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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 BieITC, Enschede, The Netherlands
Commission VII, Working Group VII/2.1 on Sustainable Agriculture
Title
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This technique is costly, laborious, and mostly based on outdated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
Statements
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.
Benchmarks/Topics
Bio-Physical Conditions
Socio-Economical Conditions
Land Use System
Other Land Use Systems
Livestock Systems
Context Goals
Inputs / Implements
Outputs /
Benefits
Soil / Terrain Climate / Weather Vegetation (Crops / Flora)
Wildlife (Fauna)
Infrastructure Operation Sequence
Land
Land Use Purpose(s)
Land Use
Land User(s)
Impact on land ( + or - )
Decision making / planningRequirements &
Suitability
Productivity
Impact on/from the environment
Interaction with secondary production
systems
The Concepts
The “Land Use System” (LUS) with ‘study entries’.
1.Concepts
Operation Sequences
Grazing Fallowing
1989198819751969 1979
Rainfed Cropping
J F M A M J J A S O N D
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 Harvesting Fallow
Pest AttackGermination
Trampling Hail Storm
Rill Erosion
WeedingSeeding
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.
Yie
ld
they address: growth limiting yield reducing land modifying aspects of LUSs.
Feasible
Problems
Management
Plot-to-plot variability
ProblemsProblemsProblemsProblemsProblems
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.Studies
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.Deaggregation
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 flowchart
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
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
Village boundaryStreamsVillagesRoadPathsRidgesContours
Very Bare to 50% Bare
Poorly vegetatedSomewhat vegetatedWell vegetatedPine 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
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
ND
VI
4.NDVI profiles
May-Jun-Jul 2001Aug-Sep-Oct 2001Nov-Dec-Jan 2002Feb-Mar-Apr 2002
1 km res. Spot Vegetation image (RGB Feb-Mar-Apr’02 )
W-Nizamabad
NDVI-profiles of 4 pixels in Nizamabad
Apr’98 May’02 By Decade
1. General Spot NDVI profile analysis for
Nizamabad area
NDVI India
W-Nizamabad
Unsupervised-classified Spot Vegetation image(30 classes; 1998-2002; 147 decadal images)
NDVI-profiles of 8 classes found in Nizamabad
Apr’98 May’02
By Decade
W-Nizamabad
Unsupervised Classification
NDVI Classes
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.Classes
Apr’99 Apr’00 Apr’01 Apr’02
250
200
150
100
50
0 Apr’98
ND
VI
Rice during Rabi Forest
Dryland Crops
Water
NDVI data from decadal Spot-Vegetation Images; 1 km pixels
Clouds
14 NDVI profiles across 4 years
50
100
150
200
Jun
e
Au
g
Oct
Dec
Feb
Ap
r
Forest
Water
Rice during Rabi
Dryland Crops light soils
Clo
ud
s
Clo
ud
s
Cotton Dryland Crops heavy soils
Rice during Kharif
Final avg. NDVI-profiles of the 14 vegetation classes
The NDVI profiles
Conclusion 2: Mixed pixels (1 km) generate ‘intermediate’ NDVI-profiles.
Conclusion 1: Profiles can be used for monitoring purposes.
Nizam.Profiles
Initial Map
comparison of the 2 Maps
Mandal Boundaries (10 km grid)
Kotagiri
Birkur
Rice in Rabi
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
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 Jan’00)
IrrigatedRainfed
Heavy soils
RainfedLight soils
Rice in Rabi & Kharif
NDVI-profiles on a DEM
DEMs, IRS
Rice in Rabi & Kharif
IrrigatedRainfedHeavy soils
RainfedLight soils
jun jul aug sep oct nov dec jan feb mar apr may
Rice
Sugarcane ( + 1 ratoon)
Sunflower
or Groundnut
or Black Gram
or Green Gram
or Red Gram
+ Rice (dominant)
+ Wheat, Sunflower, or Groundnut
or Safflower
Cotton
Sorghum
or Sunflower
or Groundnut
or Bengal Gram
RabiKharif (monsoon)
Summer Irrig
ate
dR
ain
fed
; he
av
y s
oil
Ra
infe
d; lig
ht s
oil
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
Surveying using Mobile GIS Techniques
The objective is to test use of mobile
GIS equipment for detailed fieldwork.
5.Mobile GIS
Jan. 2000
IRS-Image (23m Multi-spectral fused with 6m Pan)
Mar. 2002
Mar. 2002
IRS-Image (23m)Example 1: Mapping Plots
Digitized “in the field” in
Sep 2002
Plot Polygons
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
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 !!!
1881m2
1038m2
2 Fields
Experiences
Image Segmentation based on Object-Oriented Analysis
The objective is to identify primary
sample units (plots) for agricultural
surveys.
6.Segmentation
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