Aerial photography is considered an important management tool in agriculture. Aerial photography allows researchers to detect spatial variability and understand the causes of the variability such as planter skips, drought stress, weeds and water erosion. In agricultural research it allows researchers to differentiate healthy vegetation from unhealthy and access plant biomass and moisture levels. The photographs are also useful to document trends and changes in the landscape. original data source http://lter.kbs.msu.edu/datasets/44 Resources in this dataset:Resource Title: Website Pointer to html file. File Name: Web Page, url: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-kbs&identifier=41 Webpage with information and links to data files for download

description: Habitat Sites are natural areas that support wildlilfe on both public and privately owned land. Approximately 3,800 of the 146,240 acres within the city limits serve as habitat sites. Among the 97 individual habitat sites identified in 2004 using mapping tools, aerial imagery, site-visits, and previous inventory studies, most are located along the Chicago River and on the shorelines of Lake Michigan and Lake Calumet natural areas that support wildlife. To view or use this file, special GIS software such as Google Earth is required. To download, right-click the "Download" link above and choose "Save link as."; abstract: Habitat Sites are natural areas that support wildlilfe on both public and privately owned land. Approximately 3,800 of the 146,240 acres within the city limits serve as habitat sites. Among the 97 individual habitat sites identified in 2004 using mapping tools, aerial imagery, site-visits, and previous inventory studies, most are located along the Chicago River and on the shorelines of Lake Michigan and Lake Calumet natural areas that support wildlife. To view or use this file, special GIS software such as Google Earth is required. To download, right-click the "Download" link above and choose "Save link as."

The U.S. Geological Survey's Upper Midwest Environmental Sciences Center (UMESC) collects aerial photography of the Upper Mississippi River System (UMRS) floodplain on a regular basis. These data are used to support the Center's long-term goals of understanding the UMRS and developing useful products for the Long Term Resource Monitoring Program (LTRMP). In 2000, 1:16,000-scale true color aerial photos were collected on the Mississippi River from Cairo, IL to Minneapolis, MN and the on Illinois River from its confluence with the Mississippi near Grafton, IL to Lake Michigan/Chicago, IL. The photos were collected using a 60% stereo overlap between photos in the same flight line and a 30% overlap between flight lines. At the time this document was prepared, UMESC was in the process of scanning, georeferencing and compiled approximately every other photo into georeferenced mosaics for the navigation pools. These mosaics are served as compressed .sid images, so they'll be easier to download vial the Internet. Note: While the photos have been georeferenced, they have not been orthorectified.

The India Lights platform shows light output at night for 20 years for 600,000 villages across India. The Defense Meteorological Satellite Program (DMSP) has taken pictures of the Earth every night from 1993 to 2013. Researchers at the University of Michigan, in collaboration with the World Bank, used the DMSP images to extract the data you see on the India Lights platform. Each point you see on the map represents the light output of a specific village at a specific point in time. On the district level, the map also allows you to filter to view villages that have participated in India’s flagship electrification program. This tremendous trove of data can be used to look at changes in light output, which can be used to complement research about electrification in the country. About the Data: The DMSP raster images have a resolution of 30 arc-seconds, equal to roughly 1 square kilometer at the equator. Each pixel of the image is assigned a number on a relative scale from 0 to 63, with 0 indicating no light output and 63 indicating the highest level of output. This number is relative and may change depending on the gain settings of the satellite’s sensor, which constantly adjusts to current conditions as it takes pictures throughout the day and at night. Methodology To derive a single measurement, the light output values were extracted from the raster image for each date for the pixels that correspond to each village's approximate latitude and longitude coordinates. We then processed the data through a series of filtering and aggregation steps. First, we filtered out data with too much cloud cover and solar glare, according to recommendations from the National Oceanic and Atmospheric Administration (NOAA). We aggregated the resulting 4.4 billion data points by taking the median measurement for each village over the course of a month. We adjusted for differences among satellites using a multiple regression on year and satellite to isolate the effect of each satellite. To analyze data on the state and district level, we also determined the median village light output within each administrative boundary for each month in the twenty-year time span. These monthly aggregates for each village, district, and state are the data that we have made accessible through the API. To generate the map and light curve visualizations that are presented on this site, we performed some additional data processing. For the light curves, we used a rolling average to smooth out the noise due to wide fluctuations inherent in satellite measurements. For the map, we took a random sample of 10% of the villages, stratified over districts to ensure good coverage across regions of varying village density. Acknowledgments The India Lights project is a collaboration between Development Seed, The World Bank, and Dr. Brian Min at the University of Michigan. •Satellite base map © Mapbox. •India village locations derived from India VillageMap © 2011-2015 ML Infomap. •India population data and district boundaries © 2011-2015 ML Infomap. •Data for reference map of Uttar Pradesh, India, from Natural Earth Data •Banerjee, Sudeshna Ghosh; Barnes, Douglas; Singh, Bipul; Mayer, Kristy; Samad, Hussain. 2014. Power for all : electricity access challenge in India. A World Bank study. Washington, DC ; World Bank Group. •Hsu, Feng-Chi, Kimberly Baugh, Tilottama Ghosh, Mikhail Zhizhin, and Christopher Elvidge. "DMSP-OLS Radiance Calibrated Nighttime Lights Time Series with Intercalibration." Remote Sensing 7.2 (2015): 1855-876. Web. •Min, Brian. Monitoring Rural Electrification by Satellite. Tech. World Bank, 30 Dec. 2014. Web. •Min, Brian. Power and the Vote: Elections and Electricity in the Developing World. New York and Cambridge: Cambridge University Press. 2015. •Min, Brian, and Kwawu Mensan Gaba. Tracking Electrification in Vietnam Using Nighttime Lights. Remote Sensing 6.10 (2014): 9511-529. •Min, Brian, and Kwawu Mensan Gaba, Ousmane Fall Sarr, Alassane Agalassou. Detection of Rural Electrification in Africa using DMSP-OLS Night Lights Imagery. International Journal of Remote Sensing 34.22 (2013):8118-8141. Disclaimer Country borders or names do not necessarily reflect the World Bank Group's official position. The map is for illustrative purposes and does not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries.

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The U.S. Geological Survey's Upper Midwest Environmental Sciences Center (UMESC) collects aerial photography of the Upper Mississippi River System (UMRS) floodplain on a regular basis. These data are used to support the Center's long-term goals of understanding the UMRS and developing useful products for the Long Term Resource Monitoring Program (LTRMP). In 2000, 1:16,000-scale true color aerial photos were collected on the Mississippi River from Cairo, IL to Minneapolis, MN and the on Illinois River from its confluence with the Mississippi near Grafton, IL to Lake Michigan/Chicago, IL. The photos were collected using a 60% stereo overlap between photos in the same flight line and a 30% overlap between flight lines. At the time this document was prepared, UMESC was in the process of scanning, georeferencing and compiled approximately every other photo into georeferenced mosaics for the navigation pools. These mosaics are served as compressed .sid images, so they'll be easier to download vial the Internet. Note: While the photos have been georeferenced, they have not been orthorectified.

This dataset combines the work of several different projects to create a seamless data set for the contiguous United States. Data from four regional Gap Analysis Projects and the LANDFIRE project were combined to make this dataset. In the northwestern United States (Idaho, Oregon, Montana, Washington and Wyoming) data in this map came from the Northwest Gap Analysis Project. In the southwestern United States (Colorado, Arizona, Nevada, New Mexico, and Utah) data used in this map came from the Southwest Gap Analysis Project. The data for Alabama, Florida, Georgia, Kentucky, North Carolina, South Carolina, Mississippi, Tennessee, and Virginia came from the Southeast Gap Analysis Project and the California data was generated by the updated California Gap land cover project. The Hawaii Gap Analysis project provided the data for Hawaii. In areas of the county (central U.S., Northeast, Alaska) that have not yet been covered by a regional Gap Analysis Project, data from the Landfire project was used. Similarities in the methods used by these projects made possible the combining of the data they derived into one seamless coverage. They all used multi-season satellite imagery (Landsat ETM+) from 1999-2001 in conjunction with digital elevation model (DEM) derived datasets (e.g. elevation, landform) to model natural and semi-natural vegetation. Vegetation classes were drawn from NatureServe's Ecological System Classification (Comer et al. 2003) or classes developed by the Hawaii Gap project. Additionally, all of the projects included land use classes that were employed to describe areas where natural vegetation has been altered. In many areas of the country these classes were derived from the National Land Cover Dataset (NLCD). For the majority of classes and, in most areas of the country, a decision tree classifier was used to discriminate ecological system types. In some areas of the country, more manual techniques were used to discriminate small patch systems and systems not distinguishable through topography. The data contains multiple levels of thematic detail. At the most detailed level natural vegetation is represented by NatureServe's Ecological System classification (or in Hawaii the Hawaii GAP classification). These most detailed classifications have been crosswalked to the five highest levels of the National Vegetation Classification (NVC), Class, Subclass, Formation, Division and Macrogroup. This crosswalk allows users to display and analyze the data at different levels of thematic resolution. Developed areas, or areas dominated by introduced species, timber harvest, or water are represented by other classes, collectively refered to as land use classes; these land use classes occur at each of the thematic levels. Raster data in both ArcGIS Grid and ERDAS Imagine format is available for download at http://gis1.usgs.gov/csas/gap/viewer/land_cover/Map.aspx Six layer files are included in the download packages to assist the user in displaying the data at each of the Thematic levels in ArcGIS. In adition to the raster datasets the data is available in Web Mapping Services (WMS) format for each of the six NVC classification levels (Class, Subclass, Formation, Division, Macrogroup, Ecological System) at the following links. http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Class_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Subclass_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Formation_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Division_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_NVC_Macrogroup_Landuse/MapServer http://gis1.usgs.gov/arcgis/rest/services/gap/GAP_Land_Cover_Ecological_Systems_Landuse/MapServer