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Orchard LAI Estimation and Land-use Correlation using Geospatial Technology
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Transcript of Orchard LAI Estimation and Land-use Correlation using Geospatial Technology
Orchard LAI Estimation and Land-use Correlation using Geospatial
TechnologyByBy
Dr. Sudhanshu Sekhar PandaDr. Sudhanshu Sekhar PandaAssociate Professor, GIS/Environmental Science
Joshua Nolan & Lee IrmingerJoshua Nolan & Lee Irminger
Undergraduate Student
Institute of Environmental Spatial Analysis
BackgroundBackground
Major quantity of soil and plant available water is lost by evapotranspiration (ET).
Most Irrigation scheduling for crop is conducted based on ET. The loss of water due to ET varies with different crops
including horticultural plants. It is difficult to estimate ET for large orchards. Hydrologic parameters like stomatal conductance, soil
moisture, leaf area index (LAI), plant canopy temperature, and wind velocity are functions of plant ET.
These hydrologic attributes together can be modeled to estimate ET.
BackgroundBackground
Remote sensing has the proven ability for measuring some of Remote sensing has the proven ability for measuring some of those parameters in quick and cost-effective mannerthose parameters in quick and cost-effective manner
Hydrologic parameters like stomatal conductance, soil moisture, LAI, and plant canopy temperature can be estimated on a spatial basis using remotely sensed imagery.
Horticultural plants of different species and growth stages Horticultural plants of different species and growth stages demonstrate different values for these hydrologic parameters demonstrate different values for these hydrologic parameters that ultimately help estimate the ET.that ultimately help estimate the ET.
Geospatial Technology has the ability to decipher those plants of different species and growth stages in an orchard.
ObjectivesObjectives
The objectives of this study is to 1. Estimate LAI of a blueberry orchard (Z-Blu
Farm) and surrounding windbreaker forest cover using high resolution orthoimagery.
2. Conduct the land-use classification or orchard speciation using Object-based Image Analysis (OBIA) technique.
3. Correlate the LAI to different species present in the orchard.
Study AreaStudy Area
Z-Blu Farm (130 acres blueberry Z-Blu Farm (130 acres blueberry plantation) in Nahunta, GAplantation) in Nahunta, GA
Very Well managed orchardVery Well managed orchard Two varieties of blueberry – High Two varieties of blueberry – High
Bush and Rabbit Eye and 3 stages of Bush and Rabbit Eye and 3 stages of blueberry plants due to expansion of blueberry plants due to expansion of the orchard since 2004the orchard since 2004
Other land-uses present are forest Other land-uses present are forest (windbreaker), bare soil, and grass(windbreaker), bare soil, and grass
Data & SoftwareData & Software
6” (15 cm)6” (15 cm) resolution Color Infrared (CIR) imagery ( resolution Color Infrared (CIR) imagery (RR, , GG, , and and NIRNIR bands) through bands) through Photoscience Geospatial Inc.Photoscience Geospatial Inc.
Field LAI data with LAI-2200 Canopy Analyzer (Field LAI data with LAI-2200 Canopy Analyzer (LI-COR LI-COR Biosciences, Lincoln, NEBiosciences, Lincoln, NE ) )
IDRISI Taiga (IDRISI Taiga (Clark Labs, Clark University, Worcester, MAClark Labs, Clark University, Worcester, MA) ) softwaresoftware
eCognition (eCognition (Trimble Geospatial, Westminster, COTrimble Geospatial, Westminster, CO) ) Developer softwareDeveloper software
QT Modeler (QT Modeler (Applied Imagery, Silver Spring, MDApplied Imagery, Silver Spring, MD) software) software ArcGIS 10 (ArcGIS 10 (ESRI, Redlands, CAESRI, Redlands, CA) software) software
Orthoimagery (15 cm resolution)Orthoimagery (15 cm resolution)
LAI-2200 Plant Canopy AnalyzerLAI-2200 Plant Canopy Analyzer
LAI-2200 Calibration & Data LAI-2200 Calibration & Data Collection Collection
LAI Raster Development LAI Raster Development
LAI raster was developed for the Z-Blu LAI raster was developed for the Z-Blu orchard using the relationship developed orchard using the relationship developed by Schultz and Engman (2000) by Schultz and Engman (2000)
LAI = - ln (SAVI + .371)/.48 LAI = - ln (SAVI + .371)/.48 Which calculates LAI from the soil Which calculates LAI from the soil
adjusted vegetation index (adjusted vegetation index (SAVISAVI) from ) from NAIP orthoimageryNAIP orthoimagery
SAVI Raster Development SAVI Raster Development
SAVISAVI raster is created by the algorithm developed raster is created by the algorithm developed Huete (1988)Huete (1988)
where r and ir are spectral reflectance from the R-and NIR-band images, respectively, and the L is a constant that represents the vegetation density.
Huete (1988) defined the optimal adjustment factor of L = 0.25 to be considered for higher vegetation density in the field, L = 0.5 for intermediate vegetation density, and L = 1 for the low vegetation density.
OBIA Based Image SegmentationOBIA Based Image Segmentation
Individual bands (R-, G-, and NIR-band) were Individual bands (R-, G-, and NIR-band) were extracted from the CIR imageextracted from the CIR image
LiDAR data was used to produce ground elevation LiDAR data was used to produce ground elevation raster (DEM) and the plant height raster (nDSM) raster (DEM) and the plant height raster (nDSM) using the using the QT Modeler QT Modeler softwaresoftware
NDVI raster was developed using the algorithmNDVI raster was developed using the algorithm
Another raster was developed following the WATER algorithm developed by Nolan (2011)
where r , g and ir are spectral reflectance from the R-, G- and NIR-band images, respectively
NDVI = (ir - r) / (ir + r)
WATER = (g - ir) /(g + ir)
Rule Sets Developed for Image Rule Sets Developed for Image SegmentationSegmentation
All these rasters (All these rasters (previous slideprevious slide) were ) were imported to eCognition softwareimported to eCognition software
Rule sets were developed for distinguishing Rule sets were developed for distinguishing individual classes including blueberry individual classes including blueberry species from the study area imagespecies from the study area image
Multiresolution segmentation (Multiresolution segmentation (MRSMRS) or ) or other segmentation techniques were used other segmentation techniques were used in many steps to have classes distinguishedin many steps to have classes distinguished
Rule Sets (Rule Sets (Step by StepStep by Step))
Rule Sets (Rule Sets (Step by StepStep by Step))
Rule Sets (Rule Sets (Step by StepStep by Step))
Rule Sets (Rule Sets (Step by StepStep by Step))
Rule Set Example from eCognitionRule Set Example from eCognition
Rule Set Example from eCognition Rule Set Example from eCognition ((Contd…Contd…))
Blueberry LAI CorrelationBlueberry LAI Correlation
Segmentation of the orchard image Segmentation of the orchard image provided different blueberry species along provided different blueberry species along with bare soil and grass.with bare soil and grass.
Visual correlation was conducted on Visual correlation was conducted on classified image versus LAI rasterclassified image versus LAI raster
The field collected LAI values were The field collected LAI values were correlated with the LAI values obtained correlated with the LAI values obtained from the image analysis to observe from the image analysis to observe relationship.relationship.
SAVI RasterSAVI Raster
NDVI RasterNDVI Raster
LAI RasterLAI Raster
OBIA Segmentation Raster SeriesOBIA Segmentation Raster Series((Tall & Short Features separationTall & Short Features separation))
Multiresolution Segmentation for Differentiating Multiresolution Segmentation for Differentiating Various Classes of Tall ObjectsVarious Classes of Tall Objects
Multiresolution Segmentation for Differentiating Multiresolution Segmentation for Differentiating Various Classes of Short ObjectsVarious Classes of Short Objects
Trees & Buildings SeparationTrees & Buildings Separation
Evergreen & Deciduous Trees Evergreen & Deciduous Trees SeparationSeparation
Water & Shadow SeparationWater & Shadow Separation
Bare Soil ClassificationBare Soil Classification
High Bush Blueberry Variety Distinction High Bush Blueberry Variety Distinction (NDVI between 0.2 and 0.4)
Rabbit Eye Blueberry Variety Distinction Rabbit Eye Blueberry Variety Distinction (compactness greater than 3 )
Chessboard Segmentation to Chessboard Segmentation to Obtain Smaller ObjectObtain Smaller Object
MRS Region Grow Segmentation to MRS Region Grow Segmentation to Obtain Larger Objects in Rabbit EyeObtain Larger Objects in Rabbit Eye
Grass ClassificationGrass Classification
Final Segmented ImageFinal Segmented Image
LAI Raster & Field LAI LAI Raster & Field LAI Collection LocationsCollection Locations
Field LAI Collected Values Field LAI Collected Values
LAI Raster & Field LAI LAI Raster & Field LAI CorrelationCorrelation
ConclusionsConclusions
LAI raster was developed using the SAVI raster of the study area 0.15 cm resolution CIR.
The LAI values very well visually correlated with two blueberry varieties (High Bush and Rabbit Eye) in the field.
The LAI values from the LAI raster and the values collected from field did not correlate well. It was attributed to the faulty LICOR instrumentWe will collect LAI data in the field later with a better
instrument
ConclusionsConclusions
The OBIA based image segmentation process was very exhaustive but worth every extra rule sets we develop
SAVI raster very clearly distinguished different vegetation features in the image including both blueberry varieties (High Bush and Rabbit Eye) We would use that in the OBIA segmentation process and it
would save a great deal of effort Ground truth will be conducted later for accuracy assessment
but initial assessment shows the result more than 80% accurate Ground truth points were collected in 2009 for a previous study
but 2010 orthoimagery is used in this study Different stages (year of plantation) of blueberry plants in the
orchard will be separated with the nDSM raster in a later stage
AcknowledgementAcknowledgement
This work was supported in part by the Georgia Space Grant Program, managed by the Georgia Institute of Technology on behalf of the National Aeronautics and Space Administration, and by State and Federal Funds allocated to Georgia Agricultural Experiment Stations
Hatch project GEO1654..
SAVI Raster
LAI raster for entire land-use (Ranges from -7.59 to -11.377)
LAI raster showing the LAI values (very homogenous) of only Kudzu (Ranges from -11 to -11.315)