scholarly journals Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

2019 ◽  
Vol 11 (8) ◽  
pp. 924 ◽  
Author(s):  
Zhang ◽  
Dong ◽  
Liu ◽  
Gao ◽  
Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Tidal Flat ◽  
Large Scale ◽  
Tidal Flats ◽  
Google Earth ◽  
Landsat 8 ◽  
Intertidal Flats ◽  
Multi Temporal ◽  
Google Earth Engine

Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping.

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

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.

scholarly journals BUILDING OF TEMPERATURE MAPS OF NOVOSIBIRSK CITY BASED ON LANDSAT 8 DATA

2021 ◽  
Vol 4 (1) ◽  
pp. 52-59
Author(s):  
Elena A. Mamash ◽  
Igor A. Pestunov ◽  
Dmitrii L. Chubarov
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Google Earth ◽  
Underlying Surface ◽  
Landsat 8 ◽  
Google Earth Engine ◽  
Engine System ◽  
Temperature Maps

An algorithm for constructing temperature maps of the underlying surface based on a multi-time series of atmospheric corrected satellite data from Landsat 8, implemented in the Google Earth Engine system, is presented. The results of the construction of temperature maps of Novosibirsk using this algorithm are discussed.

scholarly journals CHINA FOREST COVER EXTRACTION BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 855-862 ◽  
Author(s):  
Y. T. Guo ◽  
X. M. Zhang ◽  
T. F. Long ◽  
W. L. Jiao ◽  
G. J. He ◽  
...  
Keyword(s):  
Random Forest ◽  
Large Scale ◽  
Forest Cover ◽  
Survey Methods ◽  
Coniferous Forest ◽  
Google Earth ◽  
Forest Area ◽  
Landsat 8 ◽  
Forest Change ◽  
Google Earth Engine

Abstract. Forest cover rate is the principal indice to reflect the forest acount of a nation and region. In view of the difficulty of accurately calculating large-scale forest area by traditional statistical survey methods, it is proposed to extract China forest area based on Google Earth Engine platform. Trained by the enough samples selected through the Google Earth software, there are nine different random forest classifiers applicable to their corresponding zones. Using Landsat 8 surface reflectance data of 2018 year and the modified forest partition map, China forest cover is generated on the Google Earth Engine platform. The accuracy of China's forest coverage achieves 89.08%, while the accuracy of Global Forest Change datasets of Maryland university and Japan’s ALOS Forest/Non-Forest forest product reach 87.78% and 84.57%. Besides, the precision of tropical/subtropical forest, temperate coniferous forest as well as nonforest region are 83.25%, 87.94% and 97.83%, higher than those of other’s accuracy. Our results show that by means of the random forest algorithm and enough samples, tropical and subtropical broadleaf forest, temperate coniferous forest and nonforest partition can be extracted more accurately. Through the computation of forest cover, our result shows that China has a area of 220.42 million hectare in 2018.

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

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.

scholarly journals A Classification of Tidal Flat Wetland Vegetation Combining Phenological Features with Google Earth Engine

2021 ◽  
Vol 13 (3) ◽  
pp. 443
Author(s):  
Nan Wu ◽  
Runhe Shi ◽  
Wei Zhuo ◽  
Chao Zhang ◽  
Bingchan Zhou ◽  
...  
Keyword(s):  
Tidal Flat ◽  
Vegetation Index ◽  
Google Earth ◽  
Wetland Vegetation ◽  
Species Level ◽  
Landsat 8 ◽  
Harmonic Model ◽  
Temporal Features ◽  
Google Earth Engine ◽  
Time Series Images

The composition and distribution of wetland vegetation is critical for ecosystem diversity and sustainable development. However, tidal flat wetland environments are complex, and obtaining effective satellite imagery is challenging due to the high cloud coverage. Moreover, it is difficult to acquire phenological feature data and extract species-level wetland vegetation information by using only spectral data or individual images. To solve these limitations, statistical features, temporal features, and phenological features of multiple Landsat 8 time-series images obtained via the Google Earth Engine (GEE) platform were compared to extract species-level wetland vegetation information from Chongming Island, China. The results indicated that (1) a harmonic model obtained the phenological characteristics of wetland vegetation better than the raw vegetation index (VI) and the Savitzky–Golay (SG) smoothing method; (2) classification based on the combination of the three features provided the highest overall accuracy (85.54%), and the phenological features (represented by the amplitude and phase of the harmonic model) had the greatest impact on the classification; and (3) the classification result from the senescence period was more accurate than that from the green period, but the annual mapping result on all seasons was the most accurate. The method described in this study can be applied to overcome the impacts of the complex environment in tidal flat wetlands and to effectively classify wetland vegetation species using GEE. This study could be used as a reference for the analysis of the phenological features of other areas or vegetation types.

scholarly journals A large-scale change monitoring of wetlands using time series Landsat imagery on Google Earth Engine: a case study in Newfoundland

2020 ◽  
Vol 57 (8) ◽  
pp. 1102-1124
Author(s):  
M. Mahdianpari ◽  
H. Jafarzadeh ◽  
J. E. Granger ◽  
F. Mohammadimanesh ◽  
B. Brisco ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Scale Change ◽  
Large Scale Change ◽  
Google Earth Engine ◽  
Change Monitoring

scholarly journals Drought Monitoring in Gresik Regency East Java using Satellite Image Time Series and Google Earth Engine

2021 ◽  
Vol 936 (1) ◽  
pp. 012003
Author(s):  
Rosmalisa Dwiyaniek ◽  
Bangun Muljo Sukojo ◽  
Filsa Bioresita
Keyword(s):  
Time Series ◽  
Satellite Image ◽  
Data Retrieval ◽  
Google Earth ◽  
Landsat 8 ◽  
Severe Drought ◽  
Dry Land ◽  
Aerial Photos ◽  
Two Factors ◽  
Google Earth Engine

Abstract Gresik is one of the areas with severe drought levels in East Java. This drought disaster caused by low rainfall and the high average surface temperature in an area. These two factors are currently difficult to predict due to uncertain climate change, this is also related to the global warming that is happening. This disaster cannot be completely avoided but can be minimized. This research was conducted to periodically check or time series of droughts by utilizing the Google Earth Engine platform. Drought identification obtained from multitemporal Landsat 8 satellite imagery with the TVDI (Temperature Vegetation Dryness Index) algorithm and field data retrieval in the form of aerial photos using a thermal camera from the DJI Mavic Enterprise Dual Thermal. From this study, it can be monitoring the distribution of drought in the 2015-2020 period in Gresik Regency occurred in 9 sub-districts, there are Wringinanom, Driyorejo, Kedamean, Balongpanggang, Benjeng, Menganti sub-districts as well as several areas in Duduksampeyan, Cerme and Panceng sub-districts. The identification of dry land also correlates well with the rainfall that occurs, namely?100 mm/month, which has low rainfall during drought events.

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.

Rapid, robust, and automated mapping of tidal flats in China using time series Sentinel-2 images and Google Earth Engine

2021 ◽  
Vol 255 ◽  
pp. 112285 ◽  
Author(s):  
Mingming Jia ◽  
Zongming Wang ◽  
Dehua Mao ◽  
Chunying Ren ◽  
Chao Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Tidal Flats ◽  
Google Earth ◽  
Automated Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

scholarly journals DETEKSI SEBARAN MUATAN PADATAN TERSUSPENSI DENGAN MODEL EMPIRIS DAN MODEL SEMI-ANALITIK DI PERAIRAN BEKASI

2020 ◽  
Vol 12 (2) ◽  
pp. 341-351
Author(s):  
Pingkan Mayestika Afgatiani ◽  
Maryani Hartuti ◽  
Syarif Budhiman
Keyword(s):  
Time Series ◽  
Mean Absolute Error ◽  
Absolute Error ◽  
Google Earth ◽  
Landsat 8 ◽  
Surface Reflectance ◽  
Mean Square ◽  
Itu Model ◽  
Google Earth Engine

Salah satu parameter dalam kualitas air adalah muatan padatan tersuspensi (MPT). Muatan padatan tersuspensi terdiri dari lumpur, pasir dan jasad renik yang disebabkan pengikisan tanah yang terbawa ke badan air. Penelitian ini bertujuan untuk mendeteksi sedimen tersuspensi di perairan Bekasi. Landsat 8 digunakan untuk analisis padatan tersuspensi dengan platform Google Earth Engine dengan membandingkan antara model empiris dan semi-analitik. Alur studi ini meliputi deliniasi wilayah non air menggunakan data citra surface reflectance, analisis MPT, dan visualisasi. Selanjutnya dilakukan validasi dengan data in situ, pemilihan model dan implementasi time series. Hasil deteksi MPT tertampil dengan tampilan warna yang berbeda sesuai dengan konsentrasinya. Hasil uji validasi dengan data in situ menunjukkan nilai Normalized Mean Absolute Error (NMAE) model semi-analitik lebih mendekati syarat minimum yaitu sebesar 66,8%, berbeda jauh dengan model empiris sebesar 43768%. Nilai Root Mean Square Error (RMSE) pun terlihat bahwa model semi-analitik menghasilkan nilai yang jauh lebih kecil sebesar 51,4 dan model empiris sebesar 58577,2. Hal ini menunjukkan bahwa model semi-analitik memiliki nilai yang lebih baik dalam mendeteksi sebaran MPT. Analisis time series menunjukkan bahwa persebaran MPT tahun 2015 – 2019 di perairan pesisir memiliki sebaran MPT yang sangat tinggi, karena banyaknya tambak dan muara sungai. Oleh karena itu, model semi-analitik lebih direkomendasikan untuk mengestimasi konsentrasi MPT dibandingkan dengan model empiris.

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