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Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: closed (6 November 2022) | Viewed by 15528

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Special Issue Editors

Prof. Dr. Zhaoming Zhang

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: global burned area extraction; land surface reflectance retrieval; thermal infrared remote sensing; vegetation parameters (leaf area index, biomass) mapping

Dr. Tengfei Long

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Guest Editor

Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: forest and burned area mapping; spatial photogrammetry; image registration; land-cover change; noctilucent remote sensing
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Dr. Mengmeng Wang

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Guest Editor

Department of Remote Sensing Science, China University of Geosciences, Wuhan 430079, China
Interests: landsat surface temperature retrieval; local climate zone classification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Deforestation typically releases carbon from the terrestrial biosphere to the atmosphere as CO₂ (carbon dioxide), while recovering vegetation in abandoned agricultural or logged land removes CO₂ from the atmosphere and sequesters it in vegetation biomass and soil carbon. Carbon budget estimation from vegetation dynamics receives a great deal of scientific attention. The key state variables and parameters of vegetation, i.e., the forest cover and its change, the content of chlorophyll, biomass, tree height, forest burned area, and leaf area index, have impacts on the vegetation carbon budget. Combining remote sensing and ecological modeling reveals a promising avenue in vegetation carbon budget investigation. This Special Issue seeks the most recent research on gaining the key vegetation parameters using the SAR interferometry, multispectral lidar, hyperspectral remote sensing, and unmanned aerial vehicle remote sensing incorporated into an ecological process model with a carbon budget model, to evaluate the spatio-temporal dynamics of both carbon storage and carbon budget of vegetation, assessing the influence of these vegetation parameters on vegetation carbon storage.

Prof. Dr. Zhaoming Zhang
Dr. Tengfei Long
Dr. Mengmeng Wang
Guest Editors

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Keywords

airborne lidar for 3D vegetation mapping
tree height retrieval
vegetation classification using hyperspectral remote sensing
forest cover and forest change mapping
vegetation parameters (chlorophyll content, biomass, leaf area index) mapping
forest burned area mapping
forest carbon storage

Published Papers (6 papers)

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Research

19 pages, 30077 KiB

Open AccessArticle

Capability of Phenology-Based Sentinel-2 Composites for Rubber Plantation Mapping in a Large Area with Complex Vegetation Landscapes

by Hongzhong Li, Longlong Zhao, Luyi Sun, Xiaoli Li, Jin Wang, Yu Han, Shouzhen Liang and Jinsong Chen

Remote Sens. 2022, 14(21), 5338; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215338 - 25 Oct 2022

Cited by 4 | Viewed by 1669

Abstract

Mapping rubber plantations in a large area is still challenging in high-cloud-cover and complex-vegetation landscapes. Existing studies were often confined to the discrimination of rubber trees from natural forests and rarely concerned other tropical tree species. The Sentinel-2 constellation, with improved spatial, spectral, and temporal resolution, offers new opportunities to improve previous efforts. In this paper, four Hainan Sentinel-2 composites were generated based on the detailed phenological stages delineation of rubber trees. The random forest classifier with different phenological stage combinations was utilized to discuss the capability of Sentinel-2 composites to map rubber plantations. The optimal resultant rubber plantation map had a producer’s accuracy, user’s accuracy, and F1 score of 81%, 84.4%, and 0.83, respectively. According to the rubber plantation map in 2020, there was a total of 5473 km² rubber plantations in Hainan, which was 2.93% higher than the statistical data from the Hainan Statistical Yearbook. According to the Hainan Statistical Yearbook, the area-weighted accuracy at the county level was 82.47%. The mean decrease in accuracy (MDA) was used to assess the feature importance of the four phenological stages. Results showed that the recovery growth stage played the most important role, and the resting stage was the least important. Moreover, in terms of the combinations of phenological stages, any dataset group with two phenological stages was sufficient for rubber tree discrimination. These findings were instrumental in facilitating the rubber plantation mapping annually. This study has demonstrated the potential of Sentinel-2 data, with the phenology-based image-compositing technique, for mapping rubber plantations in large areas with complex vegetation landscapes. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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19 pages, 6056 KiB

Open AccessArticle

Vegetation Monitoring for Mountainous Regions Using a New Integrated Topographic Correction (ITC) of the SCS + C Correction and the Shadow-Eliminated Vegetation Index

by Hong Jiang, Ailin Chen, Yongfeng Wu, Chunying Zhang, Zhaohui Chi, Mengmeng Li and Xiaoqin Wang

Remote Sens. 2022, 14(13), 3073; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14133073 - 26 Jun 2022

Cited by 9 | Viewed by 1958

Abstract

The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 Operational Land Imager (OLI) multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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18 pages, 7359 KiB

Open AccessArticle

Carbon Sink under Different Carbon Density Levels of Forest and Shrub, a Case in Dongting Lake Basin, China

by Lingqiao Kong, Fei Lu, Enming Rao and Zhiyun Ouyang

Remote Sens. 2022, 14(11), 2672; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14112672 - 02 Jun 2022

Cited by 3 | Viewed by 1835

Abstract

Terrestrial ecosystems play a critical role in the global carbon cycle and climate change mitigation. Studying the temporal and spatial dynamics of carbon sink and the driving mechanisms at the regional scale provides an important basis for ecological restoration and ecosystem management. Taking the Dongting Lake Basin as an example, we assessed the carbon sinks of forest and shrub from 2000 to 2020 based on the maps of biomass that were obtained by remote sensing, and analyzed the dynamics of carbon sinks that were contributed by different biomass carbon density levels of constant forest and shrub and new afforestation over the past two decades. The results showed that the carbon sink of forest and shrub in the Dongting Lake Basin grew rapidly from 2000 to 2020: carbon sink increased from 64.64 TgC between 2000 and 2010, to 382.56 TgC between 2010 and 2020. The continuous improvement of biomass carbon density has made a major contribution to carbon sink, especially the carbon density increase in low carbon density forests and shrubs. Carbon-dense forests and shrubs realized their contribution to carbon sink in the second decade after displaying negative carbon sink in the first decade. Carbon sink from new afforestation increased 61.16% from the first decade to the second decade, but the contribution proportion decreased. The overall low carbon density of forest and shrub in the Dongting Lake Basin and their carbon sink dynamics indicated their huge carbon sequestration potential in the future. In addition to continuously implementing forest protection and restoration projects to promote afforestation, the improvement of ecosystem quality should be paid more attention in ecosystem management for areas like Dongting Lake Basin, where ecosystems, though severely degraded, are important and fragile, to realize their huge carbon sequestration potential. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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20 pages, 12886 KiB

Open AccessArticle

Siamese Detail Difference and Self-Inverse Network for Forest Cover Change Extraction Based on Landsat 8 OLI Satellite Images

by Yantao Guo, Tengfei Long, Weili Jiao, Xiaomei Zhang, Guojin He, Wei Wang, Yan Peng and Han Xiao

Remote Sens. 2022, 14(3), 627; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030627 - 28 Jan 2022

Cited by 6 | Viewed by 3011

Abstract

In the context of carbon neutrality, forest cover change detection has become a key topic of global environmental monitoring. As a large-scale monitoring technique, remote sensing has received obvious attention in various land cover observation applications. With the rapid development of deep learning, remote sensing change detection combined with deep neural network has achieved high accuracy. In this paper, the deep neural network is used to study forest cover change with Landsat images. The main research ideas are as follows. (1) A Siamese detail difference neural network is proposed, which uses a combination of concatenate weight sharing mode and subtract weight sharing mode to improve the accuracy of forest cover change detection. (2) The self-inverse network is introduced to detect the change of forest increase by using the sample data set of forest decrease, which realizes the transfer learning of the sample data set and improves the utilization rate of the sample data set. The experimental results on Landsat 8 images show that the proposed method outperforms several Siamese neural network methods in forest cover change extraction. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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16 pages, 4224 KiB

Open AccessArticle

Spatial Representativeness of Gross Primary Productivity from Carbon Flux Sites in the Heihe River Basin, China

by Tao Yu, Qiang Zhang and Rui Sun

Remote Sens. 2021, 13(24), 5016; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13245016 - 10 Dec 2021

Cited by 2 | Viewed by 2280

Abstract

Studying the spatial representativeness of carbon flux measurement data for typical land cover types can provide important information for benchmarking Earth system models and validating multiple-scale remote sensing products. In our study, daily gross primary productivity (GPP) was firstly derived from eddy covariance observation systems and seasonal variations in field GPP were analyzed at nine flux tower sites for typical land cover types in the Heihe River Basin, China. Then, the real-time footprint distance and climate footprint distance of the field GPP were obtained by using a footprint source area model. Lastly, multiple-scale GPP products were validated at footprint scale, and the impacts (measurement height, surface roughness and turbulent state of the atmosphere) on the footprint distance of field GPP were analyzed. The results of this paper demonstrated that climate footprint distances ranged from about 500 m to 1500 m for different land cover types in the Heihe River Basin. The accuracy was higher when validating MODIS GPP products at footprint scale (R² = 0.56, RMSE = 3.07 g C m⁻² d⁻¹) than at field scale (R² = 0.51, RMSE = 3.34 g C m⁻² d⁻¹), and the same situation occurred in the validation of high-resolution downscaled GPP (R² = 0.85, RMSE = 1.34 g C m⁻² d⁻¹ when validated at footprint scale; R² = 0.82, RMSE = 1.47 g C m⁻² d⁻¹ when validated at field scale). The results of this study provide information about the footprints of field GPP for typical land cover types in arid and semi-arid areas in Northwestern China, and reveal that precision may be higher when validating multiple-scale remote sensing GPP products at the footprint scale than at the field scale. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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27 pages, 45532 KiB

Open AccessArticle

Study on the Relationship between Topological Characteristics of Vegetation Ecospatial Network and Carbon Sequestration Capacity in the Yellow River Basin, China

by Minzhe Fang, Guoxin Si, Qiang Yu, Huaguo Huang, Yuan Huang, Wei Liu and Hongqiong Guo

Remote Sens. 2021, 13(23), 4926; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13234926 - 04 Dec 2021

Cited by 30 | Viewed by 3036

Abstract

Achieving carbon neutrality is a necessary effort to rid humanity of a catastrophic climate and is a goal for China in the future. Ecological space plays an important role in the realization of carbon neutrality, but the relationship between the structure of vegetation ecological space and vegetation carbon sequestration capacity has been the focus of research. In this study, we extracted the base data from MODIS products and other remote sensing products, and then combined them with the MCR model to construct a vegetation ecospatial network in the Yellow River Basin in 2018. Afterward, we calculated the topological indicators of ecological nodes in the network and analyzed the relationship between the carbon sequestration capacity (net biome productivity) of ecological nodes and these topological indicators in combination with the Biome-BGC model. The results showed that there was a negative linear correlation between the betweenness centrality of forest nodes and their carbon sequestration capacity in the Yellow River Basin (p < 0.05, R² = 0.59). On the other hand, there was a positive linear correlation between the clustering coefficient of grassland nodes and their carbon sequestration capacity (p < 0.01, R² = 0.49). In addition, we briefly evaluated the vegetation ecospatial network in the Yellow River BASIN and suggested its optimization direction under the background of carbon neutrality in the future. Increasing the carbon sequestration capacity of vegetation through the construction of national ecological projects is one of the ways to achieve carbon neutrality, and this study provides a reference for the planning of future national ecological projects in the Yellow River Basin. Furthermore, this is also a case study of the application of remote sensing in vegetation carbon budgeting. Full article

(This article belongs to the Special Issue Remote Sensing for Vegetation Mapping and Its Application in Carbon Budget)

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