Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands

Buddenbaum, Henning; Seeling, Stephan; Hill, Joachim

doi:10.1080/01431161.2013.776721

Cited by 45 publications

(29 citation statements)

References 35 publications

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“…Hyperspectral imaging from space has a great potential to become one of the most important information sources for identifying critical, drought-affected forest sites, particularly in synergy with higher spatial resolution data acquired at increased repetition rates (e.g., Sentinel-2 and -3). Research priorities are focusing on testing efficient spectral indicators, alongside the refinement of algorithms for forest-type mapping, the detection of structural changes and gradients in forest ecosystems (e.g., [60]) and forward and inverse radiative transfer modeling. Controlled laboratory and field experiments will continue to provide important backup information for algorithmic optimization.…”

Section: Development Of the Enmap Science Planmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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Imaging spectroscopy, also known as hyperspectral remote sensing, is based on the characterization of Earth surface materials and processes through spectrally-resolved measurements of the light interacting with matter. The potential of imaging spectroscopy for Earth remote sensing has been demonstrated since the 1980s. However, most of the developments and applications in imaging spectroscopy have largely relied on airborne spectrometers, as the amount and quality of space-based imaging spectroscopy data remain relatively low to date. The upcoming Environmental Mapping and Analysis Program (EnMAP) German imaging spectroscopy mission is intended to fill this gap. An overview of the main characteristics and current status of the mission is provided in this contribution. The core payload of EnMAP consists of a dual-spectrometer instrument measuring in the optical spectral range between 420 and 2450 nm with a spectral sampling distance varying between 5 and 12 nm and a reference signal-to-noise ratio of 400:1 in the visible and near-infrared and 180:1 in the shortwave-infrared parts of the spectrum. EnMAP images will cover a 30 km-wide area in the across-track direction with a ground sampling distance of 30 m. An across-track tilted observation capability will enable a target revisit time of up to four days at the Equator and better at high latitudes. EnMAP will contribute to the development and exploitation of spaceborne imaging spectroscopy applications by making high-quality data freely available to scientific users worldwide.

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Section: Development Of the Enmap Science Planmentioning

confidence: 99%

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

et al. 2015

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“…Radiometric correction involved sensor calibration and correction of atmospheric and topographic effects with the AtCPro© radiative transfer code [41,42], which is based on the 5S concept [43]. A limited number of spectral bands with too low signal-to-noise-ratio (1330-1490 nm and 1770-1990 nm) were eliminated [44].…”

Section: Airborne Hymap Imagerymentioning

confidence: 99%

Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume

et al. 2015

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Abstract:The availability of accurate and timely information on timber volume is important for supporting operational forest management. One option is to combine statistical concepts (e.g., small area estimates) with specifically designed terrestrial sampling strategies to provide estimations also on the level of administrative units such as forest districts. This may suffice for economic assessments, but still fails to provide spatially explicit information on the distribution of timber volume within these management units. This type of information, however, is needed for decision-makers to design and implement appropriate management operations. The German federal state of Rhineland-Palatinate is currently implementing an object-oriented database that will also allow the direct integration of Earth observation data products. This work analyzes the suitability of forthcoming multi-and hyperspectral satellite imaging systems for producing local distribution maps for timber volume of Norway spruce, one of the most economically important tree species. In combination with site-specific inventory data, fully processed hyperspectral data sets (HyMap) were used to simulate datasets of the forthcoming EnMAP and Sentinel-2 systems to establish adequate models for estimating timber volume OPEN ACCESSRemote Sens. 2015, 1 12010 maps. The analysis included PLS regression and the k-NN method. Root Mean Square Errors between 21.6% and 26.5% were obtained, where k-NN performed slightly better than PLSR. It was concluded that the datasets of both simulated sensor systems fulfill accuracy requirements to support local forest management operations and could be used in synergy. Sentinel-2 can provide meaningful volume distribution maps in higher geometric resolution, while EnMAP, due to its hyperspectral coverage, can contribute complementary information, e.g., on biophysical conditions.

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“…While the outcomes of Aderson et al for observing tree species abundances structures were improved after the integration of data (Anderson, et al, 2008). Buddenbaum et al, 2013, andHeinzel andKoch, 2012, used a combination of multi-sensor data for tree classifications. Buddenbaum et al use fusion of data to generate RGB images from a combination of FW LiDAR and hyperspectral features, although the fusion limits the dimensionality of a classifier (Buddenbaum et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Buddenbaum et al, 2013, andHeinzel andKoch, 2012, used a combination of multi-sensor data for tree classifications. Buddenbaum et al use fusion of data to generate RGB images from a combination of FW LiDAR and hyperspectral features, although the fusion limits the dimensionality of a classifier (Buddenbaum et al, 2013). Further, in their study, three different classifiers were implemented and the Support Vector Machines (SVMs) returns the best results.…”

Section: Introductionmentioning

confidence: 99%

Alignment of Hyperspectral Imagery and Full-Waveform Lidar Data for Visualisation and Classification Purposes

Miltiadou

Warren

Grant

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VI, WG VI/4KEY WORDS: Integration, Hyperspectral Imagery, Full-waveform LiDAR, Voxelisation, Visualisation, Tree coverage maps ABSTRACT:The overarching aim of this paper is to enhance the visualisations and classifications of airborne remote sensing data for remote forest surveys. A new open source tool is presented for aligning hyperspectral and full-waveform LiDAR data. The tool produces coloured polygon representations of the scanned areas and aligned metrics from both datasets. Using data provided by NERC ARSF, tree coverage maps are generated and projected into the polygons. The 3D polygon meshes show well-separated structures and are suitable for direct rendering with commodity 3D-accelerated hardware allowing smooth visualisation. The intensity profile of each wave sample is accumulated into a 3D discrete density volume building a 3D representation of the scanned area. The 3D volume is then polygonised using the Marching Cubes algorithm. Further, three user-defined bands from the hyperspectral images are projected into the polygon mesh as RGB colours. Regarding the classifications of full-waveform LiDAR data, previous work used extraction of point clouds while this paper introduces a new approach of deriving information from the 3D volume representation and the hyperspectral data. We generate aligned metrics of multiple resolutions, including the standard deviation of the hyperspectral bands and width of the reflected waveform derived from the volume. Tree coverage maps are then generated using a Bayesian probabilistic model and due to the combination of the data, higher accuracy classification results are expected.* Corresponding author. This is useful to know for communication with the appropriate person in cases with more than one author.

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Fusion of full-waveform lidar and imaging spectroscopy remote sensing data for the characterization of forest stands

Cited by 45 publications

References 35 publications

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

The EnMAP Spaceborne Imaging Spectroscopy Mission for Earth Observation

Assessing the Suitability of Future Multi- and Hyperspectral Satellite Systems for Mapping the Spatial Distribution of Norway Spruce Timber Volume

Alignment of Hyperspectral Imagery and Full-Waveform Lidar Data for Visualisation and Classification Purposes

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