Combined Use of Optical and Synthetic Aperture Radar Data for REDD+ Applications in Malawi

Recent developments in satellite data availability allow tropical forest monitoring to expand in two ways: (1) dense time series foster the development of new methods for mapping and monitoring dry tropical forests and (2) the combination of optical data and synthetic aperture radar (SAR) data reduces the problems resulting from frequent cloud cover and yields additional information. This paper covers both issues by analyzing the possibilities of using optical (Sentinel-2) and SAR (Sentinel-1) time series data for forest and land cover mapping for REDD+ (Reducing Emissions from Deforestation and Forest Degradation) applications in Malawi. The challenge is to combine these different data sources in order to make optimal use of their complementary information content. We compare the results of using different input data sets as well as of two methods for data combination. Results show that time-series of optical data lead to better results than mono-temporal optical data (+8% overall accuracy for forest mapping). Combination of optical and SAR data leads to further improvements: +5% in overall accuracy for land cover and +1.5% for forest mapping. With respect to the tested combination methods, the data-based combination performs slightly better (+1% overall accuracy) than the result-based Bayesian combination

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

Drought Impact on Phenology and Green Biomass Production of Alpine Mountain Forest—Case Study of South Tyrol 2001–2012 Inspected with MODIS Time Series

Ecological balance and biodiversity of the alpine forest is endangered by global and local climatic extremes. It spurs a need for comprehensive forest monitoring, including in depth analyses of drought impact on the alpine woodland ecosystems. Addressing an arising knowledge gap, we identified and analyzed 2002–2012 aridity related responses within the alpine mountain forest of South Tyrol. The study exploited a S-mode PCA (Principal Component Analysis) based synergy between meteorological conditions rendered by the scPDSI (self-calibrated Palmer Drought Severity Index) and forest status approximated through MODIS (Moderate Resolution Imaging Spectroradiometer) derived NDVI (Normalized Difference Vegetation Index) and NDII7 (Normalized Difference Infrared Index based on MODIS band 7) time series. Besides characterizing predominant forest temporal response to drought, we identified corresponding spatial footprints of drought impact, as well as examined aridity-related changes in forest phenology and biomass production. The latter was further evaluated in relation to forest type, elevation, aspect and slope. Recognized meteorological conditions highlighted: prolonged 2003–2007 mild to extreme drought, and overall regional drying tendencies. Arising remotely sensed forest responses accounted on localized decline in foliage water content and/or photosynthetic activity, but also indicated regions where forest condition improved despite the meteorological stress. Perceived variability in the forest response to drought conditions was governed by geographic location, species structure, elevation and exposition, and featured complexity of the alpine forest ecosystem. Among the inspected biophysical factors elevation had the strongest influence on forest phenology and green biomass production under meteorological stress conditions. Stands growing above 1400 m a.s.l. demonstrated initial increase in annual biomass growth at the beginning of the dry spell in 2003. Conversely, woodlands at lower altitudes comprising considerable share of hardwood species were more prone to biomass decline in 2003, but experienced an overall upturn in biomass production during the following years of the dry spell. Aspect showed moderate effect on drought-related phenology and green biomass production responses. Diverse forest ecosystem responses identified in this study were in line with known local and regional analyses, but also shed some new light on drought induced alternation of forest status

Low altitude LiDAR and TLS point clouds for improved tree detection

1042582658Austrian Research Promotion Agency (FFG

Forest Assessment Using High Resolution SAR Data in X-Band

Novel radar satellite missions also include sensors operating in X-band at very high resolution. The presented study reports methodologies, algorithms and results on forest assessment utilizing such X-band satellite images, namely from TerraSAR-X and COSMO-SkyMed sensors. The proposed procedures cover advanced stereo-radargrammetric and interferometric data processing, as well as image segmentation and image classification. A core methodology is the multi-image matching concept for digital surface modeling based on geometrically constrained matching. Validation of generated surface models is made through comparison with LiDAR data, resulting in a standard deviation height error of less than 2 meters over forest. Image classification of forest regions is then based on X-band backscatter information, a canopy height model and interferometric coherence information yielding a classification accuracy above 90%. Such information is then directly used to extract forest border lines. High resolution X-band sensors deliver imagery that can be used for automatic forest assessment on a large scale

Mapping Tropical Rainforest Canopy Disturbances in 3D by COSMO-SkyMed Spotlight InSAR-Stereo Data to Detect Areas of Forest Degradation

Assessment of forest degradation has been emphasized as an important issue for emission calculations, but remote sensing based detecting of forest degradation is still in an early phase of development. The use of optical imagery for degradation assessment in the tropics is limited due to frequent cloud cover. Recent studies based on radar data often focus on classification approaches of 2D backscatter. In this study, we describe a method to detect areas affected by forest degradation from digital surface models derived from COSMO-SkyMed X-band Spotlight InSAR-Stereo Data. Two test sites with recent logging activities were chosen in Cameroon and in the Republic of Congo. Using the full resolution COSMO-SkyMed digital surface model and a 90-m resolution Shuttle Radar Topography Mission model or a mean filtered digital surface model we calculate difference models to detect canopy disturbances. The extracted disturbance gaps are aggregated to potential degradation areas and then evaluated with respect to reference areas extracted from RapidEye and Quickbird optical imagery. Results show overall accuracies above 75% for assessing degradation areas with the presented methods

Representation of an alpine treeline ecotone in SPOT 5 HRG data

An ecotone is a zone of vegetation transition between two communities, often resulting from a natural or anthropogenic environmental gradient. In remotely sensed imagery, an ecotone may appear as an edge, a boundary of mixed pixels or a zone of continuous variation, depending on the spatial scale of the vegetation communities and their transition zone in relation to the spatial resolution of the imagery. Often in image classification, an ecotone is either ignored if it falls within a width of one or two pixels, or part of it may be mapped as a separate vegetation community if it covers an area of several pixel widths. A soft classification method, such as probability mapping, is inherently appealing for mapping vegetation transition. Ideally, the probability of membership each pixel has to each vegetation class corresponds with the proportional composition of vegetation classes per pixel. In this paper we investigate the use of class probability mapping to produce a softened classification of an alpine treeline ecotone in Austria using a SPOT 5 HRG image. Here the transition with altitude is from dense subalpine forest to treeless alpine meadow and herbaceous vegetation. The posterior probabilities from a Maximum Likelihood algorithm are shown to reflect the land-cover composition of mixed pixels in the ecotone. The relationships between the posterior probability of class membership for the two end-member classes of ‘scrub and forest’ and ‘non-forest vegetation’ and the percentage ground cover of these vegetation classes (enumerated in 15 quadrats from 1:1500 aerial photographs) were highly significant: r2 = 0.83 and r2 = 0.85 respectively (p < 0.001, n = 15). We identify thresholds (alpha-cuts) in the posterior probabilities of class membership of ‘scrub and forest’ and ‘non-forest vegetation’ to map the alpine treeline ecotone as a transition zone of five intermediate vegetation classes between the end-member communities. In addition, we investigate the representation of the ecotone as a ratio between the posterior probabilities of ‘scrub and forest’ and ‘non-forest vegetation’. This displays the vegetation transition without imposing subjective boundaries, and has greater emphasis on the ecotone transition rather than on the end-member communities. We comment on the fitness for purpose of the different ways investigated for representing the alpine treeline ecotone

Multiple View Geometry in Remote Sensing: An Empirical Study Based on Pléiades Satellite Images

In contrast to the fields of computer vision and photogrammetry, multiple view geometry has not been extensively exploited in the remote sensing domain so far. Therefore, an empirical study is conducted based on multi view Pléiades data that depicts a scene from multiple orbits and multiple incidence angles. First, an accuracy analysis of the 2D and 3D geo-location performance is elaborated showing that ground control points can be modelled with a root mean square residual error below 30 cm in East, North, and height. Second, digital surface models are reconstructed from all possible stereo pairs and are additionally fused in the multiple view geometry sense. It is shown that employing more data increases the accuracy of the digital surface model while reducing the amount of the nonreconstructed regions

Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping

International audienceFrequent cloud cover and fast regrowth often hamper topical forest disturbance monitoring with optical data. This study aims at overcoming these limitations by combining dense time series of optical (Sentinel-2 and Landsat 8) and SAR data (Sentinel-1) for forest disturbance mapping at test sites in Peru and Gabon. We compare the accuracies of the individual disturbance maps from optical and SAR time series with the accuracies of the combined map. We further evaluate the detection accuracies by disturbance patch size and by an area-based sampling approach. The results show that the individual optical and SAR based forest disturbance detections are highly complementary, and their combination improves all accuracy measures. The overall accuracies increase by about 3% in both areas, producer accuracies of the disturbed forest class increase by up to 25% in Peru when compared to only using one sensor type. The assessment by disturbance patch size shows that the amount of detections of very small disturbances (< 0.2 ha) can almost be doubled by using both data sets: for Gabon 30% as compared to 15.7-17.5%, for Peru 80% as compared to 48.6-65.7%