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Dynamics of Heat Spots and Sustainable Agriculture
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Dynamics of Heat Spots and Sustainable Agriculture

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Agriculture".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 9497

Special Issue Editors

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
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

Special Issue Information

Dear Colleagues,

Due to the commitment made by several nations to reduce global warming through the Paris Agreement in 2015, there is a need to produce more food sustainably. This Special Issue will highlight new research on sustainable agriculture models and hotspot reduction. We accept submissions on remote sensing applications for environmental monitoring; quantification of heat stations in agricultural areas and biomes, among others; sustainable food systems; and dynamics of carbon emission in agricultural areas. Submissions focused on sustainable agriculture and climate change are also encouraged.

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

  • fire foci
  • remote sensing
  • sustainable agriculture
  • carbon emission

Published Papers (4 papers)

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Research

20 pages, 6617 KiB  
Article
Application of Geospatial Techniques in Evaluating Spatial Variability of Commercially Harvested Mangoes in Bangladesh
by Md Moniruzzaman, Md. Sorof Uddin, Md. Abdullah Elias Akhter, Akshar Tripathi and Khan Rubayet Rahaman
Sustainability 2022, 14(20), 13495; https://doi.org/10.3390/su142013495 - 19 Oct 2022
Cited by 2 | Viewed by 1643
Abstract
Mango is widely known as a popular fruit in South Asia, including Bangladesh. The country is a significant producer of different local and exotic varieties of mangoes in different geographic locations. Therefore, a study of fruit maturity at diverse locations and climatic conditions [...] Read more.
Mango is widely known as a popular fruit in South Asia, including Bangladesh. The country is a significant producer of different local and exotic varieties of mangoes in different geographic locations. Therefore, a study of fruit maturity at diverse locations and climatic conditions becomes critical for a sustainable mango production. In responding to this need, this study evaluates the variability of a few selected commercial mango (Mangifera indica L.) varieties and their maturity timeline with respect to spatial extent (longitudinal-latitudinal variations), elevation profile, and time. Remote sensing technology has been widely used for horticultural applications to study fruit phenology, maturity, harvesting time, and for mapping locational differences. In doing so, we have employed remotely sensed data, such as the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) of 30 m spatial resolution, GPM IMERGM precipitation datasets (0.1 × 0.1 degree), NASA GLDAS (Global Land Data Assimilation System) surface skin temperature (0.25 × 0.25 degree), and Noah Land Surface Model L4 3-hourly soil moisture content datasets (0.25 × 0.25 degree). Alongside these, an intensive field data collection campaign has been carried out for 2019 and 2020. It was found that 1° locational variations may result in approximately 2–5 days delay of mango harvesting. The outcome of this study may enhance the appropriate planning of harvesting, production, and the commercialization of mango selection in specific geographic setting for a sustainable harvest and production system in the country. Full article
(This article belongs to the Special Issue Dynamics of Heat Spots and Sustainable Agriculture)
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12 pages, 2750 KiB  
Article
Soybean Cultivars Identification Using Remotely Sensed Image and Machine Learning Models
by Ricardo Gava, Dthenifer Cordeiro Santana, Mayara Favero Cotrim, Fernando Saragosa Rossi, Larissa Pereira Ribeiro Teodoro, Carlos Antonio da Silva Junior and Paulo Eduardo Teodoro
Sustainability 2022, 14(12), 7125; https://doi.org/10.3390/su14127125 - 10 Jun 2022
Cited by 9 | Viewed by 1944
Abstract
Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate [...] Read more.
Using remote sensing combined with machine learning (ML) techniques is a promising approach to classify soybean cultivars. Therefore, the objectives of this study are (i) to verify which input dataset configuration (using only spectral bands, only vegetation indices, or both) is more accurate in the identification of soybean cultivars, and (ii) to verify which ML technique is more accurate in the identification of soybean cultivars. Information was extracted from five central irrigation pivots in the same region and with the same sowing date in the 2015/2016 crop year, in which each pivot was cultivated with a different cultivar, in which the cultivars used were: CV1—P98y12 RR, CV2—Desafio RR, CV3—M6410 IPRO, CV4—M7110 IPRO, and CV5—NA5909 RR. A cloud-free orbital image of the site was acquired from the Google Earth Engine platform. In addition to the spectral bands alone, a total of 13 vegetation indices were calculated. The models tested were: artificial neural networks (ANN), radial basis function network (RBF), decision tree algorithms J48 (DT) and reduced error pruning tree (REP), random forest (RF), and support vector machine (SVM). The five soybean cultivars were classified by the six-machine learning (ML) models in stratified randomized cross-validation with k-fold = 10 and 10 repetitions (100 runs for each model). After obtaining the r and MAE statistics, analysis of variance was performed considering a 6 × 3 factorial scheme (models versus inputs) with 10 repetitions (folds). The means were grouped by the Scott–Knott test at 5% probability. The spectral bands were the most accurate among the tested inputs in the identification of soybean cultivars. ANN was the most accurate model in identifying soybean cultivars. Full article
(This article belongs to the Special Issue Dynamics of Heat Spots and Sustainable Agriculture)
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19 pages, 6262 KiB  
Article
Spatiotemporal Analysis of Fire Foci and Environmental Degradation in the Biomes of Northeastern Brazil
by José Francisco de Oliveira-Júnior, Munawar Shah, Ayesha Abbas, Washington Luiz Félix Correia Filho, Carlos Antonio da Silva Junior, Dimas de Barros Santiago, Paulo Eduardo Teodoro, David Mendes, Amaury de Souza, Elinor Aviv-Sharon, Vagner Reis Silveira, Luiz Claudio Gomes Pimentel, Elania Barros da Silva, Mohd Anul Haq, Ilyas Khan, Abdullah Mohamed and El-Awady Attia
Sustainability 2022, 14(11), 6935; https://doi.org/10.3390/su14116935 - 06 Jun 2022
Cited by 12 | Viewed by 2653
Abstract
Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in [...] Read more.
Forest fires destroy productive land throughout the world. In Brazil, mainly the Northeast of Brazil (NEB) is strongly affected by forest fires and bush fires. Similarly, there is no adequate study of long-term data from ground and satellite-based estimation of fire foci in NEB. The objectives of this study are: (i) to evaluate the spatiotemporal estimation of fires in NEB biomes via environmental satellites during the long term over 1998–2018, and (ii) to characterize the environmental degradation in the NEB biomes via orbital products during 1998–2018, obtained from the Burn Database (BDQueimadas) for 1794 municipalities. The spatiotemporal variation is estimated statistically (descriptive, exploratory and multivariate statistics) from the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Standardized Precipitation Index (SPI) through the Climate Hazards Group InfraRed Precipitation Station (CHIRPS). Moreover, we identify 10 homogeneous groups of fire foci (G1–G10) with a total variance of 76.5%. The G1 group is the most extended group, along with the G2 group, the exception being the G3 group. Similarly, the G4–G10 groups have a high percentage of hotspots, with more values in the municipality of Grajaú, which belongs to the agricultural consortium. The gradient of fire foci from the coast to the interior of the NEB is directly associated with land use/land cover (LULC) changes, where the sparse vegetation category and areas without vegetation are mainly involved. The Caatinga and Cerrado biomes lose vegetation, unlike the Amazon and Atlantic Forest biomes. The fires detected in the Cerrado and Atlantic Forest biomes are the result of agricultural consortia. Additionally, the two periods 2003–2006 and 2013–2018 show periods of severe and prolonged drought due to the action of El Niño. Full article
(This article belongs to the Special Issue Dynamics of Heat Spots and Sustainable Agriculture)
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15 pages, 4303 KiB  
Article
CO2Flux Model Assessment and Comparison between an Airborne Hyperspectral Sensor and Orbital Multispectral Imagery in Southern Amazonia
by João Lucas Della-Silva, Carlos Antonio da Silva Junior, Mendelson Lima, Paulo Eduardo Teodoro, Marcos Rafael Nanni, Luciano Shozo Shiratsuchi, Larissa Pereira Ribeiro Teodoro, Guilherme Fernando Capristo-Silva, Fabio Henrique Rojo Baio, Gabriel de Oliveira, José Francisco de Oliveira-Júnior and Fernando Saragosa Rossi
Sustainability 2022, 14(9), 5458; https://doi.org/10.3390/su14095458 - 01 May 2022
Cited by 6 | Viewed by 2571
Abstract
In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems [...] Read more.
In environmental research, remote sensing techniques are mostly based on orbital data, which are characterized by limited acquisition and often poor spectral and spatial resolutions in relation to suborbital sensors. This reflects on carbon patterns, where orbital remote sensing bears devoted sensor systems for CO2 monitoring, even though carbon observations are performed with natural resources systems, such as Landsat, supported by spectral models such as CO2Flux adapted to multispectral imagery. Based on the considerations above, we have compared the CO2Flux model by using four different imagery systems (Landsat 8, PlanetScope, Sentinel-2, and AisaFenix) in the northern part of the state of Mato Grosso, southern Brazilian Amazonia. The study area covers three different land uses, which are primary tropical forest, bare soil, and pasture. After the atmospheric correction and radiometric calibration, the scenes were resampled to 30 m of spatial resolution, seeking for a parametrized comparison of CO2Flux, as well as NDVI (Normalized Difference Vegetation Index) and PRI (Photochemical Reflectance Index). The results obtained here suggest that PlanetScope, MSI/Sentinel-2, OLI/Landsat-8, and AisaFENIX can be similarly scaled, that is, the data variability along a heterogeneous scene in evergreen tropical forest is similar. We highlight that the spatial-temporal dynamics of rainfall seasonality relation to CO2 emission and uptake should be assessed in future research. Our results provide a better understanding on how the merge and/or combination of different airborne and orbital datasets that can provide reliable estimates of carbon emission and absorption within different terrestrial ecosystems in southern Amazonia. Full article
(This article belongs to the Special Issue Dynamics of Heat Spots and Sustainable Agriculture)
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