scholarly journals UTILIZATION OF SENTINEL-2 IMAGERY IN THE ESTIMATION OF PLASTICS AMONG FLOATING DEBRIS ALONG THE COAST OF MANILA BAY

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 177-184
Author(s):  
M. L. R. Gonzaga ◽  
M. T. S. Wong ◽  
A. C. Blanco ◽  
J. A. Principe
Keyword(s):  
Vegetation Index ◽  
The Philippines ◽  
Training Data ◽  
Economic Activities ◽  
Plastic Pollution ◽  
Different Types ◽  
Manila Bay ◽  
Supervised Classification Methods ◽  
Selection Of ◽  
Sentinel 2

Abstract. With the Philippines ranking as the third largest source of plastics that end up in the oceans, there is a need to further explore methodologies that will become an aid in plastic waste removal from the ocean. Manila Bay is a natural harbor in the Philippines that serves as the center of different economic activities. However, the bay is also threatened with plastic pollution due to increasing population and industrial activities. BASECO is one of the areas in Manila Bay where clean-up activities are focused as this is where trash accumulates. Sentinel-2 images are provided free of charge by the European Commission's Copernicus Programme. Satellite images from June 2019 to May 2020 were inspected, then cloud-free images were downloaded. After downloading and pre-processing, spectral data of different types of plastic such as shipping pouch, bubble wrap, styrofoam, PET bottle, sando bag and snack packaging that were measured by a spectrometer during a fieldwork by the Development of Integrated Mapping, Monitoring, and Analytical Network System for Manila Bay and Linked Environments (project MapABLE) were utilized in the selection of training data. Then, indices such as the Normalized Vegetation Index (NDVI), Floating Debris Index (FDI) and Plastic Index (PI) from previous studies were analyzed for further separation of classes used as training data. These training data served as an input to the two supervised classification methods, Naive Bayes and Mixture Tuned Matched Filtering (MTMF). Both methods were validated by reports and articles from Philippine agencies indicating the spots where trash frequently accumulates.

scholarly journals Outlier Detection at the Parcel-Level in Wheat and Rapeseed Crops Using Multispectral and SAR Time Series

2021 ◽  
Vol 13 (5) ◽  
pp. 956
Author(s):  
Florian Mouret ◽  
Mohanad Albughdadi ◽  
Sylvie Duthoit ◽  
Denis Kouamé ◽  
Guillaume Rieu ◽  
...  
Keyword(s):  
Outlier Detection ◽  
Experimental Validation ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Images ◽  
Vegetation Indexes ◽  
Crop Development ◽  
Different Types ◽  
Selection Of ◽  
Sentinel 2

This paper studies the detection of anomalous crop development at the parcel-level based on an unsupervised outlier detection technique. The experimental validation is conducted on rapeseed and wheat parcels located in Beauce (France). The proposed methodology consists of four sequential steps: (1) preprocessing of synthetic aperture radar (SAR) and multispectral images acquired using Sentinel-1 and Sentinel-2 satellites, (2) extraction of SAR and multispectral pixel-level features, (3) computation of parcel-level features using zonal statistics and (4) outlier detection. The different types of anomalies that can affect the studied crops are analyzed and described. The different factors that can influence the outlier detection results are investigated with a particular attention devoted to the synergy between Sentinel-1 and Sentinel-2 data. Overall, the best performance is obtained when using jointly a selection of Sentinel-1 and Sentinel-2 features with the isolation forest algorithm. The selected features are co-polarized (VV) and cross-polarized (VH) backscattering coefficients for Sentinel-1 and five Vegetation Indexes for Sentinel-2 (among us, the Normalized Difference Vegetation Index and two variants of the Normalized Difference Water). When using these features with an outlier ratio of 10%, the percentage of detected true positives (i.e., crop anomalies) is equal to 94.1% for rapeseed parcels and 95.5% for wheat parcels.

scholarly journals MAPPING OF <i>SARGASSUM</i> DISTRIBUTION IN THE EASTERN COAST OF SOUTHERN LEYTE USING SENTINEL 2 SATELLITE IMAGERY

2019 ◽  
Vol XLII-4/W19 ◽  
pp. 289-295
Author(s):  
J. J. Lasquites ◽  
A. C. Blanco ◽  
A. Tamondong
Keyword(s):  
Satellite Imagery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Accuracy Assessment ◽  
Marine Ecosystem ◽  
Brown Seaweed ◽  
The Philippines ◽  
Eastern Coast ◽  
Department Of Agriculture ◽  
Sentinel 2

Abstract. Sargassum is a brown seaweed distributed in the Philippines and recognized as an additional source of income for fishing communities. Due to uncontrolled harvesting of the seaweed, the Department of Agriculture regulated its collection and harvesting by imposing seasonal restrictions. Hence, the need to identify the locations and cover of healthy Sargassum is vital to address the demand in the market while maintaining ecological balance in the marine ecosystem. Two Sentinel-2 satellite imagery (10 m resolution) acquired on December 08, 2017 (peak growth) and May 27, 2018 (senescence stage) were used to map the presence of Sargassum in the eastern coast of Southern Leyte. Supervised classification using maximum likelihood algorithm and accuracy assessment were conducted before generating the map. Three classes were considered namely Sargassum, clouds and land. Furthermore, Anselin Local Moran’s I (cluster and outlier analysis) was conducted to determine which areas have significant clustering of “healthy” Sargassum using the normalized difference vegetation index (NDVI). For both image dates, high classification accuracies of Sargassum were obtained in the islands. However, there are misclassifications of Sargassum in Silago (UA = 78.72%) and Hinunangan (PA = 82.35%) using the May image. Furthermore, misclassification of Sargassum were obtained in Silago (PA = 93.6%) and Hinundayan (PA = 96.23%) using the December image. Clusters of high NDVI values are more evident in December. Healthy Sargassum are apparent in the coast of Silago and mostly found near shore and in rocky substrates.

scholarly journals Crop NDVI Monitoring Based on Sentinel 1

2019 ◽  
Vol 11 (12) ◽  
pp. 1441 ◽  
Author(s):  
Roberto Filgueiras ◽  
Everardo Chartuni Mantovani ◽  
Daniel Althoff ◽  
Elpídio Inácio Fernandes Filho ◽  
Fernando França da Cunha
Keyword(s):  
Cloud Cover ◽  
Vegetation Index ◽  
Temporal Analysis ◽  
Agricultural Crops ◽  
Radar Sensors ◽  
Great Reliability ◽  
Phenological Cycle ◽  
Regression Techniques ◽  
Selection Of ◽  
Sentinel 2

Monitoring agricultural crops is necessary for decision-making in the field. However, it is known that in some regions and periods, cloud cover makes this activity difficult to carry out in a systematic way throughout the phenological cycle of crops. This circumstance opens up opportunities for techniques involving radar sensors, resulting in images that are free of cloud effects. In this context, the objective of this work was to obtain a normalized different vegetation index (NDVI) cloudless product (NDVInc) by modeling Sentinel 2 NDVI using different regression techniques and the Sentinel 1 radar backscatter as input. To do this, we used four pairs of Sentinel 2 and Sentinel 1 images on coincident days, aiming to achieve the greatest range of NDVI values for agricultural crops (soybean and maize). These coincident pairs were the only ones in which the percentage of clouds was not equal to 100% for 33 central pivot areas in western Bahia, Brazil. The dataset used for NDVInc modeling was divided into two subsets: training and validation. The training and validation datasets were from the period from 24 June 2017 to 19 July 2018 (four pairs of images). The best performing model was used in a temporal analysis from 02 October 2017 to 08 August 2018, totaling 55 Sentinel 2 images and 25 Sentinel 1 images. The selection of the best regression algorithm was based on two validation methodologies: K-fold cross-validation (k = 10) and holdout. We tested four modeling approaches with eight regression algorithms. The random forest was the algorithm that presented the best statistical metrics, regardless of the validation methodology and the approach used. Therefore, this model was applied to a time series of Sentinel 1 images in order to demonstrate the robustness and applicability of the model created. We observed that the data derived from Sentinel 1 allowed us to model, with great reliability, the NDVI of agricultural crops throughout the phenological cycle, making the methodology developed in this work a relevant solution for the monitoring of various regions, regardless of cloud cover.

scholarly journals Development of a Rapid Mangrove Zonation Mapping Workflow Using Sentinel 2-Derived Indices and Biophysical Dataset

2021 ◽  
Vol 2 ◽  
Author(s):  
Alvin B. Baloloy ◽  
Ariel C. Blanco ◽  
Sahadev Sharma ◽  
Kazuo Nadaoka
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
The Philippines ◽  
Distribution Analysis ◽  
Rhizophora Stylosa ◽  
Area Index ◽  
Zonation Maps ◽  
Mangrove Zonation ◽  
Sentinel 2

Moderate to high resolution satellite imageries are commonly used in mapping mangrove cover from local to global scales. In addition to extent information, studies such as mangrove composition, ecology, and distribution analysis require further information on mangrove zonation. Mangrove zonation refers to unique sections within a mangrove forest being dominated by a similar family, genus, or species. This can be observed both in natural and planted mangrove forests. In this study, a mapping workflow was developed to detect zonation in test mangrove forest sites in Katunggan-It Ibajay (KII) Ecopark (Aklan), Bintuan (Coron), Bogtong, and Sagrada (Busuanga) in the Philippines and Fukido Mangrove Park (Ishigaki, Japan) using Sentinel-2 imagery. The methodology was then applied to generate a nationwide mangrove zonation map of the Philippines for year 2020. Combination of biophysical products, water, and vegetation indices were used as classification inputs including leaf area index (LAI), fractional vegetation cover (FVC), fraction of photosynthetically-active radiation (FAPAR), Canopy chlorophyll content (Cab), canopy water content (Cw), Normalized Difference Vegetation Index (NDVI), modified normalized difference water index (MNDWI), modified chlorophyll absorption in reflectance index (MCARI), and red-edge inflection point (REIP). Mangrove extents were first mapped using either the Maximum Likelihood Classification (MLC) algorithm or the Mangrove Vegetation Index (MVI)-based methodology. The biophysical and vegetation indices within these areas were stacked and transformed through Principal Component Analysis (PCA). Regions of Interest (ROIs) were selected on the PCA bands as training input to the MLC. Results show that mangrove zonation maps can highlight the major mangrove zones in the study sites, commonly limited up to genera level only except for genera with only one known species thriving in the area. Four zones were detected in KII Ecopark: Avicennia zone, Nypa zone, Avicennia mixed with Nypa zone, and mixed mangroves zones. For Coron and Busuanga, the mapped mangrove zones are mixed mangroves, Rhizophora zone and sparse/damaged zones. Three zones were detected in Fukido site: Rhizophora stylosa-dominant zone, Bruguiera gymnorrhiza-dominant zone, and mixed mangrove zones. The zonation maps were validated using field plot data and orthophotos generated from Unmanned Aerial System (UAS) surveys, with accuracies ranging from 75 to 100%.

scholarly journals IMPROVING LULC CLASSIFICATION FROM SATELLITE IMAGERY USING DEEP LEARNING – EUROSAT DATASET

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 369-376
Author(s):  
H. Yassine ◽  
K. Tout ◽  
M. Jaber
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Satellite Imagery ◽  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Feature ◽  
Classification Performance ◽  
Training Data ◽  
Data Set ◽  
Sentinel 2

Abstract. Machine learning (ML) has proven useful for a very large number of applications in several domains. It has realized a remarkable growth in remote-sensing image analysis over the past few years. Deep Learning (DL) a subset of machine learning were applied in this work to achieve a better classification of Land Use Land Cover (LULC) in satellite imagery using Convolutional Neural Networks (CNNs). EuroSAT benchmarking data set is used as training data set which uses Sentinel-2 satellite images. Sentinel-2 provides images with 13 spectral feature bands, but surprisingly little attention has been paid to these features in deep learning models. The majority of applications focused only on using RGB due to high availability of the RGB models in computer vision. While RGB gives an accuracy of 96.83% using CNN, we are presenting two approaches to improve the classification performance of Sentinel-2 images. In the first approach, features are extracted from 13 spectral feature bands of Sentinel-2 instead of RGB which leads to accuracy of 98.78%. In the second approach features are extracted from 13 spectral bands of Sentinel-2 in addition to calculated indices used in LULC like Blue Ratio (BR), Vegetation index based on Red Edge (VIRE) and Normalized Near Infrared (NNIR), etc. which gives a better accuracy of 99.58%.

SiDroForest  Siberian Drone-mapped Forest inventory

2021 ◽  
Author(s):  
Femke van Geffen ◽  
Birgit Heim ◽  
Ulrike Herzschuh ◽  
Luidmila Pestryakova ◽  
Evgenii Zakharov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Satellite Data ◽  
Forest Structure ◽  
Vegetation Index ◽  
Point Clouds ◽  
Training Data ◽  
Data Repository ◽  
Surface Model ◽  
Marine Research ◽  
Sentinel 2

&lt;p&gt;To gain a better understanding of global carbon storage and albedo feedback mechanisms it is important to have insights into high latitude vegetation change. Boreal forest compositions are changing in response to changes in climate, which in turn can lead to feedbacks in regional and global climate through altered carbon cycles and albedo dynamics. Circumpolar boreal forests represent close to 30% of all forested area on the planet, between 900 and 1,200 million ha. These forests are located primarily in Alaska, Canada, and Russia. Due to the remote location of these forests and the short seasons without snow, data collected on the boreal vegetation is limited.&amp;#160;&lt;/p&gt;&lt;p&gt;The proposed dataset is an attempt to remedy data scarcity whilst providing adjusted data for machine learning practices.We present a dataset containing diverse formats of forest structure information that covers two important vegetation transition zones in Siberia: the Evergreen - Summergreen transition zone in Central Yakutia and the northern treeline in Chukotka (NE Siberia).&lt;/p&gt;&lt;p&gt;This dataset contains data from the locations covered by fieldwork was performed by the Alfred Wegener Institute for Polar and Marine research, (AWI) and the North-Eastern Federal University of Yakutsk&amp;#8203; (NEFU). The fieldwork upscaled through the addition of Red Green Blue(RGB) UAV (Unmanned Aerial Vehicle) camera data and Sentinel-2 satellite data cropped to a 5 km radius around the fieldwork sites. The dataset is created with the aim of providing ground truth validation and training data to be used in various vegetation related machine learning tasks .&lt;/p&gt;&lt;p&gt;The dataset contains:&lt;/p&gt;&lt;p&gt;1.Labelled individual trees per 30x30 m plot assigned in field work with additional data on species, height, crown width, and biomass.&lt;/p&gt;&lt;p&gt;2.Structure from Motion (SfM)point clouds that provide 3D information about the forest structure, included generated Canopy Height Model (CHM), Digital Elevation Model (DEM) and a Digital Surface Model (DSM) per 50x50 m.&lt;/p&gt;&lt;p&gt;3.Multispectral Sentinel-2 satellite data (10 m ) cropped to a 5km radius with generated a NDVI(normalized difference vegetation index), available in three seasons: Early Summer, Peak Summer and Late Summer.&lt;/p&gt;&lt;p&gt;4.Extracted tree crowns with species information and a synthetically generated large (10.000 samples) dataset for training machine leaning algorithms.&lt;/p&gt;&lt;p&gt;The dataset will be made publicly available on&amp;#160;the data repository&amp;#160;PANGAEA.&lt;/p&gt;

scholarly journals SPATIO-TEMPORAL MAPPING AND ANALYSIS OF MANGROVE EXTENTS AROUND MANILA BAY USING LANDSAT SATELLITE IMAGERY AND MANGROVE VEGETATION INDEX (MVI)

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 103-108
Author(s):  
M. Conopio ◽  
A. B. Baloloy ◽  
J. Medina ◽  
A. C. Blanco
Keyword(s):  
Mangrove Forest ◽  
Vegetation Index ◽  
Land Reclamation ◽  
Detection Algorithm ◽  
The Philippines ◽  
Rapid Decline ◽  
Wide Range ◽  
Mangrove Vegetation ◽  
Manila Bay ◽  
Landsat Satellite

Abstract. Mangroves are considered one of the most undervalued ecosystems in the world. It provides shelter to a wide range of species and protection from natural hazards to coastal communities. The Philippines, being a country with long coastlines, benefits greatly from mangroves. Historically, it had 400,000–500,000 hectares of mangroves forest in 1920, which declined to 120,000 hectares in 1994 due to rapid industrialization, particularly the conversion of these forests into aquaculture such as fishponds Mangrove forest in the Philippines saw a rapid decline between 1920 and 1994 due to aquaculture conversion and land reclamation Mangrove Vegetation Index (MVI), an established mangrove detection algorithm, was applied on Landsat satellite images of Manila Bay to map the extent of the mangrove forest from 1990 to 2020. Thirteen time-series maps were produced. Area computation showed that the coastline of Bulacan had the most mangroves, while the coastline of Metro Manila had the least throughout the years.

scholarly journals Machine learning for improved data analysis of biological aerosol using the WIBS

2018 ◽  
Author(s):  
Simon Ruske ◽  
David O. Topping ◽  
Virginia E. Foot ◽  
Andrew P. Morse ◽  
Martin W. Gallagher
Keyword(s):  
Ultraviolet Light ◽  
Fungal Spores ◽  
Training Data ◽  
Gradient Boosting ◽  
Classification Error ◽  
Data Sets ◽  
Data Preparation ◽  
Different Types ◽  
Laboratory Training ◽  
Selection Of

Abstract. Primary biological aerosol including bacteria, fungal spores and pollen have important implications for public health and the environment. Such particles may have different concentrations of chemical fluorophores and will provide different responses in the presence of ultraviolet light which potentially could be used to discriminate between different types of biological aerosol. Development of ultraviolet light induced fluorescence (UV-LIF) instruments such as the Wideband Integrated Bioaerosol Sensor (WIBS) has made is possible to collect size, morphology and fluorescence measurements in real-time. However, it is unclear without studying responses from the instrument in the laboratory, the extent to which we can discriminate between different types of particles. Collection of laboratory data is vital to validate any approach used to analyse the data and to ensure that the data available is utilised as effectively as possible. In this manuscript we test a variety of methodologies on traditional reference particles and a range of laboratory generated aerosols. Hierarchical Agglomerative Clustering (HAC) has been previously applied to UV-LIF data in a number of studies and is tested alongside other algorithms that could be used to solve the classification problem: Density Based Spectral Clustering and Noise (DBSCAN), k-means and gradient boosting. Whilst HAC was able to effectively discriminate between the reference particles, yielding a classification error of only 1.8 %, similar results were not obtained when testing on laboratory generated aerosol where the classification error was found to be between 11.5 % and 24.2 %. Furthermore, there is a worryingly large uncertainty in this approach in terms of the data preparation and the cluster index used, and we were unable attain consistent results across the different sets of laboratory generated aerosol tested. The best results were obtained using gradient boosting, where the misclassification rate was between 4.38 % and 5.42 %. The largest contribution to this error was the pollen samples where 28.5 % of the samples were misclassified as fungal spores. The technique was also robust to changes in data preparation provided a fluorescent threshold was applied to the data. Where laboratory training data is unavailable, DBSCAN was found to be an potential alternative to HAC. In the case of one of the data sets where 22.9 % of the data was left unclassified we were able to produce three distinct clusters obtaining a classification error of only 1.42 % on the classified data. These results could not be replicated however for the other data set where 26.8 % of the data was not classified and a classification error of 13.8 % was obtained. This method, like HAC, also appeared to be heavily dependent on data preparation, requiring different selection of parameters dependent on the preparation used. Further analysis will also be required to confirm our selection of parameters when using this method on ambient data. There is a clear need for the collection of additional laboratory generated aerosol to improve interpretation of current databases and to aid in the analysis of data collected from an ambient environment. New instruments with a greater resolution are likely improve on current discrimination between pollen, bacteria and fungal spores and even between their different types, however the need for extensive laboratory training data sets will grow as a result.

scholarly journals Sub-Pixel Crop Type Classification Using PROBA-V 100 m NDVI Time Series and Reference Data from Sentinel-2 Classifications

2019 ◽  
Vol 11 (11) ◽  
pp. 1370 ◽  
Author(s):  
Petar Dimitrov ◽  
Qinghan Dong ◽  
Herman Eerens ◽  
Alexander Gikov ◽  
Lachezar Filchev ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Training Data ◽  
Support Vector ◽  
Pixel Classification ◽  
Good Correspondence ◽  
Winter Cereals ◽  
Ndvi Time Series ◽  
Sentinel 2

This paper presents the results of a sub-pixel classification of crop types in Bulgaria from PROBA-V 100 m normalized difference vegetation index (NDVI) time series. Two sub-pixel classification methods, artificial neural network (ANN) and support vector regression (SVR) were used where the output was a set of area fraction images (AFIs) at 100 m resolution with pixels containing estimated area fractions of each class. High-resolution maps of two test sites derived from Sentinel-2 classifications were used to obtain training data for the sub-pixel classifications. The estimated area fractions have a good correspondence with the true area fractions when aggregated to regions of 10 × 10 km2, especially when the SVR method was used. For the five dominant classes in the test sites the R2 obtained after the aggregation was 86% (winter cereals), 81% (sunflower), 92% (broad-leaved forest), 89% (maize), and 67% (grasslands) when the SVR method was used.

scholarly journals An Operational Framework for Mapping Irrigated Areas at Plot Scale Using Sentinel-1 and Sentinel-2 Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2584
Author(s):  
Hassan Bazzi ◽  
Nicolas Baghdadi ◽  
Ghaith Amin ◽  
Ibrahim Fayad ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Time Series ◽  
Selection Criteria ◽  
Vegetation Index ◽  
Data Availability ◽  
Training Data ◽  
Classification Model ◽  
In Situ Data ◽  
Ndvi Time Series ◽  
Sentinel 2

In this study, we present an operational methodology for mapping irrigated areas at plot scale, which overcomes the limitation of terrain data availability, using Sentinel-1 (S1) C-band SAR (synthetic-aperture radar) and Sentinel-2 (S2) optical time series. The method was performed over a study site located near Orléans city of north-central France for four years (2017 until 2020). First, training data of irrigated and non-irrigated plots were selected using predefined selection criteria to obtain sufficient samples of irrigated and non-irrigated plots each year. The training data selection criteria is based on two irrigation metrics; the first one is a SAR-based metric derived from the S1 time series and the second is an optical-based metric derived from the NDVI (normalized difference vegetation index) time series of the S2 data. Using the newly developed irrigation event detection model (IEDM) applied for all S1 time series in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations, an irrigation weight metric was calculated for each plot. Using the NDVI time series, the maximum NDVI value achieved in the crop cycle was considered as a second selection metric. By fixing threshold values for both metrics, a dataset of irrigated and non-irrigated samples was constructed each year. Later, a random forest classifier (RF) was built for each year in order to map the summer agricultural plots into irrigated/non-irrigated. The irrigation classification model uses the S1 and NDVI time series calculated over the selected training plots. Finally, the proposed irrigation classifier was validated using real in situ data collected each year. The results show that, using the proposed classification procedure, the overall accuracy for the irrigation classification reaches 84.3%, 93.0%, 81.8%, and 72.8% for the years 2020, 2019, 2018, and 2017, respectively. The comparison between our proposed classification approach and the RF classifier built directly from in situ data showed that our approach reaches an accuracy nearly similar to that obtained using in situ RF classifiers with a difference in overall accuracy not exceeding 6.2%. The analysis of the obtained classification accuracies of the proposed method with precipitation data revealed that years with higher rainfall amounts during the summer crop-growing season (irrigation period) had lower overall accuracy (72.8% for 2017) whereas years encountering a drier summer had very good accuracy (93.0% for 2019).

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