scholarly journals Object-Based Classification of Greenhouses Using Sentinel-2 MSI and SPOT-7 Images: A Case Study from Anamur (Mersin), Turkey

2020 ◽  
Vol 13 ◽  
pp. 2769-2777 ◽  
Author(s):  
Filiz Bektas Balcik ◽  
Gizem Senel ◽  
Cigdem Goksel
Keyword(s):  
Object Based ◽  
Sentinel 2

scholarly journals LAND USE AND LAND COVER CLASSIFICATION OF SENTINEL 2-A: ST PETERSBURG CASE STUDY

2019 ◽  
Vol XLII-1/W2 ◽  
pp. 13-16 ◽  
Author(s):  
M. Cavur ◽  
H. S. Duzgun ◽  
S. Kemec ◽  
D. C. Demirkan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Remotely Sensed Data ◽  
Vector Machines ◽  
Kappa Value ◽  
Sentinel 2

<p><strong>Abstract.</strong> Land use and land cover (LULC) maps in many areas have been used by companies, government offices, municipalities, and ministries. Accurate classification for LULC using remotely sensed data requires State of Art classification methods. The SNAP free software and ArcGIS Desktop were used for analysis and report. In this study, the optical Sentinel-2 images were used. In order to analyze the data, an object-oriented method was applied: Supported Vector Machines (SVM). An accuracy assessment is also applied to the classified results based on the ground truth points or known reference pixels. The overall classification accuracy of 83,64% with the kappa value of 0.802 was achieved using SVM. The study indicated that of SVM algorithms, the proposed framework on Sentinel-2 imagery results is satisfactory for LULC maps.</p>

scholarly journals Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1419 ◽  
Author(s):  
Mathias Wessel ◽  
Melanie Brandmeier ◽  
Dirk Tiede
Keyword(s):  
Machine Learning ◽  
Tree Species ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Inventory Data ◽  
Oak Trees ◽  
Object Based ◽  
Sentinel 2

We use freely available Sentinel-2 data and forest inventory data to evaluate the potential of different machine-learning approaches to classify tree species in two forest regions in Bavaria, Germany. Atmospheric correction was applied to the level 1C data, resulting in true surface reflectance or bottom of atmosphere (BOA) output. We developed a semiautomatic workflow for the classification of deciduous (mainly spruce trees), beech and oak trees by evaluating different classification algorithms (object- and pixel-based) in an architecture optimized for distributed processing. A hierarchical approach was used to evaluate different band combinations and algorithms (Support Vector Machines (SVM) and Random Forest (RF)) for the separation of broad-leaved vs. coniferous trees. The Ebersberger forest was the main project region and the Freisinger forest was used in a transferability study. Accuracy assessment and training of the algorithms was based on inventory data, validation was conducted using an independent dataset. A confusion matrix, with User´s and Producer´s Accuracies, as well as Overall Accuracies, was created for all analyses. In total, we tested 16 different classification setups for coniferous vs. broad-leaved trees, achieving the best performance of 97% for an object-based multitemporal SVM approach using only band 8 from three scenes (May, August and September). For the separation of beech and oak trees we evaluated 54 different setups, the best result achieved an accuracy of 91% for an object-based, SVM, multitemporal approach using bands 8, 2 and 3 of the May scene for segmentation and all principal components of the August scene for classification. The transferability of the model was tested for the Freisinger forest and showed similar results. This project points out that Sentinel-2 had only marginally worse results than comparable commercial high-resolution satellite sensors and is well-suited for forest analysis on a tree-stand level.

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 SPATIAL PROCESSING OF SENTINEL IMAGERY FOR MONITORING OF ACACIA FOREST DEGRADATION IN LAKE NAKURU RIPARIAN RESERVE

2020 ◽  
Vol V-3-2020 ◽  
pp. 525-532
Author(s):  
A. Osio ◽  
M. T. Pham ◽  
S. Lefèvre
Keyword(s):  
National Parks ◽  
Optical Sensors ◽  
Vegetation Indices ◽  
Forest Degradation ◽  
Lake Nakuru ◽  
Object Based ◽  
Extent Of Damage ◽  
Forest Rehabilitation ◽  
Sentinel 2

Abstract. Tree degradation in National Parks poses a serious risk to the birds and animals and to a larger extent the general ecosystem. The essence of Forest degradation mapping is to detect the extent of damage on the trees over time, hence providing stakeholders with a basis for forest rehabilitation and intervention. The study proposes a workflow for detection and classification of degrading acacia vegetation along Lake Nakuru riparian reserve. Inspired by previous research on the use of Dual Polarized Sentinel 1 Ground Range Detected (GRD) data for vegetation detection, a set of six Sentinel 1 GRD and Sentinel 2 MSI of corresponding dates (2018–2019) were used. Our study confirms the existing correlation between vegetation indices derived from optical sensors and the backscatter indices from S1 SAR image of the same land cover classes. Factors that were used in validating the results include some comparisons between pixelwise and object-based classification, with a focus on the underlying segmentation and classification algorithms, the polarimetric attributes (VV+VH intensity bands) and the reflectance bands (NIR, SWIR &amp; GREEN), the Haralick features (GLCM) vs. some geometric attributes (area &amp; moment of inertia). Classification carried out on the temporal datasets considering geometric attributes and the Random Forest classifier yielded the highest Overall Accuracy (OA) with 94.25 %, and a Kappa coefficient of 0.90.

scholarly journals Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data: A case study from Almería (Spain)

2016 ◽  
Vol 52 ◽  
pp. 403-411 ◽  
Author(s):  
Antonio Novelli ◽  
Manuel A. Aguilar ◽  
Abderrahim Nemmaoui ◽  
Fernando J. Aguilar ◽  
Eufemia Tarantino
Keyword(s):  
Performance Evaluation ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Object Based ◽  
Sentinel 2

scholarly journals Land Cover Classification of Complex Agroecosystems in the Non-Protected Highlands of the Galapagos Islands

2019 ◽  
Vol 12 (1) ◽  
pp. 65 ◽  
Author(s):  
Francisco J. Laso ◽  
Fátima L. Benítez ◽  
Gonzalo Rivas-Torres ◽  
Carolina Sampedro ◽  
Javier Arce-Nazario
Keyword(s):  
Land Cover ◽  
Invasive Plants ◽  
Mixed Forest ◽  
High Spectral Resolution ◽  
Object Based ◽  
Pioneer Plants ◽  
Land Cover Map ◽  
Aerial Vehicle ◽  
Sentinel 2

The humid highlands of the Galapagos are the islands’ most biologically productive regions and a key habitat for endemic animal and plant species. These areas are crucial for the region’s food security and for the control of invasive plants, but little is known about the spatial distribution of its land cover. We generated a baseline high-resolution land cover map of the agricultural zones and their surrounding protected areas. We combined the high spatial resolution of PlanetScope images with the high spectral resolution of Sentinel-2 images in an object-based classification using a RandomForest algorithm. We used images collected with an unmanned aerial vehicle (UAV) to verify and validate our classified map. Despite the astounding diversity and heterogeneity of the highland landscape, our classification yielded useful results (overall Kappa: 0.7, R2: 0.69) and revealed that across all four inhabited islands, invasive plants cover the largest fraction (28.5%) of the agricultural area, followed by pastures (22.3%), native vegetation (18.6%), food crops (18.3%), and mixed forest and pioneer plants (11.6%). Our results are consistent with historical trajectories of colonization and abandonment of the highlands. The produced dataset is designed to suit the needs of practitioners of both conservation and agriculture and aims to foster collaboration between the two areas.

scholarly journals A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery

2021 ◽  
Vol 13 (5) ◽  
pp. 937
Author(s):  
Payam Najafi ◽  
Bakhtiar Feizizadeh ◽  
Hossein Navid
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Crop Residue ◽  
Soil Surface ◽  
Machine Learning Algorithms ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Tillage Methods ◽  
Sentinel 2

Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen’s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen’s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques.

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