30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and 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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Wildfire Damage Assessment over Australia Using Sentinel-2 Imagery and MODIS Land Cover Product within the Google Earth Engine Cloud Platform

Remote Sensing ◽

10.3390/rs13020220 ◽

2021 ◽

Vol 13 (2) ◽

pp. 220

Author(s):

Seyd Teymoor Seydi ◽

Mehdi Akhoondzadeh ◽

Meisam Amani ◽

Sahel Mahdavi

Keyword(s):

Land Cover ◽

Google Earth ◽

Burned Area ◽

Accurate Estimation ◽

Second Phase ◽

Natural Ecosystems ◽

Change Analysis ◽

Burned Areas ◽

Google Earth Engine ◽

Sentinel 2

Wildfires are major natural disasters negatively affecting human safety, natural ecosystems, and wildlife. Timely and accurate estimation of wildfire burn areas is particularly important for post-fire management and decision making. In this regard, Remote Sensing (RS) images are great resources due to their wide coverage, high spatial and temporal resolution, and low cost. In this study, Australian areas affected by wildfire were estimated using Sentinel-2 imagery and Moderate Resolution Imaging Spectroradiometer (MODIS) products within the Google Earth Engine (GEE) cloud computing platform. To this end, a framework based on change analysis was implemented in two main phases: (1) producing the binary map of burned areas (i.e., burned vs. unburned); (2) estimating burned areas of different Land Use/Land Cover (LULC) types. The first phase was implemented in five main steps: (i) preprocessing, (ii) spectral and spatial feature extraction for pre-fire and post-fire analyses; (iii) prediction of burned areas based on a change detection by differencing the pre-fire and post-fire datasets; (iv) feature selection; and (v) binary mapping of burned areas based on the selected features by the classifiers. The second phase was defining the types of LULC classes over the burned areas using the global MODIS land cover product (MCD12Q1). Based on the test datasets, the proposed framework showed high potential in detecting burned areas with an overall accuracy (OA) and kappa coefficient (KC) of 91.02% and 0.82, respectively. It was also observed that the greatest burned area among different LULC classes was related to evergreen needle leaf forests with burning rate of over 25 (%). Finally, the results of this study were in good agreement with the Landsat burned products.

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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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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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Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100458 ◽

2020 ◽

pp. 100458

Author(s):

Michelle Li Ern Ang ◽

Dirk Arts ◽

Danielle Crawford ◽

Bonifacio V. Labatos ◽

Khanh Duc Ngo ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Analysis ◽

Google Earth ◽

Cover Time ◽

Web Based ◽

Series Analysis ◽

Google Earth Engine

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Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.03.014 ◽

2020 ◽

Vol 163 ◽

pp. 312-326 ◽

Cited By ~ 9

Author(s):

Xinxin Wang ◽

Xiangming Xiao ◽

Zhenhua Zou ◽

Luyao Hou ◽

Yuanwei Qin ◽

...

Keyword(s):

Time Series ◽

Coastal Wetlands ◽

Google Earth ◽

Landsat Images ◽

Google Earth Engine

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Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

Remote Sensing ◽

10.3390/rs11243023 ◽

2019 ◽

Vol 11 (24) ◽

pp. 3023 ◽

Cited By ~ 9

Author(s):

Shuai Xie ◽

Liangyun Liu ◽

Xiao Zhang ◽

Jiangning Yang ◽

Xidong Chen ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Vegetation Index ◽

Time Series Data ◽

Land Cover Classification ◽

Google Earth ◽

Thematic Mapper ◽

Series Data ◽

Land Cover Mapping ◽

Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

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

Remote Sensing ◽

10.3390/rs12152411 ◽

2020 ◽

Vol 12 (15) ◽

pp. 2411 ◽

Cited By ~ 2

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.

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

Remote Sensing ◽

10.3390/rs12071201 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1201 ◽

Cited By ~ 3

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Eufemia Tarantino

Keyword(s):

Big Data ◽

Land Cover ◽

Open Data ◽

Google Earth ◽

Classification Algorithm ◽

Specific Class ◽

Landsat Images ◽

Sparse Vegetation ◽

Main Technique ◽

Google Earth Engine

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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Assessing Landscape Fire Hazard by Multitemporal Automatic Classification of Landsat Time Series Using the Google Earth Engine in West-Central Spain

Forests ◽

10.3390/f10060518 ◽

2019 ◽

Vol 10 (6) ◽

pp. 518 ◽

Cited By ~ 4

Author(s):

Natalia Quintero ◽

Olga Viedma ◽

Itziar R. Urbieta ◽

José M. Moreno

Keyword(s):

Time Series ◽

Forest Fires ◽

Fire Hazard ◽

Sustainable Use ◽

Google Earth ◽

Burned Area ◽

Change Metrics ◽

Google Earth Engine ◽

The Stability

Annual Land Use and Land Cover (LULC) maps are needed to identify the interaction between landscape changes and wildland fires. Objectives: In this work, we determined fire hazard changes in a representative Mediterranean landscape through the classification of annual LULC types and fire perimeters, using a dense Landsat Time Series (LTS) during the 1984–2017 period, and MODIS images. Methods: We implemented a semiautomatic process in the Google Earth Engine (GEE) platform to generate annual imagery free of clouds, cloud shadows, and gaps. We compared LandTrendr (LT) and FormaTrend (FT) algorithms that are widely used in LTS analysis to extract the pixel tendencies and, consequently, assess LULC changes and disturbances such as forest fires. These algorithms allowed us to generate the following change metrics: type, magnitude, direction, and duration of change, as well as the prechange spectral values. Results and conclusions: Our results showed that the FT algorithm was better than the LT algorithm at detecting low-severity changes caused by fires. Likewise, the use of the change metrics’ type, magnitude, and direction of change increased the accuracy of the LULC maps by 4% relative to the ones obtained using only spectral and topographic variables. The most significant hazardous LULC change processes observed were: deforestation and degradation (mainly by fires), encroachment (i.e., invasion by shrublands) due to agriculture abandonment and forest fires, and hazardous densification (from open forests and agroforestry areas). Although the total burned area has decreased significantly since 1985, the landscape fire hazard has increased since the second half of the twentieth century. Therefore, it is necessary to implement fire management plans focused on the sustainable use of shrublands and conifer forests; this is because the stability in these hazardous vegetation types is translated into increasing fuel loads, and thus an elevated landscape fire hazard.

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Multispectral Image Super-Resolution Burned-Area Mapping Based on Space-Temperature Information

Remote Sensing ◽

10.3390/rs11222695 ◽

2019 ◽

Vol 11 (22) ◽

pp. 2695

Author(s):

Peng Wang ◽

Lei Zhang ◽

Gong Zhang ◽

Benzhou Jin ◽

Henry Leung

Keyword(s):

Objective Function ◽

Multispectral Imaging ◽

Accurate Determination ◽

Super Resolution ◽

Burned Area ◽

Landsat 8 ◽

Random Walker ◽

Burned Areas ◽

Burned Area Mapping ◽

Super Resolution Mapping

Multispectral imaging (MI) provides important information for burned-area mapping. Due to the severe conditions of burned areas and the limitations of sensors, the resolution of collected multispectral images is sometimes very rough, hindering the accurate determination of burned areas. Super-resolution mapping (SRM) has been proposed for mapping burned areas in rough images to solve this problem, allowing super-resolution burned-area mapping (SRBAM). However, the existing SRBAM methods do not use sufficiently accurate space information and detailed temperature information. To improve the mapping accuracy of burned areas, an improved SRBAM method utilizing space–temperature information (STI) is proposed here. STI contains two elements, a space element and a temperature element. We utilized the random-walker algorithm (RWA) to characterize the space element, which encompassed accurate object space information, while the temperature element with rich temperature information was derived by calculating the normalized burn ratio (NBR). The two elements were then merged to produce an objective function with space–temperature information. The particle swarm optimization algorithm (PSOA) was employed to handle the objective function and derive the burned-area mapping results. The dataset of the Landsat-8 Operational Land Imager (OLI) from Denali National Park, Alaska, was used for testing and showed that the STI method is superior to the traditional SRBAM method.

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