scholarly journals Obtención de coberturas del suelo agropecuarias en imágenes satelitales Sentinel-2 con la inyección de imágenes de dron usando Random Forest en Google Earth Engine

2020 ◽  
pp. 49
Author(s):  
M. Ramírez ◽  
L. Martínez ◽  
M. Montilla ◽  
O. Sarmiento ◽  
J. Lasso ◽  
...  
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Spatial Resolution ◽  
Supervised Classification ◽  
Reduction Process ◽  
Google Earth ◽  
Accurate Information ◽  
Pass Filter ◽  
Google Earth Engine ◽  
Sentinel 2

<p><span lang="EN-US">To obtain accurate information on land cover changes in the agricultural sector, we propose a supervised classification method that integrates Sentinel-2 satellite imagery with images surveyed from Remote Piloted Aircraft Systems (RPAS). The methodology was implemented on the Google Earth Engine platform. Initially, the Sentinel-2 imagery collection was integrated into a single image through a median reduction process. Subsequently, the high-pass filter (HPF) pansharpening image fusion method was applied to the thermal spectral bands to obtain a final spatial resolution of 10 m. To perform the integration of the two image sources, the RPAS image was normalized by using a 5X5 gaussian texture filter and the pixel was resampled to five times its original size. This procedure was performed iteratively until reaching the spatial resolution of the Sentinel-2 imagery. Besides, the following inputs were added to the classification: the spectral indices calculated from the Sentinel-2 and RPAS bands (e.g. NDVI, NDWI, SIPI, GARI); altimetric information and slopes of the zone derived from the SRTM DEM. The supervised classification was done by using the Random Forest technique (Machine Learning). The land cover seed reference to perform the classification was manually captured by a thematic expert, then, this reference was distributed in 70% for the training of the Random Forest algorithm and in 30% to validate the classification. The results show that the incorporation of the RPAS image improves thematic accuracy indicators by an average of 3% compared to a classification made exclusively with Sentinel-2 imagery.</span></p>

scholarly journals SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 663-670
Author(s):  
E. Belcore ◽  
M. Piras ◽  
E. Wozniak
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Western Alps ◽  
Computational Effort ◽  
Google Earth ◽  
Random Forest Classification ◽  
Importance Analysis ◽  
Google Earth Engine ◽  
Sentinel 2

Abstract. Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945.

scholarly journals Identificação das Dunas do Atacama (Norte do Chile) a partir da avaliação de três algoritmos no Google Earth Engine

2021 ◽  
Vol 14 (6) ◽  
pp. 3294
Author(s):  
Leonel Enrique Sánchez ◽  
Joselisa Maria Chaves ◽  
Washington J.S. Franca Rocha ◽  
Jocimara S. B. Lobão ◽  
Plínio Martins Falcão
Keyword(s):  
Random Forest ◽  
Random Sampling ◽  
Supervised Classification ◽  
Accuracy Assessment ◽  
Simple Random Sampling ◽  
Google Earth ◽  
Assessment Process ◽  
Northern Chile ◽  
Google Earth Engine ◽  
Sentinel 2

As dunas correspondem a processos de sedimentação eólica, que podem estar tanto nas áreas costeiras marinhas, como no interior do continente com algumas diferenças na modelagem. No Sul do deserto do Atacama, no Norte do Chile, há um conjunto de seis campos de dunas intermontanhas chamadas Mar de Dunas do Atacama, as quais têm tipologias complexas de dunas do deserto, que podem ser ativas, semiativas ou estabilizadas. O seu monitoramento é conveniente para conhecer detalhes sobre a possível invasão de areias das dunas ao sul do rio Copiapó. Dessa forma, esta pesquisa tem como objetivo avaliar os métodos de classificação supervisionada Random Forest, CART e SmileCART através de duas metodologias de amostragens, aleatória e estratificada, numa imagem Landsat 5 na plataforma em nuvem Google Earth Engine, a fim de verificar qual método oferece o melhor resultado para o mapeamento do Mar de Dunas do Atacama. Para conseguir este objetivo, foram criados polígonos de classes para a realização da amostragem aleatória estratificada e chave de interpretação para amostragem aleatória simples. O processo de avaliação da acurácia foi feito através de imagem Sentinel 2 com a aplicação dos índices de Simultaneidade Geográfica, Erros de Comissão e Omissão, e Exatidão Global. Observou-se como resultados para os algoritmos testados, que os três algoritmos foram eficientes para o mapeamento das Dunas do Atacama, entretanto, a técnica de classificação supervisionada por CART, com a metodologia da amostragem aleatória simples, representou o melhor desempenho.      Identification of the Atacama Dunes (Northern Chile) from the evaluation of three algorithms on Google Earth EngineA B S T R A C TThe dunes correspond to wind sedimentation processes, which can be found both in marine coastal areas and in the interior of the continent with some differences in modeling. In the south of the Atacama desert, in northern Chile, there are a set of six inter-mountain dune fields called Mar de Dunas do Atacama, which have complex types of desert dunes, which can be active, semi-active or stabilized. Its monitoring is convenient to know details about the possible invasion of sand from the dunes south of the Copiapó River. Thus, this research aims to evaluate the supervised classification methods Random Forest, CART and SmileCART through two sampling methodologies, random and stratified, in a Landsat 5 image on the Google Earth Engine cloud platform, in order to verify which method offers the best result for mapping the Atacama Dunes Sea. In order to achieve this objective, class polygons were created to perform stratified random sampling and the interpretation key for simple random sampling. The accuracy assessment process was performed using a Sentinel 2 image with the application of the Geographic Simultaneity indices and the Commission and Omission Errors. It was observed as results for the tested algorithms, that the three algorithms were efficient for mapping the Atacama Dunes, however, the CART supervised classification technique, with the simple random sampling methodology, represents the best performance.

ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

2021 ◽  
pp. 777
Author(s):  
Andi Tenri Waru ◽  
Athar Abdurrahman Bayanuddin ◽  
Ferman Setia Nugroho ◽  
Nita Rukminasari
Keyword(s):  
Random Forest ◽  
Google Earth ◽  
Multi Temporal ◽  
Google Earth Engine ◽  
Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

scholarly journals Land Cover Classification using Google Earth Engine and Random Forest Classifier—The Role of Image Composition

2020 ◽  
Vol 12 (15) ◽  
pp. 2411 ◽  
Author(s):  
Thanh Noi Phan ◽  
Verena Kuch ◽  
Lukas W. Lehnert
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Land Cover ◽  
Land Cover Classification ◽  
High Accuracy ◽  
Google Earth ◽  
Temporal Aggregation ◽  
Google Earth Engine ◽  
Time Series Images ◽  
Land Cover Maps

Land cover information plays a vital role in many aspects of life, from scientific and economic to political. Accurate information about land cover affects the accuracy of all subsequent applications, therefore accurate and timely land cover information is in high demand. In land cover classification studies over the past decade, higher accuracies were produced when using time series satellite images than when using single date images. Recently, the availability of the Google Earth Engine (GEE), a cloud-based computing platform, has gained the attention of remote sensing based applications where temporal aggregation methods derived from time series images are widely applied (i.e., the use the metrics such as mean or median), instead of time series images. In GEE, many studies simply select as many images as possible to fill gaps without concerning how different year/season images might affect the classification accuracy. This study aims to analyze the effect of different composition methods, as well as different input images, on the classification results. We use Landsat 8 surface reflectance (L8sr) data with eight different combination strategies to produce and evaluate land cover maps for a study area in Mongolia. We implemented the experiment on the GEE platform with a widely applied algorithm, the Random Forest (RF) classifier. Our results show that all the eight datasets produced moderately to highly accurate land cover maps, with overall accuracy over 84.31%. Among the eight datasets, two time series datasets of summer scenes (images from 1 June to 30 September) produced the highest accuracy (89.80% and 89.70%), followed by the median composite of the same input images (88.74%). The difference between these three classifications was not significant based on the McNemar test (p > 0.05). However, significant difference (p < 0.05) was observed for all other pairs involving one of these three datasets. The results indicate that temporal aggregation (e.g., median) is a promising method, which not only significantly reduces data volume (resulting in an easier and faster analysis) but also produces an equally high accuracy as time series data. The spatial consistency among the classification results was relatively low compared to the general high accuracy, showing that the selection of the dataset used in any classification on GEE is an important and crucial step, because the input images for the composition play an essential role in land cover classification, particularly with snowy, cloudy and expansive areas like Mongolia.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

&lt;p&gt;Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Our results show that: (1) The highest producer&amp;#8217;s accuracies (the reason why using producer&amp;#8217;s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer&amp;#8217;s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future. &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Crop Area Mapping in Southern and Central Malawi With Google Earth Engine

2021 ◽  
Vol 3 ◽  
Author(s):  
Seth Peterson ◽  
Greg Husak
Keyword(s):  
Spatial Resolution ◽  
Test Data ◽  
Google Earth ◽  
Reference Points ◽  
Sub Saharan Africa ◽  
High Temporal Resolution ◽  
Smallholder Farms ◽  
Google Earth Engine ◽  
Crop Area ◽  
Sentinel 2

Agriculture in sub-Saharan Africa consists primarily of smallholder farms of rainfed crops. Historically, satellite data were too coarse to account for the heterogeneity in these landscapes. Sentinel-2 data have improved spectral resolution and much higher spatial resolution (10 m) than previously available satellites with global coverage, such as Landsat or MODIS, making mapping smallholder farms possible. Spectral mixture analysis was used to convert the Sentinel-2 signal into fractions of green vegetation, non-photosynthetic vegetation, soil, and shade endmembers. Very high spatial resolution imagery in Google Earth Pro was used to identify locations of crop and natural vegetation classes, with over 20,000 reference points interpreted. The high temporal resolution of Sentinel-2 (5 days repeat) allows for classification of landcover based on the phenological signal, with natural areas having smoothly varying amounts of photosynthetic vegetation annually, while cropped areas show more abrupt changes, and also the presence of bare soil due to agricultural activity at some point during the year. We summarized the endmember values using monthly medians, extracted values for the reference data points, randomly split them into training and test data sets, and input the training data into the random forests algorithm in Google Earth Engine to map crop area. We divided southern and central Malawi into tiles, and found crop/no crop classification accuracies on the test data for each tile to be between 87 and 93%. The 10 m map of crop area was aggregated to the district level and showed an R2 of 0.74 with ground-based statistics from the Malawi government and 0.79 with a remotely sensed product developed by the USGS.

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

2021 ◽  
Author(s):  
Crismeire Isbaex ◽  
Ana Margarida Coelho
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Forest Biomass ◽  
Local Scale ◽  
Google Earth ◽  
Biomass Estimation ◽  
Promising Tool ◽  
Google Earth Engine ◽  
Sentinel 2

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Evaluating Combinations of Temporally Aggregated Sentinel-1, Sentinel-2 and Landsat 8 for Land Cover Mapping with Google Earth Engine

2019 ◽  
Vol 11 (3) ◽  
pp. 288 ◽  
Author(s):  
Luis Carrasco ◽  
Aneurin O’Neil ◽  
R. Morton ◽  
Clare Rowland
Keyword(s):  
Land Cover ◽  
Satellite Data ◽  
Cloud Cover ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Temporal Aggregation ◽  
Landsat 8 ◽  
Composite Approach ◽  
Google Earth Engine ◽  
Sentinel 2

Land cover mapping of large areas is challenging due to the significant volume of satellite data to acquire and process, as well as the lack of spatial continuity due to cloud cover. Temporal aggregation—the use of metrics (i.e., mean or median) derived from satellite data over a period of time—is an approach that benefits from recent increases in the frequency of free satellite data acquisition and cloud-computing power. This enables the efficient use of multi-temporal data and the exploitation of cloud-gap filling techniques for land cover mapping. Here, we provide the first formal comparison of the accuracy between land cover maps created with temporal aggregation of Sentinel-1 (S1), Sentinel-2 (S2), and Landsat-8 (L8) data from one-year and test whether this method matches the accuracy of traditional approaches. Thirty-two datasets were created for Wales by applying automated cloud-masking and temporally aggregating data over different time intervals, using Google Earth Engine. Manually processed S2 data was used for comparison using a traditional two-date composite approach. Supervised classifications were created, and their accuracy was assessed using field-based data. Temporal aggregation only matched the accuracy of the traditional two-date composite approach (77.9%) when an optimal combination of optical and radar data was used (76.5%). Combined datasets (S1, S2 or S1, S2, and L8) outperformed single-sensor datasets, while datasets based on spectral indices obtained the lowest levels of accuracy. The analysis of cloud cover showed that to ensure at least one cloud-free pixel per time interval, a maximum of two intervals per year for temporal aggregation were possible with L8, while three or four intervals could be used for S2. This study demonstrates that temporal aggregation is a promising tool for integrating large amounts of data in an efficient way and that it can compensate for the lower quality of automatic image selection and cloud masking. It also shows that combining data from different sensors can improve classification accuracy. However, this study highlights the need for identifying optimal combinations of satellite data and aggregation parameters in order to match the accuracy of manually selected and processed image composites.

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