Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing Applied to the Environment and Sustainability

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 19924

Share This Special Issue

Special Issue Editors

Dr. Carlos Antonio Da Silva Junior

E-Mail Website
Guest Editor

Department of Geography, State University of Mato Grosso (UNEMAT), Sinop 78550-000, MT, Brazil
Interests: remote sensing; LULCC; environment; spatial analysis; climate change
Special Issues, Collections and Topics in MDPI journals

Dr. Paulo Eduardo Teodoro

E-Mail Website
Guest Editor

Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, MT, Brazil
Interests: statistics; multivariate analysis; plant breeding; biometrics; remote sensing; sensors; genomic selection; geostatistics; precision agriculture
Special Issues, Collections and Topics in MDPI journals

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

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. Sustainability 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 2400 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

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

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

15 pages, 23346 KiB

Open AccessArticle

The “New Transamazonian Highway”: BR-319 and Its Current Environmental Degradation

by Mendelson Lima, Dthenifer Cordeiro Santana, Ismael Cavalcante Maciel Junior, Patricia Monique Crivelari da Costa, Pedro Paulo Gomes de Oliveira, Raul Pio de Azevedo, Rogerio de Souza Silva, Ubiranei de Freitas Marinho, Valdinete da Silva, Juliana Aparecida Arantes de Souza, Fernando Saragosa Rossi, Rafael Coll Delgado, Larissa Pereira Ribeiro Teodoro, Paulo Eduardo Teodoro and Carlos Antonio da Silva Junior

Sustainability 2022, 14(2), 823; https://doi.org/10.3390/su14020823 - 12 Jan 2022

Cited by 7 | Viewed by 3055

Abstract

The Brazilian government intends to complete the paving of the BR-319 highway, which connects Porto Velho in the deforestation arc region with Manaus in the middle of the Amazon Forest. This paving is being planned despite environmental legislation, and there is concern that its effectiveness will cause additional deforestation, threatening large portions of forest, conservation units (CUs), and indigenous lands (ILs) in the surrounding areas. In this study, we evaluated environmental degradation along the BR-319 highway from 2008 to 2020 and verified whether highway maintenance has contributed to deforestation. For this purpose, we created a 20 km buffer adjacent to the BR-319 highway and evaluated variables extracted from remote sensing information between 2008 and 2020. Fire foci, burned areas, and rainfall data were used to calculate a drought index using statistical tests for a time series. Furthermore, these were related to data on deforestation, CUs, and ILs using principal component analysis and Pearson’s correlation. Our results showed that 743 km² of forest was deforested during the period evaluated, most of which occurred in the last four years. A total of 16,472 fire foci were identified. Both deforestation and fire foci occurred mainly outside the CUs and ILs. The most affected areas were close to capital cities, and after resuming road maintenance in 2015, deforestation increased outside the capital cities. Current government policy for Amazon occupation promotes deforestation and will compromise Brazil’s climate goals of reducing greenhouse gas (GHG) emissions and deforestation. Full article

(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)

► Show Figures

Figure 1

20 pages, 11933 KiB

Open AccessArticle

Long-Term Impact of Transhumance Pastoralism and Associated Disturbances in High-Altitude Forests of Indian Western Himalaya

by Shiekh Marifatul Haq, Umer Yaqoob, Eduardo Soares Calixto, Manoj Kumar, Inayat Ur Rahman, Abeer Hashem, Elsayed Fathi Abd_Allah, Maha Abdullah Alakeel, Abdulaziz A. Alqarawi, Mohnad Abdalla, Fayaz A. Lone, Muhammad Azhar Khan, Uzma Khan and Farhana Ijaz

Sustainability 2021, 13(22), 12497; https://doi.org/10.3390/su132212497 - 12 Nov 2021

Cited by 14 | Viewed by 2130

Abstract

The Himalayan Mountains are geodynamical important, featuring a wide climatic range with a rich diversity of flora, fauna, human communities, culture, and social set-up. In recent decades, due to constant anthropogenic pressure and considerable changes witnessed in the climate of the region, species of this region are threatened. Here, we assessed the impact of nomadic settlement and associated disturbances on plant species composition, diversity parameters, ecosystem properties, and fire incidence in high-altitude forests of Western Himalaya, India. Based on the distance between nomadic settlement location and forest, we classified forest as near nomadic settlement (NNS) or away nomadic settlement (ANS) forest types. We found a significant variation in plant species composition between forest types. Three species, namely, Sibbaldia cuneata, Poa annua, and Abies pindrow, contribute 25% of the cumulative variation in plant species composition. Studying live plants, we found a significant difference only for density, in which ANS had a higher average density than NNS. Considering dead plants, we found a significant difference in all nine plant-related parameters evaluated between sites. NNS had a higher value of all parameters evaluated, except for height, which was higher in ANS sites. ANS forest type show 1.3 times more average carbon stock (160.39 ± 59.03 MgCha⁻¹; mean ± SD) than NNS forest type (120.40 ± 51.74 MgCha⁻¹). We found a significant difference in plant diversity evaluated between forest types. ANS had higher values of Margalef and Fisher diversity but lower values of evenness. We found that NSS had significantly higher values of fire incidences, whereas ANS has a higher normalized differential vegetation index and enhanced vegetation index. Overall, our study showed that species composition, diversity, and fire incidence are strongly impacted due to nomadic settlements. These findings are paramount for designing appropriate livelihood options for indigenous communities and management policies of the long-term forest harvest to achieve global goals and the UN Decade on Ecosystem Restoration targets (2021–2030) to protect the sustainable development of forest mountainous regions. Full article

(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)

► Show Figures

Figure 1

17 pages, 3116 KiB

Open AccessArticle

Analyzing the Relationship between Animal Diversity and the Remote Sensing Vegetation Parameters: The Case of Xinjiang, China

by Jinhui Wu, Haoxin Li, Huawei Wan, Yongcai Wang, Chenxi Sun and Hongmin Zhou

Sustainability 2021, 13(17), 9897; https://doi.org/10.3390/su13179897 - 03 Sep 2021

Cited by 7 | Viewed by 2623

Abstract

An explicit analysis of the impact for the richness of species of the vegetation phenological characteristics calculated from various remote sensing data is critical and essential for biodiversity conversion and restoration. This study collected long-term the Normalized Difference Vegetation Index (NDVI), the Leaf Area Index (LAI), the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and the Fractional Vegetation Cover (FVC), and calculated the six vegetation phenological characteristic parameters: the mean of the growing season, the mean of the mature season, the mean of the withered season, the annual difference value, the annual cumulative value, and the annual standard deviation in the Xinjiang Uygur Autonomous Region. The relationships between the vegetation phenological characteristics and the species richness of birds and mammals were analyzed in spatial distribution. The main findings include: (1) The correlation between bird diversity and vegetation factors is greater than that of mammals. (2) For remote sensing data, FAPAR is the most important vegetation parameter for both birds and mammals. (3) For vegetation phenological characteristics, the annual cumulative value of the LAI is the most crucial vegetation phenological parameter for influencing bird diversity distribution, and the annual difference value of the NDVI is the most significant driving factor for mammal diversity distribution. Full article

(This article belongs to the Special Issue Remote Sensing Applied to the Environment and Sustainability)

► Show Figures

Figure 1

21 pages, 6548 KiB

Open AccessArticle

Impact Assessments of Typhoon Lekima on Forest Damages in Subtropical China Using Machine Learning Methods and Landsat 8 OLI Imagery

by Xu Zhang, Guangsheng Chen, Lingxiao Cai, Hongbo Jiao, Jianwen Hua, Xifang Luo and Xinliang Wei

Sustainability 2021, 13(9), 4893; https://doi.org/10.3390/su13094893 - 27 Apr 2021

Cited by 15 | Viewed by 2366

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 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)

► Show Figures

Figure 1

13 pages, 3891 KiB

Open AccessArticle

Retrieval and Evaluation of Chlorophyll-A Spatiotemporal Variability Using GF-1 Imagery: Case Study of Qinzhou Bay, China

by Ze-Lin Na, Huan-Mei Yao, Hua-Quan Chen, Yi-Ming Wei, Ke Wen, Yi Huang and Peng-Ren Liao

Sustainability 2021, 13(9), 4649; https://doi.org/10.3390/su13094649 - 22 Apr 2021

Cited by 7 | Viewed by 1707

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 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)

► Show Figures

Figure 1

14 pages, 3233 KiB

Open AccessArticle

Estimation of Spatial and Seasonal Variability of Soil Erosion in a Cold Arid River Basin in Hindu Kush Mountainous Region Using Remote Sensing

by Ziauddin Safari, Sayed Tamim Rahimi, Kamal Ahmed, Ahmad Sharafati, Ghaith Falah Ziarh, Shamsuddin Shahid, Tarmizi Ismail, Nadhir Al-Ansari, Eun-Sung Chung and Xiaojun Wang

Sustainability 2021, 13(3), 1549; https://doi.org/10.3390/su13031549 - 02 Feb 2021

Cited by 5 | Viewed by 2794

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. 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)

► Show Figures

Figure 1

20 pages, 17373 KiB

Open AccessArticle

Modeling Flash Floods and Induced Recharge into Alluvial Aquifers Using Multi-Temporal Remote Sensing and Electrical Resistivity Imaging

by Omnia El-Saadawy, Ahmed Gaber, Abdullah Othman, Abotalib Z. Abotalib, Mohammed El Bastawesy and Mohamed Attwa

Sustainability 2020, 12(23), 10204; https://doi.org/10.3390/su122310204 - 07 Dec 2020

Cited by 35 | Viewed by 3320

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 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)

► Show Figures

Journal Menu

Journal Browser

Remote Sensing Applied to the Environment and Sustainability

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI