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Title. Novel Approaches to use RS-Products for Mapping and Studying Agricultural Land Use Systems. Dr. C.A.J.M. de Bie ITC, Enschede, The Netherlands Commission VII, Working Group VII/2.1 on Sustainable Agriculture.

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  1. Title Novel Approaches to use RS-Products for Mapping and Studying Agricultural Land Use Systems Dr. C.A.J.M. de Bie ITC, Enschede, The Netherlands Commission VII, Working Group VII/2.1 on Sustainable Agriculture Presented are novel methods that support production of agricultural land use information as required to provide timely spatial information to generate food security policies and that support land use planning studies.

  2. Statements Opening Statements • In many developing countries there is a general paucity of land use information. • At national level, many countries now seek to monitor land use change as a basis for policy guidelines and action. • Agricultural land use surveys often rely on a “Multiple area frame” sampling technique. • This technique is costly, laborious, and mostly based on outdated Arial Photos (APs). • Use of new high resolution RS-images (e.g. Aster of 15m) and of multi-temporal NDVI images (e.g. Spot of 1km) make better and more efficient approaches feasible.

  3. Benchmarks/Topics Defining Benchmarks • Options are discussed to improve the quality and efficiency of geo-information production with emphasis on agricultural land uses. • Attention is drawn to the dynamic aspects of land use systems, with crop calendar information as focal point. • Emphasis is put on recognizing plots as primary sample units to survey for collecting agricultural land use data. Topics Presented Elementary concepts to carry out agricultural sustainability studies, De-aggregation of tabular crop statistics to 1km pixel crop maps, Merging image analysis results, Classifying images using NDVI profiles and known crop calendars, Surveying using mobile GIS techniques, and Image Segmentation based on object-oriented analysis.

  4. 1.Concepts Context Socio - Economical Conditions Goals Land User(s) Bio - Physical Conditions Land Use System Land Land Use Soil / Terrain Land Use Climate / Weather Purpose(s) Vegetation (Crops / Flora) Inputs / Outputs Wildlife (Fauna) / Implements Operation Benefits Sequence Infrastructure Other Land Use Systems Livestock Systems 1 The Concepts The “Land Use System” (LUS) with ‘study entries’. Decision making / planning Requirements & Suitability Productivity Impact on land ( + or - ) Impact on/from the environment Interaction with secondary production systems

  5. Oper.Seq. Operation Sequences 1969 1975 1979 1988 1989 Grazing Fallowing Rainfed Cropping 1989 1988 J F M A M J J A S O N D Operations NPK Applic. Ploughing Weeding Seeding Harvesting Fallow Observations Pest Attack Germination Rill Erosion Trampling Hail Storm The “Operation Sequence” impacts on ‘sustainability’ aspects. Land Use System Illustrating land use operations Land Use … many aim to control growth limiting, and yield reducing land aspects. and land use obser-vations … many relate to growth limiting, and yield reducing land aspects. Land

  6. Sust.Studies Problems Problems Problems Problems we study this gap. Plot-to-plot variability Feasible Problems Yield Problems Management What do sustainability studies do ? they address: growthlimiting yieldreducing land modifyingaspects of LUSs.  They relate differences in land and management aspects to differences in system performances.  They use survey data from many plots.

  7. 2.Deaggregation 2 De-aggregation of Tabular Crop Statistics to 1km Pixel Crop Maps The objective is to map where crops are grown using a “mix” of existing GIS-information and crop statistics.

  8. GIS flowchart Masked and Classified District Map Mask of: parks,reserves, urban, water, and trees Table of number of pixels by district % of area to maize = 1.9 if Mod.Suit. + 2.7 if Suit. + 6.9 if Class-11 + 3.0 if Class-15 + 32.6 if Class-25 + 17.8 if Class-26 + 12.3 if Class-27 + 34.1 if Class-29 + 15.5 if Class-30 (N=110; Adj.R-Sq=74%) Regression Masked Apply to masked maps FAO Maize Suitability Map(values from 0 to 100) Maize Crop Statistics (5 yrs) by district 30 NOAA NDVI Classes(1km pixels)

  9. 3.Merging Images 3 Merging Image Analysis Results The objective is to optimize use of high resolution satellite imagery to delineate ‘hard’ and ‘soft’ map units.

  10. TM: hard-soft ‘Hard’ map units Merged product ‘Soft’ map units Represents: bush, pasture, fields, deciduous trees, etc. Often specific vegetation types can be clearly distinguished, while others can not. TM 453 Classified pine trees and shade NDVI Distinguishing them is ‘season dependant’

  11. TM+GIS Results can be presented with relevant digitized lines at large scale. 1 km grid and used, e.g. for local level land use planning. Village boundary Streams Villages Road Paths Ridges Contours Very Bare to 50% Bare Poorly vegetated Somewhat vegetated Well vegetated Pine Trees

  12. 4.NDVI profiles NDVI 4 Year Data 4 Classifying images using NDVI profiles and known crop calendars The objective is to identify areas having different crop calendars. The relation and interpretation quality of classified 1km NDVI time series at country and at local-level is explored to ascertain their link with crop calendar information.

  13. NDVI India Aug-Sep-Oct 2001 Feb-Mar-Apr 2002 May-Jun-Jul 2001 Nov-Dec-Jan 2002 By Decade Apr’98 May’02 NDVI-profiles of 4 pixels in Nizamabad 1 km res. Spot Vegetation image (RGB Feb-Mar-Apr’02 ) W-Nizamabad 1. General Spot NDVI profile analysis for Nizamabad area

  14. NDVI Classes W-Nizamabad By Decade W-Nizamabad Apr’98 May’02 NDVI-profiles of 8 classes found in Nizamabad Unsupervised-classified Spot Vegetation image(30 classes; 1998-2002; 147 decadal images) Unsupervised Classification

  15. Nizam.Classes First the NDVI-profiles were classified unsupervised into 30 vegetation classes Original classes 15 20 25 27 29 Then the profiles were visually grouped into 14 more general classes 1,2,23 18,19,24 28,30 21,22,26 14,16,17 3,4 8,10,12 6,7,9 5,11,13 2. Detailed Spot NDVI profile analysis for Nizamabad area The expert now classifies “supervised” the intermediate product. Gets out of the image series “what is in them”.

  16. Nizam.Profiles 250 Forest NDVI data from decadal Spot - Vegetation Images; 1 km pixels Rice during 200 Kharif Rice during Dryland Crops light soils Rabi Forest 200 Dryland Rice during Rabi Crops 150 150 Cotton Dryland Crops Clouds Clouds NDVI heavy soils 100 100 50 Water Water Clouds 50 0 Oct Feb Apr Dec Aug Apr’98 Apr’99 Apr’00 Apr’01 Apr’02 June Final avg. NDVI-profiles of the 14 vegetation classes 14 NDVI profiles across 4 years The NDVI profiles Conclusion 1: Profiles can be used for monitoring purposes. Conclusion 2: Mixed pixels (1 km) generate ‘intermediate’ NDVI-profiles.

  17. Compare Maps Initial Map comparison of the 2 Maps Nizamabad 3. Comparing the two Spot NDVI profile maps Final Nizamabad map with 14 classes Conclusion 3: Post-classification process provided ‘more refined’ results.

  18. DEMs, IRS Rainfed Heavy soils Irrigated Rainfed Light soils Rice in Rabi & Kharif IRS Image (18 Jan’00) NDVI-profiles on a DEM 4. Spatial validation of NDVI map units Conclusion 4: Patterns identified agree well with terrain features. Conclusion 5: Patterns identified agree well with a 23 m IRS image.

  19. Crop Calendar Rabi Kharif (monsoon) Summer + Rice (dominant) Rice Irrigated + Wheat, Sunflower, or Groundnut Sugarcane ( + 1 ratoon) Sunflower Rainfed; light soil or Groundnut or Black Gram or Green Gram or Red Gram Cotton Rainfed; heavy soil Rainfed Heavy soils Sorghum Irrigated or Safflower or Bengal Gram jun jul aug sep oct nov dec jan feb mar apr may or Sunflower Rainfed Light soils or Groundnut Rice in Rabi & Kharif 5. Linking the Spot NDVI profiles to crop calendars Conclusion 6: Crop calendar groups can easily be linked to profiles. Conclusion 7: Having crop calendar information at plot level is a must.

  20. 5.Mobile GIS 5 Surveying using Mobile GIS Techniques The objective is to test use of mobile GIS equipment for detailed fieldwork.

  21. Plot Polygons IRS-Image (23m) IRS-Image (23m Multi-spectral fused with 6m Pan) Mar. 2002 Digitized “in the field” in Sep 2002 Jan. 2000 Mar. 2002 Example 1: Mapping Plots

  22. Road Lines Example 2: Mapping roads in hills Often, roads are poorly mapped on topo-sheets, while (15m resolution) images of e.g. mountainous areas hardly show roads. Roads digitized in Ghazi on a topo-sheet and on an Aster image (Febr.2001; scale 1:25,000). Digitizing roads by GPS in hills proved very useful, and accurate enough to fill the short-comings.

  23. Experiences GPS-iPaq experiences Once all is done well….experience shows too many advantages and even a dependancy of using the equipment during fieldwork !!! • Not for GIS amateurs • Requires user to know facts on projection systems ‘properly’ • Requires proper preparation: • of geo-referencing images • of compressing images • of iPaq and GPS settings 1038m2 1881m2 2 Fields

  24. 6.Segmentation 6 Image Segmentation based on Object-Oriented Analysis The objective is to identify primary sample units (plots) for agricultural surveys.

  25. AFS benefits Plot boundaries are seen on images but not used during classification on a pixel-by-pixel basis. Plot boundaries are the primary sample units during agricultural surveys. Image segmentation, before classification (using eCognition) recovers this ‘loss’. Area frame sampling techniques can greatly benefit from Image segmentation. Aster image (15m) of Garmsar, Iran.

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