scholarly journals Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, a Case Study in Mozambique

2020 ◽  
Vol 12 (8) ◽  
pp. 1279 ◽  
Author(s):  
Sosdito Mananze ◽  
Isabel Pôças ◽  
Mário Cunha
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Google Earth ◽  
Agricultural Landscapes ◽  
Landsat 8 ◽  
Textural Features ◽  
Observation Data ◽  
Ground Truth Data ◽  
Spectral Bands ◽  
Google Earth Engine

Land cover maps obtained at high spatial and temporal resolutions are necessary to support monitoring and management applications in areas with many smallholder and low-input agricultural systems, as those characteristic in Mozambique. Various regional and global land cover products based on Earth Observation data have been developed and made publicly available but their application in regions characterized by a large variety of agro-systems with a dynamic nature is limited by several constraints. Challenges in the classification of spatially heterogeneous landscapes, as in Mozambique, include the definition of the adequate spatial resolution and data input combinations for accurately mapping land cover. Therefore, several combinations of variables were tested for their suitability as input for random forest ensemble classifier aimed at mapping the spatial dynamics of smallholder agricultural landscape in Vilankulo district in Mozambique. The variables comprised spectral bands from Landsat 7 ETM+ and Landsat 8 OLI/TIRS, vegetation indices and textural features and the classification was performed within the Google Earth Engine cloud computing for the years 2012, 2015, and 2018. The study of three different years aimed at evaluating the temporal dynamics of the landscape, typically characterized by high shifting nature. For the three years, the best performing variables included three selected spectral bands and textural features extracted using a window size of 25. The classification overall accuracy was 0.94 for the year 2012, 0.98 for 2015, and 0.89 for 2018, suggesting that the produced maps are reliable. In addition, the areal statistics of the class classified as agriculture were very similar to the ground truth data as reported by the Serviços Distritais de Actividades Económicas (SDAE), with an average percentage deviation below 10%. When comparing the three years studied, the natural vegetation classes are the predominant covers while the agriculture is the most important cause of land cover changes.

scholarly journals Pixel- vs. Object-Based Landsat 8 Data Classification in Google Earth Engine Using Random Forest: The Case Study of Maiella National Park

2021 ◽  
Vol 13 (12) ◽  
pp. 2299
Author(s):  
Andrea Tassi ◽  
Daniela Gigante ◽  
Giuseppe Modica ◽  
Luciano Di Martino ◽  
Marco Vizzari
Keyword(s):  
Land Cover ◽  
National Park ◽  
Time Window ◽  
Central Italy ◽  
Google Earth ◽  
Data Cube ◽  
Landsat 8 ◽  
Change Analysis ◽  
Object Based ◽  
Google Earth Engine

With the general objective of producing a 2018–2020 Land Use/Land Cover (LULC) map of the Maiella National Park (central Italy), useful for a future long-term LULC change analysis, this research aimed to develop a Landsat 8 (L8) data composition and classification process using Google Earth Engine (GEE). In this process, we compared two pixel-based (PB) and two object-based (OB) approaches, assessing the advantages of integrating the textural information in the PB approach. Moreover, we tested the possibility of using the L8 panchromatic band to improve the segmentation step and the object’s textural analysis of the OB approach and produce a 15-m resolution LULC map. After selecting the best time window of the year to compose the base data cube, we applied a cloud-filtering and a topography-correction process on the 32 available L8 surface reflectance images. On this basis, we calculated five spectral indices, some of them on an interannual basis, to account for vegetation seasonality. We added an elevation, an aspect, a slope layer, and the 2018 CORINE Land Cover classification layer to improve the available information. We applied the Gray-Level Co-Occurrence Matrix (GLCM) algorithm to calculate the image’s textural information and, in the OB approaches, the Simple Non-Iterative Clustering (SNIC) algorithm for the image segmentation step. We performed an initial RF optimization process finding the optimal number of decision trees through out-of-bag error analysis. We randomly distributed 1200 ground truth points and used 70% to train the RF classifier and 30% for the validation phase. This subdivision was randomly and recursively redefined to evaluate the performance of the tested approaches more robustly. The OB approaches performed better than the PB ones when using the 15 m L8 panchromatic band, while the addition of textural information did not improve the PB approach. Using the panchromatic band within an OB approach, we produced a detailed, 15-m resolution LULC map of the study area.

scholarly journals Land Cover Classification Based on Cloud Computing Platform

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Nghia Viet Nguyen ◽  
Thu Hoai Thi Trinh ◽  
Hoa Thi Pham ◽  
Trang Thu Thi Tran ◽  
Lan Thi Pham ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Computer Hardware ◽  
Commercial Software ◽  
Computing Platform ◽  
Land Cover Data ◽  
Cloud Computing Platform ◽  
Google Earth Engine

Land cover is a critical factor for climate change and hydrological models. The extraction of land cover data from remote sensing images has been carried out by specialized commercial software. However, the limitations of computer hardware and algorithms of the commercial software are costly and make it take a lot of time, patience, and skills to do the classification. The cloud computing platform Google Earth Engine brought a breakthrough in 2010 for analyzing and processing spatial data. This study applied Object-based Random Forest classification in the Google Earth Engine platform to produce land cover data in 2010 in the Vu Gia - Thu Bon river basin. The classification results showed 7 categories of land cover consisting of plantation forest, natural forest, paddy field, urban residence, rural residence, bare land, and water surface, with an overall accuracy of 73.9% and kappa of 0.70.

ANALISIS SURFACE URBAN HEAT ISLAND MENGGUNAKAN TEKNIK PENGINDERAAN JAUH BERBASIS CLOUD COMPUTING PADA GOOGLE EARTH ENGINE DI KOTA SAMARINDA

2021 ◽  
pp. 75
Author(s):  
Bayu Elwantyo Bagus Dewantoro ◽  
Pavita Almira Natani ◽  
Zumrotul Islamiah
Keyword(s):  
Cloud Computing ◽  
Urban Heat Island ◽  
Google Earth ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Boundary Analysis ◽  
Urban Heat ◽  
Surface Urban Heat Island ◽  
Google Earth Engine

Peningkatan intensitas pembangunan fisik dan sosial di kawasan perkotaan Samarinda sebagai indikator kemajuan suatu kawasan perkotaan secara tidak langsung berdampak terhadap stabilitas kondisi atmosfer. Fenomena urban heat island sebagai turunan dari dinamika iklim mikro perkotaan sebagai dampak dari pembangunan fisik dan sosial tersebut semakin meluas, sehingga kebutuhan akan teknik monitoring yang efektif dan efisien menjadi sangat penting. Penginderaan jauh mampu melakukan pemantauan dan deteksi titik panas dalam rangka mitigasi dan pengendalian efek urban heat island dalam cakupan wilayah yang luas dengan waktu singkat. Penelitian ini berfokus pada kajian surface urban heat island (SUHI) yang bertujuan untuk mengetahui distribusi spasial intensitas SUHI di Kota Samarinda. Metode yang digunakan pada penelitian ini berupa integrasi teknik penginderaan jauh dan cloud computing pada Google Earth Engine menggunakan band termal citra Landsat 8 OLI/TIRS serta analisis statistik citra menggunakan Buffer Boundary Analysis untuk identifikasi potensi terjadinya SUHI di Kota Samarinda. Ekstraksi suhu permukaan diperoleh dari persamaan Planck yang diintegrasikan dengan koreksi atmosfer untuk koreksi emisivitas permukaan menggunakan syntax dengan bahasa Javacript pada Google Earth Engine. Hasil pengolahan menunjukkan adanya potensi SUHI dengan intensitas tinggi dengan nilai 3,001-6,000°C pada radius 5 km dari pusat kota dan intensitas semakin turun seiring radius yang semakin jauh dari pusat kota. Secara administratif, intensitas SUHI tertinggi relatif berada pada kecamatan Samarinda Kota, Samarinda Ilir, dan Samarinda Seberang dengan rentang intensitas SUHI sebesar 1,5001-6,000°C, sementara intensitas SUHI terendah relatif berada pada kecamatan Sungai Kunjang dan Palaran dengan rentang intensitas SUHI sebesar -10,000-1,500°C.

scholarly journals Mapping land cover change over continental Africa using Landsat and Google Earth Engine cloud computing

PLoS ONE ◽  
2017 ◽  
Vol 12 (9) ◽  
pp. e0184926 ◽  
Author(s):  
Alemayehu Midekisa ◽  
Felix Holl ◽  
David J. Savory ◽  
Ricardo Andrade-Pacheco ◽  
Peter W. Gething ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Land Cover ◽  
Land Cover Change ◽  
Google Earth ◽  
Google Earth Engine

scholarly journals CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE

Irriga ◽  
2020 ◽  
Vol 25 (1) ◽  
pp. 160-169
Author(s):  
Cesar De Oliveira Ferreira Silva
Keyword(s):  
Remote Sensing ◽  
Cloud Computing ◽  
Supervised Classification ◽  
Source Code ◽  
Regression Trees ◽  
Google Earth ◽  
Landsat 8 ◽  
Classification And Regression ◽  
Google Earth Engine

CLASSIFICAÇÃO SUPERVISIONADA DE ÁREA IRRIGADA UTILIZANDO ÍNDICES ESPECTRAIS DE IMAGENS LANDSAT-8 COM GOOGLE EARTH ENGINE   CÉSAR DE OLIVEIRA FERREIRA SILVA1   1 Departamento de Engenharia Rural, Faculdade de Ciências Agronômicas, Universidade Estadual Paulista (UNESP) Campus de Botucatu. Avenida Universitária, n° 3780, Altos do Paraíso, CEP: 18610-034, Botucatu – SP, Brasil, e-mail: [email protected].     1 RESUMO   Identificar áreas de irrigação usando imagens de satélite é um desafio que encontra em soluções de computação em nuvem um grande potencial, como na ferramenta Google Earth Engine (GEE), que facilita o processo de busca, filtragem e manipulação de grandes volumes de dados de sensoriamento remoto sem a necessidade de softwares pagos ou de download de imagens. O presente trabalho apresenta uma implementação de classificação supervisionada de áreas irrigadas e não-irrigadas na região de Sorriso e Lucas do Rio Verde/MT com o algoritmo Classification and Regression Trees (CART) em ambiente GEE utilizando as bandas 2-7 do satélite Landsat-8 e os índices NDVI, NDWI e SAVI. A acurácia da classificação supervisionada foi de 99,4% ao utilizar os índices NDWI, NDVI e SAVI e de 98,7% sem utilizar esses índices, todas consideradas excelentes. O tempo de processamento médio, refeito 10 vezes, foi de 52 segundos, considerando todo o código-fonte desenvolvido desde a filtragem das imagens até a conclusão da classificação. O código-fonte desenvolvido é apresentado em anexo de modo a difundir e incentivar o uso do GEE para estudos de inteligência espacial em irrigação e drenagem por sua usabilidade e fácil manipulação.   Keywords: computação em nuvem, sensoriamento remoto, hidrologia, modelagem.     SILVA, C. O .F SUPERVISED CLASSIFICATION OF IRRIGATED AREA USING SPECTRAL INDEXES FROM LANDSAT-8 IMAGES WITH GOOGLE EARTH ENGINE     2 ABSTRACT   Identifying irrigation areas using satellite images is a challenge that finds great potential in cloud computing solutions as the Google Earth Engine (GEE) tool, which facilitates the process of searching, filtering and manipulating large volumes of remote sensing data without the need for paid software or image downloading. The present work presents an implementation of the supervised classification of irrigated and rain-fed areas in the region of Sorriso and Lucas do Rio Verde/MT with the Classification and Regression Trees (CART) algorithm in GEE environment using bands 2-7 of the Landsat- 8 and the NDVI, NDWI and SAVI indices. The accuracy of the supervised classification was 99.4% when using NDWI, NDVI and SAVI indices and 98.7% without using these indices, which were considered excellent. The average processing time, redone 10 times, was 52 seconds, considering all the source code developed from the filtering of the images to the conclusion of the classification. The developed source code is available in the appendix in order to disseminate and encourage the use of GEE for studies of spatial intelligence in irrigation and drainage due to its usability and easy manipulation.   Keywords: cloud computing, remote sensing, hydrology, modeling.

scholarly journals 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

2019 ◽  
Vol 11 (5) ◽  
pp. 489 ◽  
Author(s):  
Tengfei Long ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Weili Jiao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Google Earth ◽  
Burned Area ◽  
Landsat 8 ◽  
Landsat Images ◽  
Burned Areas ◽  
Random Decision Forests ◽  
Burned Area Mapping ◽  
Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

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.

scholarly journals A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data

2021 ◽  
Author(s):  
Robert Pazúr ◽  
Nica Huber ◽  
Dominique Weber ◽  
Christian Ginzler ◽  
Bronwyn Price
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Use Planning ◽  
Agricultural Land ◽  
Google Earth ◽  
Agricultural Landscapes ◽  
Training Data ◽  
Growing Seasons ◽  
Google Earth Engine ◽  
Sentinel 2

Abstract. Agricultural landscapes support multiple functions and are of great importance for biodiversity. Heterogeneous agricultural mosaics of cropland and grassland commonly result from variable land use practices and ecosystem service demands. Switzerland’s agricultural land use is considerably spatially heterogeneous due to strong variability in conditions, especially topography and climate, thus presenting challenges to automated agricultural mapping. Nation-wide knowledge of the location of cropland and grassland is necessary for effective conservation and land use planning. We mapped the distribution of cropland and grassland across Switzerland. We used several indices largely derived from Sentinel-2 satellite imagery captured over multiple growing seasons, and parcel-based training data derived from landholder reporting. The mapping was conducted within Google Earth Engine using a random forest classifier. The resulting map has high accuracy in lowlands as well as in mountainous areas. The map will act as a base agricultural land cover dataset for researchers and  practitioners working in agricultural areas of Switzerland and interested in land cover and landscape structure. The map as well as the training data and calculation algorithms (using Google Earth Engine) are freely available for download on the Envidat platform (doi: 10.16904/envidat.205). 

Mapping Land Coverage in the Kapuas Watershed Using Machine Learning in Google Earth Engine

2020 ◽  
Vol 4 (2) ◽  
pp. 390-395
Author(s):  
Trida Ridho Fariz ◽  
Ely Nurhidayati
Keyword(s):  
Machine Learning ◽  
Land Cover ◽  
Spatial Data ◽  
Google Earth ◽  
Landsat 8 ◽  
Significant Area ◽  
Open Land ◽  
Google Earth Engine ◽  
Bodies Of Water ◽  
Processing Platform

Land cover information is essential data in the management of watersheds. The challenge in providing land cover information in the Kapuas watershed is the cloud cover and its significant area coverage, thus requiring a large image scene. The presence of a cloud-based spatial data processing platform that is Google Earth Engine (GEE) can be answered these challenges. Therefore this study aims to map land cover in the Kapuas watershed using machine learning-based classification on GEE. The process of mapping land cover in the Kapuas watershed requires about ten scenes of Landsat 8 satellite imagery. The selected year is 2019, with mapped land cover classes consisting of bodies of water, vegetation cover, open land, and built-up area. Machine learning that tested included CART, Random Forest, GMO Max Entropy, SVM Voting, and SVM Margin. The results of this study indicate that the best machine learning in mapping land cover in the Kapuas watershed is GMO Max Entropy, then CART. This research still has many limitations, especially mapped land cover classes. So that research needs to be developed with more detailed land cover classes, more diverse and multi-time input data.

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