerrato C, Ewald M, Viterbi R, Cord AE

Ruth Sonnenschein

Mountain ecosystems are biodiversity hotspots that are increasingly threatened by climate and land use/land cover changes. Long-term biodiversity monitoring programs provide unique insights into resulting adverse impacts on plant and animal species distribution. Species distribution models (SDMs) in combination with satellite remote sensing (SRS) data offer the opportunity to analyze shifts of species distributions in response to these changes in a spatially explicit way. Here, we predicted the presence probability of three different rove beetles in a mountainous protected area (Gran Paradiso National Park, GPNP) using environmental variables derived from Landsat and Aster Global Digital Elevation Model data and an ensemble modelling approach based on five different model algorithms (maximum entropy, random forest, generalized boosting models, generalized additive models, and generalized linear models). The objectives of the study were (1) to evaluate the potential of SRS data for predicting the presence of species dependent on local-scale environmental parameters at two different time periods, (2) to analyze shifts in species distributions between the years, and (3) to identify the most important species-specific SRS predictor variables. All ensemble models showed area under curve (AUC) of the receiver operating characteristics values above 0.7 and true skills statistics (TSS) values above 0.4, highlighting the great potential of SRS data. While only a small proportion of the total area was predicted as highly suitable for each species, our results suggest an increase of suitable habitat over time for the species Platydracus stercorarius and Ocypus ophthalmicus, and an opposite trend for Dinothenarus fossor. Vegetation cover was the most important predictor variable in the majority of the SDMs across all three study species. To better account for intra- and inter-annual variability of population dynamics as well as environmental conditions, a continuation of the monitoring program in GPNP as well as the employment of SRS with higher spatial and temporal resolution is recommended.

DOI Source

DOI: 10.3390/rs12010080

Journal article2019

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Marcantonio M, Ricotta C, Tonon G

Ruth Sonnenschein, Marc Zebisch

Forests cover about 30% of the Earth surface, they are among the most biodiverse terrestrial ecosystems and represent the bulk of many ecological processes and services. The assessment of biodiversity is an important and essential goal to achieve but it can results difficult, time consuming and expensive when based on field data. Remote sensing covers large areas and provides consistent quality and standardized data, which can be used to estimate species diversity. One method to estimate species diversity from remote sensing data is based on the Spectral Variation Hypothesis (SVH), which assumes that the higher the spectral variation of an image, the higher the environmental heterogeneity and the species diversity of the considered area. SVH has been tested using different spectral heterogeneity (SH) indices and measures, recently the Rao's Q index has been proposed as a new spectral variation measure to be applied to remote sensing data. In this paper, we tested the SVH in an alpine coniferous forest to estimate tree species diversity. We evaluated the performance of the Rao's Q index and compared it with another widely used SH index, the Coefficient of Variation (CV), validating them against values of Shannon's H (used as species diversity index) derived from in-situcollected data. A NDVI time-series (for 2016 and 2017) derived from the Sentinel-2A and 2B and Landsat 8 OLI satellites has been used to test the effect of the spatial grain of both the sensors and to understand the seasonality of the SVH. The results showed that the SVH is season and sensor dependent. For both years and satellites, the relation between Rao's Q and field data reached the highest R² between June and July, decreasing towards winter and spring similarly to the NDVI time-series. This relationship could be given because, when NDVI reaches its highest values, it is able to capture small variation in reflectance of different leaf traits typical of specific trees. The relation between field and spectral diversity reached a value of R² = 0.70 (2017) and R² = 0.48 (2016) for Sentinel-2 and of R² = 0.42 (2017) and R² = 0.47 (2016) for Landsat 8. CV showed the same NDVI temporal tendency. However, the relation between field-derived Shannon's H and CV was on average lower than that we found for Rao's Q. This research underlined the goodness of the Rao's Q index, the relevance of the NDVI in the study of the SVH and the importance of the multi-temporal approach.

DOI

DOI: 10.1016/j.ecoinf.2019.04.001

Conference proceedings article2017

Assessing hypertemporal Sentinel-1 coherence maps for land cover monitoring

Vicente-Guijalba F, Duro J, Notarnicola C, Jacob A, Sonnenschein R, López-Martínez C, Dabrowska-Zielinska K, Ziólkowski D,

Ruth Sonnenschein, Claudia Notarnicola, Alexander Jacob

This paper presents the main concepts and the initial analysis of the ESA SEOM project SInCohMap “EXPLOITATION OF SENTINEL-1 INTERFEROMETRIC COHERENCE FOR LAND COVER AND VEGETATION MAPPING”. The project evaluates the performance of using the interferometric coherence of S-1 time series for land cover and vegetation mapping. One of the main objectives of the project is to quantify the impact in using S-1 InSAR (Interferometric Synthetic Aperture Radar) data relative to traditional land cover and vegetation mapping using optical data (especially Sentinel-2, hereafter named S-2) or SAR-based (Synthetic Aperture Radar) approaches. In this framework, a Round-Robin consultation is used to assess the performances of the different classification methodologies.

DOI Source

DOI: 10.1109/Multi-Temp.2017.8035240

Journal article2017

Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward

Pettorelli N, Schulte to Buhne H, Tulloch A, Dubois G, Macinnis-Ng C, Queiros AM, Keith DA, Wegmann M,

Ruth Sonnenschein

Project: Building the European Biodiversity Observation Network, Improving future ecosystem benefits through earth observations

Societal, economic and scientific interests in knowing where biodiversity is, how it is faring and what can be done to efficiently mitigate further biodiversity loss and the associated loss of ecosystem services are at an all-time high. So far, however, biodiversity monitoring has primarily focused on structural and compositional features of ecosystems despite growing evidence that ecosystem functions are key to elucidating the mechanisms through which biological diversity generates services to humanity. This monitoring gap can be traced to the current lack of consensus on what exactly ecosystem functions are and how to track them at scales beyond the site level. This contribution aims to advance the development of a global biodiversity monitoring strategy by proposing the adoption of a set of definitions and a typology for ecosystem functions, and reviewing current opportunities and potential limitations for satellite remote sensing technology to support the monitoring of ecosystem functions worldwide. By clearly defining ecosystem processes, functions and services and their interrelationships, we provide a framework to improve communication between ecologists, land and marine managers, remote sensing specialists and policy makers, thereby addressing a major barrier in the field.

DOI Source

DOI: 10.1002/rse2.59

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Journal article2019

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

Torresani M, Rocchini D, Sonnenschein R, Zebisch M, Marcantonio M, Ricotta C, Tonon G

Ruth Sonnenschein, Marc Zebisch

DOI

DOI: 10.1016/j.ecoinf.2019.04.001

Ruth Sonnenschein

About

Scientific Publications

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Land Degradation Assessment and Monitoring of Drylands

Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritage

A Globally Relevant Change Taxonomy and Evidence-based Change Framework for Land Monitoring

Comparing PlanetScope and Sentinel-2 imagery for mapping mountain pines in the Sarntal Alps, Italy

Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

Assessing hypertemporal Sentinel-1 coherence maps for land cover monitoring

Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward

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Scientific Publications

Enhancing mountain grassland mapping: A comparative study with PRISMA hyperspectral, multispectral, and SAR data

Earth Observation tools to monitor and protect mountain environment: current challenges and future perspectives

Land Degradation Assessment and Monitoring of Drylands

Using machine learning and satellite data from multiple sources to analyze mining, water management, and preservation of cultural heritage

A Globally Relevant Change Taxonomy and Evidence-based Change Framework for Land Monitoring

Comparing PlanetScope and Sentinel-2 imagery for mapping mountain pines in the Sarntal Alps, Italy

Geohazards Monitoring and Assessment Using Multi-Source Earth Observation Techniques

Height variation hypothesis: A new approach for estimating forest species diversity with CHM LiDAR data

Modelling Distributions of Rove Beetles in Mountainous Areas Using Remote Sensing Data

Estimating tree species diversity from space in an alpine conifer forest: The Rao's Q diversity index meets the spectral variation hypothesis

Assessing hypertemporal Sentinel-1 coherence maps for land cover monitoring

Satellite remote sensing of ecosystem functions: opportunities, challenges and way forward