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Image classification and land cover mapping
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Image classification and land cover mapping

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Image classification and land cover mapping using OBIA

Image classification and land cover mapping using OBIA

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  • As you know land cover change is a significant contributor to environmental change. Land cover data documents how much of a region is covered by forests, wetlands, impervious surfaces, agriculture, and other land and water types. 

Image classification and land cover mapping Image classification and land cover mapping Presentation Transcript

  • Introduction to Land CoverMapping Techniques usingSatellite ImagesKabir UddinGIS and Remote Sensing AnalystInternational Centre for Integrated Mountain DevelopmentMountain Environment & Natural Resources’ Information System (MENRIS)Kathmandu, Nepalwww.icimod.orgEmail: kuddin@icimod.org kabir.Uddin.bd@gmail.com
  • Land Cover mapping• Land cover is the physical material at the surface of the earth. Land covers include grass, asphalt, trees, bare ground, water, etc.• The objective of any classification scheme is to simplify the real world in order to facilitate communication and decision making.• Satellite Remote sensed data and GIS for land cover, its changes is a key to many diverse applications such as Environment, Forestry, Hydrology, Agriculture and Geology. Natural Resource Management, Planning and Monitoring programs depend on accurate information about the land cover in a region.
  • Land cover mapping• There are two primary methods for capturing information on land cover: field survey and analysis of remotely sensed imagery.
  • Use of land cover mapping• Local and regional planning• Disaster management• Vulnerability and risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Application of land cover mapping• Local and regional planning• Disaster management• Vulnerability and Risk Assessments• Ecological management• Monitoring the effects of climate change• Wildlife management.• Alternative landscape futures and conservation• Environmental forecasting• Environmental impact assessment• Policy development
  • Land cover mapping
  • Presentation status of landcover in the HKH region• No up to date land cover data is available• Legends used are different due to differences in objectives• Not possible for comparisons from one place to another or one year to another year• Harmonization of legends an important aspect for developing land cover database
  • Steps of land cover mapping Legend development and classification scheme Data acquisition Image rectification and enhancement Field training information Image segmentation Generate image index Assign rules Draft land cover map Validation and refining of land cover Land cover map Change assessment
  • Consultative Workshops forHarmonization Legend development
  • Data acquisition Sl No Launch Satellite/ Band/Resolution Scale Quicklook Sensor 1 Jun 2014 WorldView-2 Very high-resolution with 8 Band (Pan and Multi)* 5000 - Panchromatic 31 cm - Multispectral 1.24 m - Short-wave infrared 3.7 m 2 Oct 2009 WorldView-2 Very high-resolution with 8 Band (Pan and Multi)* 5000 -Panchromatic 46 cm -Multispectral 1.85 m (red, blue, green, near-IR, red edge, coastal, yellow, near-IR2) 3 Oct 2001 Quickbird High-resolution with 5 Band (Pan and Multi)* 10000 - Panchromatic 61 cm -Multispectral 2.44 m 4 Sep 2009 Cartosat-2 High-resolution 10000 -Panchromatic 1 m
  • Data acquisition5 Jan 2006 ALOS High-resolution 50000 -Panchromatic 2.5m -Multispectral 10m6 Oct 2003 IRS LISS IV MX High medium resolution 50000 -Multi (Green, Red and NIR) 5.8 m7 Apr 2009 Landsat7 ETM+ Medium-resolution with 8 Band (Pan and Multi)* 100000 -Panchromatic 150m -Multispectral 30m (TR 60m)8 Apr 2009 Landsat5 TM Medium-resolution with 7 Band 100000 -Multispectral 30m (TR 120m)
  • Data acquisition 185 Km Landsat image Spatial resolution 15m, 30 m Swath 185 Km
  • Data acquisition 185 Km Landsat image Spatial resolution 15m, 30 m Swath 185 Km 60 Km ASTER and SPOT image Swath 60 Km
  • Data acquisition 185 Km Landsat image Spatial resolution 15m, 30 m Swath 185 Km 60 Km ASTER and SPOT image Swath 60 Km 16 Km QuickBird, Swath 16 Km
  • Data acquisition 185 Km Landsat image Spatial resolution 15m, 30 m Swath 185 Km 60 Km ASTER and SPOT image Swath 60 Km 16 Km QuickBird, Swath 16 Km IKONOS, Swath 10 Km
  • Field samples collectionSig Id: 517 Grassland X-Coord Y-Coord 86.95601 26.52447 Permanent fresh water lakes Grass land-Imperata type
  • Sig Id: 112 X-Coord Y-Coord 86.94518 26.64141 Grass land -Imperata type
  • Sig Id: 126 X-Coord Y-Coord 86.93070 26.64202 Agriculture -(Paddy land)
  • Sig Id: 430 X-Coord Y-Coord 86.93280 26.70708 Grass land -Imperata type
  • Field samples collection
  • Image analysis for land cover mapping• The process of sorting pixels into a number of data categories based on their data file values• The process of reducing images to information classes
  • Image analysis assumptions• Similar features will have similar spectral responses.• The spectral response of a feature is unique with respect to all other features of interest.• If we quantify the spectral response of a known feature, we can use this information to find all occurrences of that feature.
  • ClassificationThere are different types of classification procedures: ● Unsupervised ● Supervised ● Knowledgebase ● Object base ● Others
  • Unsupervised classification – The process of automatically segmenting an image into spectral classes based on natural groupings found in the data – The process of identifying land cover classes and naming them ISODATA Class Names Label Class 1 Bare Class 2 Agriculture Class 3 Forest Class 4 Grass Class 5 Water
  • Supervised classification – the process of using samples of known identity (i.e., pixels already assigned to information classes) to classify pixels of unknown identity (i.e., all the other pixels in the image)
  • Object Based Classification (OBIA)• Object-Based Image Analysis also called Geographic Object-Based Image Analysis (GEOBIA) and it is a sub- discipline of geoinformation science. Object – based image analysis a technique used to analyze digital imagery. OBIA developed relatively recently compared to traditional pixel-based image analysis.• Pixel-based image analysis is based on the information in each pixel, object based image analysis is based on information from a set of similar pixels called objects or image objects.
  • Elements of object recognition• Visual/Digital – Shape – Size – Tone / colour – Texture – Shadow – Site – Association – Pattern
  • eCognition/Definiens• eCognition/Definiens software employs a flexible approach to image analysis, solution creation and adaption• Definiens Cognition Network Technology® has been developed by Nobel Laureate, Prof. Dr. Gerd Binnig and his team• In 2000, Definiens (eCognition) came in market• In 2003 Definiens Developer along with Definiens eCognition™ Server was introduced. Now, Definiens Developer 8 with updated versions is available
  • Steps for object base classification Image Segmentations Variable Operations Rule Set Rule Set Land Cover Map
  • Segmentation• The first step of an eCognition image analysis is to cut the image into pieces, which serve as building blocks for further analysis – this step is called segmentation and there is a choice of several algorithms to do this.• The next step is to label these objects according to their attributes, such as shape, color and relative position to other objects.
  • Types of Segmentation
  • Types of Segmentation Chessboard segmentation Chessboard segmentation is the simplest segmentation available as it just splits the image into square objects with a size predefined by the user.
  • Types of Segmentation Quadtree based segmentation Quadtree-based segmentation is similar to chessboard segmentation, but creates squares of differing sizes. Quadtree-based segmentation, very homogeneous regions typically produce larger squares than heterogeneous regions. Compared to multiresolution segmentation,quadtree-based segmentation is less heavy on resources.
  • Types of Segmentation Contrast split segmentation Contrast split segmentation is similar to the multi-threshold segmentation approach. The contrast split segments the scene into dark and bright image objects based on a threshold value that maximizes the contrast between them.
  • Types of Segmentation Contrast split segmentation Contrast split segmentation is similar to the multi-threshold segmentation approach. The contrast split segments the scene into dark and bright image objects based on a threshold value that maximizes the contrast between them.
  • Types of Segmentation Spectral difference segmentation Spectral difference segmentation lets you merge neighboring image objects if the difference between their layer mean intensities is below the value given by the maximum spectral difference. It is designed to refine existing segmentation results, by merging spectrally similar image objects produced by previous segmentations and therefore is a bottom-up segmentation.
  • Types of Segmentation Multiresolution segmentation Multiresolution Segmentation groups areas of similar pixel values into objects. Consequently homogeneous areas result in larger objects, heterogeneous areas in smaller ones. The Multiresolution Segmentation algorithm1 consecutively merges pixels or existing image objects. Essentially, the procedure identifies single image objects of one pixel in size and merges them with their neighbors, based on relative homogeneity criteria.
  • Multiresolution Segmentation,ParametersScale•The value of the scale parameter affects imagesegmentation by determining the size of imageobjects;•Defines the minimum size of the object throughthreshold value;•The larger the scale parameter, the more objectscan be fused and the larger the objects grow;
  • Generating arithmetic Feature  The Normalized Difference Vegetation Index (NDVI) is a standardized index allowing to generate an image displaying greenness (relative biomass)  Index values can range from -1.0 to 1.0, but vegetation values typically range between 0.1 and 0.7.  NDVI is related to vegetation is that healthy vegetation reflects very well in the near infrared part of the spectrum. It can be seen from itsmathematical definition that the NDVIof an area containing a densevegetation canopy will tend to positivevalues (say 0.3 to 0.8) while cloudsand snow fields will be characterizedby negative values of this index.NDVI = (NIR - red) / (NIR + red)
  • Land and Water Masks (LWM) Index values can range from 0 to 255, but water values typically range between 0 to 50 Water Mask = infra-red) / (green + .0001) * 100 (ETM+) Water Mask = Band 5) / (Band 2 + .0001) * 100
  • Comparing features using the 2Dfeature space plot
  • Comparing features using the 2Dfeature space plot
  • Comparing features using the 2Dfeature space plot
  • Comparing features using the 2Dfeature space plot
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Investigation of classified land cover
  • Rules
  • Software for Object BasedClassification• eCognition/Definiens• IDRISI• ERDAS Imagine• ENVI• SPRING• MADCAT
  • Accuracy AssessmentGoals: – Assess how well a classification worked – Understand how to interpret the usefulness of someone else’s classification • Some possible sources – Aerial photo interpretation – Ground truth with GPS – GIS layers – Google earth image
  • Sampling Methods Simple Random Sampling : observations are randomly placed. Stratified Random Sampling : a minimum number of observations are randomly placed in each category.
  • Sampling Methods Systematic Sampling : observations are placed at equal intervals according to a strategy. Systematic Non-Aligned Sampling : a grid provides even distribution of randomly placed observations.
  • Sampling Methods Cluster Sampling : Randomly placed “centroids” used as a base of several nearby observations. The nearby observations can be randomly selected, systematically selected, etc...
  • Accuracy Equations Number of samples correctly classified in a given class Pr oducer s accuracy = X 100 Total number of samples chosen for that class Number of samples correctly classified in a given class from the selected samples in that group User s accuracy = X 100 Total number of samples classified in that group out of entire samples selected Total Number of reference samples chosenOverall accuracy = X 100 Total number of correctly classified samples
  • Accuracy Assessment: Compare• Example: Reference Plot Class determined from Class claimed on Agreement? ID Number reference source classified map 1 Conifer Conifer Yes 2 Hardwood Conifer No 3 Water Water Yes 4 Hardwood Hardwood Yes 5 Grass Hardwood No
  • Accuracy Assessment: Compare
  • Accuracy Assessment: Compare
  • Overall (Total) Accuracy • Total accuracy – Total Accuracy: Number of correct plots / total number of plots Class types determined from reference source 50 + 13 + 8 Accuracy Total = 100 *100 = 71% # Conifer Hardwood Water Totals Class types Plots determined Conifer 50 5 2 57 Diagonals represent from sites classified classified Hardwo 14 od 13 0 27 correctly according to map reference data Water 3 5 8 16 Total 67 23 10 100 Off-diagonals were s mis-classified Total Number of reference samples chosenOverall accuracy = X 100 Total number of correctly classified samples
  • Change detection  One common type of multitemporal analysis is change detection  Change detection involves the direct comparison of two or more images to identify how areas change over time 2001 2003
  • Change detection methods 1989 2010 Image rationing Image rationing Post- Post- classification classification comparison comparison
  • Change detection methods Change vector analysis Composite multitemporal image analysis
  • Change detection methods
  • Acquiring satellite imagery
  • SPOT 5Sensor Electromagnetic spectrum Resolution Spectral bands Panchromatic 2.5 m or 5 m 0.48 - 0.71 µm B1 : green 10 m 0.50 - 0.59 µmSPOT 5 B2 : red 10 m 0.61 - 0.68 µm B3 : near infrared 10 m 0.78 - 0.89 µm B4 : mid infrared (MIR) 20 m 1.58 - 1.75 µm Monospectral 10 m 0.61 - 0.68 µm B1 : green 20 m 0.50 - 0.59 µmSPOT 4 B2 : red 20 m 0.61 - 0.68 µm B3 : near infrared 20 m 0.78 - 0.89 µm B4 : mid infrared (MIR) 20 m 1.58 - 1.75 µm Panchromatic 10 m 0.50 - 0.73 µmSPOT 1 B1 : green 20 m 0.50 - 0.59 µmSPOT 2 B2 : red 20 m 0.61 - 0.68 µmSPOT 3 B3 : near infrared 20 m 0.78 - 0.89 µm
  • Acquiring satellite imagery http://sirius.spotimage.fr/PageSearch.aspx http://sirius.spotimage.fr/PageSearch.aspx
  • IRS ImageSensor LISS-4 LISS-3 AWiFSType Mon MSS MSS MSS oRevisit period for the same 5 5 24 5territory, daysResolution, m Green 5.8 23.5 56 Red 5.8 5.8 23.5 56 NIR 5.8 23.5 56 SWIR 23.5 56Swath, km 70 23 140 740Spectral band, Green 520-590 520-590 520-590nm Red 620- 620-680 620-680 620-680 NIR 680 770-860 770-860 770-860 SWIR 1550- 1550- 1700 1700
  • IRS Imagehttp://218.248.0.130/internet/BasicLayout.jsp
  • Landsat Image
  • Landsat Imagehttp://earthexplorer.usgs.gov/
  • Landsat Imagehttp://glovis.usgs.gov/
  • Landsat Imagehttp://gcudos.gistda.or.th:8080/cudos/
  • Landsat Image
  • Landsat Imagehttp://glcf.umiacs.umd.edu/
  • Landsat Image http://mrtweb.cr.usgs.gov
  • Landsat Imagehttps://browse.digitalglobe.com/imagefinder/main.jsp;jsessionid=C5471C4D0D86AE8BADFBCBA4E3EE5E47?
  • Thank you