Dams are traditionally monitored by in-situ geodetic measurements, such as pendulum, trigonometry, and GNSS. However, due to factors like topography and practicability, many dams are only monitored by trigonometric measurements, which are carried out only once or twice a year, due to high cost and time investment. Persistent Scatterer Interferometry (PSI) offers the possibility of monitoring such infrastructures at more regular intervals. In particular, existing ground motion services relying on the PSI technique provide deformation time series for long time intervals, allowing for trend and anomaly detection in the deformation patterns. This study presents a first assessment of the German Ground Motion Service (BBD) for the monitoring of a gravity dam in the Free State of Thuringia, Germany. Despite the suboptimal dam orientation for the PSI technique, BBD data exhibits good agreement with in-situ measurements (achieving R2values above 0.5 and up to 0.8 for the descending direction). These results confirm the utility of such services for infrastructure monitoring. However, it is imperative to account for dam orientation and the update frequency of these services in operational practices.

Coastal wetlands are critical from an ecological, hydrological, and biodiversity point of view [1]. Today, these areas are under threat and have been declining every year since they were first studied [2]. Satellite images are an invaluable tool for understanding wetlands. Thanks to the revisit capability of satellites, coastal wetlands can be mapped in real time, allowing us to understand how they are changing. This work aims to map these ecosystems as accurately as possible. In order to achieve this, two types of sensors were used: a Sentinel-2 time series, a SPOT6/7 image, and altimetric data (RGE Alti©). Two automated learning algorithms were used: random forest (RF) and convolutional neural networks (CNN). Two methods were also used: a pixel approach and an object approach. The results show that the synergy of Sentinel-2 and SPOT with the contribution of indices and an object method works best, with an overall accuracy of 0.90 compared to 0.78 for a SPOT-6 pixel-by-pixel approach.

Traditional methods for monitoring embankment dams and gravity dams typically rely on on-site geodetic measurements, such as pendulum devices, trigonometry, and Global Navigation Satellite System (GNSS) technology. However, the feasibility of these instruments is often constrained by factors such as cost and accessibility. In such cases, satellite remote sensing, particularly Persistent Scatterer Interferometry (PSI), can offer a valuable alternative. Despite its cost-effectiveness in measuring deformations, PSI is not universally applicable to all dams due to various geometric factors. To address this limitation, the CR-Index provides insights into the suitability of PSI for monitoring specific structures. This study focuses on the development and application of the CR-Index for embankment dams and gravity dams in Western Germany. Based on the results, well-suited dams were equipped with innovative electronic corner reflectors to enhance their visibility in radar imagery and enable a more effective PSI analysis.

Storage of interception water in the canopy (Sc) heavily affects measurements of vegetation optical depth (VOD) from rain, dew and fog, impeding the direct retrieval of tree physiological parameters such as biomass and plant water content. This study presents a time series decomposition of VOD from Global Navigation Satellite System-Transmissometry (GNSS-T) into biomass, plant moisture content (Mg) and Sc. The experiment was conducted at eddy covariance (EC) towers in two temperate forest types in Germany, over the entire vegetation period of 2023 and under fairly wet conditions. Sc-values were 1.5 times (needleleaf) to two times (broadleaf) higher than the average diurnal Mgcycle, allowing partitioning of interception water storage from plant water. Furthermore, we found indications that Scmaxima did not linearly increase with precipitation, suggesting sensitivity of VOD to saturation effects when canopy interception storage reaches a maximum during strong precipitation events. Results indicate the sensitivity of VOD from GNSS-T to canopy wetness. This allows partitioning of canopy water storage from other VOD components and improves the usefulness of VOD as a remote sensing metric for forest canopy water relations. Moreover, it opens pathways to quantify Scand evaporation fluxes independently from EC measurements and field experiments.

Deformation monitoring is a crucial task for dam operators, particularly given the rise in extreme weather events associated with climate change. Further, quantifying the expected deformations of a dam is a central part of this endeavor. Current methods rely on in situ data (i.e., water level and temperature) to predict the expected deformations of a dam (typically represented by plumb or trigonometric measurements). However, not all dams are equipped with extensive measurement techniques, resulting in infrequent monitoring. Persistent Scatterer Interferometry (PSI) can overcome this limitation, enabling an alternative monitoring scheme for such infrastructures. This study introduces a novel monitoring approach to quantify expected deformations of gravity dams in Germany by integrating the PSI technique with in situ data. Further, it proposes a methodology to find proper statistical representations in a data-driven manner, which extends established statistical approaches. The approach demonstrates plausible deformation patterns as well as accurate predictions for validation data (mean absolute error=1.81 mm), confirming the benefits of the proposed method.

The Sentinel-1 (S-1) microwave measurements pose a unique opportunity for estimating forest parameters from satellite time series with increased data availability from overlapping orbits. Leveraging data from different orbits comes with varying viewing geometries and acquisition schedules, which can be considered in artificial neural networks.We adapt a transformer architecture for mapping the full S-1 data against median forest height values. By doing so, we propose per-orbit temporal encoders to handle different acquisition times by position, missing data by attention masking, and the addition of viewing geometry context.We show that our adjustments improve performance, with a greater impact of the additional data and a slight improvement in the masking. By optimizing the hyperparameters, our proposed method achieves a preliminary RMSE of 5.9m and an rRMSE of 35% in predicting the per-pixel vertical median forest height.

In this work, an attempt was made to determine trends in backscatter variation due to row orientation for four crop types: winter wheat, spring barley, rapeseed, and corn. An averaged backscatter from the fields of three study areas with different geographic locations was considered over 5 years (2017-2021) by considering ascending/descending orbits and different phenological groups.The results of this study indicate that VV polarization is higher when the SAR signal is directed perpendicular or parallel to the rows of winter wheat, barley, and canola (at a 0°, 90° aspect angle) and lower at 45° (90° scale). For a 10° difference in aspect angle, variation in VV was observed at one standardized unit for spring barley and winter wheat.The trend has been observed for all considered study areas, thus validating our observations.

This work serves as demonstrator, how low/medium quality UAV data can be integrated for agricultural pattern classification with convolutional neural network (CNN). The study also illustrates the potential sources of error in spectral and texture information that arise during image acquisition and processing, which can be improved during image processing and correct choice of mosaicking parameters.CNN classification of six agricultural patterns of interest (weed infested area, dry and vital crop area, dry and vital lodged crop area, bare soil area) of corn, rapeseed, winter wheat and spring barley fields. The performance of the classification is assessed on images with different units (reflectance and DN) and images with different sun lightening conditions, shadows and ‘blur’ effects (moderate/low quality data).

This study presents a comparison between satellite-based vegetation optical depth (VOD) from multi-frequency radiometry (X-, C- and L-band), VOD-derived relative water content (RWC) and auxiliary data (e.g., evapotranspiration and soil moisture), which are investigated for their sensitivity to water status of tree canopies under dry and wet conditions for a temperate forest in Thuringia, Central Germany. For this, we estimated RWC directly from VOD normalization assuming no major changes in vegetation biomass or plant structure during the study period (2015-2019).Our results show that RWC seasonalities are aligned for all investigated frequencies showing its maximum in early summer when leaves and twigs of the top and low canopy are particularly wet and photosynthetically active. Investigating drought versus non-drought years, we observed that X-band RWC is the one better capturing drought status by exhibiting low values in the extreme drought year 2018 compared to the wet year 2017 while L-band RWC reflects the ecological memory from the extreme drought conditions in 2018 in year 2019 estimates.

Microwave scattering from forests generates pixel geolocation shifts in Synthetic aperture radar (SAR) data that require an adequate representation within digital elevation models (DEM) for preprocessing. We analyze the impact of DEM properties on the radiometry and geolocation of radiometric terrain corrected (RTC) Copernicus Sentinel-1 imagery of forests to improve consistency in backscatter intensities for time series analyses. To account for the penetration depth of the C-Band sensor, we approximate the structure of stands in a temperate deciduous forest using height percentiles from Aerial Laser Scanning (ALS) point clouds in the Hainich National Park, Germany. Comparing the RTC results obtained using DEMs of SRTM, Copernicus, and ALS DEMs, the latter reduces topographically induced errors, resulting in visibly smaller effects from topography and spatially shifted information. Based on the P50 ALS vegetation elevation, results show homogeneous intensities within the same orbit and reduce variance from 2.4 dB2to 1.2 dB2in the difference of mid-range data from ascending and descending azimuth directions. Over forest, we observe lower intensities on sensor-facing and increased intensities on away-facing slopes and correlations with the illuminated pixel area (IPA) and local incidence angle. We reduce this bias with linear regressions of intensity on IPA. ALS DEMs in RTC and the proposed regression correction increase the consistency of images across orbits, measured by the inter-orbit range, throughout the selected year at our study site. We suggest the proposed method applies to other areas, requiring further testing under different forest types and topography.

This study explores the potential of Sentinel-1 time series for crop monitoring. In this study an attempt was made to use Copernicus Sentinel-1 time-series (backscatter & coherence) to map differences in crop development (growth) within fields, to define the main factors affecting radar backscatter, and to learn about plant-relevant parameters with the aim to fill gaps in Copernicus Sentinel-2 time-series with relevant spatial information for crop monitoring.

The project “South African Land Degradation Monitor” (SALDi) contributes to the German-South African Programme “Science Partnerships for the Adaptation to Complex Earth System Processes in the Region of Southern Africa” (SPACES) by addressing the dynamics and functioning of multi-use landscapes with respect to land use, land cover change, water fluxes, and implications for habitats and ecosystem services. We are utilizing time series information from Sentinel-1 and Sentinel-2 of the European Space Agency (ESA) Copernicus program. The synergetic combination of both satellites hold large potential as both systems measure different properties of the earth's surface. This study analyses the feasibility of detecting breakpoints and land surface changes over time within the six SALDi study sites. The overarching aim is to link the findings of the time series analysis to different land degradation phenomena.

Digital Elevation Models (DEM) represent fundamental data for a wide range of Earth surface process studies. In many countries high resolution DEM are difficult to acquire. Therefore, global data sets are often used for local or regional scale assessments. Here we validate eight different global scale DEM based on differential GNSS ground measurements acquired in the Lowveld savanna of Kruger National Park, South Africa. The global scale DEM include the three TanDEM-X DEM (~12 m, ~30 m, ~90 m posting), the two SRTM DEM (~30 m, ~90 m posting), the ASTER GDEM Vers. 3, the ALOS AW3D30 DEM (~30 m posting) as well as the MERIT DEM (~90 m posting).

This study explores the potential of Landsat-8 and Sentinel-2 imagery for annual grass biomass mapping in savannas. To this end, three wet season image mosaics based on Landsat-8 and Sentinel-2 were created for 2016, 2017 and 2018 over Kruger National Park (KNP), South Africa. For the purpose of calibration and validation, use was made of in situ fuel biomass values measured as part of the yearly veld condition assessment (VCA) in KNP. The satellite and reference data were fed into a random forests machine learning approach to make park-wide predictions of grass biomass and to assess the performance of Landsat-8 and Sentinel-2 predictors (i.e., surface reflectance and the normalized difference vegetation index, NDVI). Examples of the data sets used and biomass maps produced are provided together with the obtained error statistics. The latter suggest that wet season NDVI mosaics from Landsat-8 and Sentinel-2 data enable the creation of fairly reliable, annual maps of fuel biomass for KNP. These new biomass estimates represent a slight improvement over recent mapping efforts based on Sentinel-1 data [1].

In this paper, we propose a methodology for upscaling fractional vegetation cover (FVC) estimates derived from unmanned aerial vehicles (UAVs) to a larger area using freely available Sentinel-1/2 and Landsat-8 satellite data in the semi-arid Nama-Karoo biome of South Africa. To the best of our knowledge, such an approach is still lacking yet critical for understanding human-environment interactions, degradation, and the impacts of climate change in this vulnerable dryland ecosystem. The proposed approach utilizes ultra-high spatial resolution UAV imagery (i.e. <; 5 cm) to develop a high quality FVC product. The resulting product is then used to upscale and validate satellite-based estimates of FVC. The rationale for upscaling UAV estimates to the satellite-scale is not only to cover larger areas but also to exploit historical satellite time-series data, in an attempt to understand past trends and vegetation dynamics in the Nama Karoo-Grassland ecotone. The proposed method is expected to produce the first-ever high-resolution continuous maps of FVC and its changes in the above-mentioned ecosystem. Such information is of vital importance as it could help decision makers to gain a better understanding of the extent at which mechanisms such as bush encroachment, grassland expansion, or degradation are occurring in dryland ecosystems.

This study explores the potential of Sentinel-1 time series to map field heterogeneities as an additional information into optical vegetation index products into operational farming practices. The presentation explains the data processing strategies as well as their analysis and validation to calculate the crop parameters. First results on the influence of different spatio-temporal speckle filters on the time series evaluation and the derivation of a first radar vegetation index are presented as well as the characteristics of field heterogeneities that were mapped with own drone surveys.

The overarching goal of SALDi (South African Land Degradation MonItor) is to implement novel, adaptive, and sustainable tools for assessing land degradation in multi-use landscapes in South Africa. This presentation demonstrates results from hyper-temporal Sentinel-1 and -2 timeseries concerning woody cover mapping in complex savanna systems, invasive slangbos bush encroachment in grassland areas and regional soil moisture retrievals. Validation has been performed by cross-comparisons, field trips and permanently installed soil moisture networks.

This study explores the potential of multi-temporal Sentinel-1 data for herbaceous biomass mapping in savanna ecosystems. To this end, 28 Sentinel-1 backscatter intensity images are employed that have been acquired over Kruger National Park (KNP), South Africa. The time series spans one year (May 2015 to May 2016) and thus covers an entire seasonal cycle, including dry and wet season. For calibration and validation purposes, use is made of in situ fuel biomass values measured as part of the 2016 veld condition assessment (VCA) in KNP. From a methodological perspective, the satellite and reference data are fed into a random forests machine learning approach to make park-wide predictions of herbaceous biomass and to assess modeling performance based on Sentinel-1 imagery. A wall-to-wall herbaceous biomass map of KNP is presented for the period of investigation (i.e., 2015/2016). It has a spatial resolution of 20 m and is the most detailed product available for the park to date. According to the results of a ten-fold cross validation, the generated map is fairly reliable but still leaves room for improvement. This statement is supported by a coefficient of determination (R2) of 0.64, a root mean square error (RMSE) of 402.9 kg/ha, and a relative RMSE of 40.9%.

Urban structure types (USTs) are a concept in urban ecology to divide cities into units of homogenous environmental conditions. They provide a useful means to conceive efficient strategies for sustainable urban development. Cutting edge remote sensing data and methods allow for the automation of the UST classification process. This paper aims at demonstrating the robustness of a recently developed approach for area-wide mapping of 13 USTs. To this end, the experimental setup of this work includes three major cities in Germany, different sets of high-resolution multi-source data as well as statistical measures to enable the assessment of methodological accuracy and transferability. The results of this study emphasize the suitability of the presented approach with regard to classification accuracy, workflow automation, and operational readiness for future UST mapping and monitoring tasks.

Estimation of forest aboveground biomass is crucial for carbon accounting and forest management. In this paper we present the estimation of forest above ground biomass derived from a combination of Sentinel-1 and Sentinel-2 data using the Random Forest Regression approach. The research was conducted over a temperate forest in Poland, where the forest biomass is ranging from 1 to 300 t/ha. The results revealed a saturation effect around 200 t/ha. The biomass model tends to overestimate small values of aboveground biomass and underestimate larger values of biomass (greater than 250 t/ha). The overall RMSE value is equal to 60 t/ha, however it varies for biomass ranges. The independent validation performed at the forest stand level showed the best agreement for biomass ranging for 100-200 t/ha. This research was conducted as part of the ESA funded DUE GlobBiomass project.

In this work we estimated Mexican forest aboveground biomass (AGB) using Synthetic Aperture Radar (SAR) and optical remote sensing data and two different reference data: (1) extensive national forest inventory (NFl) and (2) airborne Light Detection and Ranging (LiDAR) data. For the second modelling scenario, we applied a two-stage upscaling approach: firstly AGB for the LiDAR transects were estimated and used then to calibrate satellite imagery. Furthermore, we propagated uncertainties from field measurements to LiDAR-derived AGB and to the national wall-to-wall forest AGB maps. The estimated AGB maps (NFI- and LiDAR-calibrated) showed similar goodness-of-fit statistics compared to the independent validation dataset. However, the AGB map based on two-stage up-scaling method (i.e., from field AGB to LiDAR and from LiDAR-AGB to satellite imagery) showed much higher uncertainties compared to the traditional field to satellite up-scaling.

Today, very dense synthetic aperture radar (SAR) time series are available through the framework of the European Copernicus Programme. These time series require innovative processing and preprocessing approaches including novel speckle suppression algorithms. Here we propose an image transform for hypertemporal SAR image time stacks. This proposed image transform relies on the temporal patterns only, and therefore fully preserves the spatial resolution. Specifically, we explore the potential of empirical mode decomposition (EMD), a data-driven approach to decompose the temporal signal into components of different frequencies. Based on the assumption that the high-frequency components are corresponding to speckle, these effects can be isolated and removed. We assessed the speckle filtering performance of the transform using hypertemporal Sentinel-1 data acquired over central Germany comprising 53 scenes. We investigated speckle suppression, ratio images, and edge preservation. For the latter, a novel approach was developed. Our findings suggest that EMD features speckle suppression capabilities similar to that of the Quegan filter while preserving the original image resolution.

Earth Observation time-series data are valuable information to monitor the change of the environment. But access to data and the execution of analysis tools are often time-consuming tasks and data processing knowledge is required. In order to allow user-friendly applications to be built, tools are needed to simplify the access to data archives and the analysis of such time-series data. In this work, web services for accessing and analyzing MODIS and Landsat time-series data have been developed based on the Web Processing Service specification of the Open Geospatial Consortium and made available within the Earth Observation Monitor framework. Algorithms developed to analyze vegetation changes are provided as web-based processing services in connection to the prior developed access services as well. Using the services developed, users only need to provide the geometry and the name of the dataset the user is interested in; any processing is done by the web service.

This contribution presents the results of a comparison between a TanDEM-X derived Digital Surface Model (DSM) and a high resolution, airborne LIDAR-based Digital Terrain Model (DTM) for a catchment characterized by agricultural areas and forest in Germany. It provides evidence for the overall high quality of the TanDEM-X data and guidelines for uncertainty assessment of TanDEM-X derived DSM in areas where high resolution and high quality data is not available.

The paper presents advances in regional mapping of forest growing stock volume (GSV, unit: m3/ha) using multitemporal ALOS PALSAR images. For this, an approach based on the Water Cloud Model has been used. Fine Beam Dual (FBD) and Wide Beam (WB) mode backscatter data acquired during 2010 were inverted to GSV; the individual GSV estimates were then combined to form an improved estimate. Examples of regional mapping for the boreal, temperate and tropical zone are here presented. Estimates were obtained at 25 m resolution using FBD data only and at 75 m combining multi-looked FBD estimates and WB estimates. The inclusion of multi-temporal WB data served to improve the spatial characterization of GSV. At pixel level, the discrepancy with respect to other estimates from Earth Observation data was large (relative RMSE ~ 90%). Aggregation to lower resolution (e.g., 1 km) increased the agreement and a relative RMSE mostly below 30% was obtained.

Urban structure types (UST) are an initial interest and basic instrument for monitoring, controlling and modeling tasks of urban planners and decision makers during ongoing urbanization processes. This study focuses on a method to classify UST from land cover (LC) objects, which were derived from high resolution satellite images. The topology of urban LC objects is analyzed by implementing neighborhood LC-graphs. Various graph measures are examined by their potential to distinguish between different UST, using the machine learning classifier random forest. Additionally the influence of different parameter settings of the random forest model, the reduction of training samples, and the graph measure importance is analyzed. An independent test set is classified and validated, achieving an overall accuracy of 87%. It was found that the height of the building with the highest node degree has a strong impact on the classification result.

This paper proposes a method for quality assessment of synthetic aperture radar (SAR) scenes to facilitate selection of a master scene for persistent scatterer interferometry (PSI). Parameters like perpendicular and temporal baseline or weather conditions at acquisition time are evaluated using a scoring system. By defining scoring rules every available information can be used for rating image quality thus helping to make a quick decision to find the best possible scene in a large data stack. The method can be further improved by weighting the single results as needed. The approach is successfully tested on an exemplary data stack containing 57 SAR images of ERS.

Ongoing urbanization processes have increased the demand for monitoring, controlling, and modeling services, with urban structure types as an initial interest. While urban land cover (LC) can be derived directly from high-resolution satellite images, urban land use (LU) is achieved through analyzing a combination of structural, functional, spatial, morphological, and topological attributes of the various LC classes. The objective of this letter is to distinguish urban LU classes on the basis of distances between buildings incorporated into a graph-based concept. The method was developed using cadastral data (ALK) for the German city of Rostock, then applied to the LC building objects derived from Quickbird data. Building distribution was examined and distances between buildings were used as an attribute for graph generation. Two graph measures (beta index, clustering coefficient) were analyzed resulting in two groups of LU categories. Transferability to a different urban area was tested without adaptions. Similar building distribution and LC extraction quality were found to be crucial for transferability tests. Distances between buildings are an important property for deriving LU classes, but should be accompanied by additional LC attributes to improve LU separability.

In this paper, we present some investigations based on X-band interferometric coherence for retrieving forest Growing Stock Volume (GSV). Exanimating first temporal decorrelation, a comparison indicated total (γ=0.1-0.2), (γ=0.1-0.4) and low (γ=0.4-0.9) temporal decorrelation for TSX (11 days repeat pass), CSK (1 day repeat pass) and TDX (single pass) respectively. After studying also the spatial decorrelation, it could be pointed out that the increase of the perpendicular baseline enhanced the sensitivity to the volume of the forest canopies. CSK and TDX were further investigated in order to highlight their sensitivity to GSV. Volume decorrelation seemed to be dominant, as the coherence decreased with increasing vegetation. However, TDX seemed more suitable than CSK to retrieve GSV as it showed higher correlation (r2TDX=0.64, r2CSK=0.13).

With the enhancing availability and variety of space borne Synthetic Aperture Radar (SAR) data and a growing number of analysis algorithms the need for a vital user community is increasing. Therefore the German Aerospace Center (DLR) together with the Friedrich-Schiller-University Jena (FSU) and the Technical University Munich (TUM) launched the education initiative SAR-EDU. The aim of the project is to facilitate access to expert knowledge in the scientific field of radar remote sensing. Within this effort a web portal will be created to provide seminar material on SAR basics, methods and applications to support both, lecturers and students.

ZAPÁS investigates and cross validates methodologies using both Russian and European Earth observation data to develop procedures and products for forest resource assessment and monitoring. Products include biomass change maps for the years 2007 to 2009 on a local scale, a biomass and improved land cover map on the regional scale as input to a carbon accounting model. The geographical focus of research and development is Central Siberia, which contains two administrative districts of Russia, namely Krasnoyarsk Kray and Irkutsk Oblast. The results of the terrestrial ecosystem full carbon accounting are addressed to the Federal Forest Agency as federal instance. The high resolution products comprise biomass and change maps for selected local sites. These products are addressed to support the UN FAO Forest Resources Assessment as well as the requirements of the local forest inventories.

Retrieval of forest growing stock volume (GSV) has been shown to be feasible with C-band backscatter data using hyper-temporal stacks. In this paper, we report on the generation of pan-boreal estimates of forest GSV representative for the year 2010 using Envisat ASAR ScanSAR backscatter measurements. More than 67,000 image strips acquired between October 2009 and February 2011 over the north American and the Eurasian continent have been multi-looked to 1 km pixel size, terrain geocoded to a pixel size of 0.01 degree, speckle filtered and corrected for slope-induced effects on the backscatter. Then, GSV has been retrieved with the BIOMASAR algorithm on a pixel-by-pixel basis. First results show the strong thematic accuracy of the GSV estimates due to the very large number of backscatter observations available and retained for retrieval.

Numerous studies demonstrated the potential of interferometric coherence for forest stem volume estimation in boreal forests. Coherence derived from winter images acquired at frozen conditions proved being favourable against unfrozen conditions. This also applies to PALSAR coherence, although featuring relatively large temporal baseline of at least 46 days. However, when using data acquired at unfrozen conditions, a large spread of coherence was observed at all stem volume levels. This scatter negatively effects the correlation of stem volume and coherence, and thus prevents using summer coherence for stem volume estimation. Besides environmental conditions, the different tree geometries can be assumed having an impact on the magnitude of decorrelation. So far, this impact has rarely been investigated in boreal forests. This paper presents the results of a study investigating the impact of tree species on PALSAR coherence. The results show increased scatter of coherence in summer. They also show that the coherence of dense forest is larger in summer than in winter. Eventually, a slight impact of the tree species on coherence was observed. This impact is larger in summer.

The high resolution acquisition mode of TerraSAR-X provides a new dimension in fluvial landform change detection. Here we analyzed high resolution (5 m) coherence images with temporal baselines of up to a year from the Palpa Valley in the hyper-arid coastal desert of southern Peru. The results provide evidence that this sensor is suitable for mapping the land surface changes caused by erosion and sedimentation following rainfall and runoff events in bare desert landscape units.

The meter-scale ground resolution of TerraSAR-X spotlight images promises for the first time the 3-D detection of landforms and landform changes on the microrelief scale from a satellite-based remote sensing system. Using repeat-pass pairs of high-resolution spotlight images, this paper analyzes the spatial variation of coherence on the micro- and mesorelief scale and demonstrates the high potential as well as some limitations of this approach for digital elevation model generation, geomorphological mapping, and geomorphic-change detection in contrasting landscapes of the coastal desert of southern Peru.

For many aspects of the human life the world's forests are crucial. Only microwave remote sensing provides the means of all day weather independent monitoring of those pristine areas. Multi-temporal ENVISAT ASAR and ALOS PALSAR data for mapping boreal forest in central Siberia proved to be quite successful in diverse research projects. So far, TerraSAR-X High Resolution Spotlight images have been effectively used in the verification process of the resulting land cover area maps. Part of this knowledge will be transferred within the framework of the Remote Sensing Survey of the next global Forest Resources Assessment. Altogether 350 TerraSAR-X scenes all over the cloudy tropics will be analysed in a project called FRA-SAR 2010. The combined analysis of ALOS PALSAR, ENVISAT ASAR and TerraSAR-X on five areas inside the project will foster the expertise on forest structure parameters.

This paper presents polarimetric analysis over African Savanna woodland using ALOS/PALSAR to investigate the trend between backscatter and biomass levels. An extensive field inventory was carried out combining Differential GPS and conventional topographic mapping techniques. Geographic position, basal diameter and height of trees in sampled plots were measured. Plot level biomass quantities were obtained using established allometry for the region. Geocoded ALOS/PALSAR level 1.1 and 1.5 data is checked for accuracy against existing geospatial data for the case study area. Sigma nought, Freeman and Pauli component are extracted for the sampled plots to investigate the relationship between biomass, volume and double bounce scattering. Finally a comparison of sigma nought, Freeman and Pauli components is carried out to analyze trend against volume and double bounce scattering.

The availability of reliable land cover information is crucial for a wide range of applications, like for example monitoring of land use change and land degradation as well as administrative matters in global, regional and local scales. In this paper the potential of SENTINEL-1 C-band SAR data for land cover applications, e.g. generating level-2 land cover classification products has been investigated. Therefore, the planned short revisit and dual polarization concept of SENTINEL-1 has been simulated using multi-temporal ERS-2 and ENVISAT ASAR AP C-band backscatter intensity data. For classification, several multi-temporal metrics and the minimum amount of SAR data acquired during one growing season have been analyzed to derive five basic land cover classes with accuracies greater than 85%.

Focusing on Siberia, the feasibility of using Advanced Land Observing Satellite Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) summer-intensity and winter-coherence images for large-area forest monitoring was investigated. Fine beam dual horizontal/horizontal and horizontal/vertical (polarization) intensity strip images were acquired during the summer of 2007. The processing consisted of radiometric calibration, orthorectification, and topographic normalization. The coherence was estimated from interferometric pairs with 46-day repeat-pass intervals. The pairs were acquired during the winters of 2006/2007 and 2007/2008. During both winters, suitable weather conditions that allow for low temporal decorrelation had been reported. By using PALSAR intensities and winter-coherence data, areas of forest and nonforest were separated. By combining both data types, a minimal overlap of the class signatures was observed, even though the analysis was conducted at the pixel level and no speckle filter was applied. The study concludes that the operational delineation of forest cover using PALSAR data is feasible. By applying a segmentation-based classification, an accuracy of 93% was obtained.

Retrieval of forest stem volume from synthetic aperture (SAR) backscatter and interferometric SAR (InSAR) coherence is generally performed using a model-based approach, where in situ measurements are necessary to estimate the unknown model parameters. Problems arise when in situ data are either not available or of low quality or the observables present spatial variations. In this work we present three approaches for automatic modeling and inversion of forest backscatter and coherence to retrieve forest stem volume. The three approaches exploit statistical distributions of the observables to obtain estimates for the unknowns in the model. Results shows remarkable agreement with those obtained by means of traditional modeling approaches based on in situ data.

An international initiative aimed at the harmonization and validation of existing and future land cover datasets is needed to support operational earth observation of land. The goal is to overcome current limitations of land cover datasets with respect to their compatibility and comparability and unknown accuracy. These limitations significantly hinder a variety of applications. Key entities in this effort are the Land Cover Implementation Team of Global Observation of Forest Cover/Global Observation of Land Dynamics, the Global Land Cover Network, and the CEOS Group on Calibration and Validation. In their recent efforts, they have explored and provided the methodological and organizational resources to foster such an international cooperation. The approaches described in this paper include an introduction of the UN Land Cover Classification System as a common land cover language and a basis for legend translation. All actors involved in land cover mapping are invited to participate in this initiative.

In this paper retrieval of stem volume from JERS-1 repeat-pass interferometric coherence is demonstrated for five large forest areas in central Siberia. A method for multi-temporal combination of the retrieved stem volumes from several coherence images is applied. Compared to the best results from single coherence images the retrieval accuracy is in most cases significantly improved by the multi-temporal combination. The obtained multi-temporal relative retrieval accuracy is in the range 35 - 39% for four of five areas. The best RMSE is 54 m 3 /ha.

Nation-wide monitoring of afforestation, reforestation and deforestation (ARD) activities is explicitly addressed in the Kyoto Protocol. This paper investigates the feasibility of ARD mapping using spaceborne synthetic aperture radar (SAR) and interferometric SAR (InSAR) data from the European Remote Sensing (ERS) satellite, the ENVISAT satellite and the Japanese Earth Resources Satellite (JERS). As test site the forest enterprise of Bolshe-Murtinsky, Central Siberia, was chosen since relatively long time series of ERS-ENVISAT and JERS SAR data were available spanning 1996-2004 and 1994-1997 respectively. The ERS-2 SAR and ASAR Image Mode (IM) backscatter acquired during winter under frozen conditions decreased by 1-2 dB following stand-wise logging, whereas reforestation in young stands could not be detected. The JERS SAR backscatter commonly showed stronger forest/nonforest contrast, between 2 and 4 dB depending on the weather conditions at image acquisition, whereas the repeat-pass JERS coherence was characterized by a 0.3-0.4 difference before and after deforestation activities. None of these two signatures was found to provide information on reforestation, mainly because of the too short time series with respect to the forest growth rate. To assess the possibility of using ERS-ENVISAT- and JERS-type of data for reforestation, a first-order approach has been developed. Preliminary results show that young stands can be distinguished from nonforested areas not earlier than approximately 25 years using JERS repeat-pass coherence and 30 years using ERS-ENVISAT SAR backscatter.

To assess the possibility of using L-band synthetic aperture radar (SAR) backscatter for forest stem volume mapping in the boreal zone, a comparative analysis was carried out at test sites located in Sweden, Finland and Central Siberia from which an extensive set of Japanese Earth Resources Satellite (JERS-1) images and ground data was available. The backscatter showed clear seasonal dynamics, increasing by 3-4 dB in dense forests and 1-2 dB in sparse forests when going from frozen to unfrozen conditions. To retrieve stem volume a simple L-band Water Cloud-related scattering model was used. Model training performed well in all cases, the model parameters estimates being affected by weather conditions at acquisition and test site-specific forest stand structure. At each test site the retrieval performed best under unfrozen conditions and worst under frozen conditions. The highest retrieval accuracy was achieved at the small and intensively managed test site of Kattbole in Sweden with a 25% relative RMS error. For the other test sites the much larger retrieval error could be explained as a consequence of heterogeneities in the forest stand structure, as well as differences in the ground dielectric properties and inaccuracy in the ground data

Besides so-called light use efficiency models (which, very generally, account for effects of temperature and water stress upon plant productivity), satellite-derived information may be applied to adjust Dynamic Global Vegetation Models (DGVMs). This paper attempts to assimilate satellite-derived land cover (GLC2000) into a state-of-the-art DGVM (LPJ-DGVM) and examine the impact on estimated carbon stocks. The offset between land cover information and potential fractional vegetation cover is calculated by the model and then used to adjust plant productivity. Increasing the information content of current satellite-derived land cover products would lead to more reliable estimates of parameters of the terrestrial component of the global carbon cycle.

This paper summarizes a technique to map historical (1 to 10 years old) fire scars using a vegetation index, NDSWIR, based on the SWIR and NIR. A temporal set of NDSWIR SPOT-VGT images of Siberia were segmented and then recombined with the original NDSWIR images to form a per-pixel fire scar probability map. The results show a good agreement between the fire scar probability map, hotspot data and Landsat quicklooks, any discrepancies being accounted for by the differing dates of imagery and low resolution of SPOT-VGT.

Work on Afforestation, Reforestation and Deforestation (ARD) mapping and monitoring in Siberia is presented. The accuracy requirements of a satellite based ARD data product to be useful for an independent verification of the Kyoto protocol in the first commitment period (2008-2012) are discussed. Mapping of ARD is important for national carbon emission inventories related to land use change as specified in the Kyoto protocol. The methodological requirements for remote sensing derived ARD information is outlined. The overall objective is to create a remote sensing based ARD classification system based on multi-temporal Earth observation data, multi-date forest inventory information and land cover information of test territories in Siberia. Historical (1989) and near present (2000-2003) high resolution optical satellite data (Landsat TM-5 and ETM) are pre-processed and a two-date change detection method using artificial neural networks (ANNs) is tested to derive specific ARD classes. Forest inventory information from Russian forest enterprises is used as ground truth. First results indicate that ANNs can be used successfully for ARD monitoring.

The synergetic use of a multi-sensor remote sensing concept, a comprehensive quantitative description of natural landscapes in geographical information systems form and a special type of regional ecological model is considered a relevant approach for the full accounting of terrestrial biota budgets of major greenhouse gases (FGGA). The first results of the SIBERIA-II projects show that the above approach is supposedly sufficient for quantifying the FGGA for the Northern Eurasia with details and reliability that would satisfy the requirements of the post Kyoto international negotiation process.

The objective of this study is to analyse and interpret the impact of intercepted rainfall on the thematic analyses of SAR data acquired in agricultural areas. The investigations were carried out using airborne E-SAR data at X-, C- and L-band. Furthermore, TerraSAR simulated data were analysed with respect to future spaceborne missions. The strongest effect of plant surface wetness on crop recognition was found at C-band. Single classes showed changes in user's or producer's accuracy up to 30%. For classifications based on X-and L-band data - the future TerraSAR configuration - better classification results were achieved when free vegetation water was present. Thus, the matutinal overflight time of TerraSAR over Germany can be considered as an advantage for crop mapping issues.

The overall objective of SIBERIA-II is to demonstrate the viability of full carbon accounting (including greenhouse gases (GHGs): CO2, CO, CH4, N2O, NOx) on a regional basis using the environmental tools and systems available to us today and in the near future. The region under study is Northern Eurasia, covering an area of 200 million ha and representing a significant part of the Earth's boreal biome which plays a critical role in global climate. This paper presents the temporal, spatial and spectral requirements from Dynamic Vegetation Models (DVMs) for the remote sensing products which determine the sensor collection to be used. The sensors are discussed in the context of the respective parameters: e.g. land cover and -change, biomass, freeze/thaw, fAPAR, surface temperature, soil moisture. SIBERIA-II will use high and low resolution radar data (ENVISAT ASAR (image, wide-swath and global mode), ERS-2 SAR), low and medium resolution optical data (AVHRR, MODIS, MERIS, SPOT VGT), high resolution optical data (Landsat TM5/ETM, ASTER) and scatterometer data types. This multi-sensor concept is necessary to calculate biophysical parameters and it also allows to study error propagation issues and down scaling effects for low resolution products using test areas with extensive ground truth information.

The goal of our paper is a study of the potential of ERS SAR for the ecological monitoring of the Baikal lake region, the area of global importance as economical area and natural environmental reserve. The Ust-Barguzin test site is characterized by a very complicated topography average slopes of 15/spl deg/-25/spl deg/ and appears to be severe examination for modern phase unwrapping techniques, as the area rich with layovers/foreshortenings on the SAR images. In our research we used a set of ERS SAR data obtained during tandem mission in 1997-1998. The coherence maps and DEM of the area of study were generated. A use of especially developed phase unwrapping technique allowed to generate DEM of the area from a pair of tandem scenes. An intensity images were combined into multitemporal images. Study of the multitemporal images, coherence and DEM maps allowed us to make classification of the forested/deforestated areas, the areas with various soils type and moisture. The results of DINSAR processing of the ERS data for the study area with high seismic activity are discussed also. The data collected during field trip in the beginning of September 2000 are compared with results of ERS data analysis.

Integration of optical and microwave remote sensing data is an effective way to conduct more detailed classification of the surface covers. For this work we analyzed MSU-E optical data and SIR-C quad-pol data (both C- and L-bands) that cover southeastern part of Baikal Lake region. Using two sources of data, we compared the results of several classification methods and verified them during field trip. We focused our attention mostly on forest classification. Besides we got classification results for water surfaces and marshlands.

Multitemporal radar data from the Japanese Earth Resources Satellite (JERS) satellite from the period 1993 to 1998 have been used to investigate if L-band interferometric coherence with a 44-day temporal baseline is suitable for estimations of growing-stock volume in boreal forest. Two forest regions north of Krasnoyarsk in Siberia have been used as test areas. Seasonal variations in the repeat-pass coherence have been studied, and a comparison with C-band coherence from the European Remote sensing Satellite 1 and 2 (ERS-1/2) tandem missions in 1997 and 1998 has been done. JERS coherence from the winter shows a clear correlation with the forest growing-stock volume. For the summer scenes, the spread in the values is too large to give reliable results. Acquisitions from the spring and fall show large problems with decorrelation caused by temporal changes. The results indicate potential of repeat-pass interferometric L-band coherence in winter, as will be provided by the forthcoming Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) to map growing-stock volume in Siberia and boreal forests.

This paper presents the first results from a study of multi-temporal JERS repeat pass coherence. Two test areas in a forest region north of Krasnoyarsk in Siberia have been used to investigate if L-band coherence with a 44-day temporal baseline can be used for estimations of growing stock volume in boreal forest. The available data show promising results for winter images, but for the summer scenes the standard deviations are too large to give reliable results. A comparison with ERS tandem coherence has also been done.

The objective of this study is to analyse and interpret the seasonal and diurnal changes of polarimetric parameters derived by the Cloude decomposition theorem. The investigations were carried out on polarimetric E-SAR data at L-band. The seasonal scattering behaviour of cereals strongly depends on the local incidence angle. Mainly surface scattering and double bounce processes contribute to the radar backscatter. Broad-leaf crops are characterised by a pronounced contribution of all scattering processes to the radar signal. With respect to diurnal variations of the polarimetric parameters due to dew and interception no significant changes could be determined. Nevertheless, the changes of the /spl sigma//sup 0/-values indicate an increase in volume scattering with moisture for all crops, whereby the contribution of the other mechanisms decreases for wet plant surfaces.

ERS-1 Windscatterometer measurements over Siberia were investigated to determine the potential of coarse resolution radar data for land cover mapping and monitoring. Two indices have been introduced: the slope index calculates the incidence angle dependence of the radar measurements, the radar backscatter index (RBSI) combines slope index and radar backscatter intensity. Using the RBSI, good correlations to canopy density were found and the freeze/thaw-transition can be determined. Promising correspondence was found with five-day interval time series of normalized radar cross sections and crop yield estimates.

The Spaceborne Imaging Radar-C, X-Band Synthetic Aperture Radar (SIR-C/X-SAR) was launched on the Space Shuttle Endeavour for two ten day missions in the spring and fall of 1994. Radar data from these missions are being used to better understand the dynamic global environment. During each mission, radar images of over 300 sites around the Earth were obtained, returning over a terabit of data. SIR-C/X-SAR science investigations were focused on quantifying radar's ability to estimate surface properties of importance to understanding global change; and focused studies in geology, ecology, hydrology and oceanography, as well as radar calibration and electromagnetic theory studies. In addition, the second flight featured an interferometry experiment, where digital elevation maps were obtained by interfering data from the first and second shuttle flight, and from successive days on the second flight. SIR-C/X-SAR data have been used to validate algorithms which produce maps of vegetation type and biomass; snow, soil and vegetation moisture; and the distribution of wetlands, developed with earlier aircraft data.>