Special Issue "Remote Sensing Applied to the Environment and Sustainability"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editors

Prof. Dr. Carlos Antonio Da Silva Junior
E-Mail Website
Guest Editor
Department of Geography, State University of Mato Grosso (UNEMAT), Sinop, Cáceres 78200-000, MT, Brazil
Interests: remote sensing; LULCC; environment; spatial analysis; climate change
Special Issues and Collections in MDPI journals
Prof. Dr. Paulo Eduardo Teodoro
E-Mail Website
Guest Editor
Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul, 79074-460 Mato Grosso do Sul, Brazil
Interests: deep learning; spatial analysis; climate modeling; multivariate analysis

Special Issue Information

Dear Colleagues,

With the evolution of remote sensors and cloud computing, the immeasurable amount of data generated every second globally from an increasing number of sources has changed the way of analyzing the environment and its sustainability. The way of thinking about territorial organizations through the detection and coverage of land use combined with data analysis has transformed in the last decade. The analysis of the environment (including climatic-atmospheric analyses, emissions, and fires) and its interaction with anthropic activities, especially with those in large agricultural areas, needs to be maximized to design a plan for advancing modern agriculture in a sustainable manner, while preserving the environment, thus improving land use efficiency and maximizing productivity. In this Special Issue, studies describing the results of remote sensing assessments should generate valuable insights for the scientific community and for policy implementation by authorities in different countries. In addition, we seek papers on precise and innovative ways to analyze data, such as deep learning techniques including convolutional networks, random forests, and others.

Prof. Dr. Carlos Antonio Da Silva Junior
Prof. Dr. Paulo Eduardo Teodoro
Guest Editors

Manuscript Submission Information

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Keywords

  • Biomes
  • Deforestation
  • Native forest
  • Climate change
  • Agricultural expansion
  • Orbital sensors
  • Public policy
  • Fires
  • Carbon
  • Greenhouse gases

Published Papers (4 papers)

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Research

Article
Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery
Sustainability 2021, 13(9), 4893; https://doi.org/10.3390/su13094893 - 27 Apr 2021
Viewed by 431
Abstract
Wind damage is one of the major factors affecting forest ecosystem sustainability, especially in the coastal region. Typhoon Lekima is among the top five most devastating typhoons in China and caused economic losses totaling over USD 8 billion in Zhejiang Province alone during [...] Read more.
Wind damage is one of the major factors affecting forest ecosystem sustainability, especially in the coastal region. Typhoon Lekima is among the top five most devastating typhoons in China and caused economic losses totaling over USD 8 billion in Zhejiang Province alone during 9–12 August 2019. However, there still is no assessment of its impacts on forests. Here we detected forest damage and its spatial distribution caused by Typhoon Lekima by classifying Landsat 8 OLI images using the random forest (RF) machine learning algorithm and the univariate image differencing (UID) method on the Google Earth Engine (GEE) platform. The accuracy assessment indicated a high overall accuracy (>87%) and kappa coefficient (>0.75) for forest-damage detection, as evaluated against field-investigated plot data, with better performance using the RF method. The total affected forest area by Lekima was 4598.87 km2, accounting for 8.44% of the total forest area in Zhejiang Province. The light-, moderate- and severe-damage forest areas were 2106.29 km2, 2024.26 km2 and 469.76 km2, respectively. Considering the damage severity, the net forest canopy loss fraction was 2.57%. The affected forest area and damage severity exhibited large spatial variations, which were affected by elevation, slope, precipitation and forest type. Our study indicated a larger uncertainty for affected forest area and a smaller uncertainty for the proportion of damage severity, based on multiple assessment approaches. This is among the first studies on forest damage due to typhoons at a regional scale in China, and the methods can be extended to examine the impacts of other super-strong typhoons on forests. Our study results on damage severity, spatial distribution and controlling factors could help local governments, the forest sector and forest landowners make decision on tree-planting planning and sustainable management after typhoon strikes and could also raise public and governmental awareness of typhoons’ damage on China’s inland forests. Full article
(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)
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Article
Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China
Sustainability 2021, 13(9), 4649; https://doi.org/10.3390/su13094649 - 22 Apr 2021
Viewed by 393
Abstract
Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the [...] Read more.
Chlorophyll-a (Chl-a) concentration is a measure of phytoplankton biomass, and has been used to identify ‘red tide’ events. However, nearshore waters are optically complex, making the accurate determination of the chlorophyll-a concentration challenging. Therefore, in this study, a typical area affected by the Phaeocystis ‘red tide’ bloom, Qinzhou Bay, was selected as the study area. Based on the Gaofen-1 remote sensing satellite image and water quality monitoring data, the sensitive bands and band combinations of the nearshore Chl-a concentration of Qinzhou Bay were screened, and a Qinzhou Bay Chl-a retrieval model was constructed through stepwise regression analysis. The main conclusions of this work are as follows: (1) The Chl-a concentration retrieval regression model based on 1/B4 (near-infrared band (NIR)) has the best accuracy (R2 = 0.67, root-mean-square-error = 0.70 μg/L, and mean absolute percentage error = 0.23) for the remote sensing of Chl-a concentration in Qinzhou Bay. (2) The spatiotemporal distribution of Chl-a in Qinzhou Bay is varied, with lower concentrations (0.50 μg/L) observed near the shore and higher concentrations (6.70 μg/L) observed offshore, with a gradual decreasing trend over time (−0.8). Full article
(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)
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Article
Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing
Sustainability 2021, 13(3), 1549; https://doi.org/10.3390/su13031549 - 02 Feb 2021
Viewed by 900
Abstract
An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. [...] Read more.
An approach is proposed in the present study to estimate the soil erosion in data-scarce Kokcha subbasin in Afghanistan. The Revised Universal Soil Loss Equation (RUSLE) model is used to estimate soil erosion. The satellite-based data are used to obtain the RUSLE factors. The results show that the slight (71.34%) and moderate (25.46%) erosion are dominated in the basin. In contrast, the high erosion (0.01%) is insignificant in the study area. The highest amount of erosion is observed in Rangeland (52.2%) followed by rainfed agriculture (15.1%) and barren land (9.8%) while a little or no erosion is found in areas with fruit trees, forest and shrubs, and irrigated agriculture land. The highest soil erosion was observed in summer (June–August) due to snow melting from high mountains. The spatial distribution of soil erosion revealed higher risk in foothills and degraded lands. It is expected that the methodology presented in this study for estimation of spatial and seasonal variability soil erosion in a remote mountainous river basin can be replicated in other similar regions for management of soil, agriculture, and water resources. Full article
(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)
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Article
Modeling Flash Floods and Induced Recharge into Alluvial Aquifers Using Multi-Temporal Remote Sensing and Electrical Resistivity Imaging
Sustainability 2020, 12(23), 10204; https://doi.org/10.3390/su122310204 - 07 Dec 2020
Cited by 10 | Viewed by 781
Abstract
Flash flood hazard assessments, mitigation measures, and water harvesting efforts in desert environments are often challenged by data scarcity on the basin scale. The present study, using the Wadi Atfeh catchment as a test site, integrates remote sensing datasets with field and geoelectrical [...] Read more.
Flash flood hazard assessments, mitigation measures, and water harvesting efforts in desert environments are often challenged by data scarcity on the basin scale. The present study, using the Wadi Atfeh catchment as a test site, integrates remote sensing datasets with field and geoelectrical measurements to assess flash flood hazards, suggest mitigation measures, and to examine the recharge to the alluvium aquifer. The estimated peak discharge of the 13 March 2020 flood event was 97 m3/h, which exceeded the capacity of the culverts beneath the Eastern Military Highway (64 m3/h), and a new dam was suggested, where 75% of the catchment could be controlled. The monitoring of water infiltration into the alluvium aquifer using time-lapse electrical resistivity measurements along a fixed profile showed a limited connection between the wetted surficial sediments and the water table. Throughflow is probably the main source of recharge to the aquifer rather than vertical infiltration at the basin outlet. The findings suggest further measures to avoid the negative impacts of flash floods at the Wadi Atfeh catchment and similar basins in the Eastern Desert of Egypt. Furthermore, future hydrological studies in desert environments should take into consideration the major role of the throughflow in alluvium aquifer recharge. Full article
(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)
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