scholarly journals Quantitative Soil Wind Erosion Potential Mapping for Central Asia Using the Google Earth Engine Platform

2020 ◽  
Vol 12 (20) ◽  
pp. 3430
Author(s):  
Wei Wang ◽  
Alim Samat ◽  
Yongxiao Ge ◽  
Long Ma ◽  
Abula Tuheti ◽  
...  
Keyword(s):  
Wind Speed ◽  
Central Asia ◽  
Wind Erosion ◽  
Large Scale ◽  
Google Earth ◽  
Geospatial Data ◽  
Aral Sea ◽  
Erosion Modeling ◽  
Soil Wind Erosion ◽  
Google Earth Engine

A lack of long-term soil wind erosion data impedes sustainable land management in developing regions, especially in Central Asia (CA). Compared with large-scale field measurements, wind erosion modeling based on geospatial data is an efficient and effective method for quantitative soil wind erosion mapping. However, conventional local-based wind erosion modeling is time-consuming and labor-intensive, especially when processing large amounts of geospatial data. To address this issue, we developed a Google Earth Engine-based Revised Wind Erosion Equation (RWEQ) model, named GEE-RWEQ, to delineate the Soil Wind Erosion Potential (SWEP). Based on the GEE-RWEQ model, terabytes of Remote Sensing (RS) data, climate assimilation data, and some other geospatial data were applied to produce monthly SWEP with a high spatial resolution (500 m) across CA between 2000 and 2019. The results show that the mean SWEP is in good agreement with the ground observation-based dust storm index (DSI), satellite-based Aerosol Optical Depth (AOD), and Absorbing Aerosol Index (AAI), confirming that GEE-RWEQ is a robust wind erosion prediction model. Wind speed factors primarily determined the wind erosion in CA (r = 0.7, p < 0.001), and the SWEP has significantly increased since 2011 because of the reversal of global terrestrial stilling in recent years. The Aral Sea Dry Lakebed (ASDLB), formed by shrinkage of the Aral Sea, is the most severe wind erosion area in CA (47.29 kg/m2/y). Temporally, the wind erosion dominated by wind speed has the largest spatial extent of wind erosion in Spring (MAM). Meanwhile, affected by the spatial difference of the snowmelt period in CA, the wind erosion hazard center moved from the southwest (Karakum Desert) to the middle of CA (Kyzylkum Desert and Muyunkum Desert) during spring. According to the impacts of land cover change on the spatial dynamic of wind erosion, the SWEP of bareland was the highest, while that of forestland was the lowest.

scholarly journals FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 627-633 ◽  
Author(s):  
A. Nascetti ◽  
M. Di Rita ◽  
R. Ravanelli ◽  
M. Amicuzi ◽  
S. Esposito ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Global Scale ◽  
Google Earth ◽  
Observation Data ◽  
Geometric Accuracy ◽  
Computing Platform ◽  
Aster Gdem ◽  
Google Earth Engine ◽  
Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters.<br><br> In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

Post-Soviet changes in irrigated crop production in the Amu Darya Basin

2021 ◽  
Author(s):  
Daniel Müller ◽  
Andrey Dara ◽  
Christopher Krause ◽  
Mayra Daniela Peña-Guerrero ◽  
Tillman Schmitz ◽  
...  
Keyword(s):  
Water Resources ◽  
Central Asia ◽  
Crop Production ◽  
Large Scale ◽  
Aral Sea ◽  
Crop Cultivation ◽  
Amu Darya ◽  
Crop Type ◽  
Water Scarce ◽  
Hydrological Droughts

&lt;p&gt;Water withdrawals for irrigated crop production constitute the largest global consumer of blue water resources. Monitoring the dynamics of irrigated crop cultivation allows to track changes in water consumption of irrigated cropping, which is particularly paramount in water-scarce arid and semi-arid areas. We analyzed changes in irrigated crop cultivation along with occurrence of hydrological droughts for the Amu Darya river basin of Central Asia (534,700 km&lt;sup&gt;2&lt;/sup&gt;), once the largest tributary river to the Aral Sea before large-scale irrigation projects have grossly reduced the amount of water that reaches the river delta. We used annual and seasonal spectral-temporal metrics derived from Landsat time series to quantify the three predominant cropping practices in the region (first season, second season, double cropping) for every year between 1988 and 2020. We further derived unbiased area estimates for the cropping classes at the province level based on a stratified random sample (n=2,779). Our results reveal a small yet steady decrease in irrigated second season cultivation across the basin. Regionally, we observed a gradual move away from cotton monocropping in response to the policy changes that were instigated since the mid-1990s. We compared the observed cropping dynamics to the occurrence of hydrological droughts, i.e., periods with inadequate water resources for irrigation. We find that areas with higher drought risks rely more on irrigation of the second season crops. Overall, our analysis provides the first fine-scale, annual crop type maps for the irrigated areas in the Amu Darya basin. The results shed light on how institutional changes and hydroclimatic factors that affect land-use decision-making, and thus the dynamics of crop type composition, in the vast irrigated areas of Central Asia.&lt;/p&gt;

scholarly journals The First Wetland Inventory Map of Newfoundland at a Spatial Resolution of 10 m Using Sentinel-1 and Sentinel-2 Data on the Google Earth Engine Cloud Computing Platform

2018 ◽  
Vol 11 (1) ◽  
pp. 43 ◽  
Author(s):  
Masoud Mahdianpari ◽  
Bahram Salehi ◽  
Fariba Mohammadimanesh ◽  
Saeid Homayouni ◽  
Eric Gill
Keyword(s):  
Large Scale ◽  
Remote Sensing Data ◽  
Google Earth ◽  
Wetland Mapping ◽  
Object Based ◽  
Wetland Inventory ◽  
Inventory Map ◽  
Google Earth Engine ◽  
Provincial Scale ◽  
Sentinel 2

Wetlands are one of the most important ecosystems that provide a desirable habitat for a great variety of flora and fauna. Wetland mapping and modeling using Earth Observation (EO) data are essential for natural resource management at both regional and national levels. However, accurate wetland mapping is challenging, especially on a large scale, given their heterogeneous and fragmented landscape, as well as the spectral similarity of differing wetland classes. Currently, precise, consistent, and comprehensive wetland inventories on a national- or provincial-scale are lacking globally, with most studies focused on the generation of local-scale maps from limited remote sensing data. Leveraging the Google Earth Engine (GEE) computational power and the availability of high spatial resolution remote sensing data collected by Copernicus Sentinels, this study introduces the first detailed, provincial-scale wetland inventory map of one of the richest Canadian provinces in terms of wetland extent. In particular, multi-year summer Synthetic Aperture Radar (SAR) Sentinel-1 and optical Sentinel-2 data composites were used to identify the spatial distribution of five wetland and three non-wetland classes on the Island of Newfoundland, covering an approximate area of 106,000 km2. The classification results were evaluated using both pixel-based and object-based random forest (RF) classifications implemented on the GEE platform. The results revealed the superiority of the object-based approach relative to the pixel-based classification for wetland mapping. Although the classification using multi-year optical data was more accurate compared to that of SAR, the inclusion of both types of data significantly improved the classification accuracies of wetland classes. In particular, an overall accuracy of 88.37% and a Kappa coefficient of 0.85 were achieved with the multi-year summer SAR/optical composite using an object-based RF classification, wherein all wetland and non-wetland classes were correctly identified with accuracies beyond 70% and 90%, respectively. The results suggest a paradigm-shift from standard static products and approaches toward generating more dynamic, on-demand, large-scale wetland coverage maps through advanced cloud computing resources that simplify access to and processing of the “Geo Big Data.” In addition, the resulting ever-demanding inventory map of Newfoundland is of great interest to and can be used by many stakeholders, including federal and provincial governments, municipalities, NGOs, and environmental consultants to name a few.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

&lt;p&gt;Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Our results show that: (1) The highest producer&amp;#8217;s accuracies (the reason why using producer&amp;#8217;s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer&amp;#8217;s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future. &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

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.

Environmental Approaches to Soviet Central Asia

2020 ◽  
Author(s):  
Sarah Cameron
Keyword(s):  
Central Asia ◽  
Large Scale ◽  
Animal Husbandry ◽  
Aral Sea ◽  
Biological Weapons ◽  
Soviet Regime ◽  
Cotton Production ◽  
Environmental Features ◽  
Soviet Central Asia ◽  
Bodies Of Water

The vast region known as “Soviet Central Asia” encompassed the territory of five Soviet republics, Kazakhstan, Kirgizia, Uzbekistan, Tajikistan, and Turkmenistan. Because of the region’s environmental features, particularly its aridity, historically there had been a close linkage between people and the environment in this region. But the Soviet regime set out to radically reshape this relationship, focusing on the fields of agriculture and animal husbandry, large-scale water engineering, nuclear and biological weapons testing, and medicine and public health. By focusing on the environmental impact of these policies, scholars can see how Moscow’s efforts brought many benefits to the region. Cotton production boomed, and Moscow declared the eradication of malaria. But they also left horrific scars. Josef Stalin’s program of agricultural collectivization devastated Kazakhstan, resulting in the death of more than 1.5 million people. The Aral Sea, once one of the world’s largest bodies of water, began to shrink dramatically during the Soviet era, a development due in large part to Moscow’s efforts to divert the waters that fed the sea to cotton production.

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