A multi-scale approach for retrieving proportional cover of life forms

This study presents a multi-scale procedure to derive continuous proportional cover of woody vegetation in savanna ecosystems. QuickBird data was classified to define a continuous training and validation data set of woody cover proportions. Using a regression tree algorithm based on Landsat TM data, this woody cover information was extrapolated to an area of approximately 185 km x 185 km. The resulting 30 m map of the Namibian North-eastern Kalahari Woodland was aggregated to 250 m and 500 m resolutions. Comparisons of the global MODIS VCF product with the regionally adjusted multi-scale fractional cover map indicate that VCF tree cover is generally underestimated in the study area and confusions between tree and dense shrub cover occur

Mapping Vegetation Types in a Savanna Ecosystem in Namibia: Concepts for Integrated Land Cover Assessments

The characterization and evaluation of the recent status of biodiversity and land-cover in Southern Africa’s Savannas is a major prerequisite for suitable and sustainable land management and conservation purposes. The knowledge of the spatial distribution of vegetation types is an important information source for all social benefit areas. Remote sensing techniques are essential tools for mapping and monitoring of land-cover. The development and evaluation of concepts’ for integrated land-cover assessments attracted increased interest in the remote sensing community since evolving standards for the characterization of land-cover enable an easier access and intercomparability of earth observation data. Regarding the complexity of the savanna biome in terms of the spatiotemporal heterogeneity of the vegetation structure and rainfall variability, the main research needs are addressing the assessment of the capabilities and limitations of Moderate Resolution Imaging Spectroradiometer (MODIS) time series data for mapping vegetation types in Namibia. The Random Forest framework was evaluated for mapping MODIS time series metrics in a Kalahari test site in north-eastern Namibia. In regard of the necessity to report the usefulness of the FAO and UNEP Land Cover Classification System (LCCS) in regional case studies, LCCS is evaluated in terms of the applicability in open savanna ecosystems and as ontology for the semantic integration of an in-situ vegetation database in a coarse scale mapping framework based on MODIS data. The results of the integrated use of in-situ-, Landsat, and MODIS data in a standard mapping framework are used to assess the capabilities of the methodological setups of global land-cover mapping initiatives. In order to assess the existing accuracy uncertainties of mapping savannas at global scales, the effects of composite length and varying observation periods were compared in terms of mapping accuracy. The implications for global monitoring were discussed. The determinants of precipitation amount and mapping accuracy were evaluated by comparing MODIS and Tropical Rainfall Measuring Mission (TRMM) time series. The synergistic use of multi-scale land-cover information, such as life form, cover, and height of vegetation types (in-situ), vegetation physiognomy and local patterns (Landsat), and phenology (MODIS) in an integrated ecosystem assessment framework resulted in a flexible land-cover map including a broad structural-physiognomic and a hytosociological legend. The principle of classifiers and modifiers in LCCS proved to be applicable in dry savanna ecosystems and can be confirmed as overarching land-cover ontology. Analyses of time series classifications showed that mapping accuracy increases with increasing observation period. Small composite period lengths lead to increased mapping accuracies. The relationship between mapping accuracy and observation period was observed as a function of precipitation input and the magnitude of change between land-cover stages. The integration of in-situ data in a multi-scale framework leads to improved knowledge of the regionalisation of Namibian vegetation types. On the one hand, the case study in the north-eastern Kalahari showed that multi-data mapping approaches using in-situ to ‘coarse’ MODIS time series data bear the potential of the wall-to-wall update of existing vegetation type maps. On the other hand, the global remote sensing community can extend the reference databases by integrating regional standardised biodiversity and ecotype assessments in calibration and validation activities. The studies point on the uncertainties of mapping savannas at global scales and suggest possible solutions for improvements by adapting the remotely sensed feature sets, classification methods, and integrating dynamic processes of semi-arid ecosystems in the mapping framework

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

Large-scale vegetation assessments in southern Africa: concepts and applications using multi-source remote sensing data

The interdisciplinary project structure of BIOTA Southern Africa opened the opportunity for applying integrated concepts for the spatiotemporal assessment of arid and semi-arid southern African ecosystems, including the characterization of inter-annual vegetation dynamics and large-scale land cover mapping. Due to existing high uncertainties in mapping arid and semi-arid environments, the studies on remote sensing-based vegetation mapping aimed to develop and apply land cover classifi cation techniques to derive adapted and standardized maps covering large areas along the BIOTA transect. The application of machine learning classifi cation and regression techniques proved to be useful for both fractional and categorical semi-arid land cover mapping. Key improvements were achieved by mapping vegetation types in Namibia on a national scale using time series data from the Moderate Resolution Imaging Spectroradiometer (MODIS). Synergies of multitemporal remote sensing and botanical fi eld surveys yielded a fl exible vegetation type map for the northeastern Kalahari in Namibia based on the United Nations (UN) Land Cover Classifi cation System (LCCS). The development of fractional land cover maps in north-eastern Namibia showing the percentage cover of woody vegetation, herbaceous vegetation, and bare land surface allowed for a realistic and accurate spatial description of complex and fi ne-structured semi-arid vegetation types. Time series of MODIS vegetation indices were used to map and analyse annual and inter-annual vegetation dynamics along the BIOTA Observatory transects

Unleashing the Value of Citizen Contributions

Citizen Science generates valuable outcomes that are not restricted to data. Letting those have a true impact requires us to unleash the value of Citizen contributions. The talk highlights key challenges in addressing this point

Large Area Mapping of Boreal Growing Stock Volume on an Annual and Multi-Temporal Level Using PALSAR L-Band Backscatter Mosaics

The forests of the Russian Taiga can be described as an enormous biomass and carbon reservoir. Therefore, they are of utmost importance for the global carbon cycle. Large-area forest inventories in these mostly remote regions are associated with logistical problems and high financial efforts. Remotely-sensed data from satellite platforms may have the capability to provide such huge amounts of information. This study presents an application-oriented approach to derive aboveground growing stock volume (GSV) maps using the annual large-area L-band backscatter mosaics provided by the Japan Aerospace Exploration Agency (JAXA). Furthermore, a multi-temporal map has been created to improve GSV estimation accuracy. Based on information from Russian forest inventory data, the maps were generated using the machine learning algorithm, RandomForest. The results showed the high potential of this method for an operational, large-scale and high-resolution biomass estimation over boreal forests. An RMSE from 55.2 to 63.3 m3/ha could be obtained for the annual maps. Using the multi-temporal approach, the error could be slightly reduced to 54.4 m3/ha