scholarly journals Sentinel-2 imagery usage on environmental monitoring of land use and occupation in a microwatershed in Central Brazil

Gaia Scientia ◽  
2021 ◽  
Vol 15 (1) ◽  
Author(s):  
Jean Jesus Novais ◽  
Marilusa Pinto Coelho Lacerda
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Social Aspects ◽  
Agricultural Activities ◽  
Maximum Likelihood Algorithm ◽  
Kappa Index ◽  
Central Brazil ◽  
Strategic Plans ◽  
Distrito Federal ◽  
Sentinel 2

In the last decades, sustainability concerns have increased the demand for projects and strategic plans that integrate economic and social aspects, reducing environmental impacts. In this sense, this study aims to monitor land-use adequacy in the Ribeirão Extrema microwatershed, Distrito Federal, based on cross-mapping between land-use and occupation in 2019 and agricultural aptitude map through Geographic Information Systems and Remote sensing. To this end, a hypsometric and thematic database was prepared for the region. Besides, we acquired an image from the Sentinel-2 orbital sensor of October 2019. The image was subjected to classification regarding land-use and occupation, using the MAXVER (maximum likelihood) algorithm. It was observed that 80% of use in 2019 was related to agricultural activities. Kappa index validation reached 81% accuracy. Based on the methodology, we identified 62.33% of agricultural activities occur into its capacity; 4.33%, were used above capacity, causing environmental degradation, especially in permanent preservation areas. The application of the technique was considered satisfactory because the adequacy of land-use in the studied microwatershed could be assessed in order to pursue sustainable development. Continuous analyzes can improve results.

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

DIAGNÓSTICO DO USO E COBERTURA DA TERRA NOS RESERVATÓRIOS PÚBLICOS DO MUNICÍPIO DE PAU DOS FERROS, RN, BRASIL

2020 ◽  
Vol 11 (38) ◽  
pp. 146-161
Author(s):  
Aluizio Bezerra Júnior ◽  
◽  
Agassiel Medeiros Alves ◽  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Natural Resources ◽  
Spatial Resolution ◽  
Sustainable Use ◽  
Rio Grande ◽  
Spatial Dimensions ◽  
Methodological Procedures ◽  
Remote Sensing Techniques ◽  
Sentinel 2

Research objective is to classify, measure and map the spatial dimensions of land use and land cover classes in public reservoirs 25 de Março and Dr. Pedro Diógenes Fernandes, both belonging to the municipality of Pau dos Ferros, state of Rio Grande do Norte. For the methodological procedures, remote sensing techniques (SIG Qgis version Lyon 2.12.3) were used, of the medium spatial resolution images of the SENTINEL 2 satellite, MSI sensor (Multispectral Instrument), accompanied by the interpretation key. The results showed that there is a possibility of sustainable use, since the exploration and conservation remains in balance, therefore, this research can subsidize the conservation of the use of natural resources around the reservoirs.

scholarly journals A Generic Classification Scheme for Urban Structure Types

2019 ◽  
Vol 11 (2) ◽  
pp. 173 ◽  
Author(s):  
Arthur Lehner ◽  
Thomas Blaschke
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Classification Scheme ◽  
Structure Type ◽  
Social Aspects ◽  
Urban Structure ◽  
Data Types ◽  
Urban Structures ◽  
Generic Framework ◽  
Object Based

This paper presents a proposal for a generic urban structure type (UST) scheme. Initially developed in the context of urban ecology, the UST approach is increasingly popular in the remote sensing community. However, there is no consistent and standardized UST framework. Until now, the terms land use and certain USTs are often used and described synonymously, or components of structure and use are intermingled. We suggest a generic nomenclature and a respective UST scheme that can be applied worldwide by stakeholders of different disciplines. Based on the insights of a rigorous literature analysis, we formulate a generic structural- and object-based typology, allowing for the generation of hierarchically and terminologically consistent USTs. The developed terminology exclusively focuses on morphology, urban structures and the general exterior appearance of buildings. It builds on the delimitation of spatial objects at several scales and leaves out all social aspects and land use aspects of an urban area. These underlying objects or urban artefacts and their structure- and object-related features, such as texture, patterns, shape, etc. are the core of the hierarchically structured UST scheme. Finally, the authors present a generic framework for the implementation of a remote sensing-based UST classification along with the requirements regarding sensors, data and data types.

scholarly journals DETECTING LAND USE/COVER DYNAMICS AND LAND SUITABILITY MAPPING FOR IRBID GOVERNORATE USING AN INTEGRATED APPROACH

2021 ◽  
Vol 29 (3) ◽  
pp. 263-272
Author(s):  
Alsharifa Hind Mohammad ◽  
Taleb Odeh ◽  
Maha Halalsheh ◽  
Khaldoun Shatanawi
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Change Detection ◽  
Integrated Approach ◽  
Remote Sensing And Gis ◽  
Accelerated Expansion ◽  
Agricultural Activities ◽  
Land Uses ◽  
Landsat Images ◽  
Suitability Map

This research proposes to design an approach recognizing land use/cover change for Irbid governorate from 1985 to 2015 in 10 years period bases, with an agriculture suitability map using remote sensing and GIS. In this paper, ENVI6 was used to analyse Landsat images, which helps to understand the land uses’ classes. LULC Changes results showed an increase in urban land, from 2% in 1985 reached to 11% in 2015; soil and agricultural classes had declined, in 1985 they were 74% of the total area, and reduced to 67% in 2015. Irbid Governorate’s change detection results revealed that the decline of agriculture and rock land areas is due to the accelerated expansion of urbanization, which negatively affects agricultural lands. Modelling the area showed high suitability for agricultural activities, which should be considered for the upcoming plans.

scholarly journals Remote Sensing–Based Urban Green Space Detection Using Marine Predators Algorithm Optimized Machine Learning Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-22
Author(s):  
Nhat-Duc Hoang ◽  
Xuan-Linh Tran
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Green Space ◽  
Urban Land ◽  
Urban Green Space ◽  
Urban Land Use ◽  
Urban Green ◽  
Marine Predators ◽  
Sentinel 2

Information regarding the current status of urban green space is crucial for urban land-use planning and management. This study proposes a remote sensing and data-driven solution for urban green space detection at regional scale via employment of state-of-the-art metaheuristic and machine learning approaches. Remotely sensed data obtained from Sentinel 2 satellite in the study area of Da Nang city (Vietnam) are used to construct and verify an intelligent model that hybridizes Marine Predators Algorithm (MPA) and support vector machines (SVM). SVM are employed to generalize a decision boundary that separates features characterizing statistical measurements of remote sensing data into two categories of “green space” and “nongreen space”. The MPA metaheuristic is used to optimize the SVM training phase by identifying an appropriate set of the SVM’s hyperparameters including the penalty coefficient and the kernel function parameter. Experimental results show that the proposed model which processes information provided by all of the Sentinel 2 satellite’s spectral bands can deliver a better performance than those obtained from the model based on vegetation indices. With a good classification accuracy rate of roughly 93%, an F1 score = 0.93, and an area under the receiver operating characteristic = 0.98, the newly developed model is a promising tool to assist local authority to obtain up-to-date information on urban green space and develop plans of sustainable urban land use.

Modelling biomass and biodiversity in temperate grasslands with Sentinel-1, Sentinel-2 and Unmanned Aerial Vehicles.

2021 ◽  
Author(s):  
Javier Muro ◽  
Lisa Schwarz ◽  
Florian Männer ◽  
Anja Linstädter ◽  
Olena Dubovyk
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Machine Learning Algorithms ◽  
Ecosystem Functions ◽  
Biodiversity Indicators ◽  
Biodiversity Exploratories ◽  
Improve Model ◽  
First Results ◽  
Land Use Practices ◽  
Sentinel 2

<p>Land use practices in grasslands are major determinants of their biodiversity and ecosystem functions. Relationships between biodiversity, ecosystem functions and land use practices can vary across climatic and management gradients and across scales. New generations of remote sensing sensors can model grasslands’ biomass and biodiversity parameters with relative RMSE that range between 10% and 40%. However, most of these experiments have been carried out in rather small and homogenous areas. In the project SeBAS (Sensing Biodiversity Across Scales) we are using machine learning algorithms (random forest and neural networks) to model biomass and biodiversity indicators along spatial and management gradients and across scales. Field data (above ground biomass and species inventories) was obtained during summer 2020 from the Biodiversity Exploratories: a set of 150 grassland plots across spatial and management gradients in Germany. Remote sensing information at farm level was obtained from microwave Sentinel-1 and multispectral Sentinel-2 satellites, and at plot level from a multispectral camera mounted on a UAV.</p><p>First results show the limitations of satellite images to map vegetation parameters in heterogeneous landscapes, and how the incorporation of UAV information can be used to improve model estimations of biomass production and biodiversity indicators.</p>

scholarly journals Prediction of Soil Organic Carbon under Different Land Use Types Using Sentinel-1/-2 Data in a Small Watershed

2021 ◽  
Vol 13 (7) ◽  
pp. 1229
Author(s):  
Huan Wang ◽  
Xin Zhang ◽  
Wei Wu ◽  
Hongbin Liu
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Satellite Imagery ◽  
Prediction Accuracy ◽  
Mean Squared Error ◽  
Squared Error ◽  
Land Use Types ◽  
Sentinel 2

Soil organic carbon (SOC) is a key property for evaluating soil quality. SOC is thus an important parameter of agricultural soils and needs to be regularly monitored. The aim of this study is to explore the potential of synthetic aperture radar (SAR) satellite imagery (Sentinel-1), optical satellite imagery (Sentinel-2), and digital elevation model (DEM) data to estimate the SOC content under different land use types. The extreme gradient boosting (XGboost) algorithm was used to predict the SOC content and evaluate the importance of feature variables under different land use types. For this purpose, 290 topsoil samples were collected and 49 features were derived from remote sensing images and DEM. Feature selection was carried out to prevent data redundancy. Coefficient of determination (R2), mean absolute error (MAE), mean squared error (MSE), percent root mean squared error (%RMSE), ratio of performance to interquartile range (RPIQ), and corrected akaike information criterion (AICc) were employed for evaluating model performance. The results showed that Sentinel-1 and Sentinel-2 data were both important for the prediction of SOC and the prediction accuracy of the model differed with land use types. Among them, the prediction accuracy of this model is the best for orchard (R2 = 0.86 and MSE = 0.004%), good for dry land (R2 = 0.74 and MSE = 0.008%) and paddy field (R2 = 0.66 and MSE = 0.009%). The prediction model of SOC content is effective and can provide support for the application of remote sensing data to soil property monitoring.

scholarly journals Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring

2020 ◽  
Vol 12 (18) ◽  
pp. 2919
Author(s):  
Ann-Kathrin Holtgrave ◽  
Norbert Röder ◽  
Andrea Ackermann ◽  
Stefan Erasmi ◽  
Birgit Kleinschmit
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Optical Data ◽  
Agricultural Land Use ◽  
Land Use Type ◽  
Auxiliary Data ◽  
Land Use Types ◽  
Sentinel 2

Agricultural vegetation development and harvest date monitoring over large areas requires frequent remote sensing observations. In regions with persistent cloud coverage during the vegetation season this is only feasible with active systems, such as SAR, and is limited for optical data. To date, optical remote sensing vegetation indices are more frequently used to monitor agricultural vegetation status because they are easily processed, and the characteristics are widely known. This study evaluated the correlations of three Sentinel-2 optical indices with Sentinel-1 SAR indices over agricultural areas to gain knowledge about their relationship. We compared Sentinel-2 Normalized Difference Vegetation Index, Normalized Difference Water Index, and Plant Senescence Radiation Index with Sentinel-1 SAR VV and VH backscatter, VH/VV ratio, and Sentinel-1 Radar Vegetation Index. The study was conducted on 22 test sites covering approximately 35,000 ha of four different main European agricultural land use types, namely grassland, maize, spring barley, and winter wheat, in Lower Saxony, Germany, in 2018. We investigated the relationship between Sentinel-1 and Sentinel-2 indices for each land use type considering three phenophases (growing, green, senescence). The strength of the correlations of optical and SAR indices differed among land use type and phenophase. There was no generic correlation between optical and SAR indices in our study. However, when the data were split by land use types and phenophases, the correlations increased remarkably. Overall, the highest correlations were found for the Radar Vegetation Index and VH backscatter. Correlations for grassland were lower than for the other land use types. Adding auxiliary data to a multiple linear regression analysis revealed that, in addition to land use type and phenophase information, the lower quartile and median SAR values per field, and a spatial variable, improved the models. Other auxiliary data retrieved from a digital elevation model, Sentinel-1 orbit direction, soil type information, and other SAR values had minor impacts on the model performance. In conclusion, despite the different nature of the signal generation, there were distinct relationships between optical and SAR indices which were independent of environmental variables but could be stratified by land use type and phenophase. These relationships showed similar patterns across different test sites. However, a regional clustering of landscapes would significantly improve the relationships.

scholarly journals The effect of Neutral Density Filters on drones orthomosaics classifications for land-use mapping

2020 ◽  
Author(s):  
Jean Doumit
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
High Resolution ◽  
Quality Assessment ◽  
Neutral Density ◽  
Paper Study ◽  
Visual Interpretation ◽  
Kappa Index ◽  
Urbanized Area ◽  
Index Calculation

Landscape and Street Photographers use Neutral Density (ND) Filters to enhance their photos, drones images with advanced photogrammetry software produce high-resolution orthomosaic for the production of land use maps.This paper study the effect of four different neutral density filters (ND-4, ND-8, ND-16, and ND-32) on drone orthomosaics production of a half urbanized area, a five generated orthomosaics one not filtered and four filtered were classified in a remote sensing software and compared between each other. Three comparison methods used for the comparison between orthomosaics: An image visual interpretation, kappa index calculation for land-use quality assessment, and quantity analysis of land use filter generated polygons.

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