Geography

Permanent URI for this communityhttp://hdl.handle.net/1903/2242

Browse

Subject: search.filters.subject.change detection

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    Quantifying the Spatial Variability of Annual and Seasonal Changes in Riverscape Vegetation Using Drone Laser Scanning
    (MDPI, 2021-09-07) Resop, Jonathan P.; Lehmann, Laura; Hession, W. Cully
    Riverscapes are complex ecosystems consisting of dynamic processes influenced by spatially heterogeneous physical features. A critical component of riverscapes is vegetation in the stream channel and floodplain, which influences flooding and provides habitat. Riverscape vegetation can be highly variable in size and structure, including wetland plants, grasses, shrubs, and trees. This vegetation variability is difficult to precisely measure over large extents with traditional surveying tools. Drone laser scanning (DLS), or UAV-based lidar, has shown potential for measuring topography and vegetation over large extents at a high resolution but has yet to be used to quantify both the temporal and spatial variability of riverscape vegetation. Scans were performed on a reach of Stroubles Creek in Blacksburg, VA, USA six times between 2017 and 2019. Change was calculated both annually and seasonally over the two-year period. Metrics were derived from the lidar scans to represent different aspects of riverscape vegetation: height, roughness, and density. Vegetation was classified as scrub or tree based on the height above ground and 604 trees were manually identified in the riverscape, which grew on average by 0.74 m annually. Trees had greater annual growth and scrub had greater seasonal variability. Height and roughness were better measures of annual growth and density was a better measure of seasonal variability. The results demonstrate the advantage of repeat surveys with high-resolution DLS for detecting seasonal variability in the riverscape environment, including the growth and decay of floodplain vegetation, which is critical information for various hydraulic and ecological applications.
  • Thumbnail Image
    Item
    Lidar-Imagery Fusion Reveals Rapid Coastal Forest Loss in Delaware Bay Consistent with Marsh Migration
    (MDPI, 2022-09-13) Powell, Elisabeth B.; St. Laurent, Kari A.; Dubayah, Ralph
    Tidal wetland ecosystems and their vegetation communities are broadly controlled by tidal range and inundation frequency. Sea-level rise combined with episodic flooding events are causing shifts in thresholds of vegetation species which reconstructs the plant zonation of the coastal landscape. More frequent inundation events in the upland forest are causing the forest to convert into tidal marshes, and what is left behind are swaths of dead-standing trees along the marsh–forest boundary. Upland forest dieback has been well documented in the mid-Atlantic; however, reliable methods to accurately identify this dieback over large scales are still being developed. Here, we use multitemporal Lidar and imagery from the National Agricultural Imagery Program to classify areas of forest loss in the coastal regions of Delaware. We found that 1197 ± 405 hectares of forest transitioned to non-forest over nine years, and these losses were likely driven by major coastal storms and severe drought during the study period. In addition, we report decreases in Lidar-derived canopy height in forest loss areas, suggesting forest structure changes associated with the conversion from forest to marsh. Our results highlight the potential value of integrating Lidar-derived metrics to determine specific forest characteristics that may help predict future marsh migration pathways.
  • Thumbnail Image
    Item
    TREETOP: A Shiny-based application and R package for extracting forest information from LiDAR data for ecologists and conservationists
    (Wiley, 2022-06-06) Silva, Carlos Alberta; Hudak, Andrew T.; Vierling, Lee A.; Valbuena, Ruben; Cardil, Adrian; Mohan, Midhun; Alves de Almeida, Danilo Roberti; Broadbent, Eben N.; Almeyda Zambrano, Angelica M.; Wilkinson, Ben; Sharma, Ajay; Drake, Jason B.; Medley, Paul B.; Vogel, Jason G.; Atticciati Prata, Gabriel; Atkins, Jeff W.; Hamamura, Caio; Johnson, Daniel G.; Klauberg, Carine
    Individual tree detection (ITD) and crown delineation are two of the most relevant methods for extracting detailed and reliable forest information from LiDAR (Light Detection and Ranging) datasets. However, advanced computational skills and specialized knowledge have been normally required to extract forest information from LiDAR. The development of accessible tools for 3D forest characterization can facilitate rapid assessment by stakeholders lacking a remote sensing background, thus fostering the practical use of LiDAR datasets in forest ecology and conservation. This paper introduces the treetop application, an open-source web-based and R package LiDAR analysis tool for extracting forest structural information at the tree level, including cutting-edge analyses of properties related to forest ecology and management. We provide case studies of how treetop can be used for different ecological applications, within various forest ecosystems. Specifically, treetop was employed to assess post-hurricane disturbance in natural temperate forests, forest homogeneity in industrial forest plantations and the spatial distribution of individual trees in a tropical forest. treetop simplifies the extraction of relevant forest information for forest ecologists and conservationists who may use the tool to easily visualize tree positions and sizes, conduct complex analyses and download results including individual tree lists and figures summarizing forest structural properties. Through this open-source approach, treetop can foster the practical use of LiDAR data among forest conservation and management stakeholders and help ecological researchers to further understand the relationships between forest structure and function.
  • Thumbnail Image
    Item
    Integrated use of Landsat and Corona data for long-term monitoring of forest cover change and improved representation of its patch size distribution
    (2016) Song, Danxia; Townshend, John R; Huang, Chengquan; Geography; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Forest cover change has profound impact on global carbon cycle, hydrological processes, energy balance, and biodiversity. The primary goal of this dissertation is to improve forest cover change characterization by filling a number of knowledge gaps in forest change studies. These include use of Corona data to extend satellite based forest cover change mapping back to pre-Landsat years in the 1960s, quantification of forest cover change over four decades (1960s – 2005) for a major forested province in China using Corona and Landsat data, and development of more accurate patch size-frequency modeling methods for improved representation of forest disturbances in ecosystem and other spatially explicit models. With comprehensive data coverages in the 1960s, Corona data can be used to extend Landsat-based forest change analysis by up to a decade. The usefulness of such data, however, is hindered by poor geolocation accuracy and lack of multi-spectral bands. In this study, it was demonstrated that combined use of texture features and the advanced support vector machines allowed forest mapping with accuracies of up to 95% using Corona data. Further, a semi-automated method was developed for rapid registration of Corona images with residual errors as low as 100 m. These methods were used to assess the forest cover in the 1960s in Sichuan, a major forest province in China. Together with global forest cover change products derived using Landsat data, these results revealed that the forest cover in Sichuan Province was reduced from 45.19% in the 1960s to 38.98% by 1975 and further down to 28.91% by 1990. It then stayed relatively stable between 1990 and 2005, which contradicted trends reported by inventory data. The turning point between sharp decreases before 1990 and the stable period after 1990 likely reflected transitions in forest policies from focuses on timber production to forest conservation. Representation of forest disturbances in spatially explicit ecosystem models typically relies on patch size-frequency models to allocate an appropriate amount of disturbances to each patch size level. Existing patch size-frequency models, however, do not provide accurate representation of the total disturbance area nor the patch sizes at each frequency level. In this study, a hierarchical method was developed for modeling patch size-frequency distribution. Evaluation of this method over China revealed that it greatly improved the accuracy in representing the patch size at different frequency levels and reduced error in total disturbance area estimation over existing methods from around 40% to less than 10%. The significance of this dissertation is the contribution to improve the characterization of forest cover change by extending the satellite-based forest cover change monitoring back to the 1960s and developing a more accurate patch size distribution model to represent the forest disturbance in ecosystem models. The work in the dissertation has a broader impact beyond developing methods and models, as they provide essential basis to understand the relationship between the long-term change of forest and the socioeconomic transitions. They also improve the capacities of ecosystem and other spatially explicit models to simulate the vegetation dynamics and the resultant biodiversity and carbon dynamics.
  • Thumbnail Image
    Item
    Atmospheric Correction of Landsat ETM+ Land Surface Imagery: II. Validation and Applications
    (Institute of Electrical and Electronics Engineers, 2002) Liang, Shunlin; Morisette, Jeffrey T.; Fang, Hongliang; Chen, Mingzhen; Shuey, Chad J.; Daughtry, Craig S. T.; Walthall, Charles L.
    This is the second paper of the series on atmospheric correction of ETM+ land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to 1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements; 2) apply this algorithm to correct MODIS and SeaWiFS imagery; and 3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.