{"id":122821,"date":"2012-07-31T15:42:05","date_gmt":"2012-07-31T15:42:05","guid":{"rendered":"http:\/\/www.esri.com\/arcgis-blog\/products\/product\/uncategorized\/obstruction-analysis-using-solar-radiation-graphics\/"},"modified":"2012-07-31T15:42:05","modified_gmt":"2012-07-31T15:42:05","slug":"obstruction-analysis-using-solar-radiation-graphics","status":"publish","type":"blog","link":"https:\/\/www.esri.com\/arcgis-blog\/products\/product\/analytics\/obstruction-analysis-using-solar-radiation-graphics","title":{"rendered":"Obstruction Analysis using Solar Radiation Graphics"},"author":5101,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"open","ping_status":"closed","template":"","format":"standard","meta":{"_acf_changed":false,"_searchwp_excluded":""},"categories":[23341],"tags":[24311,24321,39421],"industry":[],"product":[],"class_list":["post-122821","blog","type-blog","status-publish","format-standard","hentry","category-analytics","tag-analysis","tag-geoprocessing","tag-spatial-analyst"],"acf":{"short_description":"This blog describes the use of Geoprocessing tools including the Solar Radiation Graphics tool in the Spatial Analyst extension for evalu...","flexible_content":[{"acf_fc_layout":"content","content":"<p>This blog describes the use of Geoprocessing tools including the Solar Radiation Graphics tool in the Spatial Analyst extension for evaluating poor performing weather stations that both, recorded and, were powered by solar radiation.<\/p>\n<p><strong>Overview<\/strong><br \/>\nIn order to stimulate economic development in the agricultural sector, the Community Business Development Corporations (CBDC), responsible for the five western-most counties (Shelburne, Queens, Lunenburg, Yarmouth and Digby) in South West Nova Scotia (SWNS), assembled the funding to start an Agriculture Climate Data Project in 2011.\u00a0 The objective of this project was to measure, record, and share temperature and solar radiation observations to maximize crop production and stimulate economic development in the agricultural sector of SWNS.<\/p>\n<p><!--more-->The Applied Geomatics Research Group (AGRG) in Middleton have been monitoring the meteorological conditions in the Annapolis Valley for almost a decade and were approached by the CBDC, during the launch of the Agriculture Climate Data Project in 2011, to expand their study area to encompass all of South West Nova Scotia, Canada.\u00a0 Today the AGRG has seventy-four (74) monitoring stations deployed across SWNS. The weather stations collect temperature and solar radiation every five minutes which are downloaded to a SQL Server database, either automatically or manually depending on the weather station.<\/p>\n<p>In addition to this meteorological data, LiDAR and aerial photography for the station locations were collected in 2011.\u00a0 These data provide a high-resolution description of the surrounding landscape for each station.<\/p>\n<p>It quickly became obvious to the AGRG that the solar radiation data contained some errors. Although the AGRG had installed weather stations in open areas, some weather stations were recording little to no solar radiation at times when high values were expected. This suggested that these weather stations were obstructed\u00a0 from the sun.<\/p>\n<p>Furthermore, the weather stations are powerd by solar radiation. Limited solar radiation exposure, especially during the low light winter months, could compromise the power source of the weather station, which in turn could compromise data collection.<\/p>\n<p>It was important to identify any weather stations that may be obstructed, for example by buildings, trees, mountains, hills, etc. A 360 degree obstruction analysis was, therefore,\u00a0 performed on the SWNS weather stations using 2011 LiDAR data, a point feature shapefile of the weather stations and a series of ArcGIS 10 geoprocessing tools. This process was automated for all 74 weather stations using ArcPy.<\/p>\n<p><strong>Data<\/strong><br \/>\nLiDAR data was critical for this analysis, providing up-to-date and accurate elevation data that included ground cover, such as buildings and vegetation, which are the main causes of obstruction on the micro scale. The LiDAR data used for this project had been previously processed into a 1 meter Digital Surface Model (DSM).<\/p>\n<p>Also, a shapefile including all weather station locations was used.<\/p>\n<p><em><strong>Note<\/strong>: The LiDAR data was collected in July 2011 and therefore, only represented leaf-on vegetation. Since vegetation volume differs by season, it is normal to expect different obstruction impacts between leaf-on (summer) months and leaf-off (winter) months.<\/em><\/p>\n<p><strong>Methodology<\/strong><br \/>\nFor each weather station, the Digital Surface Models (DSM) was clipped using a 2000 meter buffer around the station\u2019s point feature.\u00a0 The initial DSM needed to be clipped because it was too large to be used as an input and since we were interested in local obstructions, expanding to 2000m was considered enough to cover all local variations.<\/p>\n<p>The <a title=\"Solar Radiation Graphics\" href=\"http:\/\/resources.arcgis.com\/en\/help\/main\/10.1\/index.html#\/\/009z000000t7000000\">Solar Radiation Graphics<\/a><em> tool <\/em>in ArcGIS 10 <em>Spatial Analyst <\/em>extension was the main geoprocessing tool used for this analysis. The <a title=\"viewshed\" href=\"http:\/\/resources.arcgis.com\/en\/help\/main\/10.1\/index.html#\/\/009z000000v3000000\">viewshed<\/a> (Figure 1) and <em>skymap <\/em>results from the <em>Solar Radiation Graphics tool <\/em>were used to delineate obstruction and weight sky sectors, depending on their solar radiation potential (i.e. sky sectors closer to the middle of the skymap have a higher solar radiation potential and vice versa).<\/p>\n<p style=\"text-align: center\"><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_viewshed.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-medium wp-image-18500 aligncenter\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_viewshed-300x300.png\" alt=\"\" width=\"300\" height=\"300\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 1. Illustration of how the viewshed derived from the Solar Radiation Graphics tool represents a hemispherical view of the surrounding topography from that point<\/p>\n<p>The clipped DSM was used as the <em>Input raster<\/em>, the weather station point feature class as the <em>Input Point Features and Height offset <\/em>of 2.5 meters was specified to account for the height of the weather stations. A <em>Sky Size <\/em>of 300 was used because a DSM was used as an <em>Input Raster <\/em>and eight (8)\u00a0\u00a0<em>Zenith <\/em>and <em>Azimuth <\/em>divisions were specified for the skymap to the future weighting process of sky sectors simple.<\/p>\n<p>Using the output skymap, <em>weighted skymaps <\/em>were created to represent the varying solar radiation potential throughout the <em>skymap<\/em>. The first was weighted on zenith positions and the second on azimuth positions using <em><a title=\"Reclassify\" href=\"http:\/\/resources.arcgis.com\/en\/help\/main\/10.1\/index.html#\/\/009z000000sr000000\">Reclassify<\/a> <\/em>(Spatial Analyst). Then, these two were combined into a general <em>weighted skymap <\/em>(Figure 2).<\/p>\n<p><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_Fig2_3.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-18497\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_Fig2_3.png\" alt=\"\" width=\"527\" height=\"225\" \/><\/a><br \/>\nFigure 2. Weighted Skymap illustrating solar radiation\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 Figure 3. Solar Radiation Zones (A: lower solar radiation potential; C: higher solar radiation potential)<\/p>\n<p>The weighted sky sectors (Figure 2) were configured into different solar radiation zones (Figure 3). The total number of raster cells was determined for each zone (Total Area).<\/p>\n<p>Then, the viewshed was overlayed onto the <em>weighted skymap <\/em>(Figure 4) using <a title=\"Con\" href=\"http:\/\/resources.arcgis.com\/en\/help\/main\/10.1\/index.html#\/\/009z00000005000000\">Con<\/a> (Spatial Analyst) and for each zone (Figure 3), the total number of unobstructed raster cells was found (<em>Unobstructed Area<\/em>).<\/p>\n<p><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_StationWE1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-18499\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_StationWE1.png\" alt=\"\" width=\"256\" height=\"229\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 4. Viewshed for station WE1 overlayed on top of the Weighted Skymap<\/p>\n<p>Then, for each of the zones illustrated in Figure 3, the proportion of unobstructed cells was determined and a normalized obstruction rank was calculated for each weather station using the following equations:<\/p>\n<p><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_equation1.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-18503 alignnone\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_equation1.png\" alt=\"\" width=\"690\" height=\"38\" \/><\/a><\/p>\n<p><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_equation2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"size-full wp-image-18504 alignnone\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_equation2.png\" alt=\"\" width=\"458\" height=\"135\" \/><\/a><\/p>\n<p><em><strong> <\/strong><\/em><\/p>\n<p><em><strong> <\/strong><\/em><\/p>\n<p style=\"text-align: left\"><em><strong> <\/strong><\/em><\/p>\n<p style=\"text-align: left\"><em><strong> <\/strong><\/em><\/p>\n<p style=\"text-align: left\"><em><strong> <\/strong><\/em><\/p>\n<p style=\"text-align: left\"><em><strong>Note:<\/strong> This example uses 3 zones (A, B &amp; C) where C has the highest solar radiation potential and A, the lowest. This was done to minimize processing time, but each sky sector could have been its own \u201czone\u201d as it had been previously weighted.<\/em><\/p>\n<p style=\"text-align: left\"><em><a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_FlowDiagram.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-18546\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_FlowDiagram.png\" alt=\"\" width=\"552\" height=\"608\" \/><\/a><br \/>\n<\/em><\/p>\n<p style=\"text-align: left\"><strong>Results<\/strong><br \/>\nResults were written to a .csv file which was joined (using <a title=\"Join\" href=\"http:\/\/resources.arcgis.com\/en\/help\/main\/10.1\/index.html#\/\/001700000064000000\">Join<\/a>) to the weather station point feature class in ArcMap 10 and symbolized using a Natural Breaks classification with 3 classes to help identify weather stations with higher obstruction. The location of weather stations with higher obstruction, symbolized in red, should be re-evaluated.\u00a0 The weather stations with little to no obstruction are symbolized in yellow and green (Figure 5).<\/p>\n<p>This analysis allowed to AGRG to identify weather stations that were more at risk of not collecting any solar radiation data or having insufficient solar power to stay on due to surrounding obstruction. Furthermore, AGRG were able to assess if any of the weather stations needed to be relocated.<a href=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_stations2.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-full wp-image-18807\" src=\"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2012\/07\/GP_Obstruction_stations2.png\" alt=\"\" width=\"624\" height=\"475\" \/><\/a><\/p>\n<p style=\"text-align: center\">Figure 5 &#8211; SWNS Weather Stations ranked<strong> <\/strong>by obstruction<\/p>\n<p style=\"text-align: left\"><em><strong>This post was contributed by Pascale Robichaud, an intern on the analysis and geoprocessing team.<\/strong><\/em><\/p>\n"}],"authors":[{"ID":5101,"user_firstname":"Linda","user_lastname":"Beale","nickname":"Linda Beale","user_nicename":"lbeale","display_name":"Linda Beale","user_email":"LBeale@esri.com","user_url":"","user_registered":"2018-03-02 00:16:44","user_description":"Dr Linda Beale is the Group Lead for Location Analytics at Esri, with an interest in sharing the value of spatial analysis with an audience ranging from those new to the discipline to those who are seeking fresh approaches and techniques.\r\n\r\nA geographer by training, Linda gained her PhD in GIS, statistics and modelling, and led the geospatial health group in the Small Area Health Statistics Unit at Imperial College London. Linda has extensive experience in the field of spatial epidemiology and has worked closely with Health Departments, the World Health Organisation and Center for Disease Control. She developed the award winning Rapid Inquiry Facility program for chronic disease modelling and was co-author on the landmark Environment and Health Atlas for England and Wales.\r\n\r\nLinda is the author of the first Esri MOOC, Going Places with Spatial Analysis, and she has published numerous peer-reviewed papers, book chapters, and been invited to keynote, present and deliver workshops at national and international conferences. Linda has worked at Esri since 2011, where her experience helps shape location analytics to provide the community with better and more powerful tools, and where she helps teach best practices and sharing of knowledge to develop understanding across the wider community.","user_avatar":"<img data-del=\"avatar\" src='https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/12\/Linda-Beale-213x200.jpg' class='avatar pp-user-avatar avatar-96 photo ' height='96' width='96'\/>"}]},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Obstruction Analysis using Solar Radiation Graphics<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/arcgis-blog\/products\/product\/analytics\/obstruction-analysis-using-solar-radiation-graphics\" \/>\n<meta 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Beale","image":{"@type":"ImageObject","inLanguage":"en-US","@id":"https:\/\/www.esri.com\/arcgis-blog\/#\/schema\/person\/image\/","url":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/12\/Linda-Beale-213x200.jpg","contentUrl":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2019\/12\/Linda-Beale-213x200.jpg","caption":"Linda Beale"},"description":"Dr Linda Beale is the Group Lead for Location Analytics at Esri, with an interest in sharing the value of spatial analysis with an audience ranging from those new to the discipline to those who are seeking fresh approaches and techniques. A geographer by training, Linda gained her PhD in GIS, statistics and modelling, and led the geospatial health group in the Small Area Health Statistics Unit at Imperial College London. Linda has extensive experience in the field of spatial epidemiology and has worked closely with Health Departments, the World Health Organisation and Center for Disease Control. She developed the award winning Rapid Inquiry Facility program for chronic disease modelling and was co-author on the landmark Environment and Health Atlas for England and Wales. Linda is the author of the first Esri MOOC, Going Places with Spatial Analysis, and she has published numerous peer-reviewed papers, book chapters, and been invited to keynote, present and deliver workshops at national and international conferences. Linda has worked at Esri since 2011, where her experience helps shape location analytics to provide the community with better and more powerful tools, and where she helps teach best practices and sharing of knowledge to develop understanding across the wider community.","sameAs":["https:\/\/x.com\/lindabeale"],"url":""}]}},"text_date":"July 31, 2012","author_name":"Linda Beale","author_page":false,"custom_image":"https:\/\/www.esri.com\/arcgis-blog\/app\/uploads\/2025\/08\/Newsroom-Keyart-Wide-1920-x-1080.jpg","primary_product":false,"tag_data":[{"term_id":24311,"name":"Analysis","slug":"analysis","term_group":0,"term_taxonomy_id":24311,"taxonomy":"post_tag","description":"","parent":0,"count":96,"filter":"raw"},{"term_id":24321,"name":"geoprocessing","slug":"geoprocessing","term_group":0,"term_taxonomy_id":24321,"taxonomy":"post_tag","description":"","parent":0,"count":129,"filter":"raw"},{"term_id":39421,"name":"Spatial Analyst","slug":"spatial-analyst","term_group":0,"term_taxonomy_id":39421,"taxonomy":"post_tag","description":"","parent":0,"count":49,"filter":"raw"}],"category_data":[{"term_id":23341,"name":"Analytics","slug":"analytics","term_group":0,"term_taxonomy_id":23341,"taxonomy":"category","description":"","parent":0,"count":1327,"filter":"raw"}],"product_data":[],"primary_product_link":"https:\/\/www.esri.com\/arcgis-blog\/","_links":{"self":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/122821","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog"}],"about":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/types\/blog"}],"author":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/users\/5101"}],"replies":[{"embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/comments?post=122821"}],"version-history":[{"count":0,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/blog\/122821\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/media?parent=122821"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/categories?post=122821"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/tags?post=122821"},{"taxonomy":"industry","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/industry?post=122821"},{"taxonomy":"product","embeddable":true,"href":"https:\/\/www.esri.com\/arcgis-blog\/wp-json\/wp\/v2\/product?post=122821"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}