PADDY YIELD ESTIMATION USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM
|
|
- Lauren Reeves
- 7 years ago
- Views:
Transcription
1 JOURNAL OF MODERN BIOTECHNOLOGY, VOL. 1, NO. 1, pp 26 30, September 2012 Copyright 2012, by Madras Institute of Biotechnology. All Right Reserved. Research Article Abstract PADDY YIELD ESTIMATION USING REMOTE SENSING AND GEOGRAPHICAL INFORMATION SYSTEM Manickam Gopperundevi* and Vijayaraghavan Kannan Center for Advanced Studies in Botany, University of Madras, Maraimalai Campus, Guindy, Chennai , Tamil Nadu, India *Corresponding Author Received: 28 June 2012; Revised: 12 July 2012; Accepted: 28 August 2012 Agriculture is the basis of Indian economy. The rapid development of Remote Sensing technology serves as the solid technological foundation for the in-depth application of Indian agricultural statistics. Satellite remote sensing techniques can provide resource managers an efficient and economical means of acquiring timely data for the development and management of our natural resources. Remote Sensing (RS) data, acquired repetitively over agricultural land help in identification and mapping of crops and also in assessing crop vigour. This paper reports on a study that has been carried out to estimate paddy yield from the Landsat-5 Thematic Mapper (TM) data in Krishnagiri taluk. In this study, the Landsat image is classified using supervised classification and then vegetation index (VI) namely the Normalized Difference Vegetation Index (NDVI) have been used together with some field data to derive relationships between NDVI and yield. A linear relationship has been obtained between NDVI with yield. The yield estimated derived from the satellite data have been found to be accurate. Keywords: Remote Sensing, Normalised Difference Vegetation Index, Vegetation Index, Yield INTRODUCTION Remote sensing and its associated image analysis technology provide access to spatial information on a planetary scale. New detectors and imaging technologies are increasing the capability of remote sensing to acquire digital spatial information at very fine resolutions in an efficient manner (Ehlers et al. 1989). Up-to-date information of features and phenomena on the earth can be derived in a short duration of time. Crop identification and prediction of yield are the main concern of remote sensing application in agriculture. Various studies have demonstrated the usefulness of satellite remote sensing data for generating the information on total irrigated area and area under different crops (Kenneth and Lee, 1984, Nageswar Rao and Mohan Kumar, 1994), condition of crops (Singh et al., 1985; Lloyd, 1989) and crop production (Hatfield 1983; Jasinski, 1990; Murthy et.al, 1996). Researchers have investigated different spectral bands for vegetation sensitivity. Remote sensing has potential not only in identifying crop classes but also in the estimation of crop area and yield. The examination of the relationships between vegetation indices and yield has been frequently studied over the years and has been shown the useful for yield prediction purposes. For example, Major et al (1986) have shown usefulness of some VIs to estimate some leaf area index (LAI), biomass and grain production for cereals from radiometric measurements. Tucker et al (1980) found a strong relationship between specific spectral data and yield. The resulting plots of spectral data against time give a distinctive pattern which when integrated could be related to yield. All these relationships have a high correlation with yield. In another study, Green and Invins (1985) also proved that VIs relate strongly to biomass, and have been found to relate directly to yield. Rudoff and Batista (1990) have indicated that spectral data when transformed into VIs have great potential to be used in wheat yield prediction models tropical regions. Since VIs have been shown to be useful for yield estimation, in this study NDVI is used for paddy yield estimation. 26 JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
2 Gopperundevi and Kannan Paddy yield estimation using remote sensing The NDVI used in this study is commonly preferred because it reduces the effects of atmospheric conditions and topographical variations. The NDVI is the ratio between the difference in the infrared and red bands and the sum of bands, i.e. the infrared bands used in the study are band 4 ( mm) and band 5 ( mm) whilst the red band is 3 ( mm) of the Landsat-5 TM data. MATERIAL AND METHODS Study area Krishnagiri district covers an area of 5143 km². Krishnagiri district is bound by Vellore and Thiruvannamalai districts to the East, State of Karnataka to the west, State of Andhra Pradesh to the North and Dharmapuri District to the south. This district is elevated from 300m to 1400m above the mean sea level. It is located between 11º 12'N to 12º 49'N Latitude, 77º 27'E to 78º 38'E Longitude (Figure 1). Methodology This study is aimed at evaluating the possibility of remotely sensed data to estimate paddy acreage as a component of crop production fore casting process. Figure 2 shows the methodology for paddy yield prediction. Combination of digital image processing and classification technique namely supervised classification; extraction of paddy by binary coding, NDVI calculation for yield estimation is performed in this study. Then Regression analysis is done between vegetation indices and yield collected in training sites. Initially the satellite image is classified using Maximum likelihood classification technique. Training sets were given to different land use/land cover classes like paddy, forest/scrub, fallow land, water bodies etc., and statistical parameters like mean, standard deviation etc. were generated for these categories. From the study of statistical parameters of crop category, two or three major crop classes could be identified. Preliminary digital analysis was carried out by using maximum likelihood classification. The training sets given during preliminary digital analysis were purified with the available ground truth for different crops and other land use/land cover classes. Once again signature sets were generated for the above mentioned classes. Subsequently maximum likelihood classification program was run for classifying the data. Figure1: Satellite image of the study area The important crops of Krishnagiri District are paddy, maize, ragi, banana, sugarcane, cotton, tamarind, coconut, mango, groundnut, vegetables and flowers. The district has an excellent scope for agri-business. Data used Satellite data were selected based on the crop calendar and cloud free data availability of the study area and it is shown in figure 1. Field visits were made for the ground truth collection for study area so as to correlate the tones and textures of different crops and other land use/land cover categories with the image interpretation key. In the predetermined test sites on toposheets, detailed information of different crops grown and other ancillary information were collected. Figure 2: Flowchart for the methodology 27 JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
3 Paddy yield estimation using remote sensing Gopperundevi and Kannan In order to relate the yield and vegetation index field data were obtained by conducting field survey and enquiring the agronomists. The average yield of paddy for healthy, moderate healthy and stressed crops in Krishnagiri area are 9000kg/ha, 7000 kg/ha and 5000 kg/ha respectively and in Kodaivasal it ranges from kg/ha, 8000 kg/ha and 7000kg/ha respectively. The µm part of the electromagnetic spectrum contains the red edge feature of the green vegetation reflectance spectrum which is exploited by standard vegetation indices. The VI values obtained from the sample plot were plotted against the grain yield. A regression fit was made to determine the relationship between yield and NDVI. RESULTS AND DISCUSSION Supervised classification The attempt was made to classify the various land uses in the study area using ERDAS image processing software by Maximum Likelihood supervised classification technique. In supervised classification, spectral signatures are developed from specified locations in the image. These specified locations are given the generic name 'training sites' and knowledge of training sites is obtained by field survey. The land Use Land Cover (LULC) images of the study area are shown in figure 3. The accuracy of classification of images of Krishnagiri is 87.50% respectively. Training areas, usually small and discrete compared to the full image, are used to train the classification algorithm to recognize land cover classes based on their spectral signatures, as found in the image. The training areas for any one land cover class need to fully represent the variability of that class within the image. There are numerous factors that can affect the training signatures of the land cover classes. Environmental factors such as differences in soil type, varying soil moisture, and health of vegetation, can affect the signature and affect the accuracy of the final thematic map. Acreage estimation Remote sensing has potential in estimation of crop area and yield in to addition to identify the different crop classes. After classifying the image using supervised classification technique, aggregation is carried out to get the crop area for the study area. Areas of different land uses are calculated initially and the details are given the Table 1. Figure 3: Classified output of Krishnagiri District Table 1: Area of Land use Land Cover in Krishnagiri SL NO. Land use Area hectares 1 Rocky area Water body Paddy Other vegetation Waste land TOTAL AREA NDVI estimation One of the most widely used indices for vegetation monitoring is the Normalized Difference Vegetation Index (NDVI), because vegetation differential absorbs visible incident solar radiant and reflects much if the infrared (NIR), data on vegetation biophysical characteristics can be derived from visible and NIR and mid- infrared portions of the electromagnetic spectrum (EMS). The NDVI approach is based on the fact that healthy vegetation has low reflectance in the visible portion of the EMS due to chlorophyll and other pigment absorption and has high reflectance in the NIR because of the internal reflectance by the mesophyll spongy tissue of green leaf. NDVI can be calculated as a ratio of red and the NIR bands of a sensor system. NDVI values range from -1 to +1, because of high reflectance in the NIR portion of the EMS, vegetation is in 28 VOLUME 1 NUMBER 1 SEPTEMBER 2012 JOURNAL OF MODERN BIOTECHNOLOGY
4 Gopperundevi and Kannan Paddy yield estimation using remote sensing represented by high NDVI values between 0.1 and 1. Conversely, non- vegetated surfaces such as water bodies yield negative values of NDVI because of the electromagnetic absorption quality of water. Bare soil areas represent NDVI values which are closest to 0 due to high reflectance in both the visible and NIR portions of the EMS. NDVI is related to the absorption of photo synthetically active radiation (PAR) and basically measures the photosynthetic capability of leaves, which is related to vegetative canopy resistance and water vapour transfer. Figure 4 shows the image of NDVI. yield estimation equation. The yield estimation equation is as follows. Yield (Kg/pixel) for Krishnagiri = x (NDVI) Figure 5: NDVI vs. yield The average yield for healthy, moderate and stressed paddy crop and over all yield of paddy are quantified and tabulated in Table 2. Table 2: Estimated yield of paddy Scatter plot Figure 4: NDVI image The VI values and paddy yield obtained for different sample points and scatter plot is plotted between them. Numerous techniques have been applied for modeling the relationship between crop yields and measured vegetation parameters. Linear regression is the most popular technique to perform the relationship significance and predictive ability. A linear analysis of investigating yield response consists of empirical analysis of large, spatial, and multivariate data sets have often been reported in the literature. Several authors have found that linear correlations between yield and vegetation properties, soil properties, vary greatly. A regression fit was made to determine the relationship between yield and VIs in this study. Yield in kg/ha is converted in yield kg/pixel and related to corresponding NDVI. A linear relationship was obtained from ten samples that were taken. Figure 5 shows the relationship and correlation for the area for NDVI versus yield. The results show there is a linear relationship between each index and yield. Thus the NDVI versus data have been selected for obtaining the SL NO. Name of the Paddy Area 1 Krishnagiri CONCLUSIONS Yield tonne ) Healthy Moderate Stressed Total (MetricYield Metric tonne The NDVI values obtained from a combination of bands 3 and 4 of the Landsat-5 TM show better correlation with yield. This is because the effects of soil and atmosphere have been considerably reduced in the NDVI. The error in the satellite yield estimate is due to the difference in the paddy phenology cycle at the time the satellite data were acquired and the time the field measurement were taken. This study indicates the usefulness of satellite remote sensing techniques in deriving agriculture information over large areas in a cost effective manner that can benefit related agencies. REFERENCES Lloyd D A phenological description of Iberian vegetation using short wave vegetation index image, International Journal of Remote Sensing 10: JOURNAL OF MODERN BIOTECHNOLOGY VOLUME 1 NUMBER 1 SEPTEMBER 2012
5 Paddy yield estimation using remote sensing Gopperundevi and Kannan Hatfield JK Remote sensing estimators of potential and actual crop yield. Remote Sensing of Environment, 13: Kenneth KE and Lee HC The identification of irrigated crop types and estimation of acreages from Landsat imagery. Photogrammetric Engineering and Remote Sensing 50: Murthy CS, Thiruvengadachari S, Raju PV and Jonna S Improved ground sampling and crop yield estimation using satellite data, International Journal of Remote Sensing 17: Nageswar Rao PP and Mohan Kumar A Crop land inventory in the command area of Krishnarajasagar project using satellite data, International Journal of Remote Sensing 15: Ehlers M, Edward G and Bedard Y Integration of remote sensing with geographic information systems: A necessary evolution. Photogrammetric Engineering and Remote Sensing 55: Green CF and Invins JD Time of sowing and yields of winter wheat. Journal of Agricultural Science 104: Jasinski MF Sensitility of the normalized difference vegetation index to sub-pixel canopy cover, soil albedo and pixel scale. Remote Sensing of Environment 32: Major DG, Schaalje GB, Asrar G Kanemasu ET Estimation of whole plant biomass and grain yield from spectral reflectance of cereals. Canadian Journal of Remote Sensing 12: Singh PNK, Sahai TP and Patel MS Spectral responses of rice crop and its relation to yield and yield attributes. International Journal of Remote Sensing 6: Rudorff BFT and Batista GT Spectral response of wheat and its relationship to agronomic variable in the tropical region. Remote Sensing of Environment 31: Tucker CJ, Holben BN, Elgin JH and Murtey JE Relation of spectral to grain yield variation. Photogrammetric Engineering and Remote Sensing 46: VOLUME 1 NUMBER 1 SEPTEMBER 2012 JOURNAL OF MODERN BIOTECHNOLOGY
Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images
Land Use/Land Cover Map of the Central Facility of ARM in the Southern Great Plains Site Using DOE s Multi-Spectral Thermal Imager Satellite Images S. E. Báez Cazull Pre-Service Teacher Program University
More informationResolutions of Remote Sensing
Resolutions of Remote Sensing 1. Spatial (what area and how detailed) 2. Spectral (what colors bands) 3. Temporal (time of day/season/year) 4. Radiometric (color depth) Spatial Resolution describes how
More informationWATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS
WATER BODY EXTRACTION FROM MULTI SPECTRAL IMAGE BY SPECTRAL PATTERN ANALYSIS Nguyen Dinh Duong Department of Environmental Information Study and Analysis, Institute of Geography, 18 Hoang Quoc Viet Rd.,
More informationAnalysis of Land Use/Land Cover Change in Jammu District Using Geospatial Techniques
Analysis of Land Use/Land Cover Change in Jammu District Using Geospatial Techniques Dr. Anuradha Sharma 1, Davinder Singh 2 1 Head, Department of Geography, University of Jammu, Jammu-180006, India 2
More informationDesign of a High Resolution Multispectral Scanner for Developing Vegetation Indexes
Design of a High Resolution Multispectral Scanner for Developing Vegetation Indexes Rishitosh kumar sinha*, Roushan kumar mishra, Sam jeba kumar, Gunasekar. S Dept. of Instrumentation & Control Engg. S.R.M
More informationRemote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite
Remote Sensing and GIS Application In Change Detection Study In Urban Zone Using Multi Temporal Satellite R.Manonmani, G.Mary Divya Suganya Institute of Remote Sensing, Anna University, Chennai 600 025
More informationDigital image processing
746A27 Remote Sensing and GIS Lecture 4 Digital image processing Chandan Roy Guest Lecturer Department of Computer and Information Science Linköping University Digital Image Processing Most of the common
More informationHow Landsat Images are Made
How Landsat Images are Made Presentation by: NASA s Landsat Education and Public Outreach team June 2006 1 More than just a pretty picture Landsat makes pretty weird looking maps, and it isn t always easy
More informationANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES
ANALYSIS OF FOREST CHANGE IN FIRE DAMAGE AREA USING SATELLITE IMAGES Joon Mook Kang, Professor Joon Kyu Park, Ph.D Min Gyu Kim, Ph.D._Candidate Dept of Civil Engineering, Chungnam National University 220
More informationReview for Introduction to Remote Sensing: Science Concepts and Technology
Review for Introduction to Remote Sensing: Science Concepts and Technology Ann Johnson Associate Director ann@baremt.com Funded by National Science Foundation Advanced Technological Education program [DUE
More informationUsing Remote Sensing to Monitor Soil Carbon Sequestration
Using Remote Sensing to Monitor Soil Carbon Sequestration E. Raymond Hunt, Jr. USDA-ARS Hydrology and Remote Sensing Beltsville Agricultural Research Center Beltsville, Maryland Introduction and Overview
More informationCROP CLASSIFICATION WITH HYPERSPECTRAL DATA OF THE HYMAP SENSOR USING DIFFERENT FEATURE EXTRACTION TECHNIQUES
Proceedings of the 2 nd Workshop of the EARSeL SIG on Land Use and Land Cover CROP CLASSIFICATION WITH HYPERSPECTRAL DATA OF THE HYMAP SENSOR USING DIFFERENT FEATURE EXTRACTION TECHNIQUES Sebastian Mader
More informationEnvironmental Remote Sensing GEOG 2021
Environmental Remote Sensing GEOG 2021 Lecture 4 Image classification 2 Purpose categorising data data abstraction / simplification data interpretation mapping for land cover mapping use land cover class
More informationSAMPLE MIDTERM QUESTIONS
Geography 309 Sample MidTerm Questions Page 1 SAMPLE MIDTERM QUESTIONS Textbook Questions Chapter 1 Questions 4, 5, 6, Chapter 2 Questions 4, 7, 10 Chapter 4 Questions 8, 9 Chapter 10 Questions 1, 4, 7
More informationAccuracy Assessment of Land Use Land Cover Classification using Google Earth
American Journal of Environmental Protection 25; 4(4): 9-98 Published online July 2, 25 (http://www.sciencepublishinggroup.com/j/ajep) doi:.648/j.ajep.2544.4 ISSN: 228-568 (Print); ISSN: 228-5699 (Online)
More informationSelecting the appropriate band combination for an RGB image using Landsat imagery
Selecting the appropriate band combination for an RGB image using Landsat imagery Ned Horning Version: 1.0 Creation Date: 2004-01-01 Revision Date: 2004-01-01 License: This document is licensed under a
More informationASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND
ASSESSMENT OF FOREST RECOVERY AFTER FIRE USING LANDSAT TM IMAGES AND GIS TECHNIQUES: A CASE STUDY OF MAE WONG NATIONAL PARK, THAILAND Sunee Sriboonpong 1 Yousif Ali Hussin 2 Alfred de Gier 2 1 Forest Resource
More informationAPPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA
APPLICATION OF GOOGLE EARTH FOR THE DEVELOPMENT OF BASE MAP IN THE CASE OF GISH ABBAY SEKELA, AMHARA STATE, ETHIOPIA Abineh Tilahun Department of Geography and environmental studies, Adigrat University,
More informationA KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW ABSTRACT
A KNOWLEDGE-BASED APPROACH FOR REDUCING CLOUD AND SHADOW Mingjun Song, Graduate Research Assistant Daniel L. Civco, Director Laboratory for Earth Resources Information Systems Department of Natural Resources
More information2.3 Spatial Resolution, Pixel Size, and Scale
Section 2.3 Spatial Resolution, Pixel Size, and Scale Page 39 2.3 Spatial Resolution, Pixel Size, and Scale For some remote sensing instruments, the distance between the target being imaged and the platform,
More information1. Introduction. V.S.S. Kiran 1, Y.K. Srivastava 2 and M. Jagannadha Rao 3
Cloud Publications International Journal of Advanced Remote Sensing and GIS 2014, Volume 3, Issue 1, pp. 592-597, Article ID Tech-273 ISSN 2320-0243 Case Study Open Access Utilization of Resourcesat LISS
More informationSupervised Classification workflow in ENVI 4.8 using WorldView-2 imagery
Supervised Classification workflow in ENVI 4.8 using WorldView-2 imagery WorldView-2 is the first commercial high-resolution satellite to provide eight spectral sensors in the visible to near-infrared
More informationRadiation Transfer in Environmental Science
Radiation Transfer in Environmental Science with emphasis on aquatic and vegetation canopy media Autumn 2008 Prof. Emmanuel Boss, Dr. Eyal Rotenberg Introduction Radiation in Environmental sciences Most
More informationAPPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING. Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO***
APPLICATION OF TERRA/ASTER DATA ON AGRICULTURE LAND MAPPING Genya SAITO*, Naoki ISHITSUKA*, Yoneharu MATANO**, and Masatane KATO*** *National Institute for Agro-Environmental Sciences 3-1-3 Kannondai Tsukuba
More informationOverview. What is EMR? Electromagnetic Radiation (EMR) LA502 Special Studies Remote Sensing
LA502 Special Studies Remote Sensing Electromagnetic Radiation (EMR) Dr. Ragab Khalil Department of Landscape Architecture Faculty of Environmental Design King AbdulAziz University Room 103 Overview What
More informationSpectral Response for DigitalGlobe Earth Imaging Instruments
Spectral Response for DigitalGlobe Earth Imaging Instruments IKONOS The IKONOS satellite carries a high resolution panchromatic band covering most of the silicon response and four lower resolution spectral
More informationENVIRONMENTAL MONITORING Vol. I - Remote Sensing (Satellite) System Technologies - Michael A. Okoye and Greg T. Koeln
REMOTE SENSING (SATELLITE) SYSTEM TECHNOLOGIES Michael A. Okoye and Greg T. Earth Satellite Corporation, Rockville Maryland, USA Keywords: active microwave, advantages of satellite remote sensing, atmospheric
More informationLand Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed
Land Use/ Land Cover Mapping Initiative for Kansas and the Kansas River Watershed Kansas Biological Survey Kansas Applied Remote Sensing Program April 2008 Previous Kansas LULC Projects Kansas LULC Map
More information1 The interaction of visible and near infrared EMR with soil
PROPERTIES OF EARTH SURFACES AND THEIR INTERACTIONS WITH ELECTROMAGNETIC RADIATION AT VISIBLE AND NEAR INFRARED WAVELENGTHS For a fixed distribution of incident radiation, the particular properties of
More informationIntroduction: Growth analysis and crop dry matter accumulation
PBIO*3110 Crop Physiology Lecture #2 Fall Semester 2008 Lecture Notes for Tuesday 9 September How is plant productivity measured? Introduction: Growth analysis and crop dry matter accumulation Learning
More informationUsing Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California
Graham Emde GEOG 3230 Advanced Remote Sensing February 22, 2013 Lab #1 Using Remote Sensing Imagery to Evaluate Post-Wildfire Damage in Southern California Introduction Wildfires are a common disturbance
More informationMapping Earth from Space Remote sensing and satellite images. Remote sensing developments from war
Mapping Earth from Space Remote sensing and satellite images Geomatics includes all the following spatial technologies: a. Cartography "The art, science and technology of making maps" b. Geographic Information
More informationMODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA
MODIS IMAGES RESTORATION FOR VNIR BANDS ON FIRE SMOKE AFFECTED AREA Li-Yu Chang and Chi-Farn Chen Center for Space and Remote Sensing Research, National Central University, No. 300, Zhongda Rd., Zhongli
More informationLectures Remote Sensing
Lectures Remote Sensing ATMOSPHERIC CORRECTION dr.ir. Jan Clevers Centre of Geo-Information Environmental Sciences Wageningen UR Atmospheric Correction of Optical RS Data Background When needed? Model
More informationTerraColor White Paper
TerraColor White Paper TerraColor is a simulated true color digital earth imagery product developed by Earthstar Geographics LLC. This product was built from imagery captured by the US Landsat 7 (ETM+)
More informationArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer
ArcGIS Agricultural Land Use Maps from the Mississippi Cropland Data Layer Fred L. Shore, Ph.D. Mississippi Department of Agriculture and Commerce Jackson, MS, USA fred_shore@nass.usda.gov Rick Mueller
More informationImage Analysis CHAPTER 16 16.1 ANALYSIS PROCEDURES
CHAPTER 16 Image Analysis 16.1 ANALYSIS PROCEDURES Studies for various disciplines require different technical approaches, but there is a generalized pattern for geology, soils, range, wetlands, archeology,
More informationVISUALIZATION OF A CROP SEASON THE INTEGRATION OF REMOTELY SENSED DATA AND SURVEY DATA
VISUALIZATION OF A CROP SEASON THE INTEGRATION OF REMOTELY SENSED DATA AND SURVEY DATA Gail Wade GIS Analyst, Spatial Analysis Research Section George Hanuschak Chief, Geospatial Information Branch Research
More informationComplex Vegetation Survey in a Fruit Plantation by Spectral Instruments
Hungarian Association of Agricultural Informatics European Federation for Information Technology in Agriculture, Food and the Environment Journal of Agricultural Informatics. 203 Vol. 4, No. 2 Complex
More informationElectromagnetic Radiation (EMR) and Remote Sensing
Electromagnetic Radiation (EMR) and Remote Sensing 1 Atmosphere Anything missing in between? Electromagnetic Radiation (EMR) is radiated by atomic particles at the source (the Sun), propagates through
More informationLanduse pattern in Perambalur district using spatial information technology
Available online at www.pelagiaresearchlibrary.com Advances in Applied Science Research, 2015, 6(6):159-166 ISSN: 0976-8610 CODEN (USA): AASRFC Landuse pattern in Perambalur district using spatial information
More informationdynamic vegetation model to a semi-arid
Application of a conceptual distributed dynamic vegetation model to a semi-arid basin, SE of Spain By: M. Pasquato, C. Medici and F. Francés Universidad Politécnica de Valencia - Spain Research Institute
More informationData Processing Flow Chart
Legend Start V1 V2 V3 Completed Version 2 Completion date Data Processing Flow Chart Data: Download a) AVHRR: 1981-1999 b) MODIS:2000-2010 c) SPOT : 1998-2002 No Progressing Started Did not start 03/12/12
More informationPreface. Ko Ko Lwin Division of Spatial Information Science University of Tsukuba 2008
1 Preface Remote Sensing data is one of the primary data sources in GIS analysis. The objective of this material is to provide fundamentals of Remote Sensing technology and its applications in Geographical
More informationVCS REDD Methodology Module. Methods for monitoring forest cover changes in REDD project activities
1 VCS REDD Methodology Module Methods for monitoring forest cover changes in REDD project activities Version 1.0 May 2009 I. SCOPE, APPLICABILITY, DATA REQUIREMENT AND OUTPUT PARAMETERS Scope This module
More informationSome elements of photo. interpretation
Some elements of photo Shape Size Pattern Color (tone, hue) Texture Shadows Site Association interpretation Olson, C. E., Jr. 1960. Elements of photographic interpretation common to several sensors. Photogrammetric
More informationDigital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction
Digital Remote Sensing Data Processing Digital Remote Sensing Data Processing and Analysis: An Introduction and Analysis: An Introduction Content Remote sensing data Spatial, spectral, radiometric and
More informationCOTTON WATER RELATIONS
COTTON WATER RELATIONS Dan R. Krieg 1 INTRODUCTION Water is the most abundant substance on the Earth s surface and yet is the most limiting to maximum productivity of nearly all crop plants. Land plants,
More informationMOVING FORWARD WITH LIDAR REMOTE SENSING: AIRBORNE ASSESSMENT OF FOREST CANOPY PARAMETERS
MOVING FORWARD WITH LIDAR REMOTE SENSING: AIRBORNE ASSESSMENT OF FOREST CANOPY PARAMETERS Alicia M. Rutledge Sorin C. Popescu Spatial Sciences Laboratory Department of Forest Science Texas A&M University
More informationRemote Sensing. Vandaag. Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem
Remote Sensing 1 Vandaag Voordelen Remote Sensing Wat is Remote Sensing? Vier elementen Remote Sensing systeem 2 Nederland Vanaf 700 km hoogte Landsat TM mozaïek 3 Europa vanaf 36000 km hoogte 4 5 Mount
More informationRiver Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models
River Flood Damage Assessment using IKONOS images, Segmentation Algorithms & Flood Simulation Models Steven M. de Jong & Raymond Sluiter Utrecht University Corné van der Sande Netherlands Earth Observation
More informationRemote Sensing and Land Use Classification: Supervised vs. Unsupervised Classification Glen Busch
Remote Sensing and Land Use Classification: Supervised vs. Unsupervised Classification Glen Busch Introduction In this time of large-scale planning and land management on public lands, managers are increasingly
More informationInformation Contents of High Resolution Satellite Images
Information Contents of High Resolution Satellite Images H. Topan, G. Büyüksalih Zonguldak Karelmas University K. Jacobsen University of Hannover, Germany Keywords: satellite images, mapping, resolution,
More informationTrend in Land Use/Land Cover Change Detection by RS and GIS Application
Trend in Land Use/Land Cover Change Detection by RS and GIS Application N. Nagarajan 1, S. Poongothai 2 1 Assistant Professor, 2 Professor Department of Civil Engineering, FEAT, Annamalai University, Tamilnadu,
More informationPartitioning the Conterminous United States into Mapping Zones for Landsat TM Land Cover Mapping
Partitioning the Conterminous United States into Mapping Zones for Landsat TM Land Cover Mapping Collin Homer Raytheon, EROS Data Center, Sioux Falls, South Dakota 605-594-2714 homer@usgs.gov Alisa Gallant
More informationAPPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED
APPLICATION OF MULTITEMPORAL LANDSAT DATA TO MAP AND MONITOR LAND COVER AND LAND USE CHANGE IN THE CHESAPEAKE BAY WATERSHED S. J. GOETZ Woods Hole Research Center Woods Hole, Massachusetts 054-096 USA
More informationU.S. Geological Survey Earth Resources Operation Systems (EROS) Data Center
U.S. Geological Survey Earth Resources Operation Systems (EROS) Data Center World Data Center for Remotely Sensed Land Data USGS EROS DATA CENTER Land Remote Sensing from Space: Acquisition to Applications
More informationSignal Strength Measurements and Coverage Estimation of Mobile Communication Network Using IRS-IC Multispectral and CARTOSAT-1 Stereo Images
Signal Strength Measurements and Coverage Estimation of Mobile Communication Network Using IRS-IC Multispectral and CARTOSAT-1 Stereo Images B. NAVEENCHANDRA 1, K. N. LOKESH 2, USHA 3, AND H.GANGADHARA
More informationIMAGINES_VALIDATIONSITESNETWORK ISSUE 1.00. EC Proposal Reference N FP7-311766. Name of lead partner for this deliverable: EOLAB
Date Issued: 26.03.2014 Issue: I1.00 IMPLEMENTING MULTI-SCALE AGRICULTURAL INDICATORS EXPLOITING SENTINELS RECOMMENDATIONS FOR SETTING-UP A NETWORK OF SITES FOR THE VALIDATION OF COPERNICUS GLOBAL LAND
More informationLCCS & GeoVIS for land cover mapping. Experience Sharing of an Exercise
LCCS & GeoVIS for land cover mapping Experience Sharing of an Exercise Forest Survey of India Subhash Ashutosh Joint Director Study Area Topographic sheet 53J4 Longitude - 78ºE - 78º15'E Latitude - 30ºN
More informationGlobal environmental information Examples of EIS Data sets and applications
METIER Graduate Training Course n 2 Montpellier - february 2007 Information Management in Environmental Sciences Global environmental information Examples of EIS Data sets and applications Global datasets
More informationThe Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories
The Role of SPOT Satellite Images in Mapping Air Pollution Caused by Cement Factories Dr. Farrag Ali FARRAG Assistant Prof. at Civil Engineering Dept. Faculty of Engineering Assiut University Assiut, Egypt.
More informationHyperspectral Satellite Imaging Planning a Mission
Hyperspectral Satellite Imaging Planning a Mission Victor Gardner University of Maryland 2007 AIAA Region 1 Mid-Atlantic Student Conference National Institute of Aerospace, Langley, VA Outline Objective
More informationMAPPING DETAILED DISTRIBUTION OF TREE CANOPIES BY HIGH-RESOLUTION SATELLITE IMAGES INTRODUCTION
MAPPING DETAILED DISTRIBUTION OF TREE CANOPIES BY HIGH-RESOLUTION SATELLITE IMAGES Hideki Hashiba, Assistant Professor Nihon Univ., College of Sci. and Tech., Department of Civil. Engrg. Chiyoda-ku Tokyo
More informationA remote sensing instrument collects information about an object or phenomenon within the
Satellite Remote Sensing GE 4150- Natural Hazards Some slides taken from Ann Maclean: Introduction to Digital Image Processing Remote Sensing the art, science, and technology of obtaining reliable information
More informationINTRODUCTION TO REMOTE SENSING
INTRODUCTION TO REMOTE SENSING Dr Robert Sanderson New Mexico State University Satellite picture of Las Cruces, NM Table of Contents Introduction...1 Electromagnetic energy...1 Reflection and absorption...2
More informationMonitoring Soil Moisture from Space. Dr. Heather McNairn Science and Technology Branch Agriculture and Agri-Food Canada heather.mcnairn@agr.gc.
Monitoring Soil Moisture from Space Dr. Heather McNairn Science and Technology Branch Agriculture and Agri-Food Canada heather.mcnairn@agr.gc.ca What is Remote Sensing? Scientists turn the raw data collected
More informationConnected Farm Field Services. Dan Rooney InfoAg Conference, July 30, 2014
Connected Farm Field Services Dan Rooney InfoAg Conference, July 30, 2014 What is Connected Farm? Connected Farm is an integrated operations management solution that combines industry-leading hardware,
More informationCIESIN Columbia University
Conference on Climate Change and Official Statistics Oslo, Norway, 14-16 April 2008 The Role of Spatial Data Infrastructure in Integrating Climate Change Information with a Focus on Monitoring Observed
More informationRemote Sensing an Introduction
Remote Sensing an Introduction Seminar: Space is the Place Referenten: Anica Huck & Michael Schlund Remote Sensing means the observation of, or gathering information about, a target by a device separated
More informationResearch on Soil Moisture and Evapotranspiration using Remote Sensing
Research on Soil Moisture and Evapotranspiration using Remote Sensing Prof. dr. hab Katarzyna Dabrowska Zielinska Remote Sensing Center Institute of Geodesy and Cartography 00-950 Warszawa Jasna 2/4 Field
More informationArturo Sanchez-Azofeifa, PhD, PEng Cassidy Rankine, Gilberto Zonta-Pastorello Centre for Earth Observation Sciences (CEOS) Earth and Atmospheric
Arturo Sanchez-Azofeifa, PhD, PEng Cassidy Rankine, Gilberto Zonta-Pastorello Centre for Earth Observation Sciences (CEOS) Earth and Atmospheric Sciences Department University of Alberta Microsoft WSN
More informationNature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data
Nature Values Screening Using Object-Based Image Analysis of Very High Resolution Remote Sensing Data Aleksi Räsänen*, Anssi Lensu, Markku Kuitunen Environmental Science and Technology Dept. of Biological
More informationSMEX04 Land Use Classification Data
Notice to Data Users: The documentation for this data set was provided solely by the Principal Investigator(s) and was not further developed, thoroughly reviewed, or edited by NSIDC. Thus, support for
More informationP.M. Rich, W.A. Hetrick, S.C. Saving Biological Sciences University of Kansas Lawrence, KS 66045
USING VIEWSHED MODELS TO CALCULATE INTERCEPTED SOLAR RADIATION: APPLICATIONS IN ECOLOGY by P.M. Rich, W.A. Hetrick, S.C. Saving Biological Sciences University of Kansas Lawrence, KS 66045 R.O. Dubayah
More informationDevelopment of Method for LST (Land Surface Temperature) Detection Using Big Data of Landsat TM Images and AWS
Development of Method for LST (Land Surface Temperature) Detection Using Big Data of Landsat TM Images and AWS Myung-Hee Jo¹, Sung Jae Kim², Jin-Ho Lee 3 ¹ Department of Aeronautical Satellite System Engineering,
More informationRemote sensing and GIS applications in coastal zone monitoring
Remote sensing and GIS applications in coastal zone monitoring T. Alexandridis, C. Topaloglou, S. Monachou, G.Tsakoumis, A. Dimitrakos, D. Stavridou Lab of Remote Sensing and GIS School of Agriculture
More informationDeficit Rainfall Insurance Payouts in Most Vulnerable Agro Climatic Zones of Tamil Nadu, India
Deficit Rainfall Insurance Payouts in Most Vulnerable Agro Climatic Zones of Tamil Nadu, India S.Senthilnathan, K.Palanisami, C.R.Ranganathan and Chieko Umetsu 2 Tamil Nadu Agricultural University, Coimbatore,
More informationMultinomial Logistics Regression for Digital Image Classification
Multinomial Logistics Regression for Digital Image Classification Dr. Moe Myint, Chief Scientist, Mapping and Natural Resources Information Integration (MNRII), Switzerland maungmoe.myint@mnrii.com KEY
More informationCalculation of Minimum Distances. Minimum Distance to Means. Σi i = 1
Minimum Distance to Means Similar to Parallelepiped classifier, but instead of bounding areas, the user supplies spectral class means in n-dimensional space and the algorithm calculates the distance between
More informationRing grave detection in high resolution satellite images of agricultural land
Ring grave detection in high resolution satellite images of agricultural land Siri Øyen Larsen, Øivind Due Trier, Ragnar Bang Huseby, and Rune Solberg, Norwegian Computing Center Collaborators: The Norwegian
More informationRemote Sensing Applications for Precision Agriculture
Remote Sensing Applications for Precision Agriculture Farm Progress Show Chris J. Johannsen, Paul G. Carter and Larry L. Biehl Department of Agronomy and Laboratory for Applications of Remote Sensing (LARS)
More informationRemote sensing and management of large irrigation projects
Remote sensing and management of large irrigation projects Lahlou O., Vidal A. in Deshayes M. (ed.). La télédétection en agriculture Montpellier : CIHEAM Options Méditerranéennes : Série A. Séminaires
More informationAnalysis of Landsat ETM+ Image Enhancement for Lithological Classification Improvement in Eagle Plain Area, Northern Yukon
Analysis of Landsat ETM+ Image Enhancement for Lithological Classification Improvement in Eagle Plain Area, Northern Yukon Shihua Zhao, Department of Geology, University of Calgary, zhaosh@ucalgary.ca,
More informationPixel-based and object-oriented change detection analysis using high-resolution imagery
Pixel-based and object-oriented change detection analysis using high-resolution imagery Institute for Mine-Surveying and Geodesy TU Bergakademie Freiberg D-09599 Freiberg, Germany imgard.niemeyer@tu-freiberg.de
More informationCrop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview. Werner Schneider
Crop Drought Stress Monitoring by Remote Sensing (DROSMON) Overview Werner Schneider Institut of Surveying, Remote Sensing and Land Information Department of Landscape, Spatial and Infrastructure Sciences
More informationPotential of Sugar cane monitoring using Synthetic Aperture Radar in Central Thailand
Potential of Sugar cane monitoring using Synthetic Aperture Radar in Central Thailand Thanit Intarat 1, Preesan Rakwatin 1, Panu Srestasathien 1, Peeteenut Triwong 2, Chanwit Tangsiriworakul 2, and Samart
More informationGeneration of Cloud-free Imagery Using Landsat-8
Generation of Cloud-free Imagery Using Landsat-8 Byeonghee Kim 1, Youkyung Han 2, Yonghyun Kim 3, Yongil Kim 4 Department of Civil and Environmental Engineering, Seoul National University (SNU), Seoul,
More informationGeospatial intelligence and data fusion techniques for sustainable development problems
Geospatial intelligence and data fusion techniques for sustainable development problems Nataliia Kussul 1,2, Andrii Shelestov 1,2,4, Ruslan Basarab 1,4, Sergii Skakun 1, Olga Kussul 2 and Mykola Lavreniuk
More informationLake Monitoring in Wisconsin using Satellite Remote Sensing
Lake Monitoring in Wisconsin using Satellite Remote Sensing D. Gurlin and S. Greb Wisconsin Department of Natural Resources 2015 Wisconsin Lakes Partnership Convention April 23 25, 2105 Holiday Inn Convention
More informationFOR375 EXAM #2 STUDY SESSION SPRING 2016. Lecture 14 Exam #2 Study Session
FOR375 EXAM #2 STUDY SESSION SPRING 2016 Lecture 14 Exam #2 Study Session INTRODUCTION TO REMOTE SENSING TYPES OF REMOTE SENSING Ground based platforms Airborne based platforms Space based platforms TYPES
More informationRESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY
RESOLUTION MERGE OF 1:35.000 SCALE AERIAL PHOTOGRAPHS WITH LANDSAT 7 ETM IMAGERY M. Erdogan, H.H. Maras, A. Yilmaz, Ö.T. Özerbil General Command of Mapping 06100 Dikimevi, Ankara, TURKEY - (mustafa.erdogan;
More informationJECAM Site Ukraine. 21 23 July 2014. Team Leader: Kussul Nataliia. JECAM/GEOGLAM Science Meeting Ottawa, Canada
Site Ukraine /GEOGLAM Science, Canada 21 23 July Team Leader: Kussul Nataliia Members: Shelestov Andrii, Skakun Sergii Site Description Kyiv oblast (SRI, area 28,000 km 2 ) & intensive observation sub-site
More informationHow to calculate reflectance and temperature using ASTER data
How to calculate reflectance and temperature using ASTER data Prepared by Abduwasit Ghulam Center for Environmental Sciences at Saint Louis University September, 2009 This instructions walk you through
More informationPlant Response to Irrigation Treatments in Arkansas Cotton
Plant Response to Irrigation Treatments in Arkansas Cotton Sreekala G. Bajwa and Earl D. Vories 1 INTRODUCTION Irrigation of cotton has been increasing throughout the mid-south. In Arkansas, 65% of the
More informationVEGETATION DETECTION IN MULTISPECTRAL REMOTE SENSING IMAGES: PROTECTIVE ROLE-ANALYSIS OF VEGETATION IN 2004 INDIAN OCEAN TSUNAMI
VEGETATION DETECTION IN MULTISPECTRAL REMOTE SENSING IMAGES: PROTECTIVE ROLE-ANALYSIS OF VEGETATION IN 2004 INDIAN OCEAN TSUNAMI Rajlaxmi Chouhan a, *, Neeraj Rao b a, b PDPM Indian Institute of Information
More informationD.S. Boyd School of Earth Sciences and Geography, Kingston University, U.K.
PHYSICAL BASIS OF REMOTE SENSING D.S. Boyd School of Earth Sciences and Geography, Kingston University, U.K. Keywords: Remote sensing, electromagnetic radiation, wavelengths, target, atmosphere, sensor,
More informationNEW GEN SUPPORT SYSTEM FOR AGRICULTURAL CROPS FOR KANCHEEPURAM DISTRICT SOUTH INDIA
NEW GEN SUPPORT SYSTEM FOR AGRICULTURAL CROPS FOR KANCHEEPURAM DISTRICT SOUTH INDIA D. Soundarrajan 1, Priyadharshini 2, Dr.M.M.Vijayalakshmi 3, Dr.E. Natarajan 4 1 Research Scholar, 3 Professor, Department
More informationTreasure Hunt. Lecture 2 How does Light Interact with the Environment? EMR Principles and Properties. EMR and Remote Sensing
Lecture 2 How does Light Interact with the Environment? Treasure Hunt Find and scan all 11 QR codes Choose one to watch / read in detail Post the key points as a reaction to http://www.scoop.it/t/env202-502-w2
More informationCollaborative research project pre agro
Collaborative research project pre agro Extraction of phenology-dependent structural information from hyperspectral, directional CHRIS data for a better derivation of canopy parameters of winter-wheat
More information