Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data
-
Published:2023-03-14
Issue:6
Volume:15
Page:1575
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Zhang Chao123, Song Tongtong1, Shi Runhe123ORCID, Hou Zhengyang4, Wu Nan5, Zhang Han1, Zhuo Wei5
Affiliation:
1. Key Laboratory of Geographic Information Sciences (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, China 2. Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China 3. Joint Laboratory for Environmental Remote Sensing and Data Assimilation, East China Normal University, Shanghai 200241, China 4. The Key Laboratory for Silviculture and Conservation (Ministry of Education), Beijing Forestry University, Beijing 100083, China 5. School of Geography and Tourism, Anhui Normal University, Wuhu 241000, China
Abstract
Urban forests are highly heterogeneous; information about the combined effect of forest classification scale and algorithm selection on the estimation accuracy for urban forests remains unclear. In this study, we chose Chongming eco-island in the mega-city of Shanghai, a national experimental carbon neutral construction plot in China, as the study object. Remote sensing estimation models (simple regression models vs. machine learning models) of forest carbon density were constructed across different classification scales (all forests, different forest types, and dominant tree species) based on high-resolution aerial photographs and Sentinel-2A remote sensing images, and a large number of field surveys and optimal models were screened by ten-fold cross-validation. The results showed that (1) in early 2020, the total forest area and carbon storage of Chongming eco-island were 307.8 km2 and 573,123.6 t, respectively, among which the areal ratios and total carbon storage ratios of evergreen broad-leaved forest, deciduous broad-leaved forest, and warm coniferous forest were 51.4% and 53.3%, 33.5% and 32.8%, and 15.1% and 13.9%, respectively. (2) The average forest carbon density of Chongming eco-island was 18.6 t/ha, among which no differences were detected among the three forest types (i.e., 17.2–19.2 t/ha), opposite to what was observed among the dominant tree species (i.e., 14.6–23.7 t/ha). (3) Compared to simple regression models, machine learning models showed an improvement in accuracy performance across all three classification scales, with average rRMSE and rBias values decreasing by 29.4% and 53.1%, respectively; compared to the all-forests classification scale, the average rRMSE and rBias across the algorithms decreased by 25.0% and 45.2% at the forest-type classification scale and by 28.6% and 44.3% at the tree species classification scale, respectively. We concluded that refining the forest classification, combined with advanced prediction procedures, could improve the accuracy of carbon storage estimates for urban forests.
Funder
Shanghai Municipal Natural Science Foundation National Natural Science Foundation of China Fundamental Research Funds for Central Universities International Cooperation Platform of Resources, Environment and Ecology, East China Normal University
Subject
General Earth and Planetary Sciences
Reference70 articles.
1. Effects of changing population or density on urban carbon dioxide emissions;Ribeiro;Nat. Commun.,2019 2. Several issues in urban forest research;Song;J. Chin. Urban For.,2004 3. Lee, S., Yim, J., Son, Y., Son, Y., and Kim, R. (2018). Estimation of Forest Carbon Stocks for National Greenhouse Gas Inventory Reporting in South Korea. Forests, 9. 4. Węgiel, A., and Polowy, K. (2020). Aboveground Carbon Content and Storage in Mature Scots Pine Stands of Different Densities. Forests, 11. 5. Above-ground carbon storage by urban trees in Leipzig, Germany: Analysis of patterns in a European city;Strohbach;Landsc. Urban Plan.,2012
|
|