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Remote Sensing
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Machine Learning in Global Change Ecology: Methods and Applications
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Machine Learning in Global Change Ecology: Methods and Applications

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: 31 August 2024 | Viewed by 4078

Special Issue Editors

Dr. Boyi Liang

E-Mail Website
Guest Editor
Precision Forestry Key Laboratory of Beijing, College of Forestry, Beijing Forestry University, Beijing 100083, China
Interests: remote sensing; machine learning; vegetation ecology; carbon cycle; GIS
Prof. Dr. Hongyan Liu

E-Mail Website
Guest Editor
College of Urban and Environmental Sciences, Peking University, No. 5, Yiheyuan Road, Haidian District, Beijing 100871, China
Interests: vegetation ecology; biogeography; remote sensing; climate change; carbon cycle
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ying Qu

E-Mail Website
Guest Editor
School of Geographical Sciences, Northeast Normal University, Changchun, China
Interests: land surface albedo; leaf area index; land cover change; quantitative remote sensing; global climate change
Special Issues, Collections and Topics in MDPI journals
Dr. Jiangzhou Xia

E-Mail Website
Guest Editor
Tianjin Key Laboratory of Water Resources and Environment, Tianjin Normal University, Tianjin 300387, China
Interests: terrestrial carbon cycle model; remote sensing and carbon cycle budget; machine learning; data-model fusion
Dr. Micol Rossini

E-Mail Website
Guest Editor
Remote Sensing of Environmental Dynamics Laboratory, Department of Earth and Environmental Sciences (DISAT), University of Milano-Bicocca, Piazza della Scienza 1, 20126 Milano, Italy
Interests: GIS and remote sensing; unmanned aerial systems (UAV); earth observation; vegetation; ecology; environment; cryosphere
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of remote sensing technology has greatly boosted the relative research on the quantitative relationship between the environment and terrestrial vegetation, which is essential for quantifying the conversion of atmospheric carbon dioxide (CO2) into plant biomass and reflecting the ability of vegetation to alter levels of atmospheric CO2. In the last 50 years, satellite remote sensing technology has provided advanced detection and research means to allow the investigation of the earth's resources, the monitoring of local and regional environmental changes and the growth of vegetation. Since the 1990s, human society has rapidly stepped into the era of “big data”, strongly facilitated by the development of new technologies such as machine learning, also supported by emergent remote sensing product with high spatiotemporal resolution. Observation technology—the ability to acquire long-term cross-scale data of ground features—has introduced significant improvements to ecological studies in global change background. The accumulation of observational data is promoting the research of ecology to move from a theory-driven to data-driven field, making it a “data-intensive science”.

This Special Issue aims to host studies covering the use of machine learning and remote sensing technology for the research of global change ecology. Topics may cover anything from the simulation of vegetation growth variables at different scales to the quantitative relationship between environmental and terrestrial vegetation parameters.

Hence, any studies into machine learning methods or application for exploring quantitative relationship between terrestrial vegetation growth and environment are welcome.

Articles may address, but are not limited, to the following topics:

  • Ecological remote sensing;
  • Machine learning applications in high spatial and temporal resolution remote sensing data processing;
  • Applications of machine learning in simulating the terrestrial ecosystem carbon and water cycles;
  • Using machine learning and remote sensing data to estimate vegetation ecological parameters;
  • Uncertainty analysis of machine learning models;
  • Evaluation of machine learning models for global change ecology;
  • Machine learning models for vegetation growth;
  • Environmental impact on vegetation growth;
  • Vegetation land cover mapping and pattern analysis;
  • Vegetation ecology;
  • Vegetation biomass;
  • Vegetation functional traits;
  • Carbon cycle/sequestration.

Dr. Boyi Liang
Prof. Dr. Hongyan Liu
Prof. Dr. Ying Qu
Dr. Jiangzhou Xia
Dr. Micol Rossini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • remote sensing
  • machine learning
  • terrestrial vegetation
  • carbon cycle
  • climate change
  • global change ecology

Published Papers (3 papers)

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17 pages, 9171 KiB  
Article
A Method of Chestnut Forest Identification Based on Time Series and Key Phenology from Sentinel-2
by Nina Xiong, Hailong Chen, Ruiping Li, Huimin Su, Shouzheng Dai and Jia Wang
Remote Sens. 2023, 15(22), 5374; https://doi.org/10.3390/rs15225374 - 16 Nov 2023
Cited by 1 | Viewed by 1002
Abstract
Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote [...] Read more.
Chestnut trees hold a prominent position in China as an economically significant forest species, offering both high economic value and ecological advantages. Identifying the distribution of chestnut forests is of paramount importance for enhancing efficient management practices. Presently, many studies are employing remote sensing imaging methods to monitor tree species. However, in comparison to the common classification of land cover types, the accuracy of tree species identification is relatively lower. This study focuses on accurately mapping the distribution of planted chestnut forests in China, particularly in the Huairou and Miyun regions, which are the main producing areas for Yanshan chestnuts in northeastern Beijing. We utilized the Google Earth Engine (GEE) cloud platform and Sentinel-2 satellite imagery to develop a method based on vegetation phenological features. This method involved identifying three distinct phenological periods of chestnut trees: flowering, fruiting, and dormancy, and extracting relevant spectral, vegetation, and terrain features. With these features, we further established and compared three machine learning algorithms for chestnut species identification: random forest (RF), decision tree (DT), and support vector machine (SVM). Our results indicated that the recognition accuracy of these algorithms ranked in descending order as RF > DT > SVM. We found that combining multiple phenological characteristics significantly improved the accuracy of chestnut forest distribution identification. Using the random forest algorithm and Sentinel-2 phenological features, we achieved an impressive overall accuracy (OA) of 98.78%, a Kappa coefficient of 0.9851, and a user’s accuracy (UA) and producer’s accuracy (PA) of 97.25% and 98.75%, respectively, for chestnut identification. When compared to field surveys and official area statistics, our method exhibited an accuracy rate of 89.59%. The implementation of this method not only offers crucial data support for soil erosion prevention and control studies in Beijing but also serves as a valuable reference for future research endeavors in this field. Full article
(This article belongs to the Special Issue Machine Learning in Global Change Ecology: Methods and Applications)
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17 pages, 18033 KiB  
Article
Long-Term Dynamics of Sandy Vegetation and Land in North China
by Zhaosheng Wang
Remote Sens. 2023, 15(19), 4803; https://doi.org/10.3390/rs15194803 - 02 Oct 2023
Viewed by 720
Abstract
Owing to the lack of long-term, continuous, large-scale, and high-resolution monitoring data and methods, we still cannot accurately understand the detailed processes of sand change in northern China. To some extent, this hinders the scientific implementation of sand prevention and control actions. To [...] Read more.
Owing to the lack of long-term, continuous, large-scale, and high-resolution monitoring data and methods, we still cannot accurately understand the detailed processes of sand change in northern China. To some extent, this hinders the scientific implementation of sand prevention and control actions. To gain a more accurate and detailed understanding of the process of sandy land change, we conducted an investigation using a reconstructed, long-term, continuous, 250 m-high spatial resolution normalized difference vegetation index (NDVI) and fractional vegetation cover (FVC) data from 1982 to 2018 to examine vegetation changes in sandy land in northern China. This study revealed that vegetation activity (NDVI slope = 0.011/a, R2 = 0.148) and vegetation coverage (FVC slope = 0.011/a, R2 = 0.080) in the northern sandy land (NSL) have slowed the desertification trend. The NSL desertification and reverse areas show decreasing and increasing trends, respectively, indicating an improvement in the degree of desertification from 1982 to 2018. Furthermore, we employed a newly proposed sandy classification method to investigate the area changes in mobile, semi-mobile, semi-fixed, and fixed sandy lands. Over the past 37 years, the total NSL area has shown a significantly weak decreasing trend (slope = −0.0009 million km2/year, r = −0.374, p = 0.023), with relatively small changes in the total area. However, the distribution area of large mobile sandy lands has significantly decreased, whereas the area of fixed sandy lands has significantly increased. Additionally, a survey of changes in the location of sandy lands revealed that 71.86% of the distribution of sandy land remained relatively fixed between 1982 and 2018, with only 28.14% of the distribution remaining in an unstable state. Stable mobile and fixed sandy lands accounted for 85.40% and 82.41% of the total area of mobile and fixed sandy lands, respectively, whereas there were more unstable sandy land distribution areas in the semi-mobile and semi-fixed sandy lands. These results indicate the alleviation of NSL desertification. The new sandy classification and monitoring methods proposed in this study will help improve the remote sensing monitoring of large-scale sand dynamics and offer new ideas for monitoring desertification on a large scale using remote sensing techniques. Full article
(This article belongs to the Special Issue Machine Learning in Global Change Ecology: Methods and Applications)
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Other

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12 pages, 2542 KiB  
Technical Note
Uncertainty of Partial Dependence Relationship between Climate and Vegetation Growth Calculated by Machine Learning Models
by Boyi Liang, Hongyan Liu, Elizabeth L. Cressey, Chongyang Xu, Liang Shi, Lu Wang, Jingyu Dai, Zong Wang and Jia Wang
Remote Sens. 2023, 15(11), 2920; https://doi.org/10.3390/rs15112920 - 03 Jun 2023
Cited by 1 | Viewed by 1644
Abstract
As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to [...] Read more.
As more machine learning and deep learning models are applied in studying the quantitative relationship between the climate and terrestrial vegetation growth, the uncertainty of these advanced models requires clarification. Partial dependence plots (PDPs) are one of the most widely used methods to estimate the marginal effect of independent variables on the predicted outcome of a machine learning model, and it is regarded as the main basis for conclusions in relevant research. As more controversies regarding the reliability of the results of the PDPs emerge, the uncertainty of the PDPs remains unclear. In this paper, we experiment with real, remote sensing data to systematically analyze the uncertainty of partial dependence relationships between four climate variables (temperature, rainfall, radiation, and windspeed) and vegetation growth, with one conventional linear model and six machine learning models. We tested the uncertainty of the PDP curves across different machine learning models from three aspects: variation, whole linear trends, and the trait of change points. Results show that the PDP of the dominant climate factor (mean air temperature) and vegetation growth parameter (indicated by the normalized difference vegetation index, NDVI) has the smallest relative variation and the whole linear trend of the PDP was comparatively stable across the different models. The mean relative variation of change points across the partial dependence curves of the non-dominant climate factors (i.e., radiation, windspeed, and rainfall) and vegetation growth ranged from 8.96% to 23.8%, respectively, which was much higher than those of the dominant climate factor and vegetation growth. Lastly, the model used for creating the PDP, rather than the relative importance of these climate factors, determines the fluctuation of the PDP output of these climate variables and vegetation growth. These findings have significant implications for using remote sensing data and machine learning models to investigate the quantitative relationships between the climate and terrestrial vegetation. Full article
(This article belongs to the Special Issue Machine Learning in Global Change Ecology: Methods and Applications)
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