High quality soil moisture datasets are required for various environmental applications. The laun... more High quality soil moisture datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1—Water (GCOM-W1) in May 2012 has provided global near-surface soil moisture data, with an average revisit frequency of two days. Since AMSR2 is a new passive microwave system in operation, it is very important to evaluate the quality of AMSR2 products before widespread utilization of the data for scientific research. In this paper, we provide a comprehensive evaluation of the AMSR2 soil moisture products retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm. The evaluation was performed for a three-year period (July 2012–June 2015) over the contiguous United States. The AMSR2 soil moisture products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ soil moisture observations of 598 monitoring stations obtained from the International Soil Moisture Network (ISMN). The accuracy of AMSR2 soil moisture product was evaluated against several types of monitoring networks, and for different land cover types and ecoregions. Three performance metrics, including mean difference (MD), root mean squared difference (RMSD), and correlation coefficient (R), were used in our accuracy assessment. Our evaluation results revealed that AMSR2 soil moisture retrievals are generally lower than in situ measurements. The AMSR2 soil moisture retrievals showed the best agreement with in situ measurements over the Great Plains and the worst agreement over forested areas. This study offers insights into the suitability and reliability of AMSR2 soil moisture products for different ecoregions. Although AMSR2 soil moisture retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.
Individual tree crown delineation is of great importance for forest inventory and management. The... more Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management.
We compared 10 established and 2 new satellite reflectance algorithms for estimating chlorophyll-... more We compared 10 established and 2 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident surface observations collected within 1 h of image acquisition to develop simple proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic or harmful algal blooms (HABs)) and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. All algorithms were compared with narrow band hyperspectral aircraft images. These images were subsequently upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl-a algorithms were then applied to the synthetic satellite images and compared to coincident surface observations of Chl-a collected from 44 sites within 1 h of aircraft acquisition of the imagery. We found several promising algorithm/satellite im-ager combinations for routine Chl-a estimation in smaller inland water bodies with operational and near-future satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl-a algorithms worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl-a algorithms worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like imagery. NDCI was the most widely applicable Chl-a algorithm with good performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery and limited performance with MODIS imagery. A new fluorescence line height " greenness " algorithm yielded the best Chl-a estimates with simulated Landsat-8 imagery.
Sinkhole mapping is critical for understanding hydrological processes and mitigating geological h... more Sinkhole mapping is critical for understanding hydrological processes and mitigating geological hazards in karst landscapes. Current methods for identifying sinkholes are primarily based on visual interpretation of low-resolution topographic maps and aerial photographs with subsequent field verification, which is labor-intensive and time-consuming. The increasing availability of high-resolution LiDAR-derived digital elevation data allows for an entirely new level of detailed delineation and analyses of small-scale geomorphologic features and landscape structures at fine scales. In this paper, we present a localized contour tree method for automated extraction of sinkholes in karst landscapes. One significant advantage of our automated approach for sinkhole extraction is that it may reduce inconsistencies and alleviate repeatability concerns associated with visual interpretation methods. In addition, the proposed method has contributed to improving the sinkhole inventory in several ways: (1) detection of non-inventoried sinkholes; (2) identification of previously inventoried sinkholes that have been filled; (3) delineation of sinkhole boundaries; and (4) characterization of sinkhole morphometric properties. We applied the method to Fillmore County in southeastern Minnesota, USA, and identified three times as many sinkholes as the existing database for the same area. The results suggest that previous visual interpretation method might significantly underestimate the number of potential sinkholes in the region. Our method holds great potential for creating and updating sinkhole inventory databases at a regional scale in a timely manner.
Flowing through Mongolia and Russia, the Selenga River is the main tributary to Lake Baikal, the ... more Flowing through Mongolia and Russia, the Selenga River is the main tributary to Lake Baikal, the world’s largest and deepest freshwater lake. The massive wetlands of the Selenga River Delta (SRD) on Lake Baikal perform important functions, including maintaining local and regional biodiversity and improving water quality. However, there exists a paucity of habitat and relevant ecological data for monitoring system-level changes. In this study, we characterized the rich habitat heterogeneity of the SRD using advanced 8-band multispectral satellite imagery coupled with a vegetation association algorithm and relatively extensive field surveys to analyze the spatial pattern of the system at multiple scales, from habitatspecific (e.g., Dense Floating Vascular habitats [Nymphoides sp.]) to coarser scales (e.g., Emergent Herbaceous; Aquatic Bed; Unconsolidated Bottom). We achieved an overall classification accuracy of 86.5 %for 22 wetland and aquatic habitat classes at the finest scale and greater than 91 % accuracy for broad vegetation and aquatic classes at more generalized scales. Our study provides the first detailed multi-scale characterization of the SRD for the conservation and management of the system and establishes baseline information for future change detection analyses.
Although remote sensing technology has long been used in wetland inventory and monitoring, the ac... more Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.
Although remote sensing technology has long been used in wetland inventory and monitoring, the ac... more Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.
This paper improved the traditional method of researching soil loss changes based on land-use dyn... more This paper improved the traditional method of researching soil loss changes based on land-use dynamic changes, geoinformation Tupu theory was introduced in to study the heterogeneity of soil erosion caused by land-use changes. With the example of Zhujiang Delta, we investigated the land-use changes and soil erosion changes over the period from 1998 to 2006. The results showed: the soil erosion intension aggravated in recent years, the average amount of soil erosion in 2006 was as much as 1.5 times of that in 1998; the change of land-use was the main reason for the change of soil erosion, and the diversity of soil erosion varied obviously with the transformation of land-use patterns; especially, soil erosion changed sharply with the mutual transformation between developing area or sand land and other land-use types correspondingly.
High quality soil moisture datasets are required for various environmental applications. The laun... more High quality soil moisture datasets are required for various environmental applications. The launch of the Advanced Microwave Scanning Radiometer 2 (AMSR2) on board the Global Change Observation Mission 1—Water (GCOM-W1) in May 2012 has provided global near-surface soil moisture data, with an average revisit frequency of two days. Since AMSR2 is a new passive microwave system in operation, it is very important to evaluate the quality of AMSR2 products before widespread utilization of the data for scientific research. In this paper, we provide a comprehensive evaluation of the AMSR2 soil moisture products retrieved by the Japan Aerospace Exploration Agency (JAXA) algorithm. The evaluation was performed for a three-year period (July 2012–June 2015) over the contiguous United States. The AMSR2 soil moisture products were evaluated by comparing ascending and descending overpass products to each other as well as comparing them to in situ soil moisture observations of 598 monitoring stations obtained from the International Soil Moisture Network (ISMN). The accuracy of AMSR2 soil moisture product was evaluated against several types of monitoring networks, and for different land cover types and ecoregions. Three performance metrics, including mean difference (MD), root mean squared difference (RMSD), and correlation coefficient (R), were used in our accuracy assessment. Our evaluation results revealed that AMSR2 soil moisture retrievals are generally lower than in situ measurements. The AMSR2 soil moisture retrievals showed the best agreement with in situ measurements over the Great Plains and the worst agreement over forested areas. This study offers insights into the suitability and reliability of AMSR2 soil moisture products for different ecoregions. Although AMSR2 soil moisture retrievals represent useful and effective measurements for some regions, further studies are required to improve the data accuracy.
Individual tree crown delineation is of great importance for forest inventory and management. The... more Individual tree crown delineation is of great importance for forest inventory and management. The increasing availability of high-resolution airborne light detection and ranging (LiDAR) data makes it possible to delineate the crown structure of individual trees and deduce their geometric properties with high accuracy. In this study, we developed an automated segmentation method that is able to fully utilize high-resolution LiDAR data for detecting, extracting, and characterizing individual tree crowns with a multitude of geometric and topological properties. The proposed approach captures topological structure of forest and quantifies topological relationships of tree crowns by using a graph theory-based localized contour tree method, and finally segments individual tree crowns by analogy of recognizing hills from a topographic map. This approach consists of five key technical components: (1) derivation of canopy height model from airborne LiDAR data; (2) generation of contours based on the canopy height model; (3) extraction of hierarchical structures of tree crowns using the localized contour tree method; (4) delineation of individual tree crowns by segmenting hierarchical crown structure; and (5) calculation of geometric and topological properties of individual trees. We applied our new method to the Medicine Bow National Forest in the southwest of Laramie, Wyoming and the HJ Andrews Experimental Forest in the central portion of the Cascade Range of Oregon, U.S. The results reveal that the overall accuracy of individual tree crown delineation for the two study areas achieved 94.21% and 75.07%, respectively. Our method holds great potential for segmenting individual tree crowns under various forest conditions. Furthermore, the geometric and topological attributes derived from our method provide comprehensive and essential information for forest management.
We compared 10 established and 2 new satellite reflectance algorithms for estimating chlorophyll-... more We compared 10 established and 2 new satellite reflectance algorithms for estimating chlorophyll-a (Chl-a) in a temperate reservoir in southwest Ohio using coincident hyperspectral aircraft imagery and dense coincident surface observations collected within 1 h of image acquisition to develop simple proxies for algal blooms in water bodies sensitive to algal blooms (especially toxic or harmful algal blooms (HABs)) and to facilitate portability between multispectral satellite imagers for regional algal bloom monitoring. All algorithms were compared with narrow band hyperspectral aircraft images. These images were subsequently upscaled spectrally and spatially to simulate 5 current and near future satellite imaging systems. Established and new Chl-a algorithms were then applied to the synthetic satellite images and compared to coincident surface observations of Chl-a collected from 44 sites within 1 h of aircraft acquisition of the imagery. We found several promising algorithm/satellite im-ager combinations for routine Chl-a estimation in smaller inland water bodies with operational and near-future satellite systems. The CI, MCI, FLH, NDCI, 2BDA and 3 BDA Chl-a algorithms worked well with CASI imagery. The NDCI, 2BDA, and 3BDA Chl-a algorithms worked well with simulated WorldView-2 and 3, Sentinel-2, and MERIS-like imagery. NDCI was the most widely applicable Chl-a algorithm with good performance for CASI, WorldView 2 and 3, Sentinel-2 and MERIS-like imagery and limited performance with MODIS imagery. A new fluorescence line height " greenness " algorithm yielded the best Chl-a estimates with simulated Landsat-8 imagery.
Sinkhole mapping is critical for understanding hydrological processes and mitigating geological h... more Sinkhole mapping is critical for understanding hydrological processes and mitigating geological hazards in karst landscapes. Current methods for identifying sinkholes are primarily based on visual interpretation of low-resolution topographic maps and aerial photographs with subsequent field verification, which is labor-intensive and time-consuming. The increasing availability of high-resolution LiDAR-derived digital elevation data allows for an entirely new level of detailed delineation and analyses of small-scale geomorphologic features and landscape structures at fine scales. In this paper, we present a localized contour tree method for automated extraction of sinkholes in karst landscapes. One significant advantage of our automated approach for sinkhole extraction is that it may reduce inconsistencies and alleviate repeatability concerns associated with visual interpretation methods. In addition, the proposed method has contributed to improving the sinkhole inventory in several ways: (1) detection of non-inventoried sinkholes; (2) identification of previously inventoried sinkholes that have been filled; (3) delineation of sinkhole boundaries; and (4) characterization of sinkhole morphometric properties. We applied the method to Fillmore County in southeastern Minnesota, USA, and identified three times as many sinkholes as the existing database for the same area. The results suggest that previous visual interpretation method might significantly underestimate the number of potential sinkholes in the region. Our method holds great potential for creating and updating sinkhole inventory databases at a regional scale in a timely manner.
Flowing through Mongolia and Russia, the Selenga River is the main tributary to Lake Baikal, the ... more Flowing through Mongolia and Russia, the Selenga River is the main tributary to Lake Baikal, the world’s largest and deepest freshwater lake. The massive wetlands of the Selenga River Delta (SRD) on Lake Baikal perform important functions, including maintaining local and regional biodiversity and improving water quality. However, there exists a paucity of habitat and relevant ecological data for monitoring system-level changes. In this study, we characterized the rich habitat heterogeneity of the SRD using advanced 8-band multispectral satellite imagery coupled with a vegetation association algorithm and relatively extensive field surveys to analyze the spatial pattern of the system at multiple scales, from habitatspecific (e.g., Dense Floating Vascular habitats [Nymphoides sp.]) to coarser scales (e.g., Emergent Herbaceous; Aquatic Bed; Unconsolidated Bottom). We achieved an overall classification accuracy of 86.5 %for 22 wetland and aquatic habitat classes at the finest scale and greater than 91 % accuracy for broad vegetation and aquatic classes at more generalized scales. Our study provides the first detailed multi-scale characterization of the SRD for the conservation and management of the system and establishes baseline information for future change detection analyses.
Although remote sensing technology has long been used in wetland inventory and monitoring, the ac... more Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.
Although remote sensing technology has long been used in wetland inventory and monitoring, the ac... more Although remote sensing technology has long been used in wetland inventory and monitoring, the accuracy and detail level of wetland maps derived with moderate resolution imagery and traditional techniques have been limited and often unsatisfactory. We explored and evaluated the utility of a newly launched high-resolution, eight-band satellite system (Worldview-2; WV2) for identifying and classifying freshwater deltaic wetland vegetation and aquatic habitats in the Selenga River Delta of Lake Baikal, Russia, using a hybrid approach and a novel application of Indicator Species Analysis (ISA). We achieved an overall classification accuracy of 86.5% (Kappa coefficient: 0.85) for 22 classes of aquatic and wetland habitats and found that additional metrics, such as the Normalized Difference Vegetation Index and image texture, were valuable for improving the overall classification accuracy and particularly for discriminating among certain habitat classes. Our analysis demonstrated that including WV2’s four spectral bands from parts of the spectrum less commonly used in remote sensing analyses, along with the more traditional bandwidths, contributed to the increase in the overall classification accuracy by ~4% overall, but with considerable increases in our ability to discriminate certain communities. The coastal band improved differentiating open water and aquatic (i.e., vegetated) habitats, and the yellow, red-edge, and near-infrared 2 bands improved discrimination among different vegetated aquatic and terrestrial habitats. The use of ISA provided statistical rigor in developing associations between spectral classes and field-based data. Our analyses demonstrated the utility of a hybrid approach and the benefit of additional bands and metrics in providing the first spatially explicit mapping of a large and heterogeneous wetland system.
This paper improved the traditional method of researching soil loss changes based on land-use dyn... more This paper improved the traditional method of researching soil loss changes based on land-use dynamic changes, geoinformation Tupu theory was introduced in to study the heterogeneity of soil erosion caused by land-use changes. With the example of Zhujiang Delta, we investigated the land-use changes and soil erosion changes over the period from 1998 to 2006. The results showed: the soil erosion intension aggravated in recent years, the average amount of soil erosion in 2006 was as much as 1.5 times of that in 1998; the change of land-use was the main reason for the change of soil erosion, and the diversity of soil erosion varied obviously with the transformation of land-use patterns; especially, soil erosion changed sharply with the mutual transformation between developing area or sand land and other land-use types correspondingly.
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Papers by Qiusheng Wu