A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock
Landsat time-series (LTS) archived the multitemporal hyperspectral images, providing freely accessible and long-term optical data for estimating forest aboveground carbon stock (ACS). Due to LTS carrying noise, there were such issues as bias, outliers, and missing values in ACS estimation. Hence, a...
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10876592/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849709099996938240 |
|---|---|
| author | Kai Huang Chenkai Teng Jialong Zhang Rui Bao Yi Liao Yunrun He Bo Qiu Mingrui Xu |
| author_facet | Kai Huang Chenkai Teng Jialong Zhang Rui Bao Yi Liao Yunrun He Bo Qiu Mingrui Xu |
| author_sort | Kai Huang |
| collection | DOAJ |
| description | Landsat time-series (LTS) archived the multitemporal hyperspectral images, providing freely accessible and long-term optical data for estimating forest aboveground carbon stock (ACS). Due to LTS carrying noise, there were such issues as bias, outliers, and missing values in ACS estimation. Hence, a new filtering method named terrain-perceive spatiotemporal filtering (TP-STF) was developed to improve the estimation accuracy. In TP-STF, landforms were classified based on the terrain data. A computer-recognizable identifier was generated by perceiving each terrain unit. Combining the discriminative criteria with the spatiotemporal information, the TP-STF adaptively selected performant filtering to reconstruct LTS. Then, the random forests regression (RFR) was employed to estimate ACS of <italic>Pinus densata</italic> in Shangri-La, Yunnan, China. Compared with the other filtering, the TP-STF method's reconstructed LTS had the best modeling accuracy and the highest prediction accuracy, with <italic>R</italic><sup>2</sup> = 0.903, RMSE = 17.049 t/hm<sup>2</sup>, <italic>P</italic> = 81.080%, and rRMSE = 19.691%. The ACS results using TP-STF and RFR were: 6.56 million tons in 1987, 6.44 million tons in 1992, 6.33 million tons in 1997, 6.35 million tons in 2002, 6.72 million tons in 2007, 6.70 million tons in 2012, and 7.04 million tons in 2017. The TP-STF could effectively denoise the LTS images in high-altitude regions, providing a new approach to improve the accuracy of remote sensing-based forest ACS estimation. |
| format | Article |
| id | doaj-art-eb8737f6884940a0bf8d147ec1285bc0 |
| institution | DOAJ |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-eb8737f6884940a0bf8d147ec1285bc02025-08-20T03:15:26ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-01186503651910.1109/JSTARS.2025.353939510876592A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon StockKai Huang0https://orcid.org/0009-0001-5690-5242Chenkai Teng1https://orcid.org/0009-0001-8336-5907Jialong Zhang2https://orcid.org/0000-0002-6969-3656Rui Bao3https://orcid.org/0000-0003-1458-1494Yi Liao4https://orcid.org/0000-0003-3995-3784Yunrun He5https://orcid.org/0009-0008-1796-8639Bo Qiu6https://orcid.org/0009-0001-5493-7718Mingrui Xu7https://orcid.org/0009-0009-8013-8096College of Soil and Water Conservation, Southwest Forestry University, Kunming, ChinaKey Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Kunming, ChinaKey Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Kunming, ChinaInstitute of Southwest Survey and Planning, National Forestry and Grassland Administration, Kunming, ChinaCollege of Mechanical and Electronic Engineering, Northwest Agriculture and Forestry University, Xianyang, ChinaKey Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Kunming, ChinaKey Laboratory of Forest Resources Conservation and Utilization in the Southwest Mountains of China Ministry of Education, Kunming, ChinaCollege of Soil and Water Conservation, Southwest Forestry University, Kunming, ChinaLandsat time-series (LTS) archived the multitemporal hyperspectral images, providing freely accessible and long-term optical data for estimating forest aboveground carbon stock (ACS). Due to LTS carrying noise, there were such issues as bias, outliers, and missing values in ACS estimation. Hence, a new filtering method named terrain-perceive spatiotemporal filtering (TP-STF) was developed to improve the estimation accuracy. In TP-STF, landforms were classified based on the terrain data. A computer-recognizable identifier was generated by perceiving each terrain unit. Combining the discriminative criteria with the spatiotemporal information, the TP-STF adaptively selected performant filtering to reconstruct LTS. Then, the random forests regression (RFR) was employed to estimate ACS of <italic>Pinus densata</italic> in Shangri-La, Yunnan, China. Compared with the other filtering, the TP-STF method's reconstructed LTS had the best modeling accuracy and the highest prediction accuracy, with <italic>R</italic><sup>2</sup> = 0.903, RMSE = 17.049 t/hm<sup>2</sup>, <italic>P</italic> = 81.080%, and rRMSE = 19.691%. The ACS results using TP-STF and RFR were: 6.56 million tons in 1987, 6.44 million tons in 1992, 6.33 million tons in 1997, 6.35 million tons in 2002, 6.72 million tons in 2007, 6.70 million tons in 2012, and 7.04 million tons in 2017. The TP-STF could effectively denoise the LTS images in high-altitude regions, providing a new approach to improve the accuracy of remote sensing-based forest ACS estimation.https://ieeexplore.ieee.org/document/10876592/Aboveground carbon stock (ACS)Landsat time-series (LTS)Shangri-Laterrain-perceive spatiotemporal filtering (TP-STF) |
| spellingShingle | Kai Huang Chenkai Teng Jialong Zhang Rui Bao Yi Liao Yunrun He Bo Qiu Mingrui Xu A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Aboveground carbon stock (ACS) Landsat time-series (LTS) Shangri-La terrain-perceive spatiotemporal filtering (TP-STF) |
| title | A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock |
| title_full | A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock |
| title_fullStr | A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock |
| title_full_unstemmed | A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock |
| title_short | A New Spatiotemporal Filtering Method to Reconstruct Landsat Time-Series for Improving Estimation Accuracy of Forest Aboveground Carbon Stock |
| title_sort | new spatiotemporal filtering method to reconstruct landsat time series for improving estimation accuracy of forest aboveground carbon stock |
| topic | Aboveground carbon stock (ACS) Landsat time-series (LTS) Shangri-La terrain-perceive spatiotemporal filtering (TP-STF) |
| url | https://ieeexplore.ieee.org/document/10876592/ |
| work_keys_str_mv | AT kaihuang anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT chenkaiteng anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT jialongzhang anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT ruibao anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT yiliao anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT yunrunhe anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT boqiu anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT mingruixu anewspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT kaihuang newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT chenkaiteng newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT jialongzhang newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT ruibao newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT yiliao newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT yunrunhe newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT boqiu newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock AT mingruixu newspatiotemporalfilteringmethodtoreconstructlandsattimeseriesforimprovingestimationaccuracyofforestabovegroundcarbonstock |