scholarly journals Intra-Annual Sentinel-2 Time-Series Supporting Grassland Habitat Discrimination

2021 ◽  
Vol 13 (2) ◽  
pp. 277
Author(s):  
Cristina Tarantino ◽  
Luigi Forte ◽  
Palma Blonda ◽  
Saverio Vicario ◽  
Valeria Tomaselli ◽  
...  
Keyword(s):  
Time Series ◽  
Majority Vote ◽  
Digital Terrain Model ◽  
Natura 2000 ◽  
Support Vector ◽  
Habitat Directive ◽  
Terrain Model ◽  
Habitat Discrimination ◽  
Grassland Habitats ◽  
Sentinel 2

The present study aims to discriminate four semi-arid grassland habitats in a Mediterranean Natura 2000 site, Southern Italy, involving 6210/E1.263, 62A0/E1.55, 6220/E1.434 and X/E1.61-E1.C2-E1.C4 (according to Annex I of the European Habitat Directive/EUropean Nature Information System (EUNIS) taxonomies). For this purpose, an intra-annual time-series of 30 Sentinel-2 images, embedding phenology information, were investigated for 2018. The methodology adopted was based on a two-stage workflow employing a Support Vector Machine classifier. In the first stage only four Sentinel-2 multi-season images were analyzed, to provide an updated land cover map from where the grassland layer was extracted. The layer obtained was then used for masking the input features to the second stage. The latter stage discriminated the four grassland habitats by analyzing several input features configurations. These included multiple spectral indices selected from the time-series and the Digital Terrain Model. The results obtained from the different input configurations selected were compared to evaluate if the phenology information from time-series could improve grassland habitats discrimination. The highest F1 values (95.25% and 80.27%) were achieved for 6210/E1.263 and 6220/E1.434, respectively, whereas the results remained stable (97,33%) for 62A0/E1.55 and quite low (75,97%) for X/E1.61-E1.C2-E1.C4. However, since for all the four habitats analyzed no single configuration resulted effective, a Majority Vote algorithm was applied to achieve a reduction in classification uncertainty.

scholarly journals Stand types discrimination comparing machine-learning algorithms in Monteverde, Canary Islands.

2018 ◽  
Vol 27 (3) ◽  
pp. eSC03 ◽  
Author(s):  
Miguel Garcia-Hidalgo ◽  
Ángela Blázquez-Casado ◽  
Beatriz Águeda ◽  
Francisco Rodriguez
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Digital Terrain Model ◽  
Image Data ◽  
Tree Height ◽  
Support Vector ◽  
Lidar Data ◽  
Support Vector Machine Algorithm ◽  
Terrain Model ◽  
Sentinel 2

Aim of study: The main objective is to determine the best machine-learning algorithm to classify the stand types of Monteverde forests combining LiDAR, orthophotography, and Sentinel-2 data, thus providing an easy and cheap method to classify Monteverde stand types.Area of study: 1500 ha forest in Monteverde, North Tenerife, Canary Islands.Material and methods: RF, SVML, SVMR and ANN algorithms are used to classify the three Monteverde stand types.  Before training the model, feature selection of LiDAR, orthophotography, and Sentinel-2 data through VSURF was carried out.  Comparison of its accuracy was performed.Main results: Five LiDAR variables were found to be the most efficient for classifying each object, while only one Sentinel-2 index and one Sentinel-2 band was valuable.  Additionally, standard deviation and mean of the Red orthophotography colour band, and ratio between Red and Green bands were also found to be suitable.  SVML is confirmed as the most accurate algorithm (0.904, 0.041 SD) while ANN showed the lowest value of 0.891 (0.073 SD).  SVMR and RF obtain 0.902 (0.060 SD) and 0.904 (0.056 SD) respectively.  SVML was found to be the best method given its low standard deviation.Research highlights: The similar high accuracy values among models confirm the importance of taking into account diverse machine-learning methods for stand types classification purposes and different explanatory variables.  Although differences between errors may not seem relevant at a first glance, due to the limited size of the study area with only three plus two categories, such differences could be highly important when working at large scales with more stand types.ADDITIONAL KEY WORDSRF algorithm, SVML algorithm, SVMR algorithm, ANN algorithm, LiDAR, orthophotography, Sentinel-2ABBREVIATIONS USEDANN, artificial neural networks algorithm; Band04, Sentinel-2 band 04 image data; BR, brezal; DTHM, digital tree height model; DTHM-2016, digital tree height model based on 2016 LiDAR data; DTM, digital terrain model; DTM-2016, digital terrain model based on 2016 LiDAR data; FBA, fayal-brezal-acebiñal; FCC, canopy cover; HEIGHT-2009, maximum height based on 2009 LiDAR data; HGR, height growth based on 2009 and 2016 LiDAR data; LA, laurisilva; NDVI705, Sentinel-2 index image data; NMF, non-Monteverde forest; NMG, non-Monteverde ground; P95-2016, height percentile 95 based on 2016 LiDAR data; RATIO R/G, ratio between Red and Green bands orthophotograph data; RED, Red band orthophotograph data; Red-SD, standard deviation of the Red band orthophotograph data; RF, random forest algorithm; SVM, support vector machine algorithm; SVML, linear support vector machine algorithm; SVMR, radial support vector machine algorithm; VSURF, variable selection using random forest. 

scholarly journals Inter-Seasonal Time Series Imagery Enhances Classification Accuracy of Grazing Resource and Land Degradation Maps in a Savanna Ecosystem

2020 ◽  
Vol 12 (1) ◽  
pp. 198 ◽  
Author(s):  
Frederick D.L. Hunter ◽  
Edward T.A. Mitchard ◽  
Peter Tyrrell ◽  
Samantha Russell
Keyword(s):  
Time Series ◽  
Land Degradation ◽  
Classification Accuracy ◽  
Grass Species ◽  
Support Vector ◽  
Invasive Shrub ◽  
Seasonal Time Series ◽  
Large Populations ◽  
Dry Seasons ◽  
Sentinel 2

In savannas, mapping grazing resources and indicators of land degradation is important for assessing ecosystem conditions and informing grazing and land management decisions. We investigated the effects of classifiers and used time series imagery—images acquired within and across seasons—on the accuracy of plant species maps. The study site was a grazed savanna in southern Kenya. We used Sentinel-2 multi-spectral imagery due to its high spatial (10–20 m) and temporal (five days) resolution with support vector machine (SVM) and random forest (RF) classifiers. The species mapped were important for grazing livestock and wildlife (three grass species), indicators of land degradation (one tree genus and one invasive shrub), and a fig tree species. The results show that increasing the number of images, including dry season imagery, results in improved classification accuracy regardless of the classifier (average increase in overall accuracy (OA) = 0.1632). SVM consistently outperformed RF, and the most accurate model and was SVM with a radial kernel using imagery from both wet and dry seasons (OA = 0.8217). Maps showed that seasonal grazing areas provide functionally different grazing opportunities and have different vegetation characteristics that are critical to a landscape’s ability to support large populations of both livestock and wildlife. This study highlights the potential of multi-spectral satellite imagery for species-level mapping of savannas.

scholarly journals CARTOGRAPHY OF MOROCCAN ARGAN TREE USING COMBINED OPTICAL AND SAR IMAGERY INTEGRATED WITH DIGITAL ELEVATION MODEL

2021 ◽  
Vol XLVI-4/W5-2021 ◽  
pp. 211-217
Author(s):  
E. Elmoussaoui ◽  
A. Moumni ◽  
A. Lahrouni
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Digital Elevation Model ◽  
Optical Data ◽  
Support Vector ◽  
Ground Truth Data ◽  
Argan Tree ◽  
Digital Elevation ◽  
Elevation Model ◽  
Sentinel 2

Abstract. Forest tree species mapping became easier due to the global availability of high spatio-temporal resolution images acquired from multiple sensors. Such data can lead to better forest resources management. Machine-learning pixel based analysis was performed to multi-spectral Sentinel-2 and Synthetic Aperture Radar Sentinel-1 time series integrated with Digital Elevation Model acquired over Argan forest of Essaouira province, Morocco. The argan tree constitutes a fundamental resource for the populations of this arid area of Morocco. This research aims to use the potential of the combination of multi-sensor data to detect, map and identify argan tree from other forest species using three Machine Learning algorithms: Support Vector Machine (SVM), Maximum Likelihood (ML) and Artificial Neural Networks (ANN). The exploited datasets included Sentinel-1 (S1), Sentinel-2 (S2) time series, Shuttle Radar Topographic Missing Digital Elevation Model (DEM) layer and Ground truth data. We tested several sets of scenarios, including single S1 derived features, single S2 time series and combined S1 and S2 derived layers with DEM scene acquisition. The best results (overall accuracy OA and Kappa coefficient K) obtained from time series of optical data (NDVI): OA = 86.87%, K = 0.84, from time series of SAR data (VV+VH/VV): OA = 45.90%, K = 0.36, from the combination of optical and SAR time series (NDVI+VH+DEM): OA = 93.01%, K = 0.914, and from the fusion of optical time series and DEM layer (NDVI+DEM): OA = 93.25%, K = 0.91. These results indicate that single-sensor (S2) integrated with the DEM layer led us to obtain the highest classification results.

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 Contribution of free satellite time-series images to mapping plant communities in the Mediterranean Natura 2000 site: the example of Biguglia Pond in Corse (France)

2020 ◽  
Vol 41 (2) ◽  
pp. 181-191
Author(s):  
Sébastien Rapinel ◽  
Clémence Rozo ◽  
Pauline Delbosc ◽  
Frédéric Bioret ◽  
Jan-Bernard Bouzillé ◽  
...  
Keyword(s):  
Time Series ◽  
Plant Communities ◽  
Conservation Status ◽  
Natura 2000 ◽  
Natural Habitats ◽  
Biogeographical Region ◽  
The Mediterranean ◽  
Natura 2000 Site ◽  
Time Series Images ◽  
Sentinel 2

Mapping plant communities, which is essential to assess the conservation status of natural habitats, is currently based mainly on time-consuming field surveys without the use of satellite data. However, free image time-series with high spatial and temporal resolution have been available since 2015. This study assessed the contribution of Sentinel-2 time-series images to mapping the spatial distribution of 18 plant communities within a Natura 2000 site (1978 ha) located on the Mediterranean biogeographical region (Corsica, France). The method was based on random forest modeling of six Sentinel-2 images acquired from 26 February to 24 October 2017, which were calibrated and validated using a field vegetation map. The results showed that the 18 plant communities were modeled correctly, with 72% overall accuracy. The uncertainty map associated with the model indicated areas that required additional field observations.

Sentinel-2 Multitemporal Bare Soil Composites for predicting soil properties using machine learning methods

2020 ◽  
Author(s):  
Daniel Zizala
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Surface Roughness ◽  
Soil Properties ◽  
Bare Soil ◽  
Soil Mapping ◽  
Digital Soil Mapping ◽  
Support Vector ◽  
Composite Formation ◽  
Sentinel 2

<p>Previous studies have shown that remote sensing data can be very useful input into soil prediction models. This input usually represents reflectance from bare soils, which, however, make up only a small part of the total area in a given part of the year. For eliminating masking effect of vegetation time series of individual images (Žížala et al. 2019; Shabou et al. 2015; Demattê et al. 2016; Blasch et al. 2015a) or multitemporal composites of spectral data can be used. Exposed Soil Composite Mapping Processor (SCMaP) (Rogge et al. 2018), Geospatial Soil Sensing System (GEOS3) (Demattê et al. 2018), Bare Soil Composite Image (Gallo et al. 2018), and Barest Pixel Composite for Agricultural Areas (Diek et al. 2017), all developed from Landsat time series, multitemporal bare soil image developed from RapidEye time series (Blasch et al. 2015b), or bare soil mosaic (Loiseau et al. 2019) derived from Sentinel-2 data can serve as examples of such composites. However, only some of the composite products have been used yet to predict soil properties. Promising results were achieved; however, the potential of these spectral composites has not yet been tested in a relevant number of studies. Further research is needed for its evaluation.</p><p>Aims of this study are to analyze and to compare the prediction ability of models using different types of multitemporal bare soil composites derived from Sentinel-2 images and their applicability for mapping soil properties in large areas. The study was conducted on a regional scale in the soil heterogeneous region of central Czechia with dissected relief and variable soil properties, where data from 100 soil profiles with soil analytics were available. Sentinel-2 images from 2016-2019 were used for composite formation in the python numpy environment. Different methods of cloud masking, bare soil identification and data aggregation (both already used in previous studies and newly derived) have been tested to compare which is the most suitable for prediction of soil properties. The principles of digital soil mapping and machine learning algorithms (random forest and support vector machine multivariate methods) were used for prediction.</p><p>Results reveal that Sentinel-2 multitemporal bare soil composites can be successfully applied in the prediction of soil properties. The setting of basic parameters of composite creation is very complex and challenging and it requires to use exact algorithms for masking clouds and bare soil. Soil moisture and surface roughness also greatly affect spectral characteristics of bare soil and thus a very important aspect of compositing is finding appropriate statistics to derive final pixel values of reflectance (minimum, mean, median, ...). One possible way to minimize the effect of moisture and surface roughness may be incorporation radar backscatter information from Sentinel-1. However, it further complicates the processing of data and makes the composite creation more complex.</p><p>The research has been supported by the project no. QK1820389 " Production of actual detailed maps of soil properties in the Czech Republic based on database of Large-scale Mapping of Agricultural Soils in Czechoslovakia and application of digital soil mapping" funding by Ministry of Agriculture.</p>

scholarly journals Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation

2021 ◽  
Vol 13 (4) ◽  
pp. 586
Author(s):  
Salvatore Praticò ◽  
Francesco Solano ◽  
Salvatore Di Fazio ◽  
Giuseppe Modica
Keyword(s):  
Time Series ◽  
Input Image ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Mountain Forest ◽  
Machine Learning Classification ◽  
Free Data ◽  
Google Earth Engine ◽  
Sentinel 2

The sustainable management of natural heritage is presently considered a global strategic issue. Owing to the ever-growing availability of free data and software, remote sensing (RS) techniques have been primarily used to map, analyse, and monitor natural resources for conservation purposes. The need to adopt multi-scale and multi-temporal approaches to detect different phenological aspects of different vegetation types and species has also emerged. The time-series composite image approach allows for capturing much of the spectral variability, but presents some criticalities (e.g., time-consuming research, downloading data, and the required storage space). To overcome these issues, the Google Earth engine (GEE) has been proposed, a free cloud-based computational platform that allows users to access and process remotely sensed data at petabyte scales. The application was tested in a natural protected area in Calabria (South Italy), which is particularly representative of the Mediterranean mountain forest environment. In the research, random forest (RF), support vector machine (SVM), and classification and regression tree (CART) algorithms were used to perform supervised pixel-based classification based on the use of Sentinel-2 images. A process to select the best input image (seasonal composition strategies, statistical operators, band composition, and derived vegetation indices (VIs) information) for classification was implemented. A set of accuracy indicators, including overall accuracy (OA) and multi-class F-score (Fm), were computed to assess the results of the different classifications. GEE proved to be a reliable and powerful tool for the classification process. The best results (OA = 0.88 and Fm = 0.88) were achieved using RF with the summer image composite, adding three VIs (NDVI, EVI, and NBR) to the Sentinel-2 bands. SVM and RF produced OAs of 0.83 and 0.80, respectively.

scholarly journals GOOGLE EARTH ENGINE: APPLICATION OF ALGORITHMS FOR REMOTE SENSING OF CROPS IN TUSCANY (ITALY)

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 291-296
Author(s):  
J. P. Clemente ◽  
G. Fontanelli ◽  
G. G. Ovando ◽  
Y. L. B. Roa ◽  
A. Lapini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Google Earth ◽  
Optical Data ◽  
Support Vector ◽  
Use Of Time ◽  
Crop Types ◽  
Google Earth Engine ◽  
Agricultural Areas ◽  
Sentinel 2

Abstract. Remote sensing has become an important mean to assess crop areas, specially for the identification of crop types. Google Earth Engine (GEE) is a free platform that provides a large number of satellite images from different constellations. Moreover, GEE provides pixel-based classifiers, which are used for mapping agricultural areas. The objective of this work is to evaluate the performance of different classification algorithms such as Minimum Distance (MD), Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART) and Na¨ıve Bayes (NB) on an agricultural area in Tuscany (Italy). Four different scenarios were implemented in GEE combining different information such as optical and Synthetic Aperture Radar (SAR) data, indices and time series. Among the five classifiers used the best performers were RF and SVM. Integrating Sentinel-1 (S1) and Sentinel-2 (S2) slightly improves the classification in comparison to the only S2 image classifications. The use of time series substantially improves supervised classifications. The analysis carried out so far lays the foundation for the integration of time series of SAR and optical data.

scholarly journals OPTIMAL DATES FOR DECIDUOUS TREE SPECIES MAPPING USING FULL YEARS SENTINEL-2 TIME SERIES IN SOUTH WEST FRANCE

2020 ◽  
Vol V-3-2020 ◽  
pp. 469-476
Author(s):  
N. Karasiak ◽  
M. Fauvel ◽  
J.-F. Dejoux ◽  
C. Monteil ◽  
D. Sheeren
Keyword(s):  
Time Series ◽  
Species Distribution ◽  
Tree Species ◽  
Satellite Image ◽  
Deciduous Tree ◽  
Support Vector ◽  
Red Oak ◽  
Spectral Bands ◽  
One Year ◽  
Sentinel 2

Abstract. The free to use Sentinel-2 (S2) sensors with 5-day revisit time at high spatial resolution in 10 spectral bands is a revolution in the remote sensing domain. Including 6 spectral bands in the near infrared, with 3 dedicated for the red-edge (where the vegetation significatively increases), these european satellites are very promising for mapping tree species distribution at a national scale. Here, we study the contribution of three one-year S2 Satellite Image Time Series (SITS) for mapping deciduous species distribution in the southwest of France. The annual cycle of vegetation (called phenology) can contribute to the identification of tree species. For some specific dates, species can have different phenological behaviours (senesence, flowering…). To train and validate the maps, we used the Support Vector Machine algorithm with a spatial cross-validation method. To train the algorithm with the same number of samples per species, we decided to undersample each class to the smallest class using a K-means clustering method. Moreover, a Sequential Feature Selection (SFS) has been implemented to detect the optimal dates per species. Our results are promising with high accuracy for Red oak andWillow (average score of the three one-year respectively F1 = 0.99, F1 = 0.94) based on the optimal dates. However, it appears that the performances when using the each full SITS are far below the optimal dates models (average ΔF1 = 0.32). We did not find, except for Willow and Red oak, that the optimal dates were the same for each year. Perspectives is to find an algorithm robust to temporal or spectral noise and to smooth the time series.

ADAPTIVE WEIGHTED LEAST SQUARES SVM BASED SNOWING MODEL FOR IMAGE DENOISING

2013 ◽  
Vol 11 (06) ◽  
pp. 1350043 ◽  
Author(s):  
SHENG ZHENG ◽  
CHANGCAI YANG ◽  
EMILE A. HENDRIKS ◽  
XIAOJUN WANG
Keyword(s):  
Least Squares ◽  
Gaussian Noise ◽  
Weighted Least Squares ◽  
Digital Terrain Model ◽  
Weighting Scheme ◽  
Support Vector ◽  
Edge Structure ◽  
Terrain Model ◽  
Non Gaussian ◽  
Local Edge

We propose a snowing model to iteratively smoothe the various image noises while preserving the important image structures such as edges and lines. Considering the gray image as a digital terrain model, we develop an adaptive weighted least squares support vector machine (LS-SVM) to iteratively estimate the optimal gray surface underlying the noisy image. The LS-SVM works on Gaussian noise while the weighted LS-SVM works on the outliers and non-Gaussian noise. To improve its performance in preserving the directional signal while suppressing the noise, the dominant orientation information of the gradients is integrated into the weighting scheme. The contribution of each attribute to the final LS-SVM model is adaptively determined in a fitness modulated, elongated, elliptical contour spread along the direction of the local edge structure. The farther away from the hyperplane in kernel space the point is, the less weight it gets, while the point on the direction of the local structure gets more weight. With adaptive weighting scheme, the robust LS-SVM smoothes most strongly along the edges, rather than across them, while it minimizes the effects of outliers, and results in strong preservation of details in the final output. The iteratively adaptive reweighted LS-SVM simulated the snowing process. The investigation on real images contaminated by mixture Gaussian noise has demonstrated that the performance of the present method is stable and reliable under noise distributions varying from Gaussian to impulsive.

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