Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

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Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine

Remote Sensing of Environment ◽

10.1016/j.rse.2017.02.021 ◽

2017 ◽

Vol 202 ◽

pp. 166-176 ◽

Cited By ~ 116

Author(s):

Huabing Huang ◽

Yanlei Chen ◽

Nicholas Clinton ◽

Jie Wang ◽

Xiaoyi Wang ◽

...

Keyword(s):

Land Cover ◽

Google Earth ◽

Landsat Images ◽

Land Cover Dynamics ◽

Google Earth Engine

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

Remote Sensing ◽

10.3390/rs11050489 ◽

2019 ◽

Vol 11 (5) ◽

pp. 489 ◽

Cited By ~ 25

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.

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Land Use/Land Cover Changes and Their Driving Factors in the Northeastern Tibetan Plateau Based on Geographical Detectors and Google Earth Engine: A Case Study in Gannan Prefecture

Remote Sensing ◽

10.3390/rs12193139 ◽

2020 ◽

Vol 12 (19) ◽

pp. 3139

Author(s):

Chenli Liu ◽

Wenlong Li ◽

Gaofeng Zhu ◽

Huakun Zhou ◽

Hepiao Yan ◽

...

Keyword(s):

Land Use ◽

Tibetan Plateau ◽

Land Cover ◽

Google Earth ◽

Driving Factors ◽

Optical Remote Sensing ◽

Land Use Land Cover ◽

Landsat Images ◽

Lulc Changes ◽

Google Earth Engine

As an important production base for livestock and a unique ecological zone in China, the northeast Tibetan Plateau has experienced dramatic land use/land cover (LULC) changes with increasing human activities and continuous climate change. However, extensive cloud cover limits the ability of optical remote sensing satellites to monitor accurately LULC changes in this area. To overcome this problem in LULC mapping in the Ganan Prefecture, 2000–2018, we used the dense time stacking of multi-temporal Landsat images and random forest algorithm based on the Google Earth Engine (GEE) platform. The dynamic trends of LULC changes were analyzed, and geographical detectors quantitatively evaluated the key driving factors of these changes. The results showed that (1) the overall classification accuracy varied between 89.14% and 91.41%, and the kappa values were greater than 86.55%, indicating that the classification results were reliably accurate. (2) The major LULC types in the study area were grassland and forest, and their area accounted for 50% and 25%, respectively. During the study period, the grassland area decreased, while the area of forest land and construction land increased to varying degrees. The land-use intensity presents multi-level intensity, and it was higher in the northeast than that in the southwest. (3) Elevation and population density were the major driving factors of LULC changes, and economic development has also significantly affected LULC. These findings revealed the main factors driving LULC changes in Gannan Prefecture and provided a reference for assisting in the development of sustainable land management and ecological protection policy decisions.

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

Remote Sensing ◽

10.3390/rs13122299 ◽

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.

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Qualifying Land Use and Land Cover Dynamics and Their Impacts on Ecosystem Service in Central Himalaya Transboundary Landscape Based on Google Earth Engine

Land ◽

10.3390/land10020173 ◽

2021 ◽

Vol 10 (2) ◽

pp. 173

Author(s):

Changjun Gu ◽

Yili Zhang ◽

Linshan Liu ◽

Lanhui Li ◽

Shicheng Li ◽

...

Keyword(s):

Land Use ◽

Ecosystem Services ◽

Land Cover ◽

Ecosystem Service ◽

Forest Cover ◽

Google Earth ◽

Forest Cover Change ◽

Sacred Landscape ◽

Lulc Changes ◽

Google Earth Engine

Land use and land cover (LULC) changes are regarded as one of the key drivers of ecosystem services degradation, especially in mountain regions where they may provide various ecosystem services to local livelihoods and surrounding areas. Additionally, ecosystems and habitats extend across political boundaries, causing more difficulties for ecosystem conservation. LULC in the Kailash Sacred Landscape (KSL) has undergone obvious changes over the past four decades; however, the spatiotemporal changes of the LULC across the whole of the KSL are still unclear, as well as the effects of LULC changes on ecosystem service values (ESVs). Thus, in this study we analyzed LULC changes across the whole of the KSL between 2000 and 2015 using Google Earth Engine (GEE) and quantified their impacts on ESVs. The greatest loss in LULC was found in forest cover, which decreased from 5443.20 km2 in 2000 to 5003.37 km2 in 2015 and which mainly occurred in KSL-Nepal. Meanwhile, the largest growth was observed in grassland (increased by 548.46 km2), followed by cropland (increased by 346.90 km2), both of which mainly occurred in KSL-Nepal. Further analysis showed that the expansions of cropland were the major drivers of the forest cover change in the KSL. Furthermore, the conversion of cropland to shrub land indicated that farmland abandonment existed in the KSL during the study period. The observed forest degradation directly influenced the ESV changes in the KSL. The total ESVs in the KSL decreased from 36.53 × 108 USD y−1 in 2000 to 35.35 × 108 USD y−1 in 2015. Meanwhile, the ESVs of the forestry areas decreased by 1.34 × 108 USD y−1. This shows that the decrease of ESVs in forestry was the primary cause to the loss of total ESVs and also of the high elasticity. Our findings show that even small changes to the LULC, especially in forestry areas, are noteworthy as they could induce a strong ESV response.

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The rapid Land Use and Land Cover change analysis using the Sentinel-2 images in Google Earth Engine: A Case Study of Xiong’an New Area from 2017 to 2020

10.1109/agro-geoinformatics50104.2021.9530306 ◽

2021 ◽

Author(s):

Jiansong Luo ◽

Rujin Yuan ◽

Yujia Cao ◽

Min Xie ◽

Yikai Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Google Earth ◽

Change Analysis ◽

Google Earth Engine ◽

Land Cover Change Analysis ◽

Sentinel 2

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Regional-scale analysis of dune-beach systems using Google Earth Engine

10.5194/egusphere-egu21-12923 ◽

2021 ◽

Author(s):

Melissa Latella ◽

Arjen Luijendijk ◽

Carlo Camporeale

Keyword(s):

Ecosystem Services ◽

Satellite Images ◽

Regional Scale ◽

Google Earth ◽

Classification Algorithm ◽

Mitigation Strategies ◽

Training Data ◽

Machine Learning Techniques ◽

Beach Width ◽

Google Earth Engine

Coastal sand dunes provide a large variety of ecosystem services, among which the inland protection from marine floods. Nowadays, this protection is fundamental, and its importance will further increase in the future due to the rise of the sea level and storm violence induced by climate change. Despite the crucial role of coastal dunes and their potential application in mitigation strategies, the phenomenon of the coastal squeeze, which is mainly caused by the urban sprawl, is progressively reducing the extents of the areas where dune can freely undergo their dynamics, thus dramatically impairing their capability of providing ecosystem services.Aiming to embed the use of satellite images in the study of coastal foredune and beach dynamics, we developed a classification algorithm that uses the satellite images and server-side functions of Google Earth Engine (GEE). The algorithm runs on the GEE Python API and allows the user to retrieve all the available images for the study site and the chosen time period from the selected sensor collection. The algorithm also filters the cloudy and saturated pixels and creates a percentile-composite image over which it applies a random forest classification algorithm. The classification is finally refined by defining a mask for land pixels only.&#160;According to the provided training data and sensor selection, the algorithm can give different outcomes, ranging from sand and vegetation maps, beach width measurements, and shoreline time evolution visualization. This very versatile tool that can be used in a great variety of applications within the monitoring and understanding of the dune-beach systems and associated coastal ecosystem services. For instance, we show how this algorithm, combined with machine learning techniques and the assimilation of real data, can support the calibration of a coastal model that gives the natural extent of the beach width and that can be, therefore, used to plan restoration activities.&#160;

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Land Cover Classification Based on Cloud Computing Platform

Journal of Southwest Jiaotong University ◽

10.35741/issn.0258-2724.55.2.61 ◽

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.

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Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs13224683 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4683

Author(s):

Masoumeh Aghababaei ◽

Ataollah Ebrahimi ◽

Ali Asghar Naghipour ◽

Esmaeil Asadi ◽

Jochem Verrelst

Keyword(s):

Vegetation Index ◽

Google Earth ◽

Optimal Time ◽

Quantitative Detection ◽

Landsat 8 ◽

Vegetation Types ◽

Landsat Images ◽

Multi Temporal ◽

Google Earth Engine ◽

Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

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