Application of Geospatial Technologies for Oil Palm Plantation Management Mohd zahlan mohd zaki Malaysia Geospatial Forum 2012 | Melaka | 6-7 March 2012.

Slides:



Advertisements
Similar presentations
Predicting and mapping biomass using remote sensing and GIS techniques; a case of sugarcane in Mumias Kenya Odhiambo J.O, Wayumba G, Inima A, Omuto C.T,
Advertisements

Xiangming Xiao Department of Botany and Microbiology, College of Arts and Sciences Center for Spatial Analysis, College of Atmospheric.
Intact Forest Landscapes and Conservation Planning in Canada Prepared by: Ryan Cheng Global Forest Watch Canada.
Geospatial Technologies Used in Land Administration Kevin Daugherty Land Administration Solutions Manager Geospatial World Forum Rotterdam, Netherlands.
Characteristics, uses, and sources Introduction to DEMs.
EFFECT OF SELECT YIELD IMPROVEMENT PROJECTS ON POTATO YIELDS “NB Potato Industry Transformation Initiative”
Application Of Remote Sensing & GIS for Effective Agricultural Management By Dr Jibanananda Roy Consultant, SkyMap Global.
Technology Enables Us To Explore Our Earth The Land and the Oceans.
Mark Coffman Spring  A contract of indemnity by which one party promises to compensate another for the financial loss incurred by the destruction.
Geographical & Environmental Modelling Dr Nigel Trodd Coventry University.
Session 131 Hazard Mapping and Modeling Supporting Emergency Response Operations using GIS and Modeling.
Characterizing Soil Erosion in Albania using Remote Sensing Ryan L. Perroy Geography Department University of California, Santa Barbara.
Teaching Critical Thinking Skills within Ag Geospatial Curriculum Ag GIS Education Symposium Pismo Beach, California January 20, 2006 Terry Brase, Associate.
Comparison of LIDAR Derived Data to Traditional Photogrammetric Mapping David Veneziano Dr. Reginald Souleyrette Dr. Shauna Hallmark GIS-T 2002 August.
Use of remote sensing on turfgrass Soil 4213 course presentation Xi Xiong April 18, 2003.
Relationships Between NDVI and Plant Physical Measurements Beltwide Cotton Conference January 6-10, 2003 Tim Sharp.
UNDERSTANDING LIDAR LIGHT DETECTION AND RANGING LIDAR is a remote sensing technique that can measure the distance to objects on and above the ground surface.
Programme for Enterprise Competitiveness Example of an Intervention: The Thai Palm Oil Sub Sector Thai-German Programme for Enterprise Competitiveness.
Module 2.1 Monitoring activity data for forests using remote sensing REDD+ training materials by GOFC-GOLD, Wageningen University, World Bank FCPF 1 Module.
REMOTE SENSING MALAYSIA (RSM)
Site-Specific Management Factors influencing plant growth Water Light Temperature Soil Compaction Drainage.
Dr. M. Ahsan Latif Department of Computer Science
Global Positioning Systems and Geographic Information Systems
A comparison of remotely sensed imagery with site-specific crop management data A comparison of remotely sensed imagery with site-specific crop management.
Application of GI-based Procedures for Soil Moisture Mapping and Crop Vegetation Status Monitoring in Romania Dr. Adriana MARICA, Dr. Gheorghe STANCALIE,
Operational Agriculture Monitoring System Using Remote Sensing Pei Zhiyuan Center for Remote Sensing Applacation, Ministry of Agriculture, China.
Lesson 7 Understanding Remote Sensing Technology.
 The textbook GIS methods section: Provides basic understanding of GIS concepts What is RS? How can we use RS for GIS, when, where and why?
1 Exploiting Multisensor Spectral Data to Improve Crop Residue Cover Estimates for Management of Agricultural Water Quality Magda S. Galloza 1, Melba M.
Page 1 CSISS Center for Spatial Information Science and Systems Remote-sensing-based Post-flood Crop Loss Assessment for Supporting Agricultural Decision.
Chapter 4. Remote Sensing Information Process. n Remote sensing can provide fundamental biophysical information, including x,y location, z elevation or.
Geomatics Tools for Inventorying and Assessing Headwaters Adam Hogg Inventory Monitoring & Assessment, Ministry of Natural Resources Eastern Region Headwaters.
Precision Agriculture an Overview. Need for Precision Agriculture (1) l In 1970, 190,500,000 ha classified as arable and permanent cropland in the USA.
Chapter 8 Remote Sensing & GIS Integration. Basics EM spectrum: fig p. 268 reflected emitted detection film sensor atmospheric attenuation.
Understanding Glacier Characteristics in Rocky Mountains Using Remote Sensing Yang Qing.
Understanding Remote Sensing Technology. Next Generation Science/Common Core Standards Addressed! CCSS.ELA Literacy. RST.9 ‐ 10.1 Cite specific textual.
GPS Aided INS for Mobile Mapping in Precision Agriculture Khurram Niaz Shaikh Supervised by: Dr. Abdul Rashid bin Mohammad Shariff Dept. of Biological.
Insert Date 1 Hurricanes-Inundation Overview Objectives: Improve forecasts of tropical cyclones and related inundation hazards to enhance mitigation decisions.
U.S. Department of the Interior U.S. Geological Survey Afghanistan Natural Resource Assessment and Reconstruction Project Geospatial Infrastructure Development:
™ Nutrient Management Planning ¨ Will these be mandated in your state?  An emerging national issue is how to account for agricultural non-point source.
A) INTRODUCTION Geographic Information System (GIS) is an important tool in agriculture and has essential use in field management. Precision farming techniques.
Remote Sensing 13/10/2009 Dr. Ahmad BinTouq URL: GEO.
State of Engineering in Precision Agriculture, Boundaries and Limits for Agronomy.
U.S. Department of the Interior U.S. Geological Survey Afghanistan Natural Resource Assessment and Reconstruction Project Geospatial Infrastructure Development:
Precision Agriculture an Overview. Precision Agriculture? Human need Environment –Hypoxia –$750,000,000 (excess N flowing down the Mississippi river/yr)
CHALLENGES FOR SUSTAINABLE AGRICULTURE ON PEATLANDS IN SARAWAK Murtedza Mohamed Faculty of Resource Science and and Technology Universiti Malaysia Sarawak.
A Remote Sensing Approach for Estimating Regional Scale Surface Moisture Luke J. Marzen Associate Professor of Geography Auburn University Co-Director.
Sensors vs. Map Based Precision Farming Chris Sechrest.
Mapping Canada’s Rangeland and Forage Resources using Earth Observation Emily Lindsay MSc Candidate – Carleton University Supervisors: Doug J. King & Andrew.
GIS: The Systematic Approach to Precise Farm Management Robert Biffle Precision Agriculture April,
Farms, sensors and satellites. Using fertilisers Farming practice are changing Growing quality crops in good yields depends on many factors, including.
Cost/Return Analysis of Precision Agriculture on Oklahoma Farms Aaron Witt April 25, 2001.
Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE.
Remote Sensing Dr. Ahmad BinTouq GEO440: GIS for Urban & Regional Planning.
Digitial Precision Agriculture and Location Intelligence Levente Klein IBM TJ Watson Research Center.
Drone applications in Forestry APEC/APFL forum, February 2017
Factsheet #11 Understanding multiscale dynamics of landscape change through the application of remote sensing & GIS Small Stream Mapping Method: Local.
Precision Agriculture
Precision Agriculture
Introduction to Geospatial Technologies in Ag
Precision Agriculture an Overview
Precision Agriculture
Precision Agriculture an Overview
Xueliang Cai, Wim Bastiaanssen
Terrain Complexity using Fuzzy Introduction Suitability using MCE
By Blake Balzan1, with Ramesh Sivanpillai PhD2
Late-Season Prediction of Wheat Grain Yield and Protein
Remote Sensing Section 3.
Precision Ag Precision agriculture (PA) refers to using information, computing and sensing technologies for production agriculture. PA application enables.
Computers in Agriculture
Presentation transcript:

Application of Geospatial Technologies for Oil Palm Plantation Management Mohd zahlan mohd zaki Malaysia Geospatial Forum 2012 | Melaka | 6-7 March 2012

2 “The location of information is what makes the information powerful!”

3 Overview Introduction Applications Oil Palm Yield Forecasting Nutrient Mapping Yield and Growth Analysis Terrain Analysis Hydrological Modelling Micro-UAV imaging

4 Introduction The Technologies Remote Sensing Global Positioning System (GPS) Geographical Information System (GIS) etc. What?

5 Why? accuracy (accurate information) cost-effectiveness better perspective, enhance communication quick decision making. Introduction (Cont.)

6 NOW When? Introduction (Cont.) 15 years Evolution of Geospatial Technology

7 Where?

8 Introduction Current Key Projects Oil Palm Yield Forecasting –Accurately forecast oil palm yield for the next financial year –Accuracy of ≥95% at estate level Site Yield Potential –Establishing achievable and realistic long term yield target based on site specific criteria What?

9 Oil Palm Yield Forecasting

10 In principles, different kind of surface materials can be distinguished from each other by differences in reflectance. 02 D (36.5 MT/ha) 01 D (32.2 MT/ha) 01 A (25.5 MT/ha) CT38 (12.5 MT/ha) Wavelength (nm) Reflectance Index Case Study: Carey Island (2009) Ground Truth Sensor: Spectroradiometer Oil Palm Yield Forecasting (Cont.)

11 Oil Palm Yield Forecasting Field : 84C Year after planting in 2007 : 23 Spectral age: Yield (CIFOS/Actual) : 19.68/19.60 MT/Ha Accuracy: 99.61% (MT/Ha/Yr) Data Source: Landsat (30m) Data Cost: Free Download

12 Oil Palm Yield Forecasting

13 Leaf Area Index & Nutrient Mapping Data Source: SPOT-XS (20m) Data Cost: RM 0.005/ha

14 Yield Performance Index (%) Variance – Actual vs. SYP

15 Growth Performance Index (%) Variance – Spectral vs. Actual

16 PALM MANAGEMENT PERFORMANCE MATRIX GROWTH PERFORMANCE YIELD PERFORMANCE EXCELLENTGOODMARGINALPOOR EXCELLENT NOT SATISFACTORY GOOD UPPER AVERAGE GOODPOOR LOWER AVERAGE MARGINAL SATISFACTORYVERY POOR POOR 1EXCELLENT Excellent growth (>95%), expect good respond to fertilization and good crop recovery standards 2GOOD Average palm growth (can further improve), likely satisfactory respond to fertilization and good crop recovery standards 3SATISFACTORY Impeded growth, suspect present of limiting factors, expect good respond to fertilization good crop recovery standards 4 NOT SATISFACTORY Excellent growth (>95%), expect good respond to fertilization but suspect poor crop recovery standards 5POOR Average palm growth, suspect present of limiting factors, likely respond to fertilization but suspect poor crop recovery standards 6VERY POOR Impeded growth, suspect present of limiting factors, expect poor respond to fertilization and/or poor crop recovery standards Palm Management Performance Index FY08/09 FY09/10

17 Data Source: Quickbird (0.68m) Data Cost: RM 0.60/ha Palm Growth Assessment High Resolution ImageDigital Image Processing: LPF Digital Image Processing: Intensity Scaling (cont.)

18 Abnormal growth, relatively small B A B A Normal palm, but was identified having leaning problem. C C Supply Palm Excellent Palm D D Vacant area Final Image Palm Growth Assessment

19 Terrain Analysis Data Source: SRTM (30m) Data Cost: Free Digital Elevation Model (DEM) Slope Analysis

20 Terrain Analysis Data Source: IFSAR (5m) Data Cost: RM 1.40/ha Digital Elevation Model (DEM) Slope Analysis

21 3D Terrain Analysis (Excise area above 25° for conservation) Data Source: SRTM (30m) Data Cost: Free Download

22 Hydrological Modelling Data Source: SRTM (30m) Data Cost: Free Download

23 Types… Advantage… Drawbacks… Potential… Micro-UAV Imaging

24 Data Source: CropCAM UAV (0.2m) Yield and Growth Analysis: Identify Problematic Areas Micro-UAV Imaging

25 Micro-UAV Imaging Hecto-copter Outbreak surveilance

26

27 Conclusions Advancement in geospatial technology provides reliable high quality data on large scale, speedily and cost effectively. 1 Data : MULTIPLE application. EXPLORE! Powerful and useful agro-management tool for precision agriculture to enable sustainable oil palm cultivation.

29 “Effort is important, but knowing where to make an effort makes all the difference”

30 Thank You