Papers by Russell Congalton
Remote Sensing, Aug 31, 2017
Remote Sensing, Jul 4, 2021
Remote Sensing, Mar 20, 2018
Remote Sensing, Jul 28, 2021
Geographies, Sep 18, 2021
American journal of remote sensing, 2019
Remote Sensing, Mar 15, 2019
Remote Sensing, Jul 23, 2020
The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, mult... more The development of unmanned aerial systems (UAS) equipped with various sensors (e.g., Lidar, multispectral sensors, and/or cameras) has provided the capability to "see" the individual trees in a forest. Individual tree crowns (ITCs) are the building blocks of precision forestry, because this knowledge allows users to analyze, model and manage the forest at the individual tree level by combing multiple data sources (e.g., remote sensing data and field surveys). Trees in the forest compete with other vegetation, especially neighboring trees, for limited resources to grow into the available horizontal and vertical space. Based on this assumption, this research developed a new region growing method that began with treetops as the initial seeds, and then segmented the ITCs, considering its growth space between the tree and its neighbors. The growth space was allocated by Euclidian distance and adjusted based on the crown size. Results showed that the over-segmentation accuracy (Oa), under-segmentation (Ua), and quality rate (QR) reached 0.784, 0.766, and 0.382, respectively, if the treetops were detected from a variable window filter based on an allometric equation for crown width. The Oa, Ua, and QR increased to 0.811, 0.853, and 0.296, respectively, when the treetops were manually adjusted. Treetop detection accuracy has a great impact on ITCs delineation accuracy. The uncertainties and limitations within this research including the interpretation error and accuracy measures were also analyzed and discussed, and a unified framework assessing the segmentation accuracy was highly suggested.
Frontiers in sustainable food systems, Sep 25, 2020
Remote Sensing, Nov 13, 2018
Mapping science, Sep 17, 1998
Remote Sensing, Dec 15, 2020
The primary goal of thematic accuracy assessment is to measure the quality of land cover products... more The primary goal of thematic accuracy assessment is to measure the quality of land cover products and it has become an essential component in global or regional land cover mapping. However, there are many uncertainties introduced in the validation process which could propagate into the derived accuracy measures and therefore impact the decisions made with these maps. Choosing the appropriate reference data sample unit is one of the most important decisions in this process. The majority of researchers have used a single pixel as the assessment unit for thematic accuracy assessment, while others have claimed that a single pixel is not appropriate. The research reported here shows the results of a simulation analysis from the perspective of positional errors. Factors including landscape characteristics, the classification scheme, the spatial scale, and the labeling threshold were also examined. The thematic errors caused by positional errors were analyzed using the current level of geo-registration accuracy achieved by several global land cover mapping projects. The primary results demonstrate that using a single-pixel as an assessment unit introduces a significant amount of thematic error. In addition, the coarser the spatial scale, the greater the impact on positional errors as most pixels in the image become mixed. A classification scheme with more classes and a more heterogeneous landscape increased the positional effect. Using a higher labeling threshold decreased the positional impact but greatly increased the number of abandoned units in the sample. This research showed that remote sensing applications should not employ a single-pixel as an assessment unit in the thematic accuracy assessment.
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Papers by Russell Congalton