Advanced Steps To Understanding Pdf Free Download
Hildegard Mccauley
Climate and weather affect all sectors of society at regional to local scales. However, the paucity of long-term observations in many parts of the globe provides a constraint on the utilization of data for applied use and scientific research. To address the need for place-based data, a number of operational gridded climate and meteorological datasets have been created (Daly et al. 1994; Mitchell et al. 2004; Abatzoglou 2013; Thornton et al. 2014; Oyler et al. 2015), in addition to remote sensing datasets that are freely available and are being increasingly used. However, the accessibility of these data to researchers, decision-makers, and the general public are limited because of challenges related to computational requirements, data storage, and software needed to work with large volumes of data.
Recognizing these limitations, recent web applications have focused on providing on-demand and dynamic visualization, extraction, and processing of precomputed data (Berrick et al. 2009; Eberle et al. 2013; Teng et al. 2016). New computing technologies, where massively parallel processors are collocated with data collections, allow for on-demand and on-the-fly generation of custom data products and visualization, thereby avoiding many limitations of the past (Moore and Hansen 2011; Baumann et al. 2016; Yang et al. 2017). The development of a cloud-computing web application for on-demand processing and visualizing climate and remote sensing data is motivated by current web application limitations, and by climate and natural resource scientist and manager needs related to drought, ecology, and agriculture that can be addressed through advanced processing and visualization of Earth observation archives.
The Climate Engine web application is hosted on the Google App Engine web server, while the source code is hosted on a GitHub repository for version control and source code management. The source code is divided into two parts: the front end, which the user sees on the web page; and the back end, which is where the requested data are formed and processed.
The front-end display of Climate Engine is viewed in a web browser and is constructed using the Twitter Bootstrap 3 Cascading Style Sheets (CSS) web framework for the navigation bars and tabs, and the overall responsive design of the site. The display contains a form for users to customize their requests and a section for displaying the response (a map layer or a time series figure with data).
The map-layer display illustrates user-requested raster output on a Google map. Climate Engine provides the ability for the user to customize the map layer (e.g., scale and color palette options) and to place optional vector images (e.g., KML, polygons, Google Fusion Tables) atop the map layer to aid in geographical orientation or to be used for spatial averaging.
The time series display illustrates a time series figure and respective data as a tabular list alongside the figure. The SVG figure is constructed using the Highcharts JavaScript graphics library, which displays the user-requested data in an interactive figure. Climate Engine provides the ability for the user to customize the time series figure (e.g., scatter, bar, or line charts) on the fly without resubmitting the request.
The beauty of this application framework is that the requests can be made from anywhere a web browser has an Internet connection, all major computing is performed using the thousands of processors via Earth Engine, and results are returned to the device for display and/or download.
Climate Engine offers both mapping and time series analysis options. Users are able to choose specific product types (remote sensing or climate), datasets (different satellite platforms or gridded climate datasets), variables (from precipitation to vegetation indices), and common calculations (climatologies or anomalies) and statistics (mean, median, maximum, minimum, total) for customized time periods. In the mapping view, users are able to modify the map layer displayed on the Google map by adjusting the color palette, transparency, and value ranges; to perform masking; and to add vector layers to the map. Users can also request values from the map or download rectangular regions of the map layer in Georeferenced Tagged Image File Format (GeoTiff) (Fig. 1). In the time series view, users are able to choose from one of three types of time series visualizations for data covering either a point location or an area average: daily values (or native temporal resolution of dataset), interannual summaries of values over a defined period, or values within a year compared to statistics from other years. Data from multiple point locations or from multiple variables can be compared at the same time. Users can dynamically interact with the resulting scalable vector graphics (SVG) figure to view values at data points, zoom in on the time series figure, toggle the display of series data, download the figure in common image formats, and download the data in comma-separated values (.csv) or Excel (.xls) file format. Climate Engine provides easy access to remote sensing and climate archives by pairing cloud-computing capabilities and a web application, thereby avoiding the computational expenses of storage and processing such large datasets.
We demonstrate the potential of Climate Engine to both the research community and decision-makers by highlighting several recent case studies related to climate, drought, fire, ecology, and agriculture. Map and time series figures shown in the case studies were all computed and downloaded using Climate Engine and edited (i.e., projection and color modifications) to create publication-quality graphics; however, readers can visit to replicate these case study maps and time series in real time.
The MODIS normalized difference vegetation index (NDVI) and LST anomalies are especially useful for evaluating regional vegetation stress due to drought. During the warm season, LST is largely a function of the ET rate due to evaporative cooling (i.e., latent heat flux). If ET is relatively low, then the LST will be relatively high, and vice versa. Figures 3c and 3d illustrate reduced NDVI and increased LST during the summer of 2012 Great Plains drought due to reduced vegetation vigor and ET, respectively. Having multiple indicators of drought that are readily accessible through on-demand cloud computing and web visualization is extremely useful for better understanding the drivers and impacts of drought from multiple perspectives and disciplines (i.e., land surface energy balance, vegetation, near-surface boundary layer).
Long-term monitoring of groundwater-dependent ecosystems (GDEs) for baseline assessments and water and land use impacts analyses is a central focus area for many western U.S. federal, state, and nongovernmental agencies. These agencies must adhere to regulations and requirements, including environmental assessments and monitoring related to sage-grouse habitat, groundwater development, and mining. A compelling example of how Climate Engine can be used for advanced GDE monitoring is shown in Fig. 6, which illustrates the coincident increase and decrease in Landsat-derived summer vegetation vigor (i.e., NDVI) beginning in 2002 for respective agricultural and adjacent spring areas located in eastern Nevada and western Utah (Figs. 6b and 6c). These changes are a consequence of groundwater pumping for agriculture, lowering of the groundwater table, and drying of the spring (Halford 2015; Huntington et al. 2016). Paired with gridMET water-year precipitation and ET0 computed with Climate Engine, Fig. 6d shows that lowering of the groundwater table has markedly changed the vegetation response to precipitation (PPT) within the spring area. Also evident is the complementary relationship between NDVI and ET0 with PPT, a novel illustration showing how atmospheric demand, supply, and vegetation response are inherently linked.
Monitoring agricultural vegetation vigor is important for assessing water use, irrigation performance, crop yields, and drought impacts and for reviewing and litigating water rights, transfers, and disputes. All of these issues are receiving high-priority attention in the western United States and in other water-limited environments around the world. The use of high-resolution satellite imagery is needed to accurately characterize spatial and temporal variations in crop productivity, phenology, and water use over large areas. Given free access to the >30-year Landsat archive combined with Google Earth Engine, rapid field-scale assessments can be readily produced using Climate Engine.
An example of a field-level analysis, where Climate Engine was used to generate maps of growing season maximum NDVI from Landsat for 2011 and 2015 over the Tulare Lake basin in the Central Valley of California is shown in Fig. 7. Figures 7a and 7b clearly illustrate the large amount of fallowing that occurred in 2015 due to the multiyear drought. Melton et al. (2015) estimated that over 2,000 km2 of Central Valley cropland was fallowed in 2015, approximately twice that of 2011. A unique and very powerful feature of Climate Engine is the ability to extract field-level time series information related to vegetation vigor for anywhere around the globe using predefined polygons, user-uploaded Keyhole Markup Language (KML) files, or by dynamically drawing polygons on the map to define areas of interest. The latter option is applied in Fig. 7c to examine field-level crop phenology stages (dormant, green-up, full cover, and senescence/harvest periods) from 2011 to 2015, clearly showing the fallowed land in 2015.
Cloud computing of environmental datasets is rapidly changing the way researchers and practitioners are conducting research, making applications, and planning for long-term application sustainability (Zhang et al. 2010; Hansen et al. 2013; Pekel et al. 2016; Yang et al. 2017). The motivation behind Climate Engine is to enable users to quickly process and visualize large datasets of climate and satellite Earth observations for advanced monitoring and process understanding and to improve early warning of drought, wildfire, and crop-failure risk at spatial scales relevant to scientists and decision-makers alike. Application features include on-demand mapping of environmental monitoring datasets, customizable analyses, time series and statistical summaries, downloadable digital data, and URL link sharing that reproduce results in real time.
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