Analysis of Long-Term Rainfall Trends Over Punjab State Derived from CHIRPS Data in the Google Earth Engine Platform

This paper analyses the spatio-temporal trends and variability in annual, seasonal, and monthly rainfall with corresponding rainy days in Bhilangana river basin, Uttarakhand Himalaya, based on stations and two gridded products. Station-based monthly rainfall and rainy days data were obtained from the India Meteorological Department (IMD) for the period from 1983 to 2008 and applied, along with two daily rainfall gridded products to establish temporal changes and spatial associations in the study area. Due to the lack of more recent ground station rainfall measurements for the basin, gridded data were then used to establish monthly rainfall spatio-temporal trends for the period 2009 to 2018. The study shows all surface observatories in the catchment experienced an annual decreasing trend in rainfall over the 1983 to 2008 period, averaging 15.75 mm per decade. Analysis of at the monthly and seasonal trend showed reduced rainfall for August and during monsoon season as a whole (10.13 and 11.38 mm per decade, respectively); maximum changes were observed in both monsoon and winter months. Gridded rainfall data were obtained from the Climate Hazard Infrared Group Precipitation Station (CHIRPS) and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). By combining the big data analytical potential of Google Earth Engine (GEE), we compare spatial patterns and temporal trends in observational and modelled precipitation and demonstrate that remote sensing products can reliably be used in inaccessible areas where observational data are scarce and/or temporally incomplete. CHIRPS reanalysis data indicate that there are in fact three significantly distinct annual rainfall periods in the basin, viz. phase 1: 1983 to 1997 (relatively high annual rainfall); phase 2: 1998 to 2008 (drought); phase 3: 2009 to 2018 (return to relatively high annual rainfall again). By comparison, PERSIANN-CDR data show reduced annual and winter precipitation, but no significant changes during the monsoon and pre-monsoon seasons from 1983 to 2008. The major conclusions of this study are that rainfall modelled using CHIRPS corresponds well with the observational record in confirming the decreased annual and seasonal rainfall, averaging 10.9 and 7.9 mm per decade respectively between 1983 and 2008, although there is a trend (albeit not statistically significant) to higher rainfall after the marked dry period between 1998 and 2008. Long-term variability in rainfall in the Bhilangana river basin has had critical impacts on the environment arising from water scarcity in this mountainous region.

Download Full-text

A weekly, near real-time dataset of the probability of large wildfire across western US forests and woodlands

10.5194/essd-2017-136 ◽

2018 ◽

Author(s):

Miranda E. Gray ◽

Luke J. Zachmann ◽

Brett G. Dickson

Keyword(s):

Real Time ◽

Learning Algorithm ◽

Google Earth ◽

Large Fire ◽

Digital Repository ◽

Wildfire Occurrence ◽

Western Us ◽

Predicted Probability ◽

Google Earth Engine

Abstract. There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near- and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250-m pixel) of the probability of large wildfire (> 405 ha) across all western US forests and woodlands, from 2005 to the present. The dataset is automatically updated on a weekly basis and in near real-time (i.e., up to the present week) using Google Earth Engine and a "Continuous Integration" pipeline. Each image in the dataset is the output of a machine-learning algorithm, trained on 10 independent, random samples of historic small and large wildfires, and represents the predicted probability of an individual pixel burning in a large fire. This novel workflow is able to integrate the short-term dynamics of fuels and weather into weekly predictions, while also integrating longer-term dynamics of fuels, climate, and the landscape. As a near real-time product, the dataset can provide operational fire managers with immediate, on-the-ground information to closely monitor changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as strategic targeting of fuels management, fire-smart development at the wildland urban interface, and analysis of trends in wildfire potential over time. Weekly large fire probability GeoTiff products from 2005 through 2017 are archived on Figshare online digital repository with the DOI 10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Near real-time weekly GeoTiff products and the entire dataset from 2005 on are also continuously uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1, and also available free of charge with a Google account. Near real-time products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets, and can be accessed with the image collection ID "users/mgray/wffr-preds" within GEE.

Download Full-text

Dynamic Monitoring of Surface Water Area during 1989–2019 in the Hetao Plain Using Landsat Data in Google Earth Engine

Water ◽

10.3390/w12113010 ◽

2020 ◽

Vol 12 (11) ◽

pp. 3010 ◽

Cited By ~ 1

Author(s):

Ruimeng Wang ◽

Haoming Xia ◽

Yaochen Qin ◽

Wenhui Niu ◽

Li Pan ◽

...

Keyword(s):

Surface Water ◽

Water Resources ◽

Water Body ◽

Vegetation Index ◽

Water Area ◽

Google Earth ◽

Body Area ◽

Hetao Plain ◽

Google Earth Engine

The spatio-temporal change of the surface water is very important to agricultural, economic, and social development in the Hetao Plain, as well as the structure and function of the ecosystem. To understand the long-term changes of the surface water area in the Hetao Plain, we used all available Landsat images (7534 scenes) and adopted the modified Normalized Difference Water Index (mNDWI), Enhanced Vegetation Index (EVI), and Normalized Difference Vegetation Index (NDVI) to map the open-surface water from 1989 to 2019 in the Google Earth Engine (GEE) cloud platform. We further analyzed precipitation, temperature, and irrigated area, revealing the impact of climate change and human activities on long-term surface water changes. The results show the following. (1) In the last 31 years, the maximum, seasonal, and annual average water body area values in the Hetao Plain have exhibited a downward trend. Meanwhile, the number of maximum, seasonal, and permanent water bodies displayed a significant upward trend. (2) The variation of the surface water area in the Hetao Plain is mainly affected by the maximum water body area, while the variation of the water body number is mainly affected by the number of minimum water bodies. (3) Precipitation has statistically significant positive effects on the water body area and water body number, which has statistically significant negative effects with temperature and irrigation. The findings of this study can be used to help the policy-makers and farmers understand changing water resources and its driving mechanism and provide a reference for water resources management, agricultural irrigation, and ecological protection.

Download Full-text

Long-Term Mapping of a Greenhouse in a Typical Protected Agricultural Region Using Landsat Imagery and the Google Earth Engine

Remote Sensing ◽

10.3390/rs12010055 ◽

2019 ◽

Vol 12 (1) ◽

pp. 55 ◽

Cited By ~ 2

Author(s):

Cong Ou ◽

Jianyu Yang ◽

Zhenrong Du ◽

Yiming Liu ◽

Quanlong Feng ◽

...

Keyword(s):

Remote Sensing ◽

Landsat Imagery ◽

Google Earth ◽

Spatiotemporal Dynamics ◽

Landscape Patterns ◽

Agricultural Region ◽

Multi Temporal ◽

Google Earth Engine ◽

Production Approach

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon’s entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies.

Download Full-text

Long-term monitoring of evapotranspiration using the SEBAL algorithm and Google Earth Engine cloud computing

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2021.05.018 ◽

2021 ◽

Vol 178 ◽

pp. 81-96

Author(s):

Leonardo Laipelt ◽

Rafael Henrique Bloedow Kayser ◽

Ayan Santos Fleischmann ◽

Anderson Ruhoff ◽

Wim Bastiaanssen ◽

...

Keyword(s):

Cloud Computing ◽

Google Earth ◽

Google Earth Engine ◽

Long Term Monitoring ◽

Term Monitoring

Download Full-text

Monitoring the Impact of Land Cover Change on Surface Urban Heat Island through Google Earth Engine: Proposal of a Global Methodology, First Applications and Problems

Remote Sensing ◽

10.3390/rs10091488 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1488 ◽

Cited By ~ 18

Author(s):

Roberta Ravanelli ◽

Andrea Nascetti ◽

Raffaella Cirigliano ◽

Clarissa Di Rico ◽

Giovanni Leuzzi ◽

...

Keyword(s):

Sustainable Development ◽

Urban Heat Island ◽

Landsat Imagery ◽

Google Earth ◽

Heat Island ◽

Urban Heat ◽

Surface Urban Heat Island ◽

Google Earth Engine ◽

Spatio Temporal

All over the world, the rapid urbanization process is challenging the sustainable development of our cities. In 2015, the United Nation highlighted in Goal 11 of the SDGs (Sustainable Development Goals) the importance to “Make cities inclusive, safe, resilient and sustainable”. In order to monitor progress regarding SDG 11, there is a need for proper indicators, representing different aspects of city conditions, obviously including the Land Cover (LC) changes and the urban climate with its most distinct feature, the Urban Heat Island (UHI). One of the aspects of UHI is the Surface Urban Heat Island (SUHI), which has been investigated through airborne and satellite remote sensing over many years. The purpose of this work is to show the present potential of Google Earth Engine (GEE) to process the huge and continuously increasing free satellite Earth Observation (EO) Big Data for long-term and wide spatio-temporal monitoring of SUHI and its connection with LC changes. A large-scale spatio-temporal procedure was implemented under GEE, also benefiting from the already established Climate Engine (CE) tool to extract the Land Surface Temperature (LST) from Landsat imagery and the simple indicator Detrended Rate Matrix was introduced to globally represent the net effect of LC changes on SUHI. The implemented procedure was successfully applied to six metropolitan areas in the U.S., and a general increasing of SUHI due to urban growth was clearly highlighted. As a matter of fact, GEE indeed allowed us to process more than 6000 Landsat images acquired over the period 1992–2011, performing a long-term and wide spatio-temporal study on SUHI vs. LC change monitoring. The present feasibility of the proposed procedure and the encouraging obtained results, although preliminary and requiring further investigations (calibration problems related to LST determination from Landsat imagery were evidenced), pave the way for a possible global service on SUHI monitoring, able to supply valuable indications to address an increasingly sustainable urban planning of our cities.

Download Full-text

Quantifying the long-term expansion and dieback of Spartina alterniflora using Google Earth Engine and object-based hierarchical random forest classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3114116 ◽

2021 ◽

pp. 1-1

Author(s):

Dandan Yan ◽

Jingtai Li ◽

Xiuying Yao ◽

Zhaoqing Luan

Keyword(s):

Random Forest ◽

Spartina Alterniflora ◽

Google Earth ◽

Random Forest Classification ◽

Forest Classification ◽

Object Based ◽

Google Earth Engine

Download Full-text

A Google Earth Engine Application to Retrieve Long-Term Surface Temperature for Small Lakes. Case: San Pedro Lagoons, Chile

Remote Sensing ◽

10.3390/rs13224544 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4544

Author(s):

María Pedreros-Guarda ◽

Rodrigo Abarca-del-Río ◽

Karen Escalona ◽

Ignacio García ◽

Óscar Parra

Keyword(s):

Quality Parameter ◽

Google Earth ◽

Water Quality Parameter ◽

Time Data ◽

La Paz ◽

In Situ Data ◽

South Central ◽

San Pedro ◽

Google Earth Engine

Lake surface water temperature (LSWT) is a crucial water quality parameter that modulates many lake and reservoir processes. Therefore, it is necessary to monitor it from a long-term perspective. Over the last decades, many methods to retrieve LSWT fields from satellite imagery have been developed. This work aims to test, implement and automate six methods. These are performed in the Google Earth Engine (GEE) platform, using 30 m spatial resolution images from Landsat 7 and 8 satellites for 2000–2020. Automated methods deliver long-term time series. Series are then calibrated with in situ data. Two-dimensional (2D) × time data fields are built on the lakes with the calibration, and a subsequent LSWT climatology is derived. Our study area is two urban lagoons with areas smaller than two (2) km2 of the city of San Pedro de la Paz, South-Central Chile. The six methods describe the seasonal variation of LSWT (Willmott’s index of agreement > 0.91, R2 > 0.67). The main difference between series is their bias. Thus, after a simple calibration, all series adequately describe the LSWT. We utilized the Pedro de la Paz lagoons to demonstrate the method’s utility. Our research demonstrates that these adjacent lagoons exhibit comparable LSWT spatial (15.5–17 ∘C) and temporal (7–25 ∘C) trends throughout the year. Differences in geographical pattern might result from the northern island’s heat impact and the existence of the Biobío river to the east. Our work represents an efficient alternative for obtaining LSWT in particular lakes and reservoirs, especially useful in medium and small-sized ones.

Download Full-text

Developing a Method for Assessing Environmental Sustainability Based on the Google Earth Engine Platform

10.21203/rs.3.rs-1009482/v1 ◽

2021 ◽

Author(s):

Xianwang Xia ◽

Chentai Jiao ◽

Shixiong Song ◽

Ling Zhang ◽

Xingyun Feng ◽

...

Keyword(s):

Sustainable Development ◽

Environmental Sustainability ◽

Google Earth ◽

Urban Agglomeration ◽

Environmental Index ◽

The Sustainable Development ◽

The Past ◽

Environmental Deterioration ◽

Google Earth Engine

Abstract Environmental sustainability is the foundation and of great significance for the sustainable development of urban agglomerations. Taking the Beijing-Tianjin-Hebei urban agglomeration as an example, we developed a method to effectively assess long-term regional environmental sustainability based on the Google Earth Engine (GEE) platform. We used the GEE to obtain 5206 Landsat remote sensing images in the region from 1983 to 2016 and developed the comprehensive environmental index (CEI) to assess regional environmental sustainability based on the theme-oriented framework proposed by the United Nations Commission on Sustainable Development. We found that the environmental sustainability of the urban agglomeration showed a trend of first rising, then falling, and then rising again in the past 30 years. The average CEI increased from 0.621 to 0.631 from 1985 to 1990, dropped to the lowest value of 0.618 in 2000, and then rose to the highest value of 0.672 in 2015. In particular, the extent of areas in which environmental sustainability improved (56% of the region) was greater than the extent of areas in which environmental deterioration occurred. The environmental sustainability of Hengshui, Xingtai and Cangzhou in the southeast of the region has been significantly improved. The method proposed in this study provides an automatic, rapid and extensible way to assess regional environmental sustainability and provides a scientific reference for improving the sustainability of the regional ecological environment.

Download Full-text

Assessing the Changes in the Moisture/Dryness of Water Cavity Surfaces in Imlili Sebkha in Southwestern Morocco by Using Machine Learning Classification in Google Earth Engine

Remote Sensing ◽

10.3390/rs12010131 ◽

2020 ◽

Vol 12 (1) ◽

pp. 131

Author(s):

Sofia Hakdaoui ◽

Anas Emran ◽

Biswajeet Pradhan ◽

Abdeljebbar Qninba ◽

Taoufik El Balla ◽

...

Keyword(s):

Salt Water ◽

Radar Data ◽

Google Earth ◽

Machine Learning Classification ◽

Server Side ◽

Radar Images ◽

Hypersaline Water ◽

Google Earth Engine ◽

Spatio Temporal

Imlili Sebkha is a stable and flat depression in southern Morocco that is more than 10 km long and almost 3 km wide. This region is mainly sandy, but its northern part holds permanent water pockets that contain fauna and flora despite their hypersaline water. Google Earth Engine (GEE) has revolutionized land monitoring analysis by allowing the use of satellite imagery and other datasets via cloud computing technology and server-side JavaScript programming. This work highlights the potential application of GEE in processing large amounts of satellite Earth Observation (EO) Big Data for the free, long-term, and wide spatio-temporal wet/dry permanent salt water cavities and moisture monitoring of Imlili Sebkha. Optical and radar images were used to understand the functions of Imlili Sebkha in discovering underground hydrological networks. The main objective of this work was to investigate and evaluate the complementarity of optical Landsat, Sentinel-2 data, and Sentinel-1 radar data in such a desert environment. Results show that radar images are not only well suited in studying desertic areas but also in mapping the water cavities in desert wetland zones. The sensitivity of these images to the variations in the slope of the topographic surface facilitated the geological and geomorphological analyses of desert zones and helped reveal the hydrological functions of Imlili Sebkha in discovering buried underground networks.

Download Full-text