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 & GREEN), the Haralick features (GLCM) vs. some geometric attributes (area & 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 Estimating Aboveground Biomass on Private Forest Using Sentinel-2 Imagery

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Askar ◽  
Narissara Nuthammachot ◽  
Worradorn Phairuang ◽  
Pramaditya Wicaksono ◽  
Tri Sayektiningsih
Keyword(s):  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Forest Degradation ◽  
Forest Monitoring ◽  
Natural Forests ◽  
Private Forests ◽  
Indonesian Government ◽  
Nations Programme ◽  
Reduce Carbon Dioxide ◽  
Sentinel 2

Private forests have a crucial role in maintaining the functioning of the Indonesian forest ecosystem especially because of the continuous degradation of natural forests. Private forests are a part of social forestry which becomes a tool for the Indonesian government to reduce carbon dioxide (CO2) emission by 26% by 2030. The United Nations Programme on Reducing Emissions from Deforestation and Forest Degradation has encouraged the Indonesian government to establish a forest monitoring system by estimating forest carbon stock using a combination of forest inventory and remote sensing. This study is aimed at assessing the potential of vegetation indices derived from Sentinel-2 for estimating aboveground biomass (AGB) of private forests. We used 45 sample plots and 7 vegetation indices to evaluate the ability of Sentinel-2 in estimating AGB on private forests. Normalised difference index (NDI) 45 exhibited a strong correlation with AGB compared to other indices (r = 0.89; R2 = 0.79). Stepwise linear regression fitted for establishing the model between field AGB and vegetation indices (R2 = 0.81). We also found that AGB in the study area based on spatial analysis was 72.54 Mg/ha. A root mean square error (RMSE) value from predicted and observed AGB was 27 Mg/ha. The AGB value in the study area is higher than the AGB value from some of forest types, and it indicates that private forests are good for biomass storage. Overall, vegetation indices from Sentinel-2 multispectral imagery can provide a good result in terms of reporting the AGB on private forests.

scholarly journals Generating Up-to-Date Crop Maps Optimized for Sentinel-2 Imagery in Israel

2021 ◽  
Vol 13 (17) ◽  
pp. 3488
Author(s):  
Keren Goldberg ◽  
Ittai Herrmann ◽  
Uri Hochberg ◽  
Offer Rozenstein
Keyword(s):  
Time Series ◽  
Mean Shift ◽  
Vegetation Indices ◽  
Gradient Boosting ◽  
Support Vector ◽  
Object Based ◽  
Accuracy Measure ◽  
Extreme Gradient Boosting ◽  
Accuracy Measures ◽  
Sentinel 2

The overarching aim of this research was to develop a method for deriving crop maps from a time series of Sentinel-2 images between 2017 and 2018 to address global challenges in agriculture and food security. This study is the first step towards improving crop mapping based on phenological features retrieved from an object-based time series on a national scale. Five main crops in Israel were classified: wheat, barley, cotton, carrot, and chickpea. To optimize the object-based classification process, different characteristics and inputs of the mean shift segmentation algorithm were tested, including vegetation indices, three-band combinations, and high/low emphasis on the spatial and spectral characteristics. Four known vegetation indices (VIs)-based time series were tested. Additionally, we compared two widely used machine learning methods for crop classification, support vector machine (SVM) and random forest (RF), in addition to a newer classifier, extreme gradient boosting (XGBoost). Lastly, we examined two accuracy measures—overall accuracy (OA) and area under the curve (AUC)—in order to optimally estimate the accuracy in the case of imbalanced class representation. Mean shift best performed when emphasizing both the spectral and spatial characteristics while using the green, red, and near-infrared (NIR) bands as input. Both accuracy measures showed that RF and XGBoost classified different types of crops with significantly greater success than achieved by SVM. Nevertheless, AUC was better able to represent the significant differences between the classification algorithms than OA was. None of the VIs showed a significantly higher contribution to the classification. However, normalized difference infrared index (NDII) with XGBoost classifier showed the highest AUC results (88%). This study demonstrates that the short-wave infrared (SWIR) band with XGBoost improves crop type classification results. Furthermore, the study emphasizes the importance of addressing imbalanced classification datasets by using a proper accuracy measure. Since object-based classification and phenological features derived from a VI-based time series are widely used to produce crop maps, the current study is also relevant for operational agricultural management and informatics at large scales.

scholarly journals Comparative Analysis of Landsat-8, Sentinel-2, and GF-1 Data for Retrieving Soil Moisture over Wheat Farmlands

2020 ◽  
Vol 12 (17) ◽  
pp. 2708 ◽  
Author(s):  
Qi Wang ◽  
Jiancheng Li ◽  
Taoyong Jin ◽  
Xin Chang ◽  
Yongchao Zhu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Optical Sensors ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Cloud Model ◽  
Vegetation Indices ◽  
Optical Data ◽  
Landsat 8 ◽  
Different Sources ◽  
Sentinel 2

Soil moisture is an important variable in ecological, hydrological, and meteorological studies. An effective method for improving the accuracy of soil moisture retrieval is the mutual supplementation of multi-source data. The sensor configuration and band settings of different optical sensors lead to differences in band reflectivity in the inter-data, further resulting in the differences between vegetation indices. The combination of synthetic aperture radar (SAR) data with multi-source optical data has been widely used for soil moisture retrieval. However, the influence of vegetation indices derived from different sources of optical data on retrieval accuracy has not been comparatively analyzed thus far. Therefore, the suitability of vegetation parameters derived from different sources of optical data for accurate soil moisture retrieval requires further investigation. In this study, vegetation indices derived from GF-1, Landsat-8, and Sentinel-2 were compared. Based on Sentinel-1 SAR and three optical data, combined with the water cloud model (WCM) and the advanced integral equation model (AIEM), the accuracy of soil moisture retrieval was investigated. The results indicate that, Sentinel-2 data were more sensitive to vegetation characteristics and had a stronger capability for vegetation signal detection. The ranking of normalized difference vegetation index (NDVI) values from the three sensors was as follows: the largest was in Sentinel-2, followed by Landsat-8, and the value of GF-1 was the smallest. The normalized difference water index (NDWI) value of Landsat-8 was larger than that of Sentinel-2. With reference to the relative components in the WCM model, the contribution of vegetation scattering exceeded that of soil scattering within a vegetation index range of approximately 0.55–0.6 in NDVI-based models and all ranges in NDWI1-based models. The threshold value of NDWI2 for calculating vegetation water content (VWC) was approximately an NDVI value of 0.4–0.55. In the soil moisture retrieval, Sentinel-2 data achieved higher accuracy than data from the other sources and thus was more suitable for the study for combination with SAR in soil moisture retrieval. Furthermore, compared with NDVI, higher accuracy of soil moisture could be retrieved by using NDWI1 (R2 = 0.623, RMSE = 4.73%). This study provides a reference for the selection of optical data for combination with SAR in soil moisture retrieval.

scholarly journals Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1280 ◽  
Author(s):  
Laura Fragoso-Campón ◽  
Elia Quirós ◽  
Julián Mora ◽  
José Antonio Gutiérrez ◽  
Pablo Durán-Barroso
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Accuracy Enhancement ◽  
Multispectral Data ◽  
Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

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 Multi-Temporal Sentinel-2 Data in Classification of Mountain Vegetation

2020 ◽  
Vol 12 (17) ◽  
pp. 2696 ◽  
Author(s):  
Martyna Wakulińska ◽  
Adriana Marcinkowska-Ochtyra
Keyword(s):  
National Parks ◽  
Vegetation Indices ◽  
European Space Agency ◽  
Principal Component ◽  
Support Vector ◽  
Electromagnetic Spectrum ◽  
Vegetation Monitoring ◽  
Multi Temporal ◽  
Mountain Vegetation ◽  
Sentinel 2

The electromagnetic spectrum registered via satellite remote sensing methods became a popular data source that can enrich traditional methods of vegetation monitoring. The European Space Agency Sentinel-2 mission, thanks to its spatial (10–20 m) and spectral resolution (12 spectral bands registered in visible-, near-, and mid-infrared spectrum) and primarily its short revisit time (5 days), helps to provide reliable and accurate material for the identification of mountain vegetation. Using the support vector machines (SVM) algorithm and reference data (botanical map of non-forest vegetation, field survey data, and high spatial resolution images) it was possible to classify eight vegetation types of Giant Mountains: bogs and fens, deciduous shrub vegetation, forests, grasslands, heathlands, subalpine tall forbs, subalpine dwarf pine scrubs, and rock and scree vegetation. Additional variables such as principal component analysis (PCA) bands and selected vegetation indices were included in the best classified dataset. The results of the iterative classification, repeated 100 times, were assessed as approximately 80% median overall accuracy (OA) based on multi-temporal datasets composed of images acquired through the vegetation growing season (from late spring to early autumn 2018), better than using a single-date scene (70%–72% OA). Additional variables did not significantly improve the results, showing the importance of spectral and temporal information themselves. Our study confirms the possibility of fully available data for the identification of mountain vegetation for management purposes and protection within national parks.

scholarly journals Object-Based Time-Constrained Dynamic Time Warping Classification of Crops Using Sentinel-2

2019 ◽  
Vol 11 (10) ◽  
pp. 1257 ◽  
Author(s):  
Ovidiu Csillik ◽  
Mariana Belgiu ◽  
Gregory P. Asner ◽  
Maggi Kelly
Keyword(s):  
Dynamic Time Warping ◽  
Vegetation Indices ◽  
Single Band ◽  
Time Constraints ◽  
Time Warping ◽  
Object Based ◽  
Crop Mapping ◽  
Dynamic Time ◽  
Sentinel 2 ◽  
Multi Band

The increasing volume of remote sensing data with improved spatial and temporal resolutions generates unique opportunities for monitoring and mapping of crops. We compared multiple single-band and multi-band object-based time-constrained Dynamic Time Warping (DTW) classifications for crop mapping based on Sentinel-2 time series of vegetation indices. We tested it on two complex and intensively managed agricultural areas in California and Texas. DTW is a time-flexible method for comparing two temporal patterns by considering their temporal distortions in their alignment. For crop mapping, using time constraints in computing DTW is recommended in order to consider the seasonality of crops. We tested different time constraints in DTW (15, 30, 45, and 60 days) and compared the results with those obtained by using Euclidean distance or a DTW without time constraint. Best classification results were for time delays of both 30 and 45 days in California: 79.5% for single-band DTWs and 85.6% for multi-band DTWs. In Texas, 45 days was best for single-band DTW (89.1%), while 30 days yielded best results for multi-band DTW (87.6%). Using temporal information from five vegetation indices instead of one increased the overall accuracy in California with 6.1%. We discuss the implications of DTW dissimilarity values in understanding the classification errors. Considering the possible sources of errors and their propagation throughout our analysis, we had combined errors of 22.2% and 16.8% for California and 24.6% and 25.4% for Texas study areas. The proposed workflow is the first implementation of DTW in an object-based image analysis (OBIA) environment and represents a promising step towards generating fast, accurate, and ready-to-use agricultural data products.

scholarly journals Possibilities of using sentinel-2 satellite data to estimate crop cover and LAI of winter wheat at different stages of development

2020 ◽  
Vol 169 ◽  
pp. 02005
Author(s):  
Feddy Mullo ◽  
Elena Prudnikova
Keyword(s):  
Winter Wheat ◽  
Satellite Data ◽  
Regression Models ◽  
Vegetation Indices ◽  
Test Plot ◽  
Fisheye Lens ◽  
Crop Cover ◽  
Wheat Development ◽  
Sentinel 2

The study was conducted on a test plot located in the Yasnogorodsky district of the Tula Region; With a camera with fisheye lens photographs were taken at different points of the plot, each point with a geographical reference. Subsequently, the possible relationship between the information extracted from the classification of the photographs using the CAN-EYE software (LAI, percentage of vegetation) and the vegetation indices (Ratio, NDVI, SAVI and EVI) calculated with spectral values obtained from the different channels of Sentinel-2 (B2, B3, B4, B5, B6, B7, B8, B8a) were evaluated. Finally, best regression models obtained for each phase of the winter wheat development were used to create the maps of LAI and percentage of vegetation. According to our results, Sentinel-2 can be successfully used to map LAI in the studied region at the shooting stage of winter wheat development with accuracy of 85 %. At other stages and for percentage of vegetation the accuracy of the models was below 50 %.

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