An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height
Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Co...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/14/3/568 |
| _version_ | 1827659042631188480 |
|---|---|
| author | Junpeng Zhao Lei Zhao Erxue Chen Zengyuan Li Kunpeng Xu Xiangyuan Ding |
| author_facet | Junpeng Zhao Lei Zhao Erxue Chen Zengyuan Li Kunpeng Xu Xiangyuan Ding |
| author_sort | Junpeng Zhao |
| collection | DOAJ |
| description | Forest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Considering the high cost of acquiring LiDAR data over large areas, we took a two-stage up-scaling approach in estimating forest canopy height and aimed to develop a method for quantifying the uncertainty of the estimation result. Based on the generalized hierarchical model-based (GHMB) estimation framework, a new estimation framework named RK-GHMB that makes use of a geostatistical method (regression kriging, RK) was developed. In this framework, the wall-to-wall forest canopy height and corresponding uncertainty in map unit scale are generated. This study was carried out by integrating plot data, sampled airborne LiDAR data, and wall-to-wall Ziyuan-3 satellite (ZY3) stereo images. The result shows that RK-GHMB can obtain a similar estimation accuracy (<i>r</i> = 0.92, <i>MAE</i> = 1.50 m) to GHMB (<i>r</i> = 0.92, <i>MAE</i> = 1.52 m) with plot-based reference data. For LiDAR-based reference data, the accuracy of RK-GHMB (<i>r</i> = 0.78, <i>MAE</i> = 1.75 m) is higher than that of GHMB (<i>r</i> = 0.75, <i>MAE</i> = 1.85 m). The uncertainties for all map units range from 1.54 to 3.60 m for the RK-GHMB results. The values change between 1.84 and 3.60 m for GHMB. This study demonstrates that this two-stage up-scaling approach can be used to monitor forest canopy height. The proposed RK-GHMB approach considers the spatial autocorrelation of neighboring data in the second modeling stage and can achieve a higher accuracy. |
| first_indexed | 2024-03-09T23:14:01Z |
| format | Article |
| id | doaj.art-c4454ebee6db43caa8bc8550a2653b50 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T23:14:01Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-c4454ebee6db43caa8bc8550a2653b502023-11-23T17:39:52ZengMDPI AGRemote Sensing2072-42922022-01-0114356810.3390/rs14030568An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy HeightJunpeng Zhao0Lei Zhao1Erxue Chen2Zengyuan Li3Kunpeng Xu4Xiangyuan Ding5Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaInstitute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, ChinaForest canopy height is an essential parameter in estimating forest aboveground biomass (AGB), growing stock volume (GSV), and carbon storage, and it can provide necessary information in forest management activities. Light direction and ranging (LiDAR) is widely used for estimating canopy height. Considering the high cost of acquiring LiDAR data over large areas, we took a two-stage up-scaling approach in estimating forest canopy height and aimed to develop a method for quantifying the uncertainty of the estimation result. Based on the generalized hierarchical model-based (GHMB) estimation framework, a new estimation framework named RK-GHMB that makes use of a geostatistical method (regression kriging, RK) was developed. In this framework, the wall-to-wall forest canopy height and corresponding uncertainty in map unit scale are generated. This study was carried out by integrating plot data, sampled airborne LiDAR data, and wall-to-wall Ziyuan-3 satellite (ZY3) stereo images. The result shows that RK-GHMB can obtain a similar estimation accuracy (<i>r</i> = 0.92, <i>MAE</i> = 1.50 m) to GHMB (<i>r</i> = 0.92, <i>MAE</i> = 1.52 m) with plot-based reference data. For LiDAR-based reference data, the accuracy of RK-GHMB (<i>r</i> = 0.78, <i>MAE</i> = 1.75 m) is higher than that of GHMB (<i>r</i> = 0.75, <i>MAE</i> = 1.85 m). The uncertainties for all map units range from 1.54 to 3.60 m for the RK-GHMB results. The values change between 1.84 and 3.60 m for GHMB. This study demonstrates that this two-stage up-scaling approach can be used to monitor forest canopy height. The proposed RK-GHMB approach considers the spatial autocorrelation of neighboring data in the second modeling stage and can achieve a higher accuracy.https://www.mdpi.com/2072-4292/14/3/568forestup-scalingregression kriginguncertaintyLiDAR |
| spellingShingle | Junpeng Zhao Lei Zhao Erxue Chen Zengyuan Li Kunpeng Xu Xiangyuan Ding An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height Remote Sensing forest up-scaling regression kriging uncertainty LiDAR |
| title | An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height |
| title_full | An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height |
| title_fullStr | An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height |
| title_full_unstemmed | An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height |
| title_short | An Improved Generalized Hierarchical Estimation Framework with Geostatistics for Mapping Forest Parameters and Its Uncertainty: A Case Study of Forest Canopy Height |
| title_sort | improved generalized hierarchical estimation framework with geostatistics for mapping forest parameters and its uncertainty a case study of forest canopy height |
| topic | forest up-scaling regression kriging uncertainty LiDAR |
| url | https://www.mdpi.com/2072-4292/14/3/568 |
| work_keys_str_mv | AT junpengzhao animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT leizhao animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT erxuechen animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT zengyuanli animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT kunpengxu animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT xiangyuanding animprovedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT junpengzhao improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT leizhao improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT erxuechen improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT zengyuanli improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT kunpengxu improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight AT xiangyuanding improvedgeneralizedhierarchicalestimationframeworkwithgeostatisticsformappingforestparametersanditsuncertaintyacasestudyofforestcanopyheight |