Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection
The age of a forest serves as a critical indicator of both its carbon stock and its capacity for carbon volume within forest ecosystems. The high spatial and temporal resolution of forest age improves the accuracy of estimates for forest biomass, storage, and carbon stocks. Obtaining reliable forest...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Ecological Indicators |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1470160X24015000 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832576536665915392 |
|---|---|
| author | Lili Zhai Mei Zan Mao Ye Jia Zhou Cong Xue Shunfa Yang Yuntong Liu |
| author_facet | Lili Zhai Mei Zan Mao Ye Jia Zhou Cong Xue Shunfa Yang Yuntong Liu |
| author_sort | Lili Zhai |
| collection | DOAJ |
| description | The age of a forest serves as a critical indicator of both its carbon stock and its capacity for carbon volume within forest ecosystems. The high spatial and temporal resolution of forest age improves the accuracy of estimates for forest biomass, storage, and carbon stocks. Obtaining reliable forest age data across various scales remains a significant challenge. This study conducted forest age mapping at a 30 m resolution over long time series, taking into account forest disturbance events, using Xinjiang forest as a case study. In 1991, the age of forests in Xinjiang was estimated utilizing the Random Forest (RF) model, incorporating both forest inventory data and remote sensing data. Utilizing Landsat Time Series Stacked (LTSS) Data from 1991 to 2022, the LandTrendr(LT) algorithm was applied to identify forest disturbance and recovery events from 1992 to 2022 using the Google Earth Engine (GEE) platform. Finally, the forest age distribution in Xinjiang from 1991 to 2022 was mapped in conjunction with the forest disturbance events. The findings revealed that: (1) the R2 values of the RF regression models for estimating forest age exceeded 0.65, with RMSEs below 22 years and RRMSEs ranging from 0.119 to 0.183, demonstrating high credibility of the simulation results; (2) the overall accuracy of the LandTrendr algorithm in identifying forest loss and gain in the study area was 85 % or higher. From 1992 to 2022, Xinjiang experienced a net increase in forest area of 28580.69 km2; (3) the forest age distribution from 1991 to 2022 indicates a predominance of young and middle-aged forests, with the proportion of old forests remaining between 30 % and 40 %, showing a gradual annual increase. This study presents a method to improve the estimation of forest age at high resolution over extended time series. It also supplies essential data for assessing carbon storage in Xinjiang’s forests, both historically and prospectively, contributing to a deeper understanding of carbon balance in forest ecosystems within arid regions. |
| format | Article |
| id | doaj-art-35a2e3e0eeb44d47984ef29fb850fe88 |
| institution | Kabale University |
| issn | 1470-160X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecological Indicators |
| spelling | doaj-art-35a2e3e0eeb44d47984ef29fb850fe882025-01-31T05:10:38ZengElsevierEcological Indicators1470-160X2025-01-01170113043Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detectionLili Zhai0Mei Zan1Mao Ye2Jia Zhou3Cong Xue4Shunfa Yang5Yuntong Liu6School of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, ChinaSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, China; Xinjiang Laboratory of Lake Environment and Resources in the Arid Zone, Urumqi, China; Corresponding author at: School of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, China.School of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, China; Xinjiang Laboratory of Lake Environment and Resources in the Arid Zone, Urumqi, ChinaSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, ChinaSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, ChinaSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, ChinaSchool of Geographic Science and Tourism, Xinjiang Normal University, Urumqi, China; Xinjiang Laboratory of Lake Environment and Resources in the Arid Zone, Urumqi, ChinaThe age of a forest serves as a critical indicator of both its carbon stock and its capacity for carbon volume within forest ecosystems. The high spatial and temporal resolution of forest age improves the accuracy of estimates for forest biomass, storage, and carbon stocks. Obtaining reliable forest age data across various scales remains a significant challenge. This study conducted forest age mapping at a 30 m resolution over long time series, taking into account forest disturbance events, using Xinjiang forest as a case study. In 1991, the age of forests in Xinjiang was estimated utilizing the Random Forest (RF) model, incorporating both forest inventory data and remote sensing data. Utilizing Landsat Time Series Stacked (LTSS) Data from 1991 to 2022, the LandTrendr(LT) algorithm was applied to identify forest disturbance and recovery events from 1992 to 2022 using the Google Earth Engine (GEE) platform. Finally, the forest age distribution in Xinjiang from 1991 to 2022 was mapped in conjunction with the forest disturbance events. The findings revealed that: (1) the R2 values of the RF regression models for estimating forest age exceeded 0.65, with RMSEs below 22 years and RRMSEs ranging from 0.119 to 0.183, demonstrating high credibility of the simulation results; (2) the overall accuracy of the LandTrendr algorithm in identifying forest loss and gain in the study area was 85 % or higher. From 1992 to 2022, Xinjiang experienced a net increase in forest area of 28580.69 km2; (3) the forest age distribution from 1991 to 2022 indicates a predominance of young and middle-aged forests, with the proportion of old forests remaining between 30 % and 40 %, showing a gradual annual increase. This study presents a method to improve the estimation of forest age at high resolution over extended time series. It also supplies essential data for assessing carbon storage in Xinjiang’s forests, both historically and prospectively, contributing to a deeper understanding of carbon balance in forest ecosystems within arid regions.http://www.sciencedirect.com/science/article/pii/S1470160X24015000Forest disturbance and recovery detectionLandTrendr algorithmRandom Forest (RF)Forest Age MappingXinjiang |
| spellingShingle | Lili Zhai Mei Zan Mao Ye Jia Zhou Cong Xue Shunfa Yang Yuntong Liu Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection Ecological Indicators Forest disturbance and recovery detection LandTrendr algorithm Random Forest (RF) Forest Age Mapping Xinjiang |
| title | Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection |
| title_full | Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection |
| title_fullStr | Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection |
| title_full_unstemmed | Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection |
| title_short | Time-series forest age estimation in Xinjiang based on forest disturbance and recovery detection |
| title_sort | time series forest age estimation in xinjiang based on forest disturbance and recovery detection |
| topic | Forest disturbance and recovery detection LandTrendr algorithm Random Forest (RF) Forest Age Mapping Xinjiang |
| url | http://www.sciencedirect.com/science/article/pii/S1470160X24015000 |
| work_keys_str_mv | AT lilizhai timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT meizan timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT maoye timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT jiazhou timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT congxue timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT shunfayang timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection AT yuntongliu timeseriesforestageestimationinxinjiangbasedonforestdisturbanceandrecoverydetection |