Query: Google earth engine mining

title: An Operational Before After Control Impact (BACI) Designed Platform for Vegetation Monitoring at Planetary Scale
abstract: In this study, we develop a vegetation monitoring framework which is applicable at a planetary scale, and is based on the BACI (Before After, Control Impact) design. This approach utilizes Google Earth Engine, a state of the art cloud computing platform. A web based application for users named EcoDash was developed. EcoDash maps vegetation using Enhanced Vegetation Index (EVI) from Moderate Resolution Imaging Spectroradiometer (MODIS) products (the MOD13A1 and MYD13A1 collections) from both Terra and Aqua sensors from the years 2000 and 2002, respectively. to detect change in vegetation, we define an EVI baseline period, and then draw results at a planetary scale using the web based application by measuring improvement or degradation in vegetation based on the user defined baseline periods. We also used EcoDash to measure the impact of deforestation and mitigation efforts by the Vietnam Forests and Deltas (VFD) program for the Nghe An and Thanh Hoa provinces in Vietnam. Using the period before 2012 as a baseline, we found that as of March 2017, 86% of the geographical area within the VFD program shows improvement, compared to only a 24% improvement in forest cover for all of Vietnam. Overall, we show how using satellite imagery for monitoring vegetation in a cloud computing environment could be a cost effective and useful tool for land managers and other practitioners

title: A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
abstract: Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI) perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m) long term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary scale cloud based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.

title: Land changes detection on Rote Island using harmonic modelling method
abstract: Rote Island is one of the islands in East Nusa Tenggara. In this island, land changes occur significantly. This land changes can be detected by Landsat images. These images are obtained from the big data engine. The big data engine used is the Google Earth Engine. This study aimed to detect land changes with harmonic modelling using multitemporal Landsat images from the big data engine. Harmonic modelling is used in monitoring changes in Normalized Difference Vegetation Index values in a multitemporal manner from Landsat images. Processing is done using the Geomatics approach. Land changes on Rote Island generally occur on coastal and savanna. Land changes on land generally have vertical deformation on its movement and horizontal on the savanna. The land changes accuracy result is 95% in 1,96s. This method can be used for rapid mapping of land changes monitoring.

title: Deep channel dynamics: A challenge for erosion management in large rivers
abstract: In this paper, we present flow and erosion problems in selected reaches of two large and dynamic river systems in South Asia, namely the Koshi River in Nepal (and India) and the Lower Brahmaputra (Jamuna) in Bangladesh. We attempted to analyse large and meso scale (short and medium term) morphological changes with a focus on the dynamics of deep channels, revealing their importance for the river and riverbank erosion management. This focus on deep channels is a key change of perspective as most morphological studies and analyses of large rivers are usually focused on sandbar and braiding dynamics. We used ground data, satellite imagery, and explorative morphological modelling to quantify and analyse the flow and morphological processes. We demonstrate how multispectral satellite imagery can be processed using Google Earth Engine to assess the spatiotemporal dynamics of morphological processes and changes. We also analysed bathymetric surveys to assess short term changes of meso scale morphology that are not fully captured by the satellite data analysis. The morphological modelling provided first results on reproducing essential processes, such as growth and migration of meso scale features, particularly deep channels, under varying flow conditions. Some features of these reaches of two rivers differ, but particularly the importance of deep channel dynamics was revealed for both. We infer that the seasonal and annual discharge variabilities are key factors for the dynamic behaviour of bank, char (island) sandbars and deep channels, particularly regarding short and mid term changes. We also infer that morphologically extreme situations do not always occur during high flows, but rather through the concentration of the flow along the deep channels during medium and lower flows.

title: Online Global Land Surface Temperature Estimation from Landsat
abstract: This study explores the estimation of land surface temperature (LST) for the globe from Landsat 5, 7 and 8 thermal infrared sensors, using different surface emissivity sources. A single channel algorithm is used for consistency among the estimated LST products, whereas the option of using emissivity from different sources provides flexibility for the algorithm's implementation to any area of interest. The Google Earth Engine (GEE) an advanced earth science data and analysis platform, allows the estimation of LST products for the globe, covering the time period from 1984 to present. To evaluate the method, the estimated LST products were compared against two reference datasets: (a) LST products derived from ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) as higher level products based on the temperature emissivity separation approach; (b) Landsat LST data that have been independently produced, using different approaches. An overall RMSE (root mean square error) of 1.52 degC was observed and it was confirmed that the accuracy of the LST product is dependent on the emissivity; different emissivity sources provided different LST accuracies, depending on the surface cover. The LST products, for the full Landsat 5, 7 and 8 archives, are estimated "on the fly" and are available on line via a web application.

title: The Development of Near Real Time Biomass and Cover Estimates for Adaptive Rangeland Management Using Landsat 7 and Landsat 8 Surface Reflectance Products
abstract: Rangelands are critical working landscapes and are the focus of considerable conservation planning efforts globally. A key conservation challenge in these landscapes is that high interannual variability in both climatic conditions and land use greatly limits the utility of outdated or static vegetation maps for management decision making. One potential solution to this problem lies in remote sensing derived information; however, prospective users must have continuous and timely access to vegetation products tailored to their needs. Google Earth Engine (GEE) can overcome the many storage, processing, and visualization barriers associated with creating ready to use remote sensing products for the public. While GEE provides a platform for building tools to analyze data and share results with users in near real time for adaptive management, monitoring products need to (1) provide accurate and stable estimates over time and (2) align with management goals and the ecology of the rangeland system in question. Here, we assess estimates of vegetation cover and above ground biomass at two dominant phenological time periods (summer/green and fall/brown) as modeled from the Landsat 7 and Landsat 8 Climatic Data Record (CDR) product. Using a best subset regression modeling approach, we modeled vegetation cover and biomass, finding that the best predictors vary by season, corresponding to vegetation phenology. We also found that sensor specific models decreased the relative differences between mapped cover and biomass estimates when comparing Landsat 7 and Landsat 8 scenes one day apart in the summer and fall. Ultimately, we developed an automated model selection process driven by sensor and vegetation greenness that can run in GEE to monitor and analyze vegetation amounts across the grazing season for adaptive management.

title: Annual Irrigation Dynamics in the U.S. Northern High Plains Derived from Landsat Satellite Data
abstract: Sustainable management of agricultural water resources requires improved understanding of irrigation patterns in space and time. We produced annual, high resolution (30 m) irrigation maps for 1999 2016 by combining all available Landsat satellite imagery with climate and soil covariables in Google Earth Engine. Random forest classification had accuracies from 92 to 100% and generally agreed with county statistics (r2 0.88 0.96) Two novel indices that integrate plant greenness and moisture information show promise for improving satellite classification of irrigation. We found considerable interannual variability in irrigation location and extent, including a near doubling between 2002 and 2016. Statistical modeling suggested that precipitation and commodity price influenced irrigated extent through time. High prices incentivized expansion to increase crop yield and profit, but dry years required greater irrigation intensity, thus reducing area in this supply limited region. Data sets produced with this approach can improve water sustainability by providing consistent, spatially explicit tracking of irrigation dynamics over time.

title: Large Scale, Multi Temporal Remote Sensing of Palaeo River Networks: A Case Study from Northwest India and its Implications for the Indus Civilisation
abstract: Remote sensing has considerable potential to contribute to the identification and reconstruction of lost hydrological systems and networks. Remote sensing based reconstructions of palaeo river networks have commonly employed single or limited time span imagery, which limits their capacity to identify features in complex and varied landscape contexts. This paper presents a seasonal multi temporal approach to the detection of palaeo rivers over large areas based on long term vegetation dynamics and spectral decomposition techniques. Twenty eight years of Landsat 5 data, a total of 1711 multi spectral images, have been bulk processed using Google Earth Engine(c) Code Editor and cloud computing infrastructure. The use of multi temporal data has allowed us to overcome seasonal cultivation patterns and long term visibility issues related to recent crop selection, extensive irrigation and land use patterns. The application of this approach on the Sutlej Yamuna interfluve (northwest India) a core area for the Bronze Age Indus Civilisation, has enabled the reconstruction of an unsuspectedly complex palaeo river network comprising more than 8000 km of palaeo channels. It has also enabled the definition of the morphology of these relict courses, which provides insights into the environmental conditions in which they operated. These new data will contribute to a better understanding of the settlement distribution and environmental settings in which this, often considered riverine, civilisation operated.

title: Analysis of satellite derived landslide at Central Nepal from 2011 to 2016
abstract: Practical landslide detection has been a challenging task for decades due to the limitations of algorithms and complicated landslide cases. Visual interpretation is still one of the most reliable methods for landslide detection. Owing to the development of machine learning, Google Earth Engine platform and free access to Landsat series images, this paper presents a pixel level landslide detection model based on change detection using the random forest method in Nepal. From the year 2012 2016, the annual Landsat images are synthesized into one image based on a ranking sequence of NDVI (normalized differential vegetation index) Six more indexes and texture information are calculated as features, covering water, snow and vegetation enhancement. DEM (digital elevation model) is used to provide landscape pattern. The synthesized features and Landsat images of every 2 nearby years, and DEM are used to detect landslides in the latter year. Our model achieves an average producer's accuracy of 68.20% and user's accuracy of 77.01% for landslide detection in terms of the 9278 x 7443 pixel study image through the 5 years. Moreover, based on the landslide detection results, we analyzed the landslide event pattern in Central Nepal from 2012 to 2016, which accords with the actual cases.

title: Green Spaces as an Indicator of Urban Health: Evaluating Its Changes in 28 Mega Cities
abstract: Urban green spaces can yield considerable health benefits to urban residents. Assessing these health benefits is a key step for managing urban green spaces for human health and wellbeing in cities. In this study, we assessed the change of health benefits generated by urban green spaces in 28 megacities worldwide between 2005 and 2015 by using availability and accessibility as proxy indicators. We first mapped land covers of 28 megacities using 10,823 scenes of Landsat images and a random forest classifier running on Google Earth Engine. We then calculated the availability and accessibility of urban green spaces using the land cover maps and gridded population data. The results showed that the mean availability of urban green spaces in these megacities increased from 27.63% in 2005 to 31.74% in 2015. The mean accessibility of urban green spaces increased from 65.76% in 2005 to 72.86% in 2015. The increased availability and accessibility of urban green spaces in megacities have brought more health benefits to their residents.