Mapping the Forest Height by Fusion of ICESat-2 and Multi-Source Remote Sensing Imagery and Topographic Information: A Case Study in Jiangxi Province, China DOI Open Access

Yichen Luo,

Shuhua Qi, Kaitao Liao

et al.

Forests, Journal Year: 2023, Volume and Issue: 14(3), P. 454 - 454

Published: Feb. 22, 2023

Forest canopy height is defined as the distance between highest point of tree and ground, which considered to be a key factor in calculating above-ground biomass, leaf area index, carbon stock. Large-scale forest monitoring can provide scientific information on deforestation degradation policymakers. The Ice, Cloud, Land Elevation Satellite-2 (ICESat-2) was launched 2018, with Advanced Topographic Laser Altimeter System (ATLAS) instrument taking task mapping transmitting data photon-counting LiDAR, offers an opportunity obtain global height. To generate high-resolution map Jiangxi Province, we integrated ICESat-2 multi-source remote sensing imagery, including Sentinel-1, Sentinel-2, Shuttle Radar Topography Mission, age Province. Meanwhile, develop four extrapolation models by random (RF), Support Vector Machine (SVM), K-nearest neighbor (KNN), Gradient Boosting Decision Tree (GBDT) link ICESat-2, spatial feature imagery. results show that: (1) moderately correlated age, making it potential predictor for mapping. (2) Compared GBDT, SVM, KNN, RF showed best predictive performance coefficient determination (R2) 0.61 root mean square error (RMSE) 5.29 m. (3) Elevation, slope, red-edge band (band 5) derived from Sentinel-2 were significantly dependent variables model. Apart that, one that relied on. In contrast, backscatter coefficients texture features Sentinel-1 not sensitive (4) There significant correlation predicted measured field measurements (R2 = 0.69, RMSE 4.02 m). nutshell, indicate method utilized this work reliably distribution at high resolution.

Language: Английский

China’s current forest age structure will lead to weakened carbon sinks in the near future DOI Creative Commons
Rong Shang, Jing M. Chen, Mingzhu Xu

et al.

The Innovation, Journal Year: 2023, Volume and Issue: 4(6), P. 100515 - 100515

Published: Sept. 16, 2023

Forests are chiefly responsible for the terrestrial carbon sink that greatly reduces buildup of CO2 concentrations in atmosphere and alleviates climate change. Current predictions sinks future have so far ignored variation forest uptake with age. Here, we predict role China's current age capacity by generating a high-resolution (30 m) map 2019 over landmass using satellite inventory data deriving growth curves measurements biomass 3,121 plots. As forests currently large proportions young middle-age stands, project will maintain high rates about 15 years. However, as grow older, their net primary productivity decline 5.0% ± 1.4% 2050, 8.4% 1.6% 2060, 16.6% 2.8% 2100, indicating weakened near future. The weakening can be potentially mitigated optimizing structure through selective logging implementing new or improved afforestation. This finding is important not only global cycle projections but also developing management strategies to enhance land alleviating effect.

Language: Английский

Citations

73
Mapping China’s planted forests using high resolution imagery and massive amounts of crowdsourced samples DOI
Kai Cheng, Yanjun Su, Hongcan Guan

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2023, Volume and Issue: 196, P. 356 - 371

Published: Jan. 17, 2023

Language: Английский

Citations

57
LiDAR GEDI derived tree canopy height heterogeneity reveals patterns of biodiversity in forest ecosystems DOI Creative Commons
Michele Torresani, Duccio Rocchini,

Alessandro Alberti

et al.

Ecological Informatics, Journal Year: 2023, Volume and Issue: 76, P. 102082 - 102082

Published: March 30, 2023

The "Height Variation Hypothesis" is an indirect approach used to estimate forest biodiversity through remote sensing data, stating that greater tree height heterogeneity (HH) measured by CHM LiDAR data indicates higher structure complexity and species diversity. This has traditionally been analyzed using only airborne which limits its application the availability of dedicated flight campaigns. In this study we relationship between diversity HH, calculated with four different indices two freely available CHMs derived from new space-borne GEDI data. first, a spatial resolution 30 m, was produced regression machine learning algorithm integrating Landsat optical information. second, 10 created Sentinel-2 images deep convolutional neural network. We tested separately in plots situated northern Italian Alps, 100 forested area Traunstein (Germany) successively all 130 cross-validation analysis. Forest density information also included as influencing factor multiple Our results show can be assess patterns ecosystems estimation HH correlated However, indicate method influenced factors including dataset choice their related resolution, calculate density. finding suggest LIDAR valuable tool ecosystems, aid global estimation.

Language: Английский

Citations

45
Mapping high-resolution forest aboveground biomass of China using multisource remote sensing data DOI Creative Commons

Qiuli Yang,

Chunyue Niu, Xiaoqiang Liu

et al.

GIScience & Remote Sensing, Journal Year: 2023, Volume and Issue: 60(1)

Published: April 26, 2023

Forest aboveground biomass (AGB) estimation is crucial for carbon cycle studies and climate change mitigation actions. However, because of limitations in timely reliable forestry surveys high-resolution remote sensing data, producing a fine resolution spatial continuous forest AGB map China challenging. Here, we combined 4789 ground-truth measurements multisource data such as recently released canopy-height product, optical spectral indexes, topographic climatological soil properties to train random regression model at 30-m resolution. The accuracy the estimated can yield R2 = 0.67 RMSE 70.71 Mg/ha. nationwide estimates show that average total storage were 97.57 ± 23.85 Mg/ha 11.06 Pg C year 2019, respectively. value uncertainty ranges from 0.68 37.80 Mg/ha, was 4.32 1.75 this study correspond reasonably well with derived grassland statistical yearbook provincial level (R2 0.61, 30.15 Mg/ha). In addition, found previous products generally underestimate compared our pixel-level measurements. provides an important alternative source be used baseline management conservation practices.

Language: Английский

Citations

45
Unveiling China’s natural and planted forest spatial–temporal dynamics from 1990 to 2020 DOI
Kai Cheng, Haitao Yang, Hongcan Guan

et al.

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2024, Volume and Issue: 209, P. 37 - 50

Published: Feb. 5, 2024

Language: Английский

Citations

33
Carbon storage through China’s planted forest expansion DOI Creative Commons
Kai Cheng, Haitao Yang, Shengli Tao

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 15, 2024

Abstract China’s extensive planted forests play a crucial role in carbon storage, vital for climate change mitigation. However, the complex spatiotemporal dynamics of forest area and its storage remain uncaptured. Here we reveal such changes from 1990 to 2020 using satellite field data. Results show doubling area, trend that intensified post-2000. These lead increasing 675.6 ± 12.5 Tg C 1,873.1 16.2 2020, with an average rate ~ 40 yr −1 . The expansion contributed 53% (637.2 5.4 C) total above increased compared growth. This proactive policy-driven has catalyzed swift increase aligning Carbon Neutrality Target 2060.

Language: Английский

Citations

33
LiDAR Data Fusion to Improve Forest Attribute Estimates: A Review DOI Creative Commons
Mattia Balestra, Suzanne Marselis, Temuulen Tsagaan Sankey

et al.

Current Forestry Reports, Journal Year: 2024, Volume and Issue: 10(4), P. 281 - 297

Published: June 21, 2024

Abstract Purpose of the Review Many LiDAR remote sensing studies over past decade promised data fusion as a potential avenue to increase accuracy, spatial-temporal resolution, and information extraction in final products. Here, we performed structured literature review analyze relevant on these topics published last main motivations applications for fusion, methods used. We discuss findings with panel experts report important lessons, challenges, future directions. Recent Findings other datasets, including multispectral, hyperspectral, radar, is found be useful variety literature, both at individual tree level area level, tree/crown segmentation, aboveground biomass assessments, canopy height, species identification, structural parameters, fuel load assessments etc. In most cases, gains are achieved improving accuracy (e.g. better classifications), resolution height). However, questions remain regarding whether marginal improvements reported range worth extra investment, specifically from an operational point view. also provide clear definition “data fusion” inform scientific community combination, integration. Summary This provides positive outlook come, while raising about trade-off between benefits versus time effort needed collecting combining multiple datasets.

Language: Английский

Citations

24
A 2020 forest age map for China with 30 m resolution DOI Creative Commons
Kai Cheng, Yu‐Ling Chen,

Tianyu Xiang

et al.

Earth system science data, Journal Year: 2024, Volume and Issue: 16(2), P. 803 - 819

Published: Feb. 7, 2024

Abstract. A high-resolution, spatially explicit forest age map is essential for quantifying carbon stocks and sequestration potential. Prior attempts to estimate on a national scale in China have been limited by sparse resolution incomplete coverage of ecosystems, attributed complex species composition, extensive areas, insufficient field measurements, inadequate methods. To address these challenges, we developed framework that combines machine learning algorithms (MLAs) remote sensing time series analysis estimating the China's forests. Initially, identify develop optimal MLAs estimation across various vegetation divisions based height, climate, terrain, soil, forest-age utilizing ascertain information. Subsequently, apply LandTrendr detect disturbances from 1985 2020, with since last disturbance serving as proxy age. Ultimately, data derived are integrated result produce 2020 China. Validation against independent plots yielded an R2 ranging 0.51 0.63. On scale, average 56.1 years (standard deviation 32.7 years). The Qinghai–Tibet Plateau alpine zone possesses oldest 138.0 years, whereas warm temperate deciduous-broadleaf averages only 28.5 years. This 30 m-resolution offers crucial insights comprehensively understanding ecological benefits forests sustainably manage resources. available at https://doi.org/10.5281/zenodo.8354262 (Cheng et al., 2023a).

Language: Английский

Citations

19
Factors affecting relative height and ground elevation estimations of GEDI among forest types across the conterminous USA DOI Creative Commons
Cangjiao Wang, Andrew J. Elmore, Izaya Numata

et al.

GIScience & Remote Sensing, Journal Year: 2022, Volume and Issue: 59(1), P. 975 - 999

Published: June 13, 2022

The Global Ecosystem Dynamics Investigation (GEDI), a new spaceborne LiDAR system of the National Aeronautics and Space Administration (NASA), has potential to revolutionize global measurements vertical vegetation structure. However, GEDI performance among different forest types factors influencing needs be evaluated against similar from existing airborne platforms. Ideally, comparisons across diverse will inform future work quantifying biomass or mapping species habitats. Thus, we compared second version L2A product (GEDI V2) with Airborne Observation Platform (AOP) leaf-on data 33 Ecological Network (NEON) sites. Comparisons were made for ground elevation relative height (RH) simulated laser scanning (ALS) waveforms discrete point cloud LiDAR. Results indicated that V2 obtained high accuracy on RH100 estimations (3σ) RMSEs 1.38 m 2.62 m, respectively. produced (RH100) all 12 %RMSE below 25%. RHs sensitive finding accuracy, RH estimation varied profiles types. For performance, greater than 21% RH95 33% variations can explained by land surface attributes, observing sensor characteristics, collection time differences between NEON Furthermore, geolocation error remains an essential factor affecting which varies cover types, especially canopy estimation. findings reported here provide insights guide enhance GEDI-based structure applications.

Language: Английский

Citations

59
Maps with 1 km resolution reveal increases in above- and belowground forest biomass carbon pools in China over the past 20 years DOI Creative Commons
Yongzhe Chen, Xiaoming Feng, Bojie Fu

et al.

Earth system science data, Journal Year: 2023, Volume and Issue: 15(2), P. 897 - 910

Published: Feb. 21, 2023

Abstract. To quantify the ecological consequences of recent nationwide restoration efforts in China, spatially explicit information on forest biomass carbon stock changes over past 20 years is critical. However, long-term tracking at national scale remains challenging as it requires continuous and high-resolution monitoring. Here, we characterize above- belowground (AGBC BGBC) forests China between 2002 2021 1 km spatial resolution by integrating multiple types remote sensing observations with intensive field measurements through regression machine learning approaches. On average, 8.6 ± 0.6 2.2 0.1 PgC were stored live China. Over last years, total pool has increased a rate 114.5 16.3 TgC yr−1 (approximately 1.1 % yr−1). The most pronounced gains occurred central to southern including Loess Plateau, Qinling mountains, southwestern karsts southeastern forests. While combined use multi-source data provides powerful tool assess changes, future research also needed explore drivers observed woody trends evaluate degree which will translate into biodiverse, healthy ecosystems that are sustainable. Annual maps for now available https://doi.org/10.6084/m9.figshare.21931161.v1 (Chen, 2023).

Language: Английский

Citations

40