High Spatial Resolution Remotely Sensed Data for Ecosystem Characterization

By M. Wulder, R. Hall, Nicholas C. Coops, S. Franklin. 2004

400 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/72389eadf5907284cdfd78bc0f6c576d9da29486

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Citations in the corpus

Putting the ‘landscape’ in landscape genetics (2007, methodology): “Recent advances in fine scale data resolution (o4 m) and analysis methods have greatly improved the spatial accuracy and precision of detecting, classifying and delineating landscape habitat characteristics in both two (Wulder et al., 2004; Greenberg et al., 2005) and Putting the ‘landscape’ in land”
An automated object-based approach for the multiscale image segmentation of forest scenes (2005, background): “A key driver in this object-based shift has been the dramatic increase in commercially available high resolution digital remote sensing imagery that is characterized by spatial resolutions 5.0 m and finer (Wulder, 1998; Wulder et al., 2004).”
Lightweight unmanned aerial vehicles will revolutionize spatial ecology (2013, background): “Indeed, data provided by these platforms are often " too general to meet regional or local objectives " in ecology (Wulder et al. 2004).”
Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology (2016, background): “Data collected through traditional remote- sensing techniques (eg manned aircraft or satellite) are often too coarse in resolution to suit fine- scale ecological studies (Wulder et al. 2004).”
Landsat continuity: Issues and opportunities for land cover monitoring (2008, background): “Very high resolution imagery tends to separate land cover into individual landscape elements (tree crowns, shrubs, gaps, etcetera) which must be reaggregated to form consistent land cover types (Wulder et al., 2004).”
Tree Species Classification with Random Forest Using Very High Spatial Resolution 8-Band WorldView-2 Satellite Data (2012, background): “It is a relevant parameter for various ecological issues such as wildlife habitat modeling and it is also becoming more and more important in sustainable forest management [1–3].”
Estimating tree crown size with spatial information of high resolution optical remotely sensed imagery (2007, background): “…have further spurred the interest in obtaining detailed canopy structure from remote sensing for a wide range of applications, such as ecology (Hurtt et al. 2003, Clark et al. 2004a,b, Wulder et al. 2004) and forest management (Read et al. 2003, White et al. 2004, Wallace and Marsh 2005).”
Using Imaging Spectroscopy to Study Ecosystem Processes and Properties (2004, background): “…radar, and vegetation indices are relatively insensitive to ecosystems with the low biomass characteristic of semiarid systems (between 0 and 60 megagrams carbon per hectare [ha]), because of these systems’ low leaf biomass and discontinuous canopies (Treuhaft et al. 2004, Wulder et al. 2004).”
Benefits of hyperspectral remote sensing for tracking plant invasions (2011, background): “This variability is because of differences in shading, and separate imaging of leaves and bark, which can make it harder to identify representative signatures of different species (Nagendra, 2001; Wulder et al., 2004).”
High-resolution image texture as a predictor of bird species richness (2006, background): “One possible solution is to use remote sensing technologies because they cover broad spatial extents yet provide detailed attribute characterization (Wulder et al., 2004).”
The role of LiDAR in sustainable forest management (2008, background): “…size) and the concomitant objects that can be characterized (trees, stands) is well understood, with high spatial resolution data enabling single tree identification and analyses, and lower spatial resolution data sources enabling coarser stand-level (or broader) analyses (Wulder et al. 2004).”
Pine plantation structure mapping using WorldView-2 multispectral image (2013, background): “Consequently, areas appear more homogeneous on moderate-resolution images than is the case for high-resolution data (Scarth, Phinn, and McAlpine 2001; Wulder et al. 2004).”