Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/15/6/1575 |
| _version_ | 1797609219568959488 |
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| author | Chao Zhang Tongtong Song Runhe Shi Zhengyang Hou Nan Wu Han Zhang Wei Zhuo |
| author_facet | Chao Zhang Tongtong Song Runhe Shi Zhengyang Hou Nan Wu Han Zhang Wei Zhuo |
| author_sort | Chao Zhang |
| collection | DOAJ |
| description | 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 km<sup>2</sup> 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. |
| first_indexed | 2024-03-11T05:57:27Z |
| format | Article |
| id | doaj.art-9b62f8ac70d34c7d8a043d7aa423a3d2 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-11T05:57:27Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-9b62f8ac70d34c7d8a043d7aa423a3d22023-11-17T13:39:00ZengMDPI AGRemote Sensing2072-42922023-03-01156157510.3390/rs15061575Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed DataChao Zhang0Tongtong Song1Runhe Shi2Zhengyang Hou3Nan Wu4Han Zhang5Wei Zhuo6Key Laboratory of Geographic Information Sciences (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Sciences (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaKey Laboratory of Geographic Information Sciences (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaThe Key Laboratory for Silviculture and Conservation (Ministry of Education), Beijing Forestry University, Beijing 100083, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241000, ChinaKey Laboratory of Geographic Information Sciences (Ministry of Education), School of Geographic Sciences, East China Normal University, Shanghai 200241, ChinaSchool of Geography and Tourism, Anhui Normal University, Wuhu 241000, ChinaUrban 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 km<sup>2</sup> 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.https://www.mdpi.com/2072-4292/15/6/1575eco-islandcarbon storagecarbon densitySentinel-2Aspatial pattern |
| spellingShingle | Chao Zhang Tongtong Song Runhe Shi Zhengyang Hou Nan Wu Han Zhang Wei Zhuo Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data Remote Sensing eco-island carbon storage carbon density Sentinel-2A spatial pattern |
| title | Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data |
| title_full | Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data |
| title_fullStr | Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data |
| title_full_unstemmed | Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data |
| title_short | Estimating the Forest Carbon Storage of Chongming Eco-Island, China, Using Multisource Remotely Sensed Data |
| title_sort | estimating the forest carbon storage of chongming eco island china using multisource remotely sensed data |
| topic | eco-island carbon storage carbon density Sentinel-2A spatial pattern |
| url | https://www.mdpi.com/2072-4292/15/6/1575 |
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