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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Forest Remote Sensing".

Deadline for manuscript submissions: closed (20 May 2022) | Viewed by 27636

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Dr. Francisco Javier Mesas Carrascosa

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Guest Editor

Department of Graphic Engineering and Geomatics, Higher Technical School of Agricultural Engineering, University of Córdoba, 14071 Córdoba, Spain
Interests: UAV; remote sensing; photogrammetry; cloud computing
Special Issues, Collections and Topics in MDPI journals

Dr. Andrés Felipe Ríos Mesa

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Co-Guest Editor

Escuela de Ingeniería, Programa Ingeniería Agroindustrial, Universidad Pontificia Bolivariana, Medellín, Colombia
Interests: Remote Sensing; Precision agriculture; Agribusiness; Food supply chain

Special Issue Information

Dear Colleague,

Good environmental, agricultural and forestry management requires efficient and effective policies. This requires data in order to determine if there is a need for new policy development and, if necessary, to adequately control, monitor and enforce its implementation. The objective is to achieve sustainable growth by meeting society's economic, social and environmental requirements without compromising natural resources or the environment. Current earth observation programs have the potential to support local, regional and national governments and non-governmental organizations in their decision-making concerning areas such as decreasing air pollution, improving water quality and supporting sustainable agriculture.

We would like to invite you to submit articles about your recent research linked to the title of this Special Issue “Remote sensing for agricultural, environmental, forestry policies”, concerning, for example, the following topics:

Monitoring, controlling and improving air and water quality
Interaction of urban areas and/or infrastructure and the environment
Urban dynamic characterization and locating settlements
Monitoring of agricultural policies
Characterization, monitoring and modelling forest and natural area dynamics
Monitoring snow cover, glacial retreat and shrinking ice sheets
Sensors and data integration: proximal and remote; satellite, manned and unmanned; active and passive; climatic and image; among others
Sustainable forestry and natural resource use
Big data and remote sensing
Machine/deep learning and remote sensing

Prof. Dr. Francisco Javier Mesas Carrascosa
Dr. Andrés Felipe Ríos Mesa
Guest Editors

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Published Papers (8 papers)

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Research

19 pages, 6357 KiB

Open AccessArticle

Improving Satellite Retrieval of Coastal Aquaculture Pond by Adding Water Quality Parameters

by Yuxuan Hou, Gang Zhao, Xiaohong Chen and Xuan Yu

Remote Sens. 2022, 14(14), 3306; https://doi.org/10.3390/rs14143306 - 8 Jul 2022

Cited by 19 | Viewed by 3320

Abstract

Coastal aquaculture is an important supply of animal proteins for human consumption, which is expanding globally. Meanwhile, extensive aquaculture may increase nutrient loadings and environmental concerns along the coast. Accurate information on aquaculture pond location is essential for coastal management. Traditional methods use morphological parameters to characterize the geometry of surface waters to differentiate artificially constructed conventional aquaculture ponds from other water bodies. However, there are other water bodies with similar morphology (e.g., saltworks, rice fields, and small reservoirs) that are difficult to distinguish from aquaculture ponds, causing a lot of omission/commissioning errors in areas with complex land-use types. Here, we develop an extraction method with shape and water quality parameters to map aquaculture ponds, including three steps: (1) Sharpen normalized difference water index to detect and binarize water pixels by the Otsu method; (2) Connect independent water pixels into water objects through the four-neighbor connectivity algorithm; and (3) Calculate the shape features and water quality features of water objects and input them into the classifier for supervised classification. We selected eight sites along the coast of China to evaluate the accuracy and generalization of our method in an environment with heterogeneous pond morphology and landscape. The results showed that six transfer characteristics including water quality characteristics improved the accuracy of distinguishing aquaculture ponds from salt pans, rice fields, and wetland parks, which typically had F1 scores > 85%. Our method significantly improved extraction efficiency on average, especially when aquaculture ponds are mixed with other morphological similar water bodies. Our identified area was in agreement with statistics data of 12 coastal provinces in China. In addition, our approach can effectively improve water objects when high-resolution remote sensing images are unavailable. This work was applied to open-source remote sensing imagery and has the potential to extract long-term series and large-scale aquaculture ponds globally. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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20 pages, 5264 KiB

Open AccessArticle

Rice Crop Monitoring Using Sentinel-1 SAR Data: A Case Study in Saku, Japan

by Shoko Kobayashi and Hiyuto Ide

Remote Sens. 2022, 14(14), 3254; https://doi.org/10.3390/rs14143254 - 6 Jul 2022

Cited by 14 | Viewed by 5328

Abstract

Global warming affects rice crop production, causing deterioration of rice grain quality. This study used C-band microwave images taken by the Sentinel-1 satellites to monitor rice crop growth with the aim to understand microwave backscatter behavior, focusing on decreases in panicle water contents with ripening, which affect C-band backscatter. Time-series changes illustrated a similar tendency across all four analysis years, showing that VV/VH ratio at an incidence angle of 45–46° stopped decreasing to be stable over the reproductive and ripening periods due to reductions in the panicle water content that allowed for greater microwave penetration into the canopy, thereby increasing panicle-related backscatter. Furthermore, multivariate regression analysis combined with field observations showed that VV and VH with the shallow incidence angles were significantly negatively correlated with panicle water content, which well demonstrated backscatter increases with plant senescence. Furthermore, it was observed that backscatter behaviors were highly consistent with changes in crop phenology and surface condition. Accordingly, Sentinel-1 images with shallow incidence angles and high revisit observation capabilities offer a strong potential for estimating panicle water content. Therefore, it seems reasonable to conclude that C-band SAR data is capable of retrieving grain filling conditions to estimate proper harvesting time. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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17 pages, 15332 KiB

Open AccessArticle

Interprovincial Joint Prevention and Control of Open Straw Burning in Northeast China: Implications for Atmospheric Environment Management

by Jing Fu, Shitao Song, Li Guo, Weiwei Chen, Peng Wang, Lingjian Duanmu, Yijing Shang, Bowen Shi and Luyan He

Remote Sens. 2022, 14(11), 2528; https://doi.org/10.3390/rs14112528 - 25 May 2022

Cited by 9 | Viewed by 2222

Abstract

Large-scale open burning of straw residues causes seasonal and severe atmospheric pollution in Northeast China. Previous studies focused on the causes or assessment of atmospheric pollution in a single city. However, studies conducted on the interaction range, degree and policy control of pollutant transport on a large scale are still to be performed. In this study, we propose combined control of straw burning by dividing region the straw burning in Northeast China in recent 20 years, determining the transport routes between main cities, and analyzing the interaction characteristics of straw burning under different scenarios. The fire point data suggest that the most intense straw burning years in Northeast China in the past 20 years occurred in the range from 2014 to 2017, mainly after the autumn harvest (October–November) and before spring cultivation (March–April). The burning areas were concentrated in the belt of Shenyang-Changchun-Harbin, the border of the three provinces and Eastern-Inner Mongolia, and the surrounding area of Hegang and Jiamusi City. The lower number of fire points before 2013 indicates that high-intensity burning has not always been the case, while the sharp decline after 2018 is mainly due to scientific control of straw burning and increased comprehensive utilization of straw. Compared with S2, the PM_2.5 concentrations increased by 6.2% in S3 and 18.7% in S4, indicating that burning in three or four provinces at the same time will significantly increase air pollution and exert a regional transmission effect. Straw burning in Northeast China is divided into six main regions based on correlation analysis and satellite fire monitoring. Under typical S3, the case analysis results indicate that there is regional transmission interaction between different cities and provinces, focusing on multi-province border cities, and it is affected by Northwest long airflow, and Southeast and Northeast short airflow. These results provide scientific and technological support for implementing the joint prevention and control plan for straw incineration in Northeast China. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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17 pages, 1801 KiB

Open AccessArticle

Harmonizing Definitions and Methods to Estimate Deforestation at the Lacandona Tropical Region in Southern Mexico

by Ana Fernández-Montes de Oca, Adrián Ghilardi, Edith Kauffer, José Alberto Gallardo-Cruz, Juan Manuel Núñez and Víctor Sánchez-Cordero

Remote Sens. 2022, 14(10), 2319; https://doi.org/10.3390/rs14102319 - 11 May 2022

Cited by 3 | Viewed by 1820

Abstract

Deforestation is a major factor reducing natural habitats, leading to tropical ecosystems and biodiversity loss worldwide. The Lacandona region in southern Mexico holds one of the largest fragments of tropical rainforest in North America. We evaluated the deforestation of the Lacandona region harmonizing concepts and methodologies. An international (FAO definition), governmental (national definition), and regional definition of deforestation with applications at different scales were analyzed and harmonized with two classification methods (likelihood and spectral angle mapper (SAM)). We used 2015 and 2018 Landsat 8 images, and likelihood and SAM classifications were applied for FAO and regional definitions of deforestation. Overall, the best evaluated classifier in quantity was likelihood for 2015 and 2018 (kappa: 0.87 and 0.70, overall accuracy: 91.8 and 80.4%, and quantity disagreement: 4.1 and 10 %, respectively). The allocation disagreement only showed exchange between classes. Nevertheless, they did not show differences between classifiers, although 2015 had less disagreement than 2018: exchange, 4.1% for likelihood and SAM; shift: 0% for likelihood and SAM. Maps based on the regional definition of deforestation showed that the likelihood classification detected 11,441 ha less deforestation than SAM (40,538 and 51,979 ha, respectively). The FAO definition of deforestation showed that likelihood classification detected 11,914 ha less deforestation than SAM classification (37,152 and 49,066 ha, respectively). Further, the likelihood classification showed 3387 ha more of deforestation according to the regional definition than the FAO definition of deforestation (40,538 and 37,152 ha, respectively). SAM classification showed that the regional definition showed 2913 ha more deforestation than the FAO definition (51,979 and 49,066, respectively). We concluded that implementation of governmental programs in the Lacandona region requires estimations based on a careful selection of deforestation definitions and methods. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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22 pages, 5869 KiB

Open AccessArticle

Grassland Phenology Response to Climate Conditions in Biobio, Chile from 2001 to 2020

by Marcelo-Alejandro Doussoulin-Guzmán, Fernando-Juan Pérez-Porras, Paula Triviño-Tarradas, Andrés-Felipe Ríos-Mesa, Alfonso García-Ferrer Porras and Francisco-Javier Mesas-Carrascosa

Remote Sens. 2022, 14(3), 475; https://doi.org/10.3390/rs14030475 - 20 Jan 2022

Cited by 14 | Viewed by 2930

Abstract

Plant phenology is affected by climate conditions and therefore provides a sensitive indicator to changes in climate. Studying the evolution and change in plant phenology aids in a better understanding of and predicting changes in ecosystems. Vegetation Indices (VIs) have been recognized for their utility in indicating vegetation activity. Understanding climatic variables and their relationship to VI support the knowledge base of how ecosystems are changing under a new climatic scenario. This study evaluates grassland growth phenology in the Biobio, Chile, biweekly with Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time series. Four growth parameters for the six agro-climatic regions were analyzed from 2001 to 2020: start and end of the season, time and value of maximum NDVI. For this purpose, the NDVI time series were smoothed using Savitzky–Golay filtering. In addition, by using monthly gridded database climate data, we studied correlations between phenology markers and rainfall, maximum temperature and minimum temperature. The results show that both the start and end of the growing season did not significantly change; however, all agro-climatic regions grow faster and more vigorously. Thus, climatic conditions in Biobio have become more conducive to grassland growth over the 2001–2020 period. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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Graphical abstract

23 pages, 4834 KiB

Open AccessArticle

Identification of Significative LiDAR Metrics and Comparison of Machine Learning Approaches for Estimating Stand and Diversity Variables in Heterogeneous Brazilian Atlantic Forest

by Rorai Pereira Martins-Neto, Antonio Maria Garcia Tommaselli, Nilton Nobuhiro Imai, Hassan Camil David, Milto Miltiadou and Eija Honkavaara

Remote Sens. 2021, 13(13), 2444; https://doi.org/10.3390/rs13132444 - 23 Jun 2021

Cited by 11 | Viewed by 4874

Abstract

Data collection and estimation of variables that describe the structure of tropical forests, diversity, and richness of tree species are challenging tasks. Light detection and ranging (LiDAR) is a powerful technique due to its ability to penetrate small openings and cracks in the forest canopy, enabling the collection of structural information in complex forests. Our objective was to identify the most significant LiDAR metrics and machine learning techniques to estimate the stand and diversity variables in a disturbed heterogeneous tropical forest. Data were collected in a remnant of the Brazilian Atlantic Forest with different successional stages. LiDAR metrics were used in three types of transformation: (i) raw data (untransformed), (ii) correlation analysis, and (iii) principal component analysis (PCA). These transformations were tested with four machine learning techniques: (i) artificial neural network (ANN), ordinary least squares (OLS), random forests (RF), and support vector machine (SVM) with different configurations resulting in 27 combinations. The best technique was determined based on the lowest RMSE (%) and corrected Akaike information criterion (AICc), and bias (%) values close to zero. The output forest variables were mean diameter at breast height (MDBH), quadratic mean diameter (QMD), basal area (BA), density (DEN), number of tree species (NTS), as well as Shannon–Waver (H’) and Simpson’s diversity indices (D). The best input data were the new variables obtained from the PCA, and the best modeling method was ANN with two hidden layers for the variables MDBH, QMD, BA, and DEN while for NTS, H’and D, the ANN with three hidden layers were the best methods. For MDBH, QMD, H’and D, the RMSE was 5.2–10% with a bias between −1.7% and 3.6%. The BA, DEN, and NTS were the most difficult variables to estimate, due to their complexity in tropical forests; the RMSE was 16.2–27.6% and the bias between −12.4% and −0.24%. The results showed that it is possible to estimate the stand and diversity variables in heterogeneous forests with LiDAR data. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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22 pages, 3788 KiB

Open AccessFeature PaperArticle

Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

by J. David Ballester-Berman and Maria Rastoll-Gimenez

Remote Sens. 2021, 13(11), 2075; https://doi.org/10.3390/rs13112075 - 25 May 2021

Cited by 5 | Viewed by 2452

Abstract

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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24 pages, 8526 KiB

Open AccessArticle

Mapping Mangrove Zonation Changes in Senegal with Landsat Imagery Using an OBIA Approach Combined with Linear Spectral Unmixing

by Florent Lombard and Julien Andrieu

Remote Sens. 2021, 13(10), 1961; https://doi.org/10.3390/rs13101961 - 18 May 2021

Cited by 10 | Viewed by 3122

Abstract

The mangrove areas in Senegal have fluctuated considerably over the last few decades, and it is therefore important to monitor the evolution of forest cover in order to orient and optimise forestry policies. This study presents a method for mapping plant formations to monitor and study changes in zonation within the mangroves of Senegal. Using Landsat ETM+ and Landsat 8 OLI images merged to a 15-m resolution with a pansharpening method, a processing chain that combines an OBIA approach and linear spectral unmixing was developed to detect changes in mangrove zonation through a diachronic analysis. The accuracy of the discriminations was evaluated with kappa indices, which were 0.8 for the Saloum delta and 0.83 for the Casamance estuary. Over the last 20 years, the mangroves of Senegal have increased in surface area. However, the dynamics of zonation differ between the two main mangrove hydrosystems of Senegal. In Casamance, a colonisation process is underway. In the Saloum, Rhizophora mangle is undergoing a process of densification in mangroves and appears to reproduce well in both regions. Furthermore, this study confirms, on a regional scale, observations in the literature noting the lack of Avicennia germinans reproduction on a local scale. In the long term, these regeneration gaps may prevent the mangrove from colonising the upper tidal zones in the Saloum. Therefore, it would be appropriate to redirect conservation policies towards reforestation efforts in the Saloum rather than in Casamance and to focus these actions on the perpetuation of Avicennia germinans rather than Rhizophora mangle, which has no difficulty in reproducing. From this perspective, it is necessary to gain a more in-depth understanding of the specific factors that promote the success of Avicennia germinans seeding. Full article

(This article belongs to the Special Issue Remote Sensing for Agricultural, Environmental and Forestry Policies)

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