scholarly journals Transformasi indeks vegetasi Citra Sentinel 2 A untuk pemetaan produktivitas lahan sawah Kabupaten Magelang

2020 ◽  
Vol 18 (1) ◽  
Author(s):  
Alfiatun Nur Khasanah ◽  
Dian Octaviani
Keyword(s):  
Sentinel 2A ◽  
Sentinel 2

Aplikasi Penginderaan Jauh dan GIS dapat digunakan untuk mendapatkan informasi spasial produksi lahan sawah. Produktivitas sawah, yang biasanya disajikan dalam data tabular, dapat dipetakan menjadi informasi spasial dengan menggunakan respon vegetasi dari citra penginderaan jauh resolusi menengah. Tujuan dari penelitian ini adalah untuk menilai rata-rata produksi sawah di Magelang menggunakan Sentinel 2A. Citra melalui tahapan pemrosesan, yaitu koreksi atmosfer serta klasifikasi multispektral untuk mendapatkan batas sawah. Survei lapangan dan analisis regresi antara survei produktivitas aktual dan indeks vegetasi dilakukan untuk mendapatkan model terbaik. Ada 8 model indeks vegetasi yang digunakan dalam penelitian ini. Indeks Vegetasi memberikan kisaran nilai koefisien korelasi 0,62 hingga 0,74. Kisaran ini dikategorikan sebagai hubungan korelasi sedang dan kuat. Nilai koefisien korelasi tertinggi ditunjukkan oleh indeks RVI sebesar 0,74, yang berarti bahwa 74% dari model dapat mewakili sampel. Produksi beras dominan di daerah penelitian adalah di kisaran 47-52 kg / 100 m2. Nilai ini di bawah rata-rata produksi di Magelang

scholarly journals A Hybrid Bio-Optical Transformation for Satellite Bathymetry Modeling Using Sentinel-2 Imagery

2019 ◽  
Vol 11 (23) ◽  
pp. 2746 ◽  
Author(s):  
Athanasios K. Mavraeidopoulos ◽  
Emmanouil Oikonomou ◽  
Athanasios Palikaris ◽  
Serafeim Poulos
Keyword(s):  
Optical Properties ◽  
Field Data ◽  
Sea Water ◽  
Aegean Sea ◽  
Multispectral Images ◽  
Inherent Optical Properties ◽  
Sentinel 2A ◽  
Satellite Bathymetry ◽  
Optical Transformation ◽  
Sentinel 2

The article presents a new hybrid bio-optical transformation (HBT) method for the rapid modelling of bathymetry in coastal areas. The proposed approach exploits free-of-charge multispectral images and their processing by applying limited manpower and resources. The testbed area is a strait between two Greek Islands in the Aegean Sea with many small islets and complex seabed relief. The HBT methodology implements semi-analytical and empirical steps to model sea-water inherent optical properties (IOPs) and apparent optical properties (AOPs) observed by the Sentinel-2A multispectral satellite. The relationships of the calculated IOPs and AOPs are investigated and utilized to classify the study area into sub-regions with similar water optical characteristics, where no environmental observations have previously been collected. The bathymetry model is configured using very few field data (training depths) chosen from existing official nautical charts. The assessment of the HBT indicates the potential for obtaining satellite derived bathymetry with a satisfactory accuracy for depths down to 30 m.

scholarly journals First Results of Phytoplankton Spatial Dynamics in Two NW-Mediterranean Bays from Chlorophyll-a Estimates Using Sentinel 2: Potential Implications for Aquaculture

2019 ◽  
Vol 11 (15) ◽  
pp. 1756 ◽  
Author(s):  
Soriano-González ◽  
Angelats ◽  
Fernández-Tejedor ◽  
Diogene ◽  
Alcaraz
Keyword(s):  
Chlorophyll A ◽  
Spatial Dynamics ◽  
Ebro Delta ◽  
Band Ratio ◽  
Environmental Forcing ◽  
Shellfish Farming ◽  
First Results ◽  
Nw Mediterranean ◽  
Sentinel 2A ◽  
Sentinel 2

Shellfish aquaculture has a major socioeconomic impact on coastal areas, thus it is necessary to develop support tools for its management. In this sense, phytoplankton monitoring is crucial, as it is the main source of food for shellfish farming. The aim of this study was to assess the applicability of Sentinel 2 multispectral imagery (MSI) to monitor the phytoplankton biomass at Ebro Delta bays and to assess its potential as a tool for shellfish management. In situ chlorophyll-a data from Ebro Delta bays (NE Spain) were coupled with several band combination and band ratio spectral indices derived from Sentinel 2A levels 1C and 2A for time-series mapping. The best results (AIC = 72.17, APD < 10%, and MAE < 0.7 mg/m3) were obtained with a simple blue-to-green ratio applied over Rayleigh corrected images. Sentinel 2–derived maps provided coverage of the farm sites at both bays allowing relating the spatiotemporal distribution of phytoplankton with the environmental forcing under different states of the bays. The applied methodology will be further improved but the results show the potential of using Sentinel 2 MSI imagery as a tool for assessing phytoplankton spatiotemporal dynamics and to encourage better future practices in the management of the aquaculture in Ebro Delta bays.

scholarly journals SenDiT: The Sentinel-2 Displacement Toolbox with Application to Glacier Surface Velocities

2019 ◽  
Vol 11 (10) ◽  
pp. 1151
Author(s):  
Teodor Nagy ◽  
Liss M. Andreassen ◽  
Robert A. Duller ◽  
Pablo J. Gonzalez
Keyword(s):  
Feature Tracking ◽  
World Wide ◽  
Satellite Images ◽  
Glacier Surface ◽  
Displacement Maps ◽  
Spatial And Temporal Changes ◽  
Glacier Velocity ◽  
Glacier Flow ◽  
Sentinel 2A ◽  
Sentinel 2

Satellite imagery represents a unique opportunity to quantify the spatial and temporal changes of glaciers world-wide. Glacier velocity has been measured from repeat satellite scenes for decades now, yet a range of satellite missions, feature tracking programs, and user approaches have made it a laborious task. To date, there has been no tool developed that would allow a user to obtain displacement maps of any specified glacier simply by establishing the key temporal, spatial and feature tracking parameters. This work presents the application and development of a unique, semi-automatic, open-source, flexible processing toolbox for the retrieval of displacement maps with a focus on obtaining glacier surface velocities. SenDiT combines the download, pre-processing, feature tracking, and postprocessing of the highest resolution Sentinel-2A and Sentinel-2B satellite images into a semi-automatic toolbox, leaving a user with a set of rasterized and georeferenced glacier flow magnitude and direction maps for their further analyses. The solution is freely available and is tailored so that non-glaciologists and people with limited geographic information system (GIS) knowledge can also benefit from it. The system can be used to provide both regional and global sets of ice velocities. The system was tested and applied on a range of glaciers in mainland Norway, Iceland, Greenland and New Zealand. It was also tested on areas of stable terrain in Libya and Australia, where sources of error involved in the feature tracking using Sentinel-2 imagery are thoroughly described and quantified.

scholarly journals THE USE OF SPECTRAL AND TEXTURAL FEATURES IN CROP TYPE MAPPING USING SENTINEL-2A IMAGES: A CASE STUDY, ÇUKUROVA REGION, TURKEY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 117-122
Author(s):  
A. Tuzcu Kokal ◽  
A. F. Sunar ◽  
A. Dervisoglu ◽  
S. Berberoglu
Keyword(s):  
Satellite Image ◽  
Support Vector ◽  
Textural Features ◽  
Factors Affecting ◽  
Object Based ◽  
Crop Types ◽  
Sentinel 2A ◽  
Occurrence Matrix ◽  
Agriculture Management ◽  
Sentinel 2

Abstract. Turkey has favorable agricultural conditions (i.e. fertile soils, climate and rainfall) and can grow almost any type of crop in many regions, making it one of the leading sectors of the economy. For sustainable agriculture management, all factors affecting the agricultural products should be analyzed on a spatial-temporal basis. Therefore, nowadays space technologies such as remote sensing are important tools in providing an accurate mapping of the agricultural fields with timely monitoring and higher repetition frequency and accuracy. In this study, object based classification method was applied to 2017 Sentinel 2 Level 2A satellite image in order to map crop types in the Adana, Çukurova region in Turkey. Support Vector Machine (SVM) was used as a classifier. Texture information were incorporated to spectral wavebands of Sentinel-2 image, to increase the classification accuracy. In this context, all of the textural features of Gray-Level Co-occurrence Matrix (GLCM) were tested and Entropy, Standard deviation, and Mean textural features were found to be the most suitable among them. Multi-spectral and textural features were used as an input separately and/or in combination to evaluate the potential of texture in differentiating crop types and the accuracy of output thematic maps. As a result, with the addition of textural features, it was observed that the Overall Accuracy and Kappa coefficient increased by 7% and 8%, respectively.

scholarly journals ASSESSMENTS OF SENTINEL-2 VEGETATION RED-EDGE SPECTRAL BANDS FOR IMPROVING LAND COVER CLASSIFICATION

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 871-874 ◽  
Author(s):  
S. Qiu ◽  
B. He ◽  
C. Yin ◽  
Z. Liao
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Relative Importance ◽  
East Texas ◽  
Spectral Bands ◽  
Red Edge ◽  
Sentinel 2A ◽  
Land Cover Maps ◽  
Confusion Matrices ◽  
Sentinel 2

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40&amp;thinsp;%, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 319 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals ­ Predicting Pinus monophylla forest cover in the Baja California desert by remote sensing

2018 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Forest Cover ◽  
Baja California ◽  
Arid Zones ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

Background. The Californian single-leaf pinyon (Pinus monophylla var. californiarum), a subspecies of the single-leaf pinyon (the world's only 1-needled pine), inhabits semi-arid zones of the Mojave Desert (southern Nevada and southeastern California, US) and also of northern Baja California (Mexico). This subspecies is distributed as a relict in the geographically isolated arid Sierra La Asamblea at elevations of between 1,010 and 1,631 m, with mean annual precipitation levels of between 184 and 288 mm. The aim of this research was i) to estimate the distribution of P. monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) by using Sentinel-2 images, and ii) to test and describe the relationship between the distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) Sentinel-2 images can be used to predict the P. monophylla distribution in the study site due to higher resolution (x3) and increased number of bands (x2) relative to Landsat-8 , and ii) the topographical variables aspect, ruggedness and slope are particularly important because they represent important microhabitat factors that can determine where conifers can become established and persist. Methods. An atmospherically corrected a 12-bit Sentinel-2A MSI image with ten spectral bands in the visible, near infrared, and short-wave infrared light region was used in combination with the normalized differential vegetation index. Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest classification including cross valuation (10-fold) were used to model the associations between presence/absence of P. monophylla and the five topographical and 18 climate variables. Results. We estimated, using supervised classification of Sentinel-2 satellite images, that P. monophylla covers 6,653 ± 46 ha in the isolated Sierra La Asamblea. The NDVI was one of the variables that contributed to the prediction and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the most influential environmental predictor variable and indicated that the probability of P. monophylla occurrence was higher than 50% when the degree of ruggedness was greater than 17.5 m. When average temperature in the warmest month increased from 23.5 to 25.2 °C, the probability of occurrence of P. monophylla decreased. Discussion. The classification accuracy was similar to that reported in other studies using Sentinel-2A MSI images. Ruggedness is known to generate microclimates and provides shade that decreases evapotranspiration from pines in desert environments. Identification of P. monophylla in the Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climate variability and change.

scholarly journals WILDFIRE DAMAGE ASSESSMENT USING MULTI-TEMPORAL SENTINEL-2 DATA

2019 ◽  
Vol XLII-3/W8 ◽  
pp. 97-102
Author(s):  
M. Chung ◽  
M. Jung ◽  
Y. Kim
Keyword(s):  
Damage Assessment ◽  
Coniferous Forest ◽  
Spectral Indices ◽  
Wildfire Occurrence ◽  
Ecological Changes ◽  
Multi Temporal ◽  
Additional Image ◽  
Image Pairs ◽  
Sentinel 2A ◽  
Sentinel 2

<p><strong>Abstract.</strong> Recently, the drastic climate changes have increased the importance of wildfire monitoring and damage assessment as well as the possibility of wildfire occurrence. Estimation of wildfire damage provides the information on wildfire-induced ecological changes and supports the decision-making process for post-fire treatment activities. For accurate wildfire damage assessment, the discrimination between disaster-induced and natural changes is crucial because they usually coupled together.</p> <p>In this study, Sentinel-2 images were employed to assess the damage from a wildfire, which occurred in the coniferous forest of Gangneung, Gangwon Province, South Korea on April 2019. The images were captured from both Sentinel-2A and -2B, shortening the temporal interval of available pre- and post-fire images. Multi-temporal image analysis was performed in both object and pixel-based with two commonly used spectral indices, NDVI and NBR. Additional image pair from the same period of 2018 was used to distinguish the fire-affected regions from the naturally changed area and compared with the results from using only one pair of images from 2019. The experimental results showed that the change detection performance could be affected by the number of image pairs and spectral indices used to discriminate burned region from unburned region. Thus it verified the significance of adequately employing annual multi-pair satellite images for wildfire damage assessment.</p>

scholarly journals Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery

2021 ◽  
Vol 13 (1) ◽  
pp. 157
Author(s):  
Jun Li ◽  
Zhaocong Wu ◽  
Zhongwen Hu ◽  
Zilong Li ◽  
Yisong Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Spatial Resolution ◽  
Short Wave ◽  
Spectral Information ◽  
Thin Cloud ◽  
Cloud Removal ◽  
Image Pairs ◽  
Sentinel 2A ◽  
Thin Cloud Removal ◽  
Sentinel 2

Thin clouds seriously affect the availability of optical remote sensing images, especially in visible bands. Short-wave infrared (SWIR) bands are less influenced by thin clouds, but usually have lower spatial resolution than visible (Vis) bands in high spatial resolution remote sensing images (e.g., in Sentinel-2A/B, CBERS04, ZY-1 02D and HJ-1B satellites). Most cloud removal methods do not take advantage of the spectral information available in SWIR bands, which are less affected by clouds, to restore the background information tainted by thin clouds in Vis bands. In this paper, we propose CR-MSS, a novel deep learning-based thin cloud removal method that takes the SWIR and vegetation red edge (VRE) bands as inputs in addition to visible/near infrared (Vis/NIR) bands, in order to improve cloud removal in Sentinel-2 visible bands. Contrary to some traditional and deep learning-based cloud removal methods, which use manually designed rescaling algorithm to handle bands at different resolutions, CR-MSS uses convolutional layers to automatically process bands at different resolution. CR-MSS has two input/output branches that are designed to process Vis/NIR and VRE/SWIR, respectively. Firstly, Vis/NIR cloudy bands are down-sampled by a convolutional layer to low spatial resolution features, which are then concatenated with the corresponding features extracted from VRE/SWIR bands. Secondly, the concatenated features are put into a fusion tunnel to down-sample and fuse the spectral information from Vis/NIR and VRE/SWIR bands. Third, a decomposition tunnel is designed to up-sample and decompose the fused features. Finally, a transpose convolutional layer is used to up-sample the feature maps to the resolution of input Vis/NIR bands. CR-MSS was trained on 28 real Sentinel-2A image pairs over the globe, and tested separately on eight real cloud image pairs and eight simulated cloud image pairs. The average SSIM values (Structural Similarity Index Measurement) for CR-MSS results on Vis/NIR bands over all testing images were 0.69, 0.71, 0.77, and 0.81, respectively, which was on average 1.74% higher than the best baseline method. The visual results on real Sentinel-2 images demonstrate that CR-MSS can produce more realistic cloud and cloud shadow removal results than baseline methods.

scholarly journals Retrieval and mapping of chlorophyll-a concentration from Sentinel-2 images in an urban river in the semiarid region of Brazil

2020 ◽  
Vol 15 (2) ◽  
pp. 1
Author(s):  
Alessandro Rhadamek Alves Pereira ◽  
João Batista Lopes ◽  
Giovana Mira de Espindola ◽  
Carlos Ernando da Silva
Keyword(s):  
Chlorophyll A ◽  
River Mouth ◽  
Water Quality Monitoring ◽  
Semiarid Region ◽  
Surface Reflectance ◽  
Chlorophyll A Concentration ◽  
In Situ Data ◽  
Sentinel 2A ◽  
Sentinel 2

Recently, the Poti river mouth region has experienced environmental impacts that resulted in a change of landscape in its dry season, highlighting the eutrophication and proliferation of phytoplankton, algae, cyanobacteria and aquatic plants. Considering the aspects related to water-quality monitoring in the semiarid region of Brazil from remote sensing, this study aimed to evaluate the performance of Sentinel-2A satellite data in the retrieval of chlorophyll-a concentration in Poti River in Teresina, Piaui, Brazil. The chlorophyll-a concentration retrieval and mapping methodology involved the study of the water surface reflectance in Sentinel-2A images and their correlation with the chlorophyll-a data collected in situ during the years 2016 and 2017. The results generated by the Chl-1, Ha et al. (2017), Chl-2, Page et al. (2018), and Chl-3, Kuhn et al. (2019) equations show the need for calibrating the algorithms used for the Poti River water components. However, the empirical algorithm Chl-2 shows a correlation has been established to identify the spatiotemporal variation of chlorophyll-a concentration along the Poti River broadly and not punctually. The spatial distribution of this pigment in maps derived from Sentinel-2A is consistent with the pattern of occurrence determined by the in situ data. Therefore, the MSI sensor proved to be a tool suitable for the retrieval and monitoring of chlorophyll-a concentration along the Poti River.

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