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Monitoring Environmental Impacts and Ecological Processes with GIS and Remotely-Sensed Data

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 5414

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Special Issue Editors

Dr. Tiziana Simoniello

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Guest Editor

National Research Council (CNR), Institute of Methodologies for Environmental Analysis (IMAA), 85050 Tito Scalo, PZ, Italy
Interests: EO data calibration and processing; land surface phenology; land degradation; RS in forestry and natural resource management; RS in ecology and conservation; EO data integration
Special Issues, Collections and Topics in MDPI journals

Dr. Gabriel Brito Costa

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Biosciences Post-Graduate Program (PPG-BIO), Federal University of Western Pará (UFOPA), Santarem 68035-110, Brazil
Interests: meteorology; environment; evapotranspiration; climate science; atmospheric physics; climate variability; climate modeling; climate dynamics; ecology; atmospheric pollution

Special Issue Information

Dear Colleagues,

The great growth of Earth observation (EO) systems together with innovative data processing and analysis solutions provide opportunities to monitor spatial–temporal changes and to evaluate the effects of human impact and climate modification on the environment and ecological processes as never before. New sensors on different platforms (e.g., GEDI on ISS, PRISMA, AHSI-Gaofen5, and TROPOMI-S5 on satellites, micro Hyper and LiDAR instruments on UAVs) make available more detailed information (higher spectral, spatial, and temporal resolutions) and a greater number of parameters. At the same time, analysis methods based on artificial intelligence techniques, such as machine and deep learning, and reasoning in conditions of uncertainty, are supporting the exploitation of the large heritage of time series of EO data (e.g., Landsat, AVHRR, SEVIRI, Spot, MODIS) acquired in the past to understand and interpret complex processes.

This Special Issue aims to summarize the state of applications and evaluate the trends of EO data acquisition and geospatial processing systems, deepening the knowledge of emerging platforms and techniques in the studies of environmental quality and ecological processes.

Topics in this Special Issue include research and innovative methods for assessing quality in environmental matrices (soil, water, vegetation, and air) and a better understanding of ecological and environmental interactions to support the development of sustainable solutions.

Spatial-temporal changes in environmental and ecological processes.
Pollutions/contaminants in environmental matrices (soil, water, vegetation, air).
EO Essential Variables (Essential Biodiversity Variables—EBV, Essential Climate Variables—ECV, Essential Geodiversity Variables—EGV, Essential Water Variables—EWV, and Essential Ocean Variables—EOV).
Biodiversity (species, habitat and distribution mapping, distribution models).
Landscape patterns, processes and interactions.
Application of passive and active EO data, imaging spectroscopy and other emerging techniques.
Next generation EO sensors and products for environment and ecosystem monitoring (PACE, SBG, CHIME, LSTM, CO2M).
GIS and EO for resource management tools and Decision Support Systems (integration models, geospatial artificial intelligence—GeoAI, etc.).

Dr. Tiziana Simoniello
Dr. Gabriel Brito Costa
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

ecosystem processes
landscape ecology
spatial data
remote sensing imagery
biodiversity monitoring
environmental quality
EO essential variables

Published Papers (3 papers)

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Research

22 pages, 5038 KiB

Open AccessArticle

Detection and Attribution of Changes in Terrestrial Water Storage across China: Climate Change versus Vegetation Greening

by Rui Kong, Zengxin Zhang, Ying Zhang, Yiming Wang, Zhenhua Peng, Xi Chen and Chong-Yu Xu

Remote Sens. 2023, 15(12), 3104; https://doi.org/10.3390/rs15123104 - 14 Jun 2023

Cited by 4 | Viewed by 1316

Abstract

Whether or not large-scale vegetation restoration will lead to a decrease in regional terrestrial water storage is a controversial topic. This study employed the Geodetector model, in conjunction with observed and satellite hydro-meteorological data, to detect the changes in terrestrial water storage anomaly (TWSA) and to identify the contributions of climate change and vegetation greening across China during the years 1982–2019. The results revealed that: (1) during the period of 1982–2019, TWSA showed a downward trend in about two thirds of the country, with significant declines in North China, southeast Tibet, and northwest Xinjiang, and an upward trend in the remaining third of the country, with significant increases mainly in the Qaidam Basin, the Yangtze River, and the Songhua River; (2) the positive correlation between normalized vegetation index (NDVI) and TWSA accounts for 48.64% of the total vegetation area across China. In addition, the response of vegetation greenness lags behind the TWSA and precipitation, and the lag time was shorter in arid and semi-arid regions dominated by grasslands, and longer in relatively humid regions dominated by forests and savannas; (3) furthermore, TWSAs decreased with the increase in NDVI and evapotranspiration (ET) in arid and semi-arid areas, and increased with the rise in NDVI and ET in the humid regions. The Geodetector model was used to detect the effects of climate, vegetation, and human factors on TWSA. It is worth mentioning that NDVI, precipitation, and ET were some of the main factors affecting TWSA. Therefore, it is essential to implement rational ecological engineering to mitigate climate change’s negative effects and maintain water resources’ sustainability in arid and semi-arid regions. Full article

(This article belongs to the Special Issue Monitoring Environmental Impacts and Ecological Processes with GIS and Remotely-Sensed Data)

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22 pages, 9398 KiB

Open AccessArticle

Comparison of Machine Learning Methods for Predicting Soil Total Nitrogen Content Using Landsat-8, Sentinel-1, and Sentinel-2 Images

by Qingwen Zhang, Mingyue Liu, Yongbin Zhang, Dehua Mao, Fuping Li, Fenghua Wu, Jingru Song, Xiang Li, Caiyao Kou, Chunjing Li and Weidong Man

Remote Sens. 2023, 15(11), 2907; https://doi.org/10.3390/rs15112907 - 02 Jun 2023

Cited by 3 | Viewed by 1657

Abstract

Soil total nitrogen (STN) is a crucial component of the ecosystem’s nitrogen pool, and accurate prediction of STN content is essential for understanding global nitrogen cycling processes. This study utilized the measured STN content of 126 sample points and 40 extracted remote sensing variables to predict the STN content and map its spatial distribution in the northeastern coastal region of Hebei Province, China, employing the random forest (RF), gradient boosting machine (GBM), and extreme gradient boosting (XGBoost) methods. The purpose was to compare the ability of remote sensing images (Landsat-8, Sentinel-1, and Sentinel-2) with different machine learning methods for predicting STN content. The research results show the following: (1) The three machine learning methods accurately predicted the STN content and the optimal model provided by the XGBoost method, with an R² of 0.627, RMSE of 0.127 g·kg⁻¹, and MAE of 0.092 g·kg⁻¹. (2) The combination of optical and synthetic aperture radar (SAR) images improved prediction accuracy, with the R² improving by 45.5%. (3) The importance of optical images is higher than that of SAR images in the RF, GBM, and XGBoost methods, with optical images accounting for 87%, 76%, and 77% importance, respectively. (4) The spatial distribution of STN content predicted by the three methods is similar. Higher STN contents are distributed in the northern part of the study area, while lower STN contents are distributed in coastal areas. The results of this study can be very useful for inventories of soil nitrogen and provide data support and method references for revealing nitrogen cycling. Full article

(This article belongs to the Special Issue Monitoring Environmental Impacts and Ecological Processes with GIS and Remotely-Sensed Data)

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20 pages, 3305 KiB

Open AccessArticle

Evaluating the Losses and Recovery of GPP in the Subtropical Mangrove Forest Directly Attacked by Tropical Cyclone: Case Study in Hainan Island

by Lan Wu, Enliang Guo, Yinghe An, Qian Xiong, Xian Shi, Xiang Zhang and Zhongyi Sun

Remote Sens. 2023, 15(8), 2094; https://doi.org/10.3390/rs15082094 - 16 Apr 2023

Cited by 1 | Viewed by 1843

Abstract

The gross primary production (GPP) of the mangrove ecosystem determines the upper limit of the scale of its “blue carbon” sink. Tropical cyclones (TCs) are among the most important extreme events that threaten the subtropical mangrove ecosystem and have a serious impact on mangrove ecosystem GPP. However, there are somewhat insufficient scientific findings on regional-scale mangrove ecosystem GPP responding to large-scale weather events such as TCs. Therefore, we selected the subtropical Hainan Island mangrove ecosystem, where more than two TCs pass through per year, as the research area; selected direct-attack TCs as the research object; and took the mangrove vegetation photosynthesis light-use efficiency model established based on the eddy covariance observation data as the tool to evaluate the loss and recovery of mangrove ecosystem GPP after TCs attacked at a regional scale. We found that the TC impacted the mangrove ecosystem GPP through the photosynthetic area and rate, and the recovery of the rate occurred prior to the recovery of the area; the loss of mangrove ecosystem GPP is inversely proportional to the distance to the center of the TC and the distance to the coastline; and the canopy height, diameter at breast height, and aspect where the tree stands significantly influence the response of the mangrove ecosystem GPP to TCs. However, the response varies for different mangrove community compositions, soil conditions, and planting densities as well as different frequencies and intensities of TCs, and they should be analyzed in detail. This study is expected to provide technical and data support for the protection of blue carbon in a subtropical island mangrove ecosystem in response to extreme events and post-disaster recovery. Full article

(This article belongs to the Special Issue Monitoring Environmental Impacts and Ecological Processes with GIS and Remotely-Sensed Data)

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