Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska
The boreal biome, the largest terrestrial biome on Earth, is increasingly vulnerable to climate change due to warming twice as rapidly as the global average. Climate change has increased the temperature, frequency, severity, and amount of area burned, which is leading to changes in the spatial exten...
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Elsevier
2025-06-01
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| Series: | Science of Remote Sensing |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666017224000762 |
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| author | Pratima Khatri-Chhetri Hans-Erik Andersen Bruce Cook Sean M. Hendryx Liz van Wagtendonk Van R. Kane |
| author_facet | Pratima Khatri-Chhetri Hans-Erik Andersen Bruce Cook Sean M. Hendryx Liz van Wagtendonk Van R. Kane |
| author_sort | Pratima Khatri-Chhetri |
| collection | DOAJ |
| description | The boreal biome, the largest terrestrial biome on Earth, is increasingly vulnerable to climate change due to warming twice as rapidly as the global average. Climate change has increased the temperature, frequency, severity, and amount of area burned, which is leading to changes in the spatial extent of forest type and species range. These rapid ecological shifts necessitate fine-scale monitoring of forest type to detect potential type conversions and guide management interventions. In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. For this purpose, we conducted forest type classification at three different levels, including 1. forest and nonforest, 2. hardwood, softwood, and nonforest, and 3. three dominant forest types, including paper birch, black spruce, white spruce, and nonforest. To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. The remote sensing data were collected under variable sky conditions (clear to overcast) throughout a 1-month growing-season period, and field data collected by United States Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis program (FIA). In this framework, we also studied the importance of topographic and remote sensing variables for the classification of forest types. We found the CNN model outperformed the XGBoost model in terms of overall accuracy and a macro average F1 score for all three different forest type classifications. The CNN model achieved an overall accuracy of 93.1% for forest or nonforest, 82.6% for hardwood, softwood, and nonforest, and 74.7% for three dominant forest types including paper birch, black spruce, and white spruce along with nonforest. Among the various topographic factors, we found that elevation was the most important factor for discriminating all forest types. In addition, we found that canopy height and vegetation indices including Photochemical Reflectance Index (PRI) (R531 & R570), Pigment Specific Normalized Difference (PSND) (R635 & R800), and Gitelson and Merzlyak (GM1) (R550 & R750) were important for differentiating between hardwood and softwood while Anthocyanin Reflectance Index (ARI1) (R550 & R700) was important for differentiating between forest and nonforest. The high-resolution forest type information can improve our ecological understanding of boreal forest dynamics, estimate above ground biomass, and carbon, and support the national forest inventory and forest managers. |
| format | Article |
| id | doaj-art-9748dc5de2134b1a935ae2334019cdc4 |
| institution | Kabale University |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-9748dc5de2134b1a935ae2334019cdc42025-01-20T04:17:51ZengElsevierScience of Remote Sensing2666-01722025-06-0111100192Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior AlaskaPratima Khatri-Chhetri0Hans-Erik Andersen1Bruce Cook2Sean M. Hendryx3Liz van Wagtendonk4Van R. Kane5School of Environmental and Forest Sciences, University of Washington, Anderson Hall, 3715 W Stevens Way NE, Seattle, WA, 98195, USA; Corresponding author.USDA Forest Service, Pacific Northwest Research Station, Seattle, WA, USANASA, Goddard Space Flight Center, Biospheric Sciences Laboratory, Greenbelt, MD, 20771, USAScale AI, 155 5th St, San Francisco, CA, 94103, USASchool of Environmental and Forest Sciences, University of Washington, Anderson Hall, 3715 W Stevens Way NE, Seattle, WA, 98195, USASchool of Environmental and Forest Sciences, University of Washington, Anderson Hall, 3715 W Stevens Way NE, Seattle, WA, 98195, USAThe boreal biome, the largest terrestrial biome on Earth, is increasingly vulnerable to climate change due to warming twice as rapidly as the global average. Climate change has increased the temperature, frequency, severity, and amount of area burned, which is leading to changes in the spatial extent of forest type and species range. These rapid ecological shifts necessitate fine-scale monitoring of forest type to detect potential type conversions and guide management interventions. In this study, we present a framework for forest type classification combining field plots and high-resolution remote sensing data using machine learning models in the boreal forest of Interior Alaska. For this purpose, we conducted forest type classification at three different levels, including 1. forest and nonforest, 2. hardwood, softwood, and nonforest, and 3. three dominant forest types, including paper birch, black spruce, white spruce, and nonforest. To achieve this goal, we compared the performance of two advanced modeling approaches, the convolutional neural network (CNN) and the XGBoost model. Our datasets included field and high-resolution topographic metrics including elevation, slope, aspect, and solar radiation and canopy height derived from lidar (1 m) and 44 vegetation indices derived from high-resolution (1 m) visible to near infrared (VNIR) hyperspectral data collected by NASA Goddard's Lidar, Hyperspectral and Thermal Imager (G-LiHT) sensor. The remote sensing data were collected under variable sky conditions (clear to overcast) throughout a 1-month growing-season period, and field data collected by United States Department of Agriculture (USDA) Forest Service, Forest Inventory and Analysis program (FIA). In this framework, we also studied the importance of topographic and remote sensing variables for the classification of forest types. We found the CNN model outperformed the XGBoost model in terms of overall accuracy and a macro average F1 score for all three different forest type classifications. The CNN model achieved an overall accuracy of 93.1% for forest or nonforest, 82.6% for hardwood, softwood, and nonforest, and 74.7% for three dominant forest types including paper birch, black spruce, and white spruce along with nonforest. Among the various topographic factors, we found that elevation was the most important factor for discriminating all forest types. In addition, we found that canopy height and vegetation indices including Photochemical Reflectance Index (PRI) (R531 & R570), Pigment Specific Normalized Difference (PSND) (R635 & R800), and Gitelson and Merzlyak (GM1) (R550 & R750) were important for differentiating between hardwood and softwood while Anthocyanin Reflectance Index (ARI1) (R550 & R700) was important for differentiating between forest and nonforest. The high-resolution forest type information can improve our ecological understanding of boreal forest dynamics, estimate above ground biomass, and carbon, and support the national forest inventory and forest managers.http://www.sciencedirect.com/science/article/pii/S2666017224000762Boreal forestDeep learningG-LiHTLidarForest type mapping |
| spellingShingle | Pratima Khatri-Chhetri Hans-Erik Andersen Bruce Cook Sean M. Hendryx Liz van Wagtendonk Van R. Kane Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska Science of Remote Sensing Boreal forest Deep learning G-LiHT Lidar Forest type mapping |
| title | Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska |
| title_full | Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska |
| title_fullStr | Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska |
| title_full_unstemmed | Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska |
| title_short | Synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of Interior Alaska |
| title_sort | synthesizing field plot and airborne remote sensing data to enhance national forest inventory mapping in the boreal forest of interior alaska |
| topic | Boreal forest Deep learning G-LiHT Lidar Forest type mapping |
| url | http://www.sciencedirect.com/science/article/pii/S2666017224000762 |
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