Mapping Forest Degradation due to Selective Logging by Means of Time Series Analysis: Case Studies in Central Africa

Detecting and monitoring forest degradation in the tropics has implications for various fields of interest (biodiversity, emission calculations, self-sustenance of indigenous communities, timber exploitation). However, remote-sensing-based detection of forest degradation is difficult, as these subtle degradation signals are not easy to detect in the first place and quickly lost over time due to fast re-vegetation. To overcome these shortcomings, a time series analysis has been developed to map and monitor forest degradation over a longer period of time, with frequent updates based on Landsat data. This time series approach helps to reduce both the commission and the omission errors compared to, e.g., bi- or tri-temporal assessments. The approach involves a series of pre-processing steps, such as geometric and radiometric adjustments, followed by spectral mixture analysis and classification of spectral curves. The resulting pixel-based classification is then aggregated to degradation areas. The method was developed on a study site in Cameroon and applied to a second site in Central African Republic. For both areas, the results were finally evaluated against visual interpretation of very high-resolution optical imagery. Results show overall accuracies in both study sites above 85% for mapping degradation areas with the presented methods

Satellite-based forest monitoring: spatial and temporal forecast of growing index and short-wave infrared band

For detecting anomalies or interventions in the field of forest monitoring we propose an approach based on the spatial and temporal forecast of satellite time series data. For each pixel of the satellite image three different types of forecasts are provided, namely spatial, temporal and combined spatio-temporal forecast. Spatial forecast means that a clustering algorithm is used to group the time series data based on the features normalised difference vegetation index (NDVI) and the short-wave infrared band (SWIR). For estimation of the typical temporal trajectory of the NDVI and SWIR during the vegetation period of each spatial cluster, we apply several methods of functional data analysis including functional principal component analysis, and a novel form of random regression forests with online learning (streaming) capability. The temporal forecast is carried out by means of functional time series analysis and an autoregressive integrated moving average model. The combination of the temporal forecasts, which is based on the past of the considered pixel, and spatial forecasts, which is based on highly correlated pixels within one cluster and their past, is performed by functional data analysis, and a variant of random regression forests adapted to online learning capabilities. For evaluation of the methods, the approaches are applied to a study area in Germany for monitoring forest damages caused by wind-storm, and to a study area in Spain for monitoring forest fires

Remote Sensing Based Two-Stage Sampling for Accuracy Assessment and Area Estimation of Land Cover Changes

Land cover change processes are accelerating at the regional to global level. The remote sensing community has developed reliable and robust methods for wall-to-wall mapping of land cover changes; however, land cover changes often occur at rates below the mapping errors. In the current publication, we propose a cost-effective approach to complement wall-to-wall land cover change maps with a sampling approach, which is used for accuracy assessment and accurate estimation of areas undergoing land cover changes, including provision of confidence intervals. We propose a two-stage sampling approach in order to keep accuracy, efficiency, and effort of the estimations in balance. Stratification is applied in both stages in order to gain control over the sample size allocated to rare land cover change classes on the one hand and the cost constraints for very high resolution reference imagery on the other. Bootstrapping is used to complement the accuracy measures and the area estimates with confidence intervals. The area estimates and verification estimations rely on a high quality visual interpretation of the sampling units based on time series of satellite imagery. To demonstrate the cost-effective operational applicability of the approach we applied it for assessment of deforestation in an area characterized by frequent cloud cover and very low change rate in the Republic of Congo, which makes accurate deforestation monitoring particularly challenging

Sentinel-based Evolution of Copernicus Land Services on Continental and Global Scale

Copernicus is a European Earth Observation (EO) programme headed by the European Commission (EC) in partnership with the European Space Agency (ESA) for the better understanding of the state and changes of our planet. Copernicus provides six operational services on the earth’s main sub-systems (i.e. Land, Atmosphere, Oceans) and on cross-cutting processes (i.e., Climate Change, Emergency and Security). These services are largely based on EO satellite data, which will rely on the fleet of ESA’s Sentinel satellites. The Copernicus Land Monitoring Service provides EO-based spatial information related to bio-geophysical variables and Land Cover/Land Use (LC/LU) characteristics as well as their changes over time. The related services are reflected in a Global, pan-European (Continental), Local Component, and an In-situ Component. The Horizon2020 project, “Evolution of Copernicus Land Services based on Sentinel data” (ECoLaSS), which will be implemented from 2017-2019 aims at developing innovative methods, algorithms and prototypes to improve and invent future pre-operational Copernicus Land services from 2020 onwards, for the pan-European and Global Components. ECoLaSS will make full use of dense Sentinel time series of optical (S-2, S-3) and Synthetic Aperture Radar (SAR) data (S-1). Rapidly evolving scientific as well as user requirements will be analyzed in support of a future pan-European roll-out of new/improved Copernicus Land Monitoring services, and the transfer to global applications. This paper will describe the ECoLaSS concept, explain the current status of Copernicus Land services, the envisaged methods for their production and present first analyses and examples. Service requirements assessment will be performed involving the main Copernicus Land stakeholders and users, and will thus steer methodological developments, such as: (i) Sentinel-1/-2/-3 time series integration, (ii) time series pre-processing methods, (iii) thematic classification and (iv) change detection from time series analysis, and (v) the development of an incremental update methodology for the Copernicus Land High Resolution Layers (HRLs). These methods will be applied on test sites, located both in Europe and Africa, prior to a prototyping phase. Larger demonstration sites representing various bio-geographic regions were selected to implement the following innovative prototypes: (i) indicators and variables from high spatial and temporal resolution data, for both the Continental and Global Component products; (ii) incremental update strategies for the main pan-European products (i.e. the HRLs); (iii) improved permanent grassland identification; (iv) crop area and crop status/parameters monitoring; (v) further novel LC/LU products. Finally, the main target to assess/benchmark all operational products in view of their innovation potential and technical excellence will be performed, leading to a strategy for an operationalization framework for a future pan-European roll-out of improved or newly developed Copernicus Land Monitoring services. ECoLaSS will promote the innovation potential of new land monitoring services and applications to diverse user communities. The project will thus contribute to a growing "Copernicus Economy" by boosting (new) Copernicus Land Services and value-added applications (Downstream Services). It is expected that such new services will provide a variety of inter-linkages with other LC/LU projects, and bring new opportunities for a wide range of dedicated applications to the market from 2020 onwards