Christian Hüttich

On the suitability of MODIS Time Series Metrics to Map Vegetation Types in Dry Savanna Ecosystems: A Case Study in the Kalahari of NE Namibia

The characterization and evaluation of the recent status of biodiversity in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. This paper presents an integrated concept for vegetation type mapping in a dry savanna ecosystem based on local scale in-situ botanical survey data with high resolution (Landsat) and coarse resolution (MODIS) satellite time series. In this context, a semi-automated training database generation procedure using object-oriented image segmentation techniques is introduced. A tree-based Random Forest classifier was used for mapping vegetation type associations in the Kalahari of NE Namibia based on inter-annual intensity- and phenology-related time series metrics. The utilization of long-term inter-annual temporal metrics delivered the best classification accuracies (Kappa = 0.93) compared with classifications based on seasonal feature sets. The relationship between annual classification accuracies and bi-annual precipitation sums was conducted using data from the Tropical Rainfall Measuring Mission (TRMM). Increased error rates occurred in years with high rainfall rates compared to dry rainy seasons. The variable importance was analyzed and showed high-rank positions for features of the Enhanced Vegetation Index (EVI) and the blue and middle infrared bands, indicating that soil reflectance was crucial information for an accurate spectral discrimination of Kalahari vegetation types. Time series features related to reflectance intensity obtained increased rank-positions compared to phenology-related metrics.

Continuous field mapping of Mediterranean wetlands using sub-pixel spectral signatures and multi-temporal Landsat data

Abstract Wetlands rank among the most diverse ecosystems on earth and function as important ecosystem service providers. Pressures on wetland ecosystems caused by human activities, such as land use transformations or agricultural intensification, lead to strong wetland degradation. Satellite-based wetland mapping still bears the most uncertainties compared to other land cover types mapping. Image classification techniques have to better adapt to specific wetland characteristics, such as spatial heterogeneity, seasonal dynamics and fuzzy transitions between different land cover classes. For this purpose, a pixel-based method for wetland delineation based on multi-temporal Landsat data in West Turkey was developed and analyzed. In addition to common vegetation indices and texture measures, the usefulness of seasonal indices was tested. Multi-temporal Landsat imagery was combined with high resolution satellite data to extract sub-pixel information of coastal and inland wetland classes based on a random forest regression algorithm. The classification achieved an overall accuracy of 79.02%. In addition to the hard wetland classification the mapping framework provides a map of fractional cover information of different wetland classes including information about fuzzy spatial transitions of highly heterogeneous distribution patterns of wetland habitats and related intra-annual seasonal dynamics. Mapping spatio-temporal wetland dynamics at continuous field scales increases the applicability of Landsat-derived maps for local-scale ecosystem monitoring and environmental management on habitat level.

Indicators of Northern Eurasia's land-cover change trends from SPOT-VEGETATION time-series analysis 1998-2005

The Boreal and Tundra ecosystems of the mid to high latitudes are sensitive indicators of environmental impacts from both climate change and direct human activities. This study uses inter‐annual and inter‐seasonal SPOT‐VGT mosaics for recent years from 1998 to 2005 covering the entire boreal ecosystems of northern Eurasia. Linear trends could be detected in the NDVI and NDWI time series that differ by season, land‐cover type, and latitude. Significant positive NDVI trends are described for spring and related negative trends for NDWI over the boreal forest zone. They indicate an earlier onset of the vegetation green‐up. Similar vegetation dynamics can be described for autumn. The tundra ecosystems of the northern Eurasia latitudes exhibit trends of negative NDVI and positive NDWI. This may be explained by earlier snowmelt and increasing amounts of surface water from positive temperature anomalies. The non‐ambiguous coarse‐scale indicators require further detailed studies to identify driving factors and amount for positive feedbacks in boreal ecosystems.

Integrating in-situ, Landsat, and MODIS data for mapping in Southern African savannas: experiences of LCCS-based land-cover mapping in the Kalahari in Namibia

Integrated ecosystem assessment initiatives are important steps towards a global biodiversity observing system. Reliable earth observation data are key information for tracking biodiversity change on various scales. Regarding the establishment of standardized environmental observation systems, a key question is: What can be observed on each scale and how can land cover information be transferred? In this study, a land cover map from a dry semi-arid savanna ecosystem in Namibia was obtained based on the UN LCCS, in-situ data, and MODIS and Landsat satellite imagery. In situ botanical relevé samples were used as baseline data for the definition of a standardized LCCS legend. A standard LCCS code for savanna vegetation types is introduced. An object-oriented segmentation of Landsat imagery was used as intermediate stage for downscaling in-situ training data on a coarse MODIS resolution. MODIS time series metrics of the growing season 2004/2005 were used to classify Kalahari vegetation types using a tree-based ensemble classifier (Random Forest). The prevailing Kalahari vegetation types based on LCCS was open broadleaved deciduous shrubland with an herbaceous layer which differs from the class assignments of the global and regional land-cover maps. The separability analysis based on Bhattacharya distance measurements applied on two LCCS levels indicated a relationship of spectral mapping dependencies of annual MODIS time series features due to the thematic detail of the classification scheme. The analysis of LCCS classifiers showed an increased significance of life-form composition and soil conditions to the mapping accuracy. An overall accuracy of 92.48% was achieved. Woody plant associations proved to be most stable due to small omission and commission errors. The case study comprised a first suitability assessment of the LCCS classifier approach for a southern African savanna ecosystem.

Multi-Source Data Processing Middleware for Land Monitoring within a Web-Based Spatial Data Infrastructure for Siberia

Land monitoring is a key issue in Earth system sciences to study environmental changes. To generate knowledge about change, e.g., to decrease uncertaincy in the results and build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and in situ measurements are available for operational monitoring of the land surface. As the availability of well-prepared geospatial time-series data for environmental research is limited, user-dependent processing steps with respect to the data source and formats pose additional challenges. In most cases, it is possible to support science with spatial data infrastructures (SDI) and services to provide such data in a processed format. A data processing middleware is proposed as a technical solution to improve interdisciplinary research using multi-source time-series data and standardized data acquisition, pre-processing, updating and analyses. This solution is being implemented within the Siberian Earth System Science Cluster (SIB-ESS-C), which combines various sources of EO data, climate data and analytical tools. The development of this SDI is based on the definition of automated and on-demand tools for data searching, ordering and processing, implemented along with standard-compliant web services. These tools, consisting of a user-friendly download, analysis and interpretation infrastructure, are available within SIB-ESS-C for operational use.

Identification of land surface temperature and albedo trends in AVHRR Pathfinder data from 1982 to 2005 for northern Siberia

The arctic regions are highly vulnerable to climate change. Climate models predict an increase in global mean temperatures for the upcoming century. The arctic environment is subject to significant changes of the land surface. Especially the changes of vegetation pattern and the phenological cycle in the taiga–tundra transition area are of high importance in climate change research. This study focuses on time series and trend analysis of land surface temperature, albedo, snow water equivalent, and normalized difference vegetation index information in the time period of 1982–2005 for northern Siberia. The findings show strong dependencies between these parameters and their inter-annual dynamics, which indicate changes in vegetation growing period. We found a strong negative correlation between land surface temperature and albedo conditions for the beginning (60–90%) of the growing season for selected hot spot trend regions in northern Siberia.

Multi-Source Data Integration and Analysis for Land Monitoring in Siberia

Land monitoring is a key issue in Earth system sciences analyzing environmental changes. To generate knowledge about changes, e.g., by decreasing uncertainties in the products and to build confidence in land change monitoring, multiple information sources are needed. Earth observation (EO) satellites and in situ measurements are available for operational monitoring of the land surface. As the availability of well-prepared geospatial time-series data for environmental research is limited, user-dependent processing steps with respect to the data source and formats pose additional challenges. Further steps are necessary for the analysis of time-series data. In most cases, it is possible to support science with spatial data infrastructures (SDI) and web services to provide data in a processed format and to provide time-series plots for further interpretation. Data processing middleware is proposed as a technical solution to improve interdisciplinary research using multi-source time-series data and standardized data acquisition, pre-processing, updating and analyses. This solution is being implemented within the Siberian Earth System Science Cluster (SIB-ESS-C), which combines various sources of EO data and climate data with a focus on vegetation and temperature data. Products from the Moderate Resolution Imaging Spectroradiometer (MODIS), in situ data from meteorological stations and high spatial resolution Landsat data are available in the processing middleware that is connected to different data providers. Analytical tools have been integrated and can be used for time-series plotting, phenological dates, trend calculations, break point detection, and data comparison using existing open-source software packages. The development of this SDI is based on the definition of automated and on-demand tools for data searching, ordering and processing, implemented along with standard-compliant web services. Therefore, open-source software is used to build up this system. The tools developed, consisting of a user-friendly data access, download, analysis and interpretation infrastructure, are available within SIB-ESS-C for operational use.

Modeling Growing Stock Volume Using SAR Data and OBIA: Effects of Scale Parameter and Textural and Geometrical Features

This study applies an object-based image classification approach to the modeling of growing stock volume (GSV) for three test sites in boreal forests of Central Siberia. Assessing GSV is of great importance in the context of climate change and modeling of the global carbon cycle. In this study, dual-polarized (HH and HV) L-band radar data are used. The main objective of this study is to improve the model accuracy of object-based GSV estimation. Thus, the applied methodology uses backscatter intensities as well as geometrical and textural features computed using Trimble eCognition Developer. Furthermore, the impacts of these feature groups and of different scale parameters on the model accuracy are analyzed. The scale parameter is of great importance in image segmentation, defining the size of the resulting objects. For modeling GSV, the random forest algorithm is used, and is trained using forest inventory data. The application of this method yields a coefficient of correlation (R²) between 0.42 and 0.51, and a relative root mean square error (RMSE) between 27% and 37%. These results reveal that the combined use of spectral, textural, and geometrical features and a smaller scale parameter enhance the model accuracy. These findings are encouraging and indicate that the model performance of object-based GSV estimation models can still be improved.

Assessment and monitoring of Siberian forest resources in the framework of the EU-Russia ZAPÁS project

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.