Mathias Schardt

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.

Assessment of the Stereo-Radargrammetric Mapping Potential of TerraSAR-X Multibeam Spotlight Data

TerraSAR-X can acquire image data in various resolutions down to a range of about 1 m. Moreover, the sensor can operate at various imaging beams and thus acquire image data at different off-nadir viewing angles. These circumstances led to a stimulation of the traditional stereo-mapping approach, as TerraSAR-X image pairs became available in high resolution and in various geometric dispositions. With respect to 3-D surface mapping, TerraSAR-X stereo data processing, therefore, is a serious alternative to synthetic aperture radar interferometry, which can be addressed as the evolving mapping technique of the last decade. Within the TerraSAR-X science program of the German Aerospace Center (DLR), high-resolution multibeam data sets in Spotlight mode were acquired for several Austrian test sites. In general, three images were obtained from either ascending or descending orbits. In order to exploit the 3-D mapping accuracy of TerraSAR-X, stereo-radargrammetric mapping techniques were applied to the data sets, thereby utilizing stereo pairs as well as multi-image data sets in various dispositions. This paper focuses on one of the selected test sites and refers to the issues of 2-D and 3-D mapping-accuracy assessment as well as to surface model and vegetation-height-model generation. Validation of these products was widely restricted to visual analysis due to the lack of adequate high-quality reference products.

Assessment of forest attributes and single-tree segmentation by means of laser scanning

In this study laser scanner canopy height metrics data from the laser scanner Toposys-1 were investigated to derive forest attributes such as timber volume, tree height, and crown area coverage for the use in forest inventories. Investigations were based both on single tree information from crown segmentation and stand-wise assessments. while the statistical stand-wise approach only utilizes mean values for stand areas, the single tree classification approach makes use of the full potential of the high resolution laser scanner data. Forest inventory parameters were classified on the base of single trees or small groups of trees using digital image processing methods such as segmentation and data filtering. Stand-wise forest inventory data and single tree information were regressed against laser-derived features. Accuracy for additional stand parameters depends on crown closure and tree species. The obtained accuracy for tree heights from the approaches described is within the accuracy of conventional field based measurements. Further, it was investigated in how far laser scanner data is appropriate to assess timber volume. The described approaches can be used operationally for stand- wise forest inventories. Especially the single tree approach can be used instead of time- and cost-intensive field work in cases when full enumeration is required.

Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics

Vegetation canopy heights derived from the SRTM 30 m grid DEM minus USGS National Elevation Data (NED) DTM were compared to three vegetation metrics derived from a medium footprint LIDAR data (LVIS) for the US Sierra Nevada forest in California. Generally the SRTM minus NED was found to underestimate the vegetation canopy height. Comparing the SRTM–NED‐derived heights as a function of the canopy percentile height (shape/vertical structure) derived from LVIS, the SRTM SAR signal was found to penetrate, on average, into about 44% of the canopy and 85% after adjustment of the data. On the canopy type analysis, it was found that the SRTM phase scattering centres occurred at 60% for red fir, 53% for Sierra mixed conifer, 50% for ponderosa pine and 50% for montane hardwood‐conifer. Whereas analysing the residual errors of the SRTM–NED minus the LVIS‐derived canopy height as a function of LVIS canopy height and cover it was observed that the residuals generally increase with increasing canopy height and cover. Likewise, the behaviour of the RMSE as a function of canopy height and cover was observed to initially increase with canopy height and cover but saturates at 50 m canopy height and 60% canopy cover. On the other hand, the behaviour of the correlation coefficient as a function of canopy height and cover was found to be high at lower canopy height (<15 m) and cover (<20%) and decrease rapidly making a depression at medium canopy heights (>15 m and <50 m) and cover (>20% and <50%). It then increases with increasing canopy height and cover yielding a plateau at canopies higher than 50 m and cover above 70%.

Forest inventory based on laser scanning and aerial photography

High-pulse-rate laser scanners are capable to detect single trees in boreal forest zone, since significant amount of laser pulses reflect directly from the ground without any interaction with the canopy. This allows detailed investigation of forest areas and the creation of a 3- dimensional tree height model. By extracting the height, location and crown dimension of the trees from the 3- dimensional tree height model and by using the tree species information available in aerial photographs and in laser scanner data, important tree attributes, such as stem volume, basal area, and age, can be estimated for single trees. By knowing the characteristics of single trees, forest characteristics for sample plots, stands and larger areas, such as stem volume per hectare [m3/ha], basal area per hectare [m2/ha], mean height, dominant height, mean age, number of stems [pc/ha] and development class, can be calculated. The advantage of the method is the capability to measure physical dimensions from the trees directly and the capability to use existing conversion formulas for stand attributes. This paper describes the methods and gives a first indication of the performance of the developed method. It is shown that tree heights of individual trees in the dominating storey can be obtained with less than 1 m standard error. In addition, the following standard errors were obtained for mean height, basal area and stem volume at stand level: 2.3 m (13.6%), 1.9 m2/ha (9.6%), and 16.5 m3/ha (9.5%), respectively, even without using the tree species information. The accuracy was better than the accuracy of conventional standwise field inventory. It was also demonstrated that laser scanner is significantly more accurate than imaging spectrometer AISA in the stand attributes retrieval.

Methods for Mapping Forest Disturbance and Degradation from Optical Earth Observation Data: a Review

Purpose of ReviewThis paper presents a review of the current state of the art in remote sensing-based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview and tabular description of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing mapping approaches: first, classical image-to-image change detection and second, time series analysis.Recent FindingsWith the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa.SummaryThe review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high-resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real-time disturbance mapping in support of operational reactive measures.

A hybrid classifier for remote sensing applications.

This paper presents a hybrid-unsupervised and supervised-classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptions while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with complex non-normally distributed classes than Maximum Likelihood. The main advantage of the Neural Gas classifier, however, is that it requires much less user interaction than other classifiers, especially Maximum Likelihood.

On the capability of different SAR polarization combinations for agricultural monitoring

This paper examines the capability of different SAR polarization combinations for agricultural monitoring. For this purpose, a time series dataset of five quad-polarized images acquired by RADARSAT-2 (C-Band) is used. The different SAR polarization combinations are generated by splitting each input dataset in two additional dual-polarization combinations synthetically. Polarimetric decomposition is realized by a new Kennaugh matrix like decomposition, while the mandatory speckle filtering is performed by a pyramidal multi-looking approach. Thus, the data is normalized in order to fulfill the requirement of a normal distribution for the subsequent maximum likelihood classification. Concluding, the accuracy assessment provides a measure for the questioned classification capability of dual-polarized images in comparison to the quad-polarized data.

Using worldwide available TerraSAR-X data to calibrate the geo-location accuracy of optical sensors

A method to calibrate the geo-location accuracy of optical sensors is presented which is based on a novel multi-modal image matching strategy. This concept enables to transfer points from highly accurate TerraSAR-X imagery to optical images. These points are then used to register the images or to update the optical sensor models. The potential of the methodology is demonstrated on Spot 5, Ikonos and RapidEye images.

Comparison of SRTM-NED data to LIDAR derived canopy metrics

Forest canopy height derived from the SRTM-NED were compared to three LIDAR vegetation metrics for the Sierra Nevada forest. Generally the SRTM-NED was found to under estimate the vegetation canopy height. The SRTM SAR signal was found to penetrate, on average, into 44% of the canopies. The residual errors as a function of LVIS canopy height and cover were found to generally increase with height and cover. Likewise, the RMSE was found to initially increase with canopy height and cover but saturates at 50 m height and 60% cover.