scholarly journals COMPARISON OF LANDSAT-8 AND SENTINEL-2 DATA FOR CLASSIFICATION OF RABI CROPS OVER KARNATAKA, INDIA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 579-584
Author(s):  
S. Paul ◽  
D. N. Kumar
Keyword(s):  
Vegetation Index ◽  
Support Vector ◽  
Growth Stages ◽  
Landsat 8 ◽  
Large Spatial Scale ◽  
Spectral Bands ◽  
Sentinel 2A ◽  
Rabi Crops ◽  
Sentinel 2

<p><strong>Abstract.</strong> Classification of crops is very important to study different growth stages and forecast yield. Remote sensing data plays a significant role in crop identification and condition assessment over a large spatial scale. Importance of Normalized Difference Indices (NDIs) along with surface reflectances of remotely sensed spectral bands have been evaluated for classification of eight types of Rabi crops utilizing the Landsat-8 and Sentinel-2 datasets and performances of both the satellites are compared. Landsat-8 and Sentinel-2A images are acquired for the location of crops and seven and nine spectral bands are utilized respectively for the classification. Experiments are carried out considering the different combinations of surface reflectances of spectral bands and optimal NDIs as features in support vector machine classifier. Optimal NDIs are selected from the set of <sup>7</sup>C<sub>2</sub> and <sup>9</sup>C<sub>2</sub> NDIs of Landsat-8 and Sentinel-2A datasets respectively using the partial informational correlation measure, a nonparametric feature selection approach. Few important vegetation indices (e.g. enhanced vegetation index) are also experimented in combination with the surface reflectances and NDIs to perform the crop classification. It has been observed that combination of surface reflectances and optimal NDIs can classify the crops more efficiently. The average overall accuracy of 80.96% and 88.16% are achieved using the Landsat-8 and Sentinel-2A datasets respectively. It has been observed that all the crop classes except Paddy and Cotton achieve producer accuracy and user accuracy of more than 75% and 85% respectively. This technique can be implemented for crop identification with adequate accessibility of crop information.</p>

scholarly journals Assessing the Accuracy of Multiple Classification Algorithms for Crop Classification Using Landsat-8 and Sentinel-2 Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1735 ◽  
Author(s):  
Amal Chakhar ◽  
Damián Ortega-Terol ◽  
David Hernández-López ◽  
Rocío Ballesteros ◽  
José F. Ortega ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Nearest Neighbor ◽  
Semiarid Region ◽  
Support Vector ◽  
Classification Algorithms ◽  
Landsat 8 ◽  
Ensemble Classifiers ◽  
Crop Classification ◽  
Sentinel 2A

The launch of Sentinel-2A and B satellites has boosted the development of many applications that could benefit from the fine resolution of the supplied information, both in time and in space. Crop classification is a necessary task for efficient land management. We evaluated the benefits of combining Landsat-8 and Sentinel-2A information for irrigated crop classification. We also assessed the robustness and efficiency of 22 nonparametric classification algorithms for classifying irrigated crops in a semiarid region in the southeast of Spain. A parcel-based approach was proposed calculating the mean normalized difference vegetation index (NDVI) of each plot and the standard deviation to generate a calibration-testing set of data. More than 2000 visited plots for 12 different crops along the study site were utilized as ground truth. Ensemble classifiers were the most robust algorithms but not the most efficient because of their low prediction rate. Nearest neighbor methods and support vector machines have the best balance between robustness and efficiency as methods for classification. Although the F1 score is close to 90%, some misclassifications were found for spring crops (e.g., barley, wheat and peas). However, crops with quite similar cycles could be differentiated, such as purple garlic and white garlic, showing the powerfulness of the developed tool.

scholarly journals A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

Geosciences ◽  
2019 ◽  
Vol 9 (9) ◽  
pp. 396 ◽  
Author(s):  
Premysl Stych ◽  
Barbora Jerabkova ◽  
Josef Lastovicka ◽  
Martin Riedl ◽  
Daniel Paluba
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Bark Beetle ◽  
National Park ◽  
Support Vector ◽  
Landsat 8 ◽  
Kappa Index ◽  
Sentinel 2

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.

scholarly journals ­Detection of Pinus monophylla forest in the Baja California desert by remote sensing

2017 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Baja California ◽  
Arid Zones ◽  
Landsat 8 ◽  
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 in 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 establish the distribution of P. monophylla var. californiarum in the Sierra La Asamblea, Baja California (Mexico) using Sentinel-2 images, and ii) to test and describe the relationship between this distribution of P. monophylla and five topographic and 18 climate variables. We hypothesized that i) the Sentinel-2 images can be used to accurately detect 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 influential because they represent important microhabitat factors that can affect where conifers can become established and persist. Methods. It was used an atmospherically corrected a 12-bit Sentinel-2A MSI image with eleven spectral bands in the visible, near infrared, and short-wave infrared light region combined with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest regression 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. Probably, P. monophylla covers 4,955 hectares in the isolated Sierra La Asamblea via supervised classification of Sentinel-2 satellite images. The NDVI was one of the variables that contributed to the detection and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the best 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 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 climatic variation and change.

scholarly journals Radiometric Top-of-Atmosphere Reflectance Consistency Assessment for Landsat 8/OLI, Sentinel-2/MSI, PROBA-V, and DEIMOS-1 over Libya-4 and RadCalNet Calibration Sites

2019 ◽  
Vol 11 (19) ◽  
pp. 2253 ◽  
Author(s):  
Sindy Sterckx ◽  
Erwin Wolters
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Data Interoperability ◽  
Clear Trend ◽  
Spectral Bands ◽  
Satellite Sensors ◽  
Sentinel 2A ◽  
Sentinel 2 ◽  
Made In ◽  
Require Data

There is a clear trend toward the use of higher spatial resolution satellite sensors. Due to the low revisit time of these sensors and frequent cloud coverage, many applications require data from different sensors to be combined in order to have more frequent observations. This raises concerns regarding data interoperability and consistency. The initial pre-requisite is that there are no radiometric differences in top-of-atmosphere (TOA) observations. This paper aims to quantitatively assess differences in the TOA signal provided by PROBA-V, Sentinel-2A and Sentinel-2B, Landsat-8, and Deimos-1 by using observations over both the Libya-4 desert calibration site and the RadCalNet sites. The results obtained over the Libya-4 site indicate that for all sensors investigated, the inter-sensor deviations are negligible, i.e., within ±2% for comparable spectral bands, with the exception of the Deimos-1 Green band. Clear BRDF (bi-directional reflectance distribution function) effects were observed over the RadCalNet sites, thereby preventing consistent conclusions on inter-sensor deviations from being made. In order to fully explore the potential of the RadCalNet sites, it is recommended that BRDF characterizations be additionally incorporated into the RadCalNet simulations and made publicly available through the distribution portal.

scholarly journals Harmonized Landsat 8 and Sentinel-2 Time Series Data to Detect Irrigated Areas: An Application in Southern Italy

2020 ◽  
Vol 12 (8) ◽  
pp. 1275 ◽  
Author(s):  
Salvatore Falanga Bolognesi ◽  
Edoardo Pasolli ◽  
Oscar Belfiore ◽  
Carlo De Michele ◽  
Guido D’Urso
Keyword(s):  
Vegetation Index ◽  
Southern Italy ◽  
Time Series Data ◽  
Series Data ◽  
Support Vector ◽  
Landsat 8 ◽  
Irrigation Season ◽  
Campania Region ◽  
Machine Learning Classification ◽  
Sentinel 2

Lack of accurate and up-to-date data associated with irrigated areas and related irrigation amounts is hampering the full implementation and compliance of the Water Framework Directive (WFD). In this paper, we describe the framework that we developed and implemented within the DIANA project to map the actual extent of irrigated areas in the Campania region (Southern Italy) during the 2018 irrigation season. For this purpose, we considered 202 images from the Harmonized Landsat Sentinel-2 (HLS) products (57 images from Landsat 8 and 145 images from Sentinel-2). Such data were preprocessed in order to extract a multitemporal Normalized Difference Vegetation Index (NDVI) map, which was then smoothed through a gap-filling algorithm. We further integrated data coming from high-resolution (4 km) global satellite precipitation Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) products. We collected an extensive ground truth in the field represented by 2992 data points coming from three main thematic classes: bare soil and rainfed (class 0), herbaceous (class 1), and tree crop (class 2). This information was exploited to generate irrigated area maps by adopting a machine learning classification approach. We compared six different types of classifiers through a cross-validation approach and found that, in general, random forests, support vector machines, and boosted decision trees exhibited the best performances in terms of classification accuracy and robustness to different tested scenarios. We found an overall accuracy close to 90% in discriminating among the three thematic classes, which highlighted promising capabilities in the detection of irrigated areas from HLS products.

scholarly journals DETECTION OF SHALLOW WATER AREA WITH MACHINE LEARNING ALGORITHMS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1269-1273 ◽  
Author(s):  
N. Yagmur ◽  
N. Musaoglu ◽  
G. Taskin
Keyword(s):  
Support Vector Machine ◽  
Shallow Water ◽  
Spatial Resolution ◽  
Water Area ◽  
Water Bodies ◽  
Support Vector ◽  
Spectral Bands ◽  
Shallow Water Area ◽  
Sentinel 2

<p><strong>Abstract.</strong> Remote sensing techniques has been widely used for detecting water bodies in especially wetlands. Different classification methods and water indices has used for this purpose and there are numerous studies for detecting water bodies. However, detecting shallow water area is difficult comparing with deep water bodies because of the mixed pixels. Akgol Wetland is chosen as study area to detect shallow water. For this purpose, Sentinel 2 satellite image, which gives more accurate results thanks to higher spatial resolution than the images having medium spatial resolution, is used. In this study, two classification approaches were applied on Sentinel 2 image to detect shallow water area. In the first approach, effectiveness of indices was determined and classification of spectral bands with indices shows higher accuracy than classification of only spectral bands by using support vector machine classification method. In the second approach, support vector machine recursive feature elimination method used for the most effective features in the first approach. Besides overall accuracy of only spectral bands is obtained as 88.10%, spectral bands and indices’ accuracy was obtained as 91.84%.</p>

scholarly journals Performance of Sentinel-2A Remote Sensing System for Urban Area Mapping in Malaysia via Pixel-Based and OBIA Methods

2021 ◽  
Vol 18 (21) ◽  
pp. 38
Author(s):  
Adhwa Amir Tan ◽  
Helmi Zulhaidi Mohd Shafri ◽  
Nur Shafira Nisa Shaharum
Keyword(s):  
Remote Sensing ◽  
Image Analysis ◽  
Spatial Resolution ◽  
Support Vector ◽  
Landsat 8 ◽  
Object Based ◽  
Fusion Images ◽  
Urban Mapping ◽  
Sentinel 2A

Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

scholarly journals ­ Predicting Pinus monophylla forest 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 ◽  
Baja California ◽  
Arid Zones ◽  
Landsat 8 ◽  
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 5,395 ± 23.29 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 Mapping of Post-Wildfire Burned Area Using a Hybrid Algorithm and Satellite Data: The Case of the Camp Fire Wildfire in California, USA

2020 ◽  
Vol 12 (4) ◽  
pp. 623 ◽  
Author(s):  
Mutiara Syifa ◽  
Mahdi Panahi ◽  
Chang-Wook Lee
Keyword(s):  
Support Vector Machine ◽  
Hybrid Algorithm ◽  
Imperialist Competitive Algorithm ◽  
Burned Area ◽  
Support Vector ◽  
Landsat 8 ◽  
Svm Algorithm ◽  
Mitigation Planning ◽  
Sentinel 2

On November 8, 2018, a devastating wildfire, known as the Camp Fire wildfire, was reported in Butte County, California, USA. Approximately 88 fatalities ensued, and 18,804 structures were damaged by the wildfire. As a response to this destructive wildfire, this study generated a pre- and post-wildfire maps to provide basic data for evacuation and mitigation planning. This study used Landsat-8 and Sentinel-2 imagery to map the pre- and post-wildfire conditions. A support vector machine (SVM) optimized by the imperialist competitive algorithm (ICA) hybrid model was compared with the non-optimized SVM algorithm for classification of the pre- and post-wildfire map. The SVM–ICA produced a better accuracy (overall accuracies of 83.8% and 83.6% for pre- and post-wildfire using Landsat-8 respectively; 90.8% and 91.8% for pre- and post-wildfire using Sentinel-2 respectively), compared to SVM without optimization (overall accuracies of 80.0% and 78.9% for pre- and post-wildfire using Landsat-8 respectively; 83.3% and 84.8% for pre- and post-wildfire using Sentinel-2 respectively. In total, eight pre- and post-wildfire burned area maps were generated; these can be used to assess the area affected by the Camp Fire wildfire as well as for wildfire mitigation planning in the future.

scholarly journals ­Detection of Pinus monophylla forest in the Baja California desert by remote sensing

2017 ◽  
Author(s):  
Jonathan G Escobar-Flores ◽  
Carlos A Lopez-Sanchez ◽  
Sarahi Sandoval ◽  
Marco A Marquez-Linares ◽  
Christian Wehenkel
Keyword(s):  
Supervised Classification ◽  
Baja California ◽  
Arid Zones ◽  
Landsat 8 ◽  
Climate Variables ◽  
Pinus Monophylla ◽  
Single Leaf ◽  
Sentinel 2A ◽  
Sentinel 2

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 in 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, 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 establish the distribution of Pinus monophylla var. californiarum in Sierra La Asamblea, Baja California (Mexico) using Sentinel-2 images, and ii) to test and describe the relationship between this distribution of Pinus monophylla and five topographic and 18 climate variables. We hypothesized that i) the Sentinel-2 images can be used to accurately detect 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 influential because they represent important microhabitat factors that can affect where conifers can become established and persist. Methods. It was used an atmospherically corrected a 12-bit Sentinel-2A MSI image with eleven spectral bands in the visible, near infrared, and short-wave infrared light region combined with the normalized differential vegetation index (NDVI). Supervised classification of this image was carried out using a backpropagation-type artificial neural network algorithm. Stepwise multivariate binominal logistical regression and Random Forest regression including cross valuation (10 fold) were used to model the associations between presence/absence of pines and the five topographical and 18 climate variables. Results. Probably, P. monophylla covers 4,955 hectares in the isolated in Sierra La Asamblea, Baja California (Mexico) via supervised classification of Sentinel-2 satellite images. The NDVI was one of the variables that contributed to the detection and clearly separated the forest cover (NDVI > 0.35) from the other vegetation cover (NDVI < 0.20). The ruggedness was the best 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 (Kappa) was similar to 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 Sierra La Asamblea as the most southern populations represents an opportunity for research on climatic tolerance and community responses to climatic variation and change.

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