Wildfire Severity Zoning Through Google Earth Engine and Fire Risk Assessment: Application of Data Mining and Fuzzy Multi-Criteria Evaluation in Zagros Forests, Iran

Abstract The arid and semi-arid regions of Zagros forests in the Middle East are constantly exposed to wildfire due to ecological conditions, and support systems are inefficient in controlling wildfires due to managerial and social weaknesses. Remote sensing and assessment tools are suitable for rapid prevention and action to identify the severity and location of a wildfire. This study investigated the natural resource management of Zagros Forestry in terms of protecting wildfire and combating forest wildfires using the NASA fire spatial data and the wildfire severity in the Google Earth Engine (GEE) platform. The land-use of the study area is produced by applying the Random Forest (RF) classification method and data from the Sentinel 2 satellite imagery for 2019. To separate the types of cultivation and vegetation of the region, the method of extracting the average vegetation index of the seasons is extracted from GEE. To evaluate fire risk, eleven human and ecological factors and two assessment models are applied to classify the probability fire risk therein. Furthermore, the outcome of AUC confirmed the Logistic Regression (LR) model; the accuracy of the LR (AUC=0.875049) model is satisfactory and is suitable for fire risk mapping in Zagros Forestry. Six high-risk areas of the wildfire were identified by MOLA, which overlap with protected areas. Out of a total of 20469.17 Ha of wildfire, 10426.41 Ha belong to these protected areas. 3826 Ha of this area were in the forests of Amygdalus spp, Quercus brant ii, pistacia Atlantica, and Quercus Infectoria, and 6600.41 Ha of it were in rangelands. Accordingly, an executive order was developed for the decision support system that reduces the risk of wildfire and helps extinguish the wildfire.

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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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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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Combination of Landsat 8 OLI and Sentinel-1 SAR Time-Series Data for Mapping Paddy Fields in Parts of West and Central Java Provinces, Indonesia

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9110663 ◽

2020 ◽

Vol 9 (11) ◽

pp. 663

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Raihan Rafif ◽

Siti Saringatin ◽

Pramaditya Wicaksono

Keyword(s):

Spatial Data ◽

Vegetation Index ◽

Google Earth ◽

Paddy Fields ◽

Series Data ◽

Landsat 8 ◽

Spectral Indices ◽

Seamless Integration ◽

Synthetic Aperture Radar Data ◽

Central Java

The rise of Google Earth Engine, a cloud computing platform for spatial data, has unlocked seamless integration for multi-sensor and multi-temporal analysis, which is useful for the identification of land-cover classes based on their temporal characteristics. Our study aims to employ temporal patterns from monthly-median Sentinel-1 (S1) C-band synthetic aperture radar data and cloud-filled monthly spectral indices, i.e., Normalized Difference Vegetation Index (NDVI), Modified Normalized Difference Water Index (MNDWI), and Normalized Difference Built-up Index (NDBI), from Landsat 8 (L8) OLI for mapping rice cropland areas in the northern part of Central Java Province, Indonesia. The harmonic function was used to fill the cloud and cloud-masked values in the spectral indices from Landsat 8 data, and smile Random Forests (RF) and Classification And Regression Trees (CART) algorithms were used to map rice cropland areas using a combination of monthly S1 and monthly harmonic L8 spectral indices. An additional terrain variable, Terrain Roughness Index (TRI) from the SRTM dataset, was also included in the analysis. Our results demonstrated that RF models with 50 (RF50) and 80 (RF80) trees yielded better accuracy for mapping the extent of paddy fields, with user accuracies of 85.65% (RF50) and 85.75% (RF80), and producer accuracies of 91.63% (RF80) and 93.48% (RF50) (overall accuracies of 92.10% (RF80) and 92.47% (RF50)), respectively, while CART yielded a user accuracy of only 84.83% and a producer accuracy of 80.86%. The model variable importance in both RF50 and RF80 models showed that vertical transmit and horizontal receive (VH) polarization and harmonic-fitted NDVI were identified as the top five important variables, and the variables representing February, April, June, and December contributed more to the RF model. The detection of VH and NDVI as the top variables which contributed up to 51% of the Random Forest model indicated the importance of the multi-sensor combination for the identification of paddy fields.

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High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine

Remote Sensing ◽

10.3390/rs11070752 ◽

2019 ◽

Vol 11 (7) ◽

pp. 752 ◽

Cited By ~ 21

Author(s):

Zhongchang Sun ◽

Ru Xu ◽

Wenjie Du ◽

Lei Wang ◽

Dengsheng Lu

Keyword(s):

High Resolution ◽

Vegetation Index ◽

Urban Land ◽

Google Earth ◽

Economic Research ◽

Optical Data ◽

Large Area ◽

Google Earth Engine ◽

Sar Data ◽

Sentinel 2

Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.

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A Long-term Spatial and Temporal Analysis of NDVI Changes in Java Island Using Google Earth Engine

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/936/1/012038 ◽

2021 ◽

Vol 936 (1) ◽

pp. 012038

Author(s):

Benedict ◽

Lalu Muhamad Jaelani

Keyword(s):

Vegetation Cover ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Image Data ◽

Temporal Analysis ◽

Google Earth ◽

Vegetation Density ◽

Angle Sensor ◽

Study Results ◽

Google Earth Engine

Abstract Java is Indonesia’s and the world’s most populous island. The increase in population on the island of Java reduces the area of forest and other vegetation covers. Landslides, floods, and other natural disasters are caused by reduced vegetation cover. Furthermore, it has the potential to lead to the extinction of flora and fauna. The Normalized Difference Vegetation Index (NDVI) can be used to monitor the vegetation cover. This study analyzes the NDVI changes value from 2005 to 2020 using Terra and Aqua MODIS image data processed using Google Earth Engine. Processing was carried out in some stages: down-setting, performing NDVI processing, calculating monthly average NDVI, calculating annual average NDVI, and analyzing. From the study results, the NDVI value of Terra and Aqua MODIS data has a solid but imperfect correlation coefficient due to differences in orbital time which causes differences in solar zenith angle, sensor viewing angle, and azimuth angle. Then from this study, it was found that overall, changes in vegetation density cover on the island of Java decreased, which was indicated by the NDVI decline rate of -0.00047/year. The most significant decrease in NDVI value occurred in the period 2015–2016, covering an area of 13994.630 km2, and the most significant increase in NDVI occurred in the period 2010–2011, covering an area of 2256.101 km2.

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Automated detection of individual juniper tree location and forest cover changes using Google Earth Engine

Annals of Forest Research ◽

10.15287/afr.2021.2145 ◽

2021 ◽

Vol 64 (1) ◽

pp. 61-72

Author(s):

Sudeera Wickramarathna ◽

Jamon Van Den Hoek ◽

Bogdan Strimbu

Keyword(s):

Random Forest ◽

Vegetation Index ◽

Forest Cover ◽

Near Infrared ◽

Google Earth ◽

Random Forest Algorithm ◽

Individual Tree ◽

Western Juniper ◽

Effective Manner ◽

Google Earth Engine

Tree detection is the first step in the appraisal of a forest, especially when the focus is monitoring the growth of tree canopy. The acquisition of annual very high-resolution aerial images by the National Agriculture Imagery Program (NAIP) and their accessibility through Google Earth Engine (GEE) supports the delineation of tree canopies and change over time in a cost and time-effective manner. The objectives of this study are to develop an automated method to detect the crowns of individual western Juniper (Juniperus occidentalis) trees and to assess the change of forest cover from multispectral 1-meter resolution NAIP images collected from 2009 to 2016 in Oregon, USA. The Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Ratio Vegetation Index (RVI) were calculated from the NAIP images, in addition to the red-green-blue-near infrared bands. To identify the most suitable approach for individual tree crown identification, we created two training datasets: one considering yearly images separately and one merging all images, irrespective of the year. We segmented individual tree crowns using a random forest algorithm implemented in GEE and seven rasters, namely the reflectance of four spectral bands as recorded by the NAIP images (i.e., the red-green-blue-near infrared) and three calculated indices (i.e., NDVI, NDWI, and RVI). We compared the estimated location of the trees, computed as the centroid of the crown, with the visually identified treetops, which were considered as validation locations. We found that tree location errors were smaller when years were analyzed individually than by merging the years. Measurements of completeness (74%), correctness (94%), and mean accuracy detection (82 %) show promising performance of the random forest algorithm in crown delineation, considering that only four original input bands were used for crown segmentation. The change in the calculated crown area for western juniper follows a sinusoidal curve, with a decrease from 2011 to 2012 and an increase from 2012 to 2014. The proposed approach has the potential to estimate individual tree locations and forest cover area dynamics at broad spatial scales using regularly collected airborne imagery with easy-to-implement methods.

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HazMapper: a global open-source natural hazard mapping application in Google Earth Engine

Natural Hazards and Earth System Science ◽

10.5194/nhess-21-1495-2021 ◽

2021 ◽

Vol 21 (5) ◽

pp. 1495-1511

Author(s):

Corey M. Scheip ◽

Karl W. Wegmann

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Volcanic Eruptions ◽

Relative Difference ◽

Google Earth ◽

Satellite Networks ◽

Pyroclastic Flows ◽

Hazard Mapping ◽

Polar Regions ◽

Google Earth Engine

Abstract. Modern satellite networks with rapid image acquisition cycles allow for near-real-time imaging of areas impacted by natural hazards such as mass wasting, flooding, and volcanic eruptions. Publicly accessible multi-spectral datasets (e.g., Landsat, Sentinel-2) are particularly helpful in analyzing the spatial extent of disturbances, however, the datasets are large and require intensive processing on high-powered computers by trained analysts. HazMapper is an open-access hazard mapping application developed in Google Earth Engine that allows users to derive map and GIS-based products from Sentinel or Landsat datasets without the time- and cost-intensive resources required for traditional analysis. The first iteration of HazMapper relies on a vegetation-based metric, the relative difference in the normalized difference vegetation index (rdNDVI), to identify areas on the landscape where vegetation was removed following a natural disaster. Because of the vegetation-based metric, the tool is typically not suitable for use in desert or polar regions. HazMapper is not a semi-automated routine but makes rapid and repeatable analysis and visualization feasible for both recent and historical natural disasters. Case studies are included for the identification of landslides and debris flows, wildfires, pyroclastic flows, and lava flow inundation. HazMapper is intended for use by both scientists and non-scientists, such as emergency managers and public safety decision-makers.

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Monitoring the spatiotemporal dynamics of surface water body of the Xiaolangdi Reservoir using Landsat-5/7/8 imagery and Google Earth Engine

Open Geosciences ◽

10.1515/geo-2020-0305 ◽

2021 ◽

Vol 13 (1) ◽

pp. 1290-1302

Author(s):

Ruimeng Wang ◽

Li Pan ◽

Wenhui Niu ◽

Rumeng Li ◽

Xiaoyang Zhao ◽

...

Keyword(s):

Surface Water ◽

Water Body ◽

Vegetation Index ◽

Yellow River ◽

Google Earth ◽

Body Type ◽

Body Area ◽

Surface Water Body ◽

Google Earth Engine ◽

Xiaolangdi Reservoir

Abstract Xiaolangdi Reservoir is a key control project to control the water and sediment in the lower Yellow River, and a timely and accurate grasp of the reservoir’s water storage status is essential for the function of the reservoir. This study used all available Landsat images (789 scenes) and adopted the modified normalized difference water index, enhanced vegetation index, and normalized difference vegetation index to map the surface water from 1999 to 2019 in Google Earth Engine (GEE) cloud platform. The spatiotemporal characteristics of the surface water body area changes in the Xiaolangdi Reservoir in the past 21 years are analyzed from the water body type division, area change, type conversion, and the driving force of the Xiaolangdi water body area changes was analyzed. The results showed that (1) the overall accuracy of the water body extraction method was 98.86%, and the kappa coefficient was 0.96; (2) the maximum water body area of the Xiaolangdi Reservoir varies greatly between inter-annual and intra-annual, and seasonal water body and permanent water body have uneven spatiotemporal distribution; (3) in the conversion of water body types, the increased seasonal water body area of the Xiaolangdi Reservoir from 1999 to 2019 was mainly formed by the conversion of permanent water body, and the reduced permanent water body area was mainly caused by non-water conversion; and (4) the change of the water body area of the Xiaolangdi Reservoir has a weak negative correlation with natural factors such as precipitation and temperature, and population. It is positively correlated with seven indicators such as runoff and regional gross domestic product (GDP). The findings of the research will provide necessary data support for the management and planning of soil and water resources in the Xiaolangdi Reservoir.

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The NDVI algorithm utilization on the google earth engine platform to monitor changes in forest density in mining area

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/886/1/012100 ◽

2021 ◽

Vol 886 (1) ◽

pp. 012100

Author(s):

Munajat Nursaputra ◽

Siti Halimah Larekeng ◽

Nasri ◽

Andi Siady Hamzah

Keyword(s):

Remote Sensing ◽

Time Series ◽

Large Scale ◽

Vegetation Index ◽

Forest Degradation ◽

Google Earth ◽

Aerial Images ◽

Forest Monitoring ◽

Forest Density ◽

Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

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