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 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 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 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.

scholarly journals Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate

2022 ◽  
Vol 961 (1) ◽  
pp. 012051
Author(s):  
Sajjad H Hasan ◽  
Amjed N M AL-Hameedawi ◽  
H S Ismael
Keyword(s):  
Land Cover ◽  
Satellite Image ◽  
Large Data ◽  
Bare Soil ◽  
Google Earth ◽  
Machine Intelligence ◽  
Classification Model ◽  
Landsat 8 ◽  
Development Environment ◽  
Google Earth Engine

Abstract As a result of the advancements that have occurred in the technical field of geomatics, particularly after the development of developmental programming environments, they have become the most important machine for conducting image analyses of satellite data, creating and modifying spatial analysis tools, and performing large data analyses at a fast rate without the need for high-end specifications on the personal computer. This study has several objectives, including the definition and popularization of the use of the power of Google Earth Engine (GEE) in the speed of conducting spatial analyzes, which cite by conducting a classification at the level of a governorate and obtaining results with speed and relatively good quality. By using the Google Earth Engine (GEE) platform and through Javascript programming language, a classification of the land cover of Wasit Governorate, Iraq was created under the supervision of a satellite image (Landsat 8) by creating a training sample, Google Maps’ High Resolution basemap imagery was used to create this map to identify classes of landcover (water, bare soil, vegetation, and urban). Each source pixel is assigned to one of the previously mentioned classes. Then to create a land cover map of the region using the Statistical Machine Intelligence and Learning Engine (SMILE) classifier from the JAVA library, which is used by Google Earth Engine (GEE) to implement these algorithms. The result is an array of pixels (raster data). The pixel value represents the class that was previously determined by the samples.

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 Assessment of Annual Composite Images Obtained by Google Earth Engine for Urban Areas Mapping Using Random Forest

2021 ◽  
Vol 13 (4) ◽  
pp. 748
Author(s):  
Zhaoming Zhang ◽  
Mingyue Wei ◽  
Dongchuan Pu ◽  
Guojin He ◽  
Guizhou Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Urban Areas ◽  
Google Earth ◽  
Landsat 8 ◽  
Annual Time Series ◽  
Google Earth Engine ◽  
Time Series Images ◽  
The Impact ◽  
Annual Time

Urban areas represent the primary source region of greenhouse gas emissions. Mapping urban areas is essential for understanding land cover change, carbon cycles, and climate change (urban areas also refer to impervious surfaces, i.e., artificial cover and structures). Remote sensing has greatly advanced urban areas mapping over the last several decades. At present, we have entered the era of big data. Long time series of satellite data such as Landsat and high-performance computing platforms such as Google Earth Engine (GEE) offer new opportunities to map urban areas. The objective of this research was to determine how annual time series images from Landsat 8 Operational Land Imager (OLI) can effectively be composed to map urban areas in three cities in China in support of GEE. Three reducer functions, ee.Reducer.min(), ee.Reducer.median(), and ee.Reducer.max() provided by GEE, were selected to construct four schemes to synthesize the annual intensive time series Landsat 8 OLI data for three cities in China. Then, urban areas were mapped based on the random forest algorithm and the accuracy was evaluated in detail. The results show that (1) the quality of annual composite images was improved significantly, particularly in reducing the impact of cloud and cloud shadows, and (2) the annual composite images obtained by the combination of multiple reducer functions had better performance than that obtained by a single reducer function. Further, the overall accuracy of urban areas mapping with the combination of multiple reducer functions exceeded 90% in all three cities in China. In summary, a suitable combination of reducer functions for synthesizing annual time series images can enhance data quality and ensure differences between characteristics and higher precision for urban areas mapping.

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 Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

2021 ◽  
Vol 256 ◽  
pp. 112318
Author(s):  
Dong Liang ◽  
Huadong Guo ◽  
Lu Zhang ◽  
Yun Cheng ◽  
Qi Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Sar Images ◽  
Google Earth Engine ◽  
The Antarctic

An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

2021 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Yan Huang
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Mangrove Area ◽  
Google Earth Engine ◽  
Forest Maps ◽  
Sentinel 2

<p>A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.</p>

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