Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

Journal Browser

Remote Sensing Applied to the Environment and Sustainability Volume Ⅱ

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 (20 July 2023) | Viewed by 11866

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, need 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.

The first volume of the Special Issue can be found at following website:

https://www.mdpi.com/journal/sustainability/special_issues/RS_appllied_to_environ

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

Jump to: Other

12 pages, 1345 KiB

Open AccessArticle

Macro- and Micronutrient Contents and Their Relationship with Growth in Six Eucalyptus Species

by Otavio Ananias Pereira da Silva, Dayane Bortoloto da Silva, Marcelo Carvalho Minhoto Teixeira-Filho, Tays Batista Silva, Cid Naudi Silva Campos, Fabio Henrique Rojo Baio, Gileno Brito de Azevedo, Gláucia Amorim Faria, Larissa Pereira Ribeiro Teodoro and Paulo Eduardo Teodoro

Sustainability 2023, 15(22), 15771; https://doi.org/10.3390/su152215771 - 9 Nov 2023

Viewed by 1033

Abstract

Knowing nutrient allocation dynamics in the tissues and the characteristics related to growth in different forest species is crucial to fertilization management and selecting better species for specific environments, ensuring greater fertilization efficiency and consequent sustainability in the forestry sector through the rational use of fertilizers. The objectives of this study were (i) to evaluate the content of macro- and micronutrients in different tissues of eucalyptus species and (ii) to relate them with their growth. The treatments were composed of six eucalyptus species (Eucalyptus camaldulensis Dehnh., Corymbia citriodora Hook., E. saligna Sm., E. grandis W. Hill ex Maiden, E. urograndis, and E. urophylla S. T. Blake). Macro- (nitrogen, phosphorus, potassium, calcium, magnesium, and sulfur) and micronutrient (boron, copper, iron, manganese, and zinc) contents were determined in the leaves, bark, and sapwood. To study the functional patterns in macro- and micronutrient contents, Canonical Variable Analysis (CVA) was performed. The first two canonical variables in nutrient content of leaves, bark, and sapwood and the growth variables of eucalyptus species accumulated values greater than 80% of variance. The species E. grandis and E. urograndis showed the highest means for volume and total height but showed no differences regarding the concentration of major elements in the tissues, except the iron content in the bark, which was higher compared to other species. CVA proved to be an excellent tool for understanding, identifying, and classifying the strategies of Eucalyptus sp. regarding the content of nutrients in the shoot biomass tissues and may support genetic improvement programs aiming at identifying potential species. Future research involving the use of remotely piloted aircraft and remote sensors could be a strategy to monitor nutrient contents in different parts of trees throughout the cycle of different eucalyptus species. Full article

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

► Show Figures

Figure 1

15 pages, 2972 KiB

Open AccessArticle

A CNN–LSTM Machine-Learning Method for Estimating Particulate Organic Carbon from Remote Sensing in Lakes

by Banglong Pan, Hanming Yu, Hongwei Cheng, Shuhua Du, Shutong Cai, Minle Zhao, Juan Du and Fazhi Xie

Sustainability 2023, 15(17), 13043; https://doi.org/10.3390/su151713043 - 29 Aug 2023

Cited by 3 | Viewed by 1365

Abstract

As particulate organic carbon (POC) from lakes plays an important role in lake ecosystem sustainability and carbon cycle, the estimation of its concentration using satellite remote sensing is of great interest. However, the high complexity and variability of lake water composition pose major challenges to the estimation algorithm of POC concentration in Class II water. This study aimed to formulate a machine-learning algorithm to predict POC concentration and compare their modeling performance. A Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) algorithm based on spectral and time sequences was proposed to construct an estimation model using the Sentinel 2 satellite images and water surface sample data of Chaohu Lake in China. As a comparison, the performances of the Backpropagation Neural Network (BP), Generalized Regression Neural Network (GRNN), and Convolutional Neural Network (CNN) models were evaluated for remote sensing inversion of POC concentration. The results show that the CNN–LSTM model obtained higher prediction precision than the BP, GRNN, and CNN models, with a coefficient of determination (R²) of 0.88, a root mean square error (RMSE) of 3.66, and residual prediction deviation (RPD) of 3.03, which are 6.02%, 22.13%, and 28.4% better than the CNN model, respectively. This indicates that CNN–LSTM effectively combines spatial and temporal information, quickly captures time-series features, strengthens the learning ability of multi-scale features, is conducive to improving estimation precision of remote sensing models, and offers good support for carbon source monitoring and assessment in lakes. Full article

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

► Show Figures

Figure 1

18 pages, 43558 KiB

Open AccessArticle

Remote Sensing Inversion of Typical Water Quality Parameters of a Complex River Network: A Case Study of Qidong’s Rivers

by Xi Zhu, Yansha Wen, Xiang Li, Feng Yan and Shuhe Zhao

Sustainability 2023, 15(8), 6948; https://doi.org/10.3390/su15086948 - 20 Apr 2023

Cited by 6 | Viewed by 1299

Abstract

The remote sensing inversion of the water quality parameters of a complex river network in the absence of historical ground data is a difficult problem in the field of remote sensing. In this paper, a sub-regional inversion method for typical water quality parameters is presented for a complex river network using Gaofen-1 satellite data. Qidong’s rivers were selected as the survey region, and different band combination models and datasets on different river sub-regions were used to perform the remote sensing inversion, which realized the inversion of the permanganate index (CODMn), ammonia nitrogen (NH3-N), total phosphorus (TP), and total nitrogen (TN) in the rivers. The results show that all the coefficients of determination (R^2) of the inversion models are larger than 0.5, indicating an increase of about 0.4 when compared with the inversion method of the whole region, indicating good relevance. Water quality data and satellite data collected at different times were used for validation, which showed good results. On the basis of the water quality inversion, the key polluted areas were extracted in combination with on-site surveys to find the pollution source in order to verify the results of the inversion. The sub-region inversion method proposed in this paper can be used for the remote sensing inversion of the water quality parameters of complex river networks in the absence of historical ground data. Full article

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

► Show Figures

Figure 1

21 pages, 5816 KiB

Open AccessArticle

Dynamics of Fire Foci in the Amazon Rainforest and Their Consequences on Environmental Degradation

by Helvécio de Oliveira Filho, José Francisco de Oliveira-Júnior, Marcos Vinícius da Silva, Alexandre Maniçoba da Rosa Ferraz Jardim, Munawar Shah, João Paulo Assis Gobo, Claudio José Cavalcante Blanco, Luiz Claudio Gomes Pimentel, Corbiniano da Silva, Elania Barros da Silva, Thelma de Barros Machado, Carlos Rodrigues Pereira, Ninu Krishnan Modon Valappil, Vijith Hamza, Mohd Anul Haq, Ilyas Khan, Abdullah Mohamed and El-Awady Attia

Sustainability 2022, 14(15), 9419; https://doi.org/10.3390/su14159419 - 1 Aug 2022

Cited by 5 | Viewed by 2489

Abstract

Burns are common practices in Brazil and cause major fires, especially in the Legal Amazon. This study evaluated the dynamics of the fire foci in the Legal Amazon in Brazil and their consequences on environmental degradation, particularly in the transformation of the forest into pasture, in livestock and agriculture areas, mining activities and urbanization. The fire foci data were obtained from the reference satellites of the BDQueimadas of the CPTEC/INPE for the period June 1998–May 2022. The data obtained were subjected to descriptive and exploratory statistical analysis, followed by a comparison with the PRODES data during 2004–2021, the DETER data (2016–2019) and the ENSO phases during the ONI index for the study area. Biophysical parameters were used in the assessment of environmental degradation. The results showed that El Niño’s years of activity and the years of extreme droughts (2005, 2010 and 2015) stand out with respect to significant increase in fire foci. Moreover, the significant numbers of fire foci indices during August, September, October and November were recorded as 23.28%, 30.91%, 15.64% and 10.34%, respectively, and these were even more intensified by the El Niño episodes. Biophysical parameters maps showed the variability of the fire foci, mainly in the south and west part of the Amazon basin referring to the Arc of Deforestation. Similarly, the states of Mato Grosso, Pará and Amazonas had the highest alerts from PRODES and DETER, and in the case of DETER, primarily mining and deforestation (94.3%) increased the environmental degradation. The use of burns for agriculture and livestock, followed by mining and wood extraction, caused the degradation of the Amazon biome. Full article

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

► Show Figures

Figure 1

17 pages, 6662 KiB

Open AccessArticle

Highway Network and Fire Occurrence in Amazonian Indigenous Lands

by Carlos F. A. Silva, Swanni T. Alvarado, Alex M. Santos, Maurício O. Andrade and Silas N. Melo

Sustainability 2022, 14(15), 9167; https://doi.org/10.3390/su14159167 - 26 Jul 2022

Cited by 8 | Viewed by 1757

Abstract

The construction and expansion of highways aiming to improve the integration of the most isolated regions in Brazil facilitated the access to many inhabited areas in the Amazon biome, but had as a consequence assisted the degradation of many of these regions. Over the last two decades, we have observed in this biome a gradual diversification and intensification of land uses through vegetation loss and an increase in fire associated with deforestation and an increase in grazing areas. We used data from several active fires products derived from 14 different satellites, available on the Brazilian National Institute for Space Research (INPE). We evaluated the influence of highway infrastructure on fire occurrence inside and around Indigenous Lands (IL) located in the Brazilian Amazon biome, from 2008 to 2021. We classified 332 ILs into “cut by highways”, “without highways”, and “with highways in a 10 km buffer”. We performed: (a) the descriptive statistics of the fire occurrence by state, by season, and by type of land use and land cover (LULC) affected by fire; (b) the spatial distribution of the active fire density; and (c) a simple linear regression model between the fire occurrence and the IL area. Our results showed that in total, 16–46% of the fires occurred within the IL in most of the states, while the 10 km buffer was the region most affected by fire. We confirmed that in the last three years there was a significant increase in the number of active fires, representing anomalies in fire occurrence across the studied period. We discussed the result implications and the role of the highway network in environmental degradation inside and around the ILs located in the Brazilian Amazon. Full article

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

► Show Figures

Figure 1

19 pages, 3018 KiB

Open AccessArticle

In-Season Monitoring of Maize Leaf Water Content Using Ground-Based and UAV-Based Hyperspectral Data

by Luís Guilherme Teixeira Crusiol, Liang Sun, Zheng Sun, Ruiqing Chen, Yongfeng Wu, Juncheng Ma and Chenxi Song

Sustainability 2022, 14(15), 9039; https://doi.org/10.3390/su14159039 - 23 Jul 2022

Cited by 12 | Viewed by 1940

Abstract

China is one the largest maize (Zea mays L.) producer worldwide. Considering water deficit as one of the most important limiting factors for crop yield stability, remote sensing technology has been successfully used to monitor water relations in the soil–plant–atmosphere system through canopy and leaf reflectance, contributing to the better management of water under precision agriculture practices and the quantification of dynamic traits. This research was aimed to evaluate the relation between maize leaf water content (LWC) and ground-based and unoccupied aerial vehicle (UAV)-based hyperspectral data using the following approaches: (I) single wavelengths, (II) broadband reflectance and vegetation indices, (III) optimum hyperspectral vegetation indices (HVIs), and (IV) partial least squares regression (PLSR). A field experiment was undertaken at the Chinese Academy of Agricultural Sciences, Beijing, China, during the 2020 cropping season following a split plot model in a randomized complete block design with three blocks. Three maize varieties were subjected to three differential irrigation schedules. Leaf-based reflectance (400–2500 nm) was measured with a FieldSpec 4 spectroradiometer, and canopy-based reflectance (400–1000 nm) was collected with a Pika-L hyperspectral camera mounted on a UAV at three assessment days. Both sensors demonstrated similar shapes in the spectral response from the leaves and canopy, with differences in reflectance intensity across near-infrared wavelengths. Ground-based hyperspectral data outperformed UAV-based data for LWC monitoring, especially when using the full spectra (Vis–NIR–SWIR). The HVI and the PLSR models were demonstrated to be more suitable for LWC monitoring, with a higher HVI accuracy. The optimal band combinations for HVI were centered between 628 and 824 nm (R² from 0.28 to 0.49) using the UAV-based sensor and were consistently located around 1431–1464 nm and 2115–2331 nm (R² from 0.59 to 0.80) using the ground-based sensor on the three assessment days. The obtained results indicate the potential for the complementary use of ground-based and UAV-based hyperspectral data for maize LWC monitoring. Full article

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

► Show Figures

Figure 1

Other

Jump to: Research

11 pages, 2017 KiB

Open AccessTechnical Note

Machine Learning Methods for Woody Volume Prediction in Eucalyptus

by Dthenifer Cordeiro Santana, Regimar Garcia dos Santos, Pedro Henrique Neves da Silva, Hemerson Pistori, Larissa Pereira Ribeiro Teodoro, Nerison Luis Poersch, Gileno Brito de Azevedo, Glauce Taís de Oliveira Sousa Azevedo, Carlos Antonio da Silva Junior and Paulo Eduardo Teodoro

Sustainability 2023, 15(14), 10968; https://doi.org/10.3390/su151410968 - 13 Jul 2023

Cited by 1 | Viewed by 898

Abstract

Machine learning (ML) algorithms can be used to predict wood volume in a faster and more accurate way, providing reliable answers in forest inventories. The objective of this work was to evaluate the performance of different ML techniques to predict the volume of eucalyptus wood, using diameter at breast height (DBH) and total height (Ht) as input variables, obtained by measuring DBH and Ht of 72 trees of six eucalyptus species (Eucalyptus camaldulensis, E. uroplylla, E. saligna, E. grandis, E. urograndis, and Corymbria citriodora). The trees were cut down in two different epochs, rendering 48 samples at 24 months and 24 samples at 48 months, and the volume of each tree was measured using the Smailian method. This research explores five machine learning models, namely artificial neural networks (ANN), K-nearest neighbor (KNN), multiple linear regression (LR), random forest (RF) and support vector machine (SVM), to estimate the volume of eucalyptus wood using DBH and Ht. Artificial neural networks achieved higher correlations between observed and estimated wood volume values. However, the RF outperformed all models by providing lower MAE and higher correlations between observed and estimated wood volume values. Therefore, RF is the most accurate for predicting wood volume in eucalyptus species. Full article

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

► Show Figures

Journal Menu

Journal Browser

Remote Sensing Applied to the Environment and Sustainability Volume Ⅱ

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (7 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI