Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads

Object-based classification of abandoned logging roads under heavy canopy using LiDAR

LiDAR-derived slope models may be used to detect abandoned logging\ud roads in steep forested terrain. An object-based classification approach to\ud abandoned logging road detection was employed in this study. First, a slope model\ud of the study site in Marin, California was created from a LiDAR derived DEM.\ud Multiresolution segmentation was applied to the slope model and road seed objects\ud were iteratively grown into candidate objects. A road classification accuracy of\ud 86% was achieved using this fully automated procedure and post processing\ud increased this accuracy to 90%. In order to assess the sensitivity of the road\ud classification to LiDAR ground point spacing, the LiDAR ground point cloud was\ud repeatedly thinned by a fraction of 0.5 and the classification procedure was\ud reapplied. The producer???s accuracy of the road classification declined from 79 %\ud with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2,\ud indicating the importance of high point density for accurate classification of\ud abandoned logging roads

Land-use impacts on water resources and protected areas: applications of state-and-transition simulation modeling of future scenarios

Human land use will increasingly contribute to habitat loss and water shortages in California, given future population projections and associated land-use demand. Understanding how land-use change may impact future water use and where existing protected areas may be threatened by land-use conversion will be important if effective, sustainable management approaches are to be implemented. We used a state-and-transition simulation modeling (STSM) framework to simulate spatially-explicit (1 km2) historical (1992‒2010) and future (2011‒2060) land-use change for 52 California counties within Mediterranean California ecoregions. Historical land use and land cover (LULC) change estimates were derived from the Farmland Mapping and Monitoring Program dataset and attributed with county-level agricultural water-use data from the California Department of Water Resources. Five future alternative land-use scenarios were developed and modeled using the historical land-use change estimates and land-use projections based on the Intergovernmental Panel on Climate Change’s Special Report on Emission Scenarios A2 and B1 scenarios. Spatial land-use transition outputs across scenarios were combined to reveal scenario agreement and a land conversion threat index was developed to evaluate vulnerability of existing protected areas to proximal land conversion. By 2060, highest LULC conversion threats were projected to impact nearly 10,500 km2 of land area within 10 km of a protected area boundary and over 18,000 km2 of land area within essential habitat connectivity areas. Agricultural water use declined across all scenarios perpetuating historical drought-related land use from 2008‒2010 and trends of annual cropland conversion into perennial woody crops. STSM is useful in analyzing land-use related impacts on water resource use as well as potential threats to existing protected land. Exploring a range of alternative, yet plausible, LULC change impacts will help to better inform resource management and mitigation strategies

Downscaling global land-use/land-cover projections for use in region-level state-and-transition simulation modeling

Global land-use/land-cover (LULC) change projections and historical datasets are typically available at coarse grid resolutions and are often incompatible with modeling applications at local to regional scales. The difficulty of downscaling and reapportioning global gridded LULC change projections to regional boundaries is a barrier to the use of these datasets in a state-and-transition simulation model (STSM) framework. Here we compare three downscaling techniques to transform gridded LULC transitions into spatial scales and thematic LULC classes appropriate for use in a regional STSM. For each downscaling approach, Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) LULC projections, at the 0.5 × 0.5 cell resolution, were downscaled to seven Level III ecoregions in the Pacific Northwest, United States. RCP transition values at each cell were downscaled based on the proportional distribution between ecoregions of (1) cell area, (2) land-cover composition derived from remotely-sensed imagery, and (3) historic LULC transition values from a LULC history database. Resulting downscaled LULC transition values were aggregated according to their bounding ecoregion and “cross-walked” to relevant LULC classes. Ecoregion-level LULC transition values were applied in a STSM projecting LULC change between 2005 and 2100. While each downscaling methods had advantages and disadvantages, downscaling using the historical land-use history dataset consistently apportioned RCP LULC transitions in agreement with historical observations. Regardless of the downscaling method, some LULC projections remain improbable and require further investigation

Estimating carbon sequestration in the piedmont ecoregion of the United States from 1971 to 2010

Abstract Background Human activities have diverse and profound impacts on ecosystem carbon cycles. The Piedmont ecoregion in the eastern United States has undergone significant land use and land cover change in the past few decades. The purpose of this study was to use newly available land use and land cover change data to quantify carbon changes within the ecoregion. Land use and land cover change data (60-m spatial resolution) derived from sequential remotely sensed Landsat imagery were used to generate 960-m resolution land cover change maps for the Piedmont ecoregion. These maps were used in the Integrated Biosphere Simulator (IBIS) to simulate ecosystem carbon stock and flux changes from 1971 to 2010. Results Results show that land use change, especially urbanization and forest harvest had significant impacts on carbon sources and sinks. From 1971 to 2010, forest ecosystems sequestered 0.25 Mg C ha−1 yr−1, while agricultural ecosystems sequestered 0.03 Mg C ha−1 yr−1. The total ecosystem C stock increased from 2271 Tg C in 1971 to 2402 Tg C in 2010, with an annual average increase of 3.3 Tg C yr−1. Conclusions Terrestrial lands in the Piedmont ecoregion were estimated to be weak net carbon sink during the study period. The major factors contributing to the carbon sink were forest growth and afforestation; the major factors contributing to terrestrial emissions were human induced land cover change, especially urbanization and forest harvest. An additional amount of carbon continues to be stored in harvested wood products. If this pool were included the carbon sink would be stronger

Object-Based Classification of Abandoned Logging Roads under Heavy Canopy Using LiDAR

LiDAR-derived slope models may be used to detect abandoned logging roads in steep forested terrain. An object-based classification approach of abandoned logging road detection was employed in this study. First, a slope model of the study site in Marin County, California was created from a LiDAR derived DEM. Multiresolution segmentation was applied to the slope model and road seed objects were iteratively grown into candidate objects. A road classification accuracy of 86% was achieved using this fully automated procedure and post processing increased this accuracy to 90%. In order to assess the sensitivity of the road classification to LiDAR ground point spacing, the LiDAR ground point cloud was repeatedly thinned by a fraction of 0.5 and the classification procedure was reapplied. The producer’s accuracy of the road classification declined from 79% with a ground point spacing of 0.91 to below 50% with a ground point spacing of 2, indicating the importance of high point density for accurate classification of abandoned logging roads