This simple study provides the technique to detect fire burnt in oil palm plantations, Riau Province, Indonesia.
The image acquisition date was June 25, 2013. As known, on late June 2013, smoke blew from burning forest in Sumatera Island to Malaysia dan Singapore. Indonesia then seen as the main perpetrator of this smoke due to forest conversion into oil palm expansion.
However, if we overlay the GIS datasets consist of Multinational Companies (MNCs) of Oil Palm Plantations, we can conclude that the fire burnt sources are came from these MNCs, which is the head office of the MNCs located in Malaysia dan Singapore.
Interpretation of Remote Sensing Images A supervised classification of remote sensing images is a processing technique that allows for the identification of materials in the image, according to their spectral signatures (see here for further definitions about remote sensing).The main advantage of this approach is that an entire image can be processed rapidly, producing the land cover classification thereof.This post is about the interpretation of remote sensing images that is a fundamental phase of the ROI creation, which is a required step for the semi-automatic classification.....
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.
Overview In 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. 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.
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. 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.
Water is not just an environmental issue – it has the power to create risks and opportunities that can impact companies, investors, and entire economies. Further proof of this point came from the annual meeting of the World Economic Forum in Davos, where water supply crises were ranked as one of the most likely and highest impact risks facing the world. To thrive sustainably, companies, investors, and governments need a deeper understanding of where and how water risks are emerging worldwide.
The World Resources Institute’s Aqueduct water risk mapping tool provides unprecedented insight into the complexities of water risk. Aqueduct’s global water risk maps are the product of three years of indicator development, data collection, and modeling. They bring together data on twelve different indicators of water risk – everything from water stress to drought to access to clean drinking water. A free online tool lets users from companies, investors, and beyond combine the twelve indicators together using preset or customized weights to create a global water risk map that is tailored to their specific concerns. Aqueduct is already being used by companies ranging fromMcDonald’s to Goldman Sachs, as well as the National Intelligence Council and academics and governments worldwide.
Recientemente, un usuario en un foro de GIS planteaba la necesidad de corregir un ráster DEM, en una zona delimitada por un archivo vectorial, con base en que éste no reflejaba la elevación producto de una densa población de árboles … Sigue leyendo...
The Semi-Automatic Classification Plugin version 1.8.0 (for QGIS 1.8) and 2.0.0 (for QGIS 2.0) has been released through the official repository.
This post is an updated tutorial for the land cover classification of remote sensing images, using the new functionalities of the Semi-Automatic Classification Plugin (see how to install and configure this plugin). In particular, we will see how to create a band set (a list of single band rasters), how to collect ROIs calculating the spectral signatures thereof, and how to perform the land cover classification.
We are going to use a sample Landsat 8 image (a subset acquired in the South of Rome, Italy) that you can download from here (data available from the U.S. Geological Survey). If you need more information about GIS and remote sensing definitions see here. At the end of this post you can find the video of this tutorial.
It’s snowing here in Berlin. And I already thought, that we wouldn’t have any white color out there before Christmas Eve. In order to check the weather forecast for the next days, I found www.openweathermap.org.
Important: Prior to reading the detailed atmospheric correction (conversion to reflectance) steps in the guide below, read the following two paragraphs about the Landsat 8 DOS Method, an easier DOS atmospheric correction surface reflectance method that should be used for Landsat 8.
Desde que o professor Abbas Rajabifard, em conjunto com outros pesquisadores da Austrália, publicou em 2000 o artigo “Das iniciativas de IDEs locais às globais: uma pirâmide de blocos de montar” (tradução livre), se consolidou como o paradigma para a integração de IDEs em diversos níveis uma pirâmide, em cuja base ficam as IDEs corporativas, que se uniriam em iniciativas locais, estaduais, nacionais, regionais até uma formar uma estrutura global. A ideia era bastante original e contribui significativamente para a visão de que estruturas padronizadas podiam comunicar-se e formar associações mais amplas. Tal raciocínio foi adotado no Plano de Ação daInfraestrutura Nacional de Dados Espaciais (INDE) no Brasil, através do planejamento da execução escalonado em ciclos, sendo o primeiro para atores federais, o segundo para estaduais e o terceiro para os demais.....
If you’re working with huge mosaic datasets, with hundreds or even thousands of rasters, then this blog is for you. I’m hoping we can clarify some questions that have come up with regards to how display ordering occurs with mosaic datasets. For you visual learners out there, enjoy the following flow chart. And for those of you who like to learn verbally, enjoy the rest of this blog.
A supervised classification of remote sensing images is a processing technique that allows for the identification of materials in the image, according to their spectral signatures (see here for further definitions about remote sensing).The main...