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

2. 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 This technique is costly, laborious, and mostly based on outdated Arial Photos (APs).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. Opening Statements Statement s

3. 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. Defining Benchmarks Topics Presented 1.Elementary concepts to carry out agricultural sustainability studies, 2.De-aggregation of tabular crop statistics to 1km pixel crop maps, 3.Merging image analysis results, 4.Classifying images using NDVI profiles and known crop calendars, 5.Surveying using mobile GIS techniques, and 6.Image Segmentation based on object-oriented analysis. Benchmark s/Topics

4. Impact on land ( + or - ) Decision making / planning Requirements & Suitability Productivity Impact on/from the environment Interaction with secondary production systems The Concepts The Land Use System (LUS) with study entries. 1.Conce pts

5. Operation Sequences GrazingFallowing 19891988197519691979 Rainfed Cropping JFMAMJJASOND 1988 1989 Observations Operations … many aim to control growth limiting, and yield reducing land aspects. … many relate to growth limiting, and yield reducing land aspects. Ploughing HarvestingFallow Pest Attack Germination Trampling Hail Storm Rill Erosion Weeding Seeding NPK Applic. Illustrating land use operations and land use obser- vations The Operation Sequence impacts on sustainability aspects. Land Use Land Land Use System Oper.Seq.

6. Yield they address: growth limiting yield reducing land modifying aspects of LUSs. Feasible Problems Management Plot-to-plot variability Problems What do sustainability studies do ? They relate differences in land and management aspects to differences in system performances. They use survey data from many plots. we study this gap. Sust.Stu dies

7. 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. 2.Deaggregatio n

8. District Map Table of number of pixels by district Maize Crop Statistics (5 yrs) by district Mask of: parks, reserves, urban, water, and trees Masked and Classified Masked FAO Maize Suitability Map (values from 0 to 100) 30 NOAA NDVI Classes (1km pixels) % 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 Apply to masked maps GIS flowc hart

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

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

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

12. 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. 4 Year Data NDVI 4.NDVI profiles

13. May-Jun-Jul 2001 Aug-Sep-Oct 2001 Nov-Dec-Jan 2002 Feb-Mar-Apr 2002 1 km res. Spot Vegetation image (RGB Feb-Mar-Apr02 ) W-Nizamabad NDVI-profiles of 4 pixels in Nizamabad Apr98May02 By Decade 1. General Spot NDVI profile analysis for Nizamabad area NDVI India

14. W-Nizamabad Unsupervised-classified Spot Vegetation image (30 classes; 1998-2002; 147 decadal images) NDVI-profiles of 8 classes found in Nizamabad Apr98May02 By Decade W-Nizamabad Unsupervised Classification NDVI Classes

15. First the NDVI-profiles were classified unsupervised into 30 vegetation classes 2. Detailed Spot NDVI profile analysis for Nizamabad area 15 20 25 27 29 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 Original classes Then the profiles were visually grouped into 14 more general classes Gets out of the image series what is in them. The expert now classifies supervised the intermediate product. Nizam.Cl asses

16. Apr99Apr00 Apr01 Apr02 250 200 150 100 50 0 Apr98 NDVI Rice during Rabi Forest Dryland Crops Water NDVI data from decadal Spot-Vegetation Images; 1 km pixels Clouds 14 NDVI profiles across 4 years The NDVI profiles Conclusion 2: Mixed pixels (1 km) generate intermediate NDVI-profiles. Conclusion 1: Profiles can be used for monitoring purposes. Nizam.Pr ofiles

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

18. Conclusion 5: Patterns identified agree well with a 23 m IRS image. 4. Spatial validation of NDVI map units Conclusion 4: Patterns identified agree well with terrain features. IRS Image (18 Jan00) Irrigated Rainfed Heavy soils Rainfed Light soils Rice in Rabi & Kharif NDVI-profiles on a DEM DEMs, IRS

19. Rice in Rabi & Kharif Irrigated Rainfed Heavy soils Rainfed Light soils 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. Crop Calendar

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

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

22. 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). Example 2: Mapping roads in hills Digitizing roads by GPS in hills proved very useful, and accurate enough to fill the short- comings. Road Lines

23. GPS-iPaq experiences 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 Once all is done well….experience shows too many advantages and even a dependancy of using the equipment during fieldwork !!! 1881m 2 1038m 2 2 Fields Experie nces

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

25. 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. Aster image (15m) of Garmsar, Iran. Area frame sampling techniques can greatly benefit from Image segmentation. AFS benefits