Utilizing Hyperspectral Imaging System in unmannedaerial vehicle (UAV) for Agricultural/Palm Oil Analysis Geo Sense Sdn. Bhd. 79A, Jalan Seri Impian 1 T06-‐03, Jln Centry Square Taman Impian Emas Block 2320 81300 Johor Bahru 63000 Cyberjaya firstname.lastname@example.org www.geosense.com.my
Geo Sense Sdn. Bhd. Brief Background • Establish May 2006 • MSC Status – in web GIS and aerial mapping • Pioneering Civilian UAV applicaTons / Services • Skudai, Johor Bahru Base Company & Cyberjaya • CollaboraTon with UTM, IMREC and Inst. Sustainable Agri of Cardoba Spain • Since 2007 -‐ R&D in unmanned aerial mapping and remote sensing • Recepient Anugerah Perdana Menteri APICTA ICT eGov. Catergory in 2007 • Vision, to become leading tech company in civilian UAV applicaTon
Sources of Aerial Imagery Light Aircraft Imaging Altitude10k – 30k feet Satellite 500-800 km Cost RC AIrcarft / UAV Altitude 500 – 2000 ft • erial Camera System A • adiometric resolution R • 0 cm – 60 cm resolution 2 (avg 30 in resolution) Cloud Issues • erial mapping survey A • PSAR (SAR) / LIDAR I • atural color (RGB) N • - 15 cm resolution 7 • urveillance / Monitoring S 200 meter90 km 3 km 300 meter 30 km 3 km
Progress Monitoring Rumah Rakyat IRDA June 2010 Oct 2010 Feb 2011
Potential ApplicationsWork Auditing /Verification Feb 2010 June 2010
Case Study 1 -‐ UAV Mapping by Geo Sense – Semporna, Sabah (5 sq km) Visual Using UAV PopulaTon: ~ 130K Mail Volume: ~ 200 per day PO: Semporna Post Oﬃce Mail delivery: limited Address: Using kampung, schools & PO Box (710 units) Bank: Maybank & BSN
House Numbering and Address Assignment by Pos Malaysia DigiTzing using GIS KAMPUNG BANGAU-‐BANGAU 2,436 houses ADDRESS SAMPLE Cikgu Ahmad No2, KampungBangauBangau PERKAMPUNGAN AIR 1 91300 Semporna, Sabah 659 houses PERKAMPUNGAN AIR 2 Lat 4.4883 N Long 118.6050 E 3,313 houses Approximately 6,000 new delivery points idenTﬁed
Base Map Preparation – Using High Res Aerial Image from UAV High resoluTon UAV Images Image digitalizing Asset Data in Base Map Base Map from high res images
3D / DTM – OrthoracTﬁcaTon Process Sojware upgrade
Scope of Work & Delivery UAV capturing images Image process & digitalizing The big mosaic (sTtched) image Image presentaTon & potenTal uTlizaTon Big Poster Tiles for quick viewing Online Tles visualizaTon Image registraTon (GIS) IntegraTon with -‐ Project GIS Project Management -‐ Project monitoring and System Online visualizaTon Quick distribuTon – images reporTng system -‐ Structure modeling Online archiving system store in DVD -‐ Decision support with WBS system
Quanta Lab Geo Sense Geo Sense, is Malaysian MSC Status company, that is using unmanned aerial vehicle (UAV) for aerial mapping and remote sensing. Geo Sense is collaboraTng with Dr. Pablo J. Zarco Tejada the Director of Laboratory for Research in QuanTtaTve Remote Sensing, under the InsTtute of Sustainable Agricultural in Cardoba, Spain in uTlizing UAV for advance remote sensing for agricultural purposes. Geo Sense
CollaboraTon of experts between Quantalab and Geo Sense Sdn. Bhd. Proﬁle Pablo J. Zarco has been Course Director and Dr Pablo J. Zarco Tejada Teaching Assistant within the Departments of Environmental Science, and Earth and Space Ph.D. in Earth and Space Science, York Science ( York University , Canada ), and Land, Air, University (Canada), 2000 and Water Resources (LAWR), University of M.Sc. in Remote Sensing, Image California Davis , in courses related to Processing and ApplicaTons. Dept. of Environmental Science and Remote Sensing. He Applied Physics, Electronic and has also collaborated in other courses at University Mechanical Engineering (APEME), of California , Davis in Precision Agriculture and University of Dundee (Scotland , UK), Environmental Remote Sensing: 1997 Since 2008, Dr Pablo has been uTlizing UAV for B.S. Agricultural Engineering (Cordoba , agri. remote sensing. Spain) Geo Sense
The collaboraTon will oﬀer Malaysia users to access to the latest technology in agricultural monitoring and analysis using UAV for quicker respond at lower cost compare with convenTonal methods. The UAV with mulT spectral camera enable to meet any on demand request for urgent requirement in any agricultural respond and analysis, eg to quickly get the assessment over agri. epidemics in paddy ﬁeld and mapping DOA IntegraTng farming area without the need to wait for satellite images or convenTonal airplane. Geo Sense
Sample of analysis from UAV hyperspec. Sensor operate by Quantalab in Spain.
ExisTng Geo Sense UAV Agricultural / Crop Monitoring /Precision farming Without hyperspec sensor – limited. Infrared Imagery Crop Analysis
Sample of large agricultural area (track record) – 1000 hectares olive farm in Cardova, Spain .
Hyperspectral imagery acquired with an UAV plaOorm over orchard crops Imagery acquired at 40 cm resoluTon and 260 bands in the 400-‐900 nm region @ 5 nm FWHM Hyperspectral imagery acquired from an UAV plaoorm and the Micro-‐Hyperspec™ Imaging Spectrometer from Headwall Photonics. Imagery acquired at 550 m AGL over an orange orchard where stress detecTon experiments are conducted by QuantaLab at the InsTtute of Sustainable Agriculture (IAS), NaTonal Research Council (CSIC), Spain.
Hyperspectral Image OrthorecQﬁcaQon AStude data acquired with an AHRS system onboard the UAV Image orthorecTﬁcaTon is conducted using aqtude data acquired with an AHRS instrument synchronized with the hyperspectral imager. Commercial sojware and IAS-‐CSIC algorithms are applied in the laboratory ajer each ﬂight campaign.
Image CalibraQon and Atmospheric CorrecQon Spectral calibraTon of the hyperspectral instrument is conducted at IAS-‐CSIC using Hg-‐Ar calibraTon lamps. Radiometric calibraTon coeﬃcients are developed in the opTcs laboratory at IAS-‐CSIC using a radiometrically calibrated integraTng sphere. Image calibraTon and atmospheric correcTon to obtain surface reﬂectance are conducted from ﬁeld-‐measured data and aerosol opTcal depth measured at the Tme of ﬂight. Radiance and reﬂectance imagery are produced ajer calibraTon algorithms are applied in QuantaLab IAS-‐CSIC Laboratory. Imagery acquired at 40 cm resoluTon, 260 bands in the 400-‐900 nm region (5 nm FWHM). Raw data Reﬂectance data
Hyperspectral Image SegmentaQon of the crop canopy Object based image analysis for automaTc tree crown idenTﬁcaTon and stress detecTon using spectral indices Hyperspectral reﬂectance image Object based analysis Stress map (object-‐based analysis) Interpolated themaTc maps obtained from object based analysis conducted on hyperspectral indices at the tree crown level. Stress maps are derived based on photosyntheTc pigment concentraTon and canopy density
Hyperspectral Reﬂectance from a water body S p e c t r a l r e ﬂ e c t a n c e extracted from diﬀerent areas of a water body
Airborne Hyperspectral Imaging for Palm Oil Analysis
IDENTIFICATION OF SELECTED OIL PALM CHARACTERISICS USING DEVELOPED SPECTRAL SIGNATURE LIBRARY
User Requirements • Needs to increase producTvity by planTng more (new estate) & improve producTvity / yields – SoluTon; Maintain good tree condiTons, by having up to date block / sectors / trees informaTon – Healthiness and nutrient status – Assets, Land use, land cover (showing assets locaTon, vegetaTon and water body) • Nutrient checking (leaves & soil) – up to individual tree – SoluTon; Soil Nutrient & Foliar Variability Mapping – showing the availability of N,P,K,Mg,B(easier for detected less nutrient area).
User Requirements • Healthiness oil palm trees map for detecTng stress trees and for esTmaTng the yield. – SoluTon; Digital “stressed” palms map. Maps showing healthy trees, “stressed” and dead palms and development of spectral signature for all palms condiTon. • LocaTons of the tree with un-‐healthy condiTon – SoluTon; Tree status. Showing tree maturity status (ageing) – Individual Oil Palm inventory countswith precise GPS locaTon Map (locaTon each tree)
What aerial imagery tells • As evaluaTon tools and diagnosTc kits, – PlantaTon and forestry – Inventory – Healthiness/Stressed/Disease – Dead Trees (Pest/Disease/Water Stress, waterlog, burnt, etc) – Species/community types – Maturity • Marine and environmental features – Inventory – Community types – Changes detecTon and analysis – Coral/sea weed mapping – Water quality (salinity, turbidity, pH, temperature, etc) • Physical Features – Roads/footpath/track/rivers/streams/topo. etc – Area EsTmaTon=Gross Area–Vacant Area=Net Area – Boundaries
Overview of works proposal – design and develop Unmanned Aerial Remote Sensing Facility For Agricultural, Forestry and Palm Oil Analysis. Preparing UAV plaoorm IntegraTng Micro Hyperspec Sensor for user unmanned remote Image capturing and image from Honeywell Photonic (US) advance cube. – operate by Geo sensing aerial vehicle plaoorm hyoperspec sensor for UAV system. Into (UAV). Need for stable, endurance Sense & Quantalab, Spain Geo Sense UAV plaoorm. and load (min. 3 kg load) system IntegraTon work is collaboraTon with Plaoorm will be provided by Geo Sense QuantaLab, Spain and Geo Sense Online access system – Design & develop client web based system for imaging database or library mulT access via Internet / Image Analysis – system for review, analysis Intranet Quantalab, Spain and decision support -‐ Geo Sense & partner -‐ Quantalab & Geo Sense
Grant Plan ExisTng Grant Spin oﬀ Hand launch glider UAV Mid range UAV System Malaysian IMU system Unmanned aerial mapping (20 kg, payload 1.5kg) (autopilot system) Per mission 30 min. endurance, Per mission 90 min 1.5 sq km per mission Min. 3 sq km per mission 6 sq km (600 hectares) per day. 10 sq km (1000 hectares) per day Remote sensing on RGB compact camera Hyperspectral Imaging demand For UAV VisualizaTon Imaging analysis Center for unmanned remote Sensing for tropical agri.