@misc{RheinwaltBookhagen2018,
  author    = {Rheinwalt, Aljoscha and Bookhagen, Bodo},
  title     = {Network-based flow accumulation for point clouds},
  series = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX},
  volume    = {10783},
  journal   = {Remote Sensing for Agriculture, Ecosystems, and Hydrology XX},
  publisher = {SPIE-INT Society of Photo-Optical Instrumentation Engineers},
  address   = {Bellingham},
  isbn      = {978-1-5106-2150-3},
  issn      = {0277-786X},
  doi       = {10.1117/12.2318424},
  pages     = {12},
  year      = {2018},
  abstract  = {Point clouds provide high-resolution topographic data which is often classified into bare-earth, vegetation, and building points and then filtered and aggregated to gridded Digital Elevation Models (DEMs) or Digital Terrain Models (DTMs). Based on these equally-spaced grids flow-accumulation algorithms are applied to describe the hydrologic and geomorphologic mass transport on the surface. In this contribution, we propose a stochastic point-cloud filtering that, together with a spatial bootstrap sampling, allows for a flow accumulation directly on point clouds using Facet-Flow Networks (FFN). Additionally, this provides a framework for the quantification of uncertainties in point-cloud derived metrics such as Specific Catchment Area (SCA) even though the flow accumulation itself is deterministic.},
  language  = {en}
}
@misc{AtmaniBookhagenSmith2022,
  author    = {Atmani, Farid and Bookhagen, Bodo and Smith, Taylor},
  title     = {Measuring Vegetation Heights and Their Seasonal Changes in the Western Namibian Savanna Using Spaceborne Lidars},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1275},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-56991},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-569915},
  pages     = {20},
  year      = {2022},
  abstract  = {The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.},
  language  = {en}
}
@article{AtmaniBookhagenSmith2022,
  author    = {Atmani, Farid and Bookhagen, Bodo and Smith, Taylor},
  title     = {Measuring vegetation heights and their seasonal changes in the Western Namibian Savanna using spaceborne lidars},
  series = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  volume    = {14},
  journal   = {Remote sensing / Molecular Diversity Preservation International (MDPI)},
  number    = {12},
  edition   = {12},
  publisher = {MDPI},
  address   = {Basel, Schweiz},
  issn      = {2072-4292},
  doi       = {10.3390/rs14122928},
  pages     = {1 -- 20},
  year      = {2022},
  abstract  = {The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) with its land and vegetation height data product (ATL08), and Global Ecosystem Dynamics Investigation (GEDI) with its terrain elevation and height metrics data product (GEDI Level 2A) missions have great potential to globally map ground and canopy heights. Canopy height is a key factor in estimating above-ground biomass and its seasonal changes; these satellite missions can also improve estimated above-ground carbon stocks. This study presents a novel Sparse Vegetation Detection Algorithm (SVDA) which uses ICESat-2 (ATL03, geolocated photons) data to map tree and vegetation heights in a sparsely vegetated savanna ecosystem. The SVDA consists of three main steps: First, noise photons are filtered using the signal confidence flag from ATL03 data and local point statistics. Second, we classify ground photons based on photon height percentiles. Third, tree and grass photons are classified based on the number of neighbors. We validated tree heights with field measurements (n = 55), finding a root-mean-square error (RMSE) of 1.82 m using SVDA, GEDI Level 2A (Geolocated Elevation and Height Metrics product): 1.33 m, and ATL08: 5.59 m. Our results indicate that the SVDA is effective in identifying canopy photons in savanna ecosystems, where ATL08 performs poorly. We further identify seasonal vegetation height changes with an emphasis on vegetation below 3 m; widespread height changes in this class from two wet-dry cycles show maximum seasonal changes of 1 m, possibly related to seasonal grass-height differences. Our study shows the difficulties of vegetation measurements in savanna ecosystems but provides the first estimates of seasonal biomass changes.},
  language  = {en}
}
@article{RheinwahltGoswamiBookhagen2019,
  author    = {Rheinwahlt, Aljoscha and Goswami, Bedartha and Bookhagen, Bodo},
  title     = {A network-based flow accumulation algorithm for point clouds},
  series = {Journal of geophysical research : Earth surface},
  volume    = {124},
  journal   = {Journal of geophysical research : Earth surface},
  number    = {7},
  publisher = {American Geophysical Union},
  address   = {Washington},
  issn      = {2169-9003},
  doi       = {10.1029/2018JF004827},
  pages     = {2013 -- 2033},
  year      = {2019},
  abstract  = {Flow accumulation algorithms estimate the steady state of flow on real or modeled topographic surfaces and are crucial for hydrological and geomorphological assessments, including delineation of river networks, drainage basins, and sediment transport processes. Existing flow accumulation algorithms are typically designed to compute flows on regular grids and are not directly applicable to arbitrarily sampled topographic data such as lidar point clouds. In this study we present a random sampling scheme that generates homogeneous point densities, in combination with a novel flow path tracing approach-the Facet-Flow Network (FFN)-that estimates flow accumulation in terms of specific catchment area (SCA) on triangulated surfaces. The random sampling minimizes biases due to spatial sampling and the FFN allows for direct flow estimation from point clouds. We validate our approach on a Gaussian hill surface and study the convergence of its SCA compared to the analytical solution. Here, our algorithm outperforms the multiple flow direction algorithm, which is optimized for divergent surfaces. We also compute the SCA of a 6-km(2)-steep, vegetated catchment on Santa Cruz Island, California, based on airborne lidar point-cloud data. Point-cloud-based SCA values estimated by our method compare well with those estimated by the D-infinity or multiple flow direction algorithm on gridded data. The advantage of computing SCA from point clouds becomes relevant especially for divergent topography and for small drainage areas: These are depicted with much more detail due to the higher sampling density of point clouds.},
  language  = {en}
}