The document describes a regression model developed to predict percent built-up land cover in Pucallpa, Peru using normalized difference vegetation index (NDVI) values derived from Landsat imagery. Landsat and Google Earth imagery were analyzed to determine percent built-up land cover around sample points. NDVI and percent built-up land cover were then used to develop a regression model. The model was able to predict percent built-up land cover with an R-squared value of 0.776, providing planners and managers a low-cost tool for rapid urban area assessment.

1. A REGRESSION MODEL FOR PREDICTING PERCENT BUILT-UP LAND COVER FROM
REMOTELY SENSED IMAGERY OF PUCALLPA, PERU
Presented by:
Drake H. Sprague
M.A. Candidate
Advisor: Dr. Maria Garcia-Quijano
Department of Geosciences
Florida Atlantic University
Boca Raton, Florida

2. Cities in LDCs absorb annually 20 – 30 million new residents due to rural-to-
urban migration of the poorest citizens (Smith, 2001)
UN (2000) estimated 74% of Latin American population in urban areas; this is
expected to increase to 81% by 2020 (UN, 2006)
Numerous related long term impacts, including loss of most fertile agricultural
lands (Imhoff et al., 1997)
Loss of life and damage to property due to disasters is greater in LDC urban
area than in those of developed countries (Montoya, 2003)
Planners and emergency managers in LDCs urgently need timely intelligence
about urban areas, however, high costs and complex analysis methods may
prevent them from acquiring the information they require
Introduction
Worldwide, urban areas are growing rapidly

3.  National census counts in most LDCs are infrequent due to high costs and data
becomes quickly outdated (Lo, 2006)
 Remote Sensing methods have been used for obtaining population using high-
resolution (submeter) imagery which may be too costly for use but infrequently in
LDCs (Gluch et al., 2006)
Moderate-resolution (10 to 30 m) imagery is, by comparison, less expensive and
its uses for deriving rapid population estimates should be considered
Introduction
How can agencies in LDCs account for rapid urban population growth?

4. Comparative costs / km²
Introduction
How much does this imagery cost?

5. Provide planners and emergency managers in LDCs with an inexpensive tool
that they can easily implement for rapid urban area assessments
Purpose

6. Develop a method to quickly assess built-up land cover for use as a proxy for
population density estimation using available resources:
Moderate-resolution satellite imagery
Free high-resolution imagery from Google Earth
Local expert knowledge
Using a regression model, predict the percentage of built-up land cover in
Pucallpa, Peru, as a function of a single variable common to the Amazon
region: Green vegetation
Key criteria: Low cost, simple and statistically robust
Objective

7. Null hypothesis:
No relationship exists between the intensity of built-up land cover and the
concentration of surrounding green vegetation in estimated from remotely
sensed data using the Normalized Difference Vegetation Index (NDVI) in the
city of Pucallpa, Peru.
Research Questions

8. Literature Review
Urban studies using Moderate Resolution Satellite Imagery in LDCs
1977, an allometric growth model was used to estimate the population in 13
Chinese cities using color composites from Band 5 (red) and Band 7 (infrared)
from Landsat Multispectral Scanner (MSS) imagery with 79-meter spatial
resolution. Best results obtained for cities of between 500,000 and 2.5 million
(Lo et al., 1977).
An urban planning study was done in 2000 using 20-meter SPOT (Systeme Pour
l’Observation de la Terre) XS (Multispectral) imagery to analyze the growth of
Ouagadougou, Burkina Faso between 1986 and 1997. The Spatial
Reclassification Kernel (SPARK) algorithm was applied to distinguish between
socio-economic regions within the city. Results found that the imagery could be
used to accurately estimate urban growth, but was too coarse in resolution to
be used with the SPARK algorithm (de Jong et al., 2000).

9. Literature Review
Urban studies using Moderate Resolution Satellite Imagery in LDCs
30 meter Landsat ETM+ imagery was used in 2006 as a basis for monitoring the
evolution of urban land cover changes in Manaus, Brazil, at the sub-pixel level
using multiple endmember spectral mixture analysis (MESMA). Results found
the vegetation and impervious surface features corresponded well with
reference data, but soil features did not, due to limitations in the reference
data (Powel et al., 2006).
• The time investment, cost and complexity of the methodology in this study
would be impractical for most agencies in LDCs, especially if a rapid assessment
of urban areas is all that is required.

10. Located in Peru’s low-altitude jungle region, 155 meters above sea-level
Between 74° 31’ and 74° 39’ W and 8° 18’ and 8° 26’ S
Map: Gobierno Regional de Ucayali, 2006
Study Site – Pucallpa, Peru

11. Landsat ETM+
Sept. 2000
Image Preprocessing:
• Image Subset
• Stack
• Register
Landsat
Sept. 2002
Image Preprocessing:
• Image Subset
• Stack
• Register
Intuitive Map
(5 urban classes)
In Situ – Pucallpa (GPS)
Readjust Class Scheme
(110 points)
NDVI
3 x 3
Filter
Derive GE %BU
Coverages
Co-registered Map
BU% & NDVI
Intersect
Training Set
75%
Validation Set
25%
Analyze
Outliers
Analyze
Outliers
RUN
MODEL
Map of
BU Urban
Intensity
Google Earth
2004
Expert Validation
Stratified
Random
Sampling
ISODATA
200
Clusters
Process Flow Chart
A Regression Model for Predicting Percent Built-up Land Cover
Using Remotely Sensed Imagery of Pucallpa, Peru

12. Landsat 5 - Thematic Mapper (TM).
Seven spectral bands over a ground swath of 185 × 175 km
30 x 30 m spatial resolution
Landsat 7 - Enhanced Thematic Mapper Plus (ETM+)
Includes the above, plus an additional Panchromatic 8th band with
15 x 15 m spatial resolution – especially useful for updating maps and
monitoring urban growth (Cheng, 2000).
LANDSAT IMAGING SYSTEMS
Data Sources

13. ETM+ acquired September 7, 2000
Provided by: Centro Internacional de Agricultura Tropical (CIAT)
ETM+ (or TM) acquired September 1, 2002
Provided by: Gobierno Regional de Ucayali
Landsat Worldwide Reference System (WRS)
Pucallpa is located within Path 006 / Row 066
Dry season acquisition date; imagery less affected by atmospheric noise than
in the wet season.
LANDSAT IMAGERY OF PUCALLPA

14. 2004 Google Earth – Digital Globe’s QuickBird Satellite Imaging System
DATA SOURCES

15. 2005 Air photo Mosaic – Fuerza Aérea del Perú (FAP)
DATA SOURCES
N

16. Landsat Scenes of Study Area
DATA SOURCES
September 1, 2000
185 × 170 km swath
(Natural Color)
Red – Band 3, Green – Band 2, Blue - Band 1
20 × 16 km (320 km²)
(False Color)
Red – Band 4, Green – Band 3, Blue - Band 2

17. Project to the Universal Transverse Mercator (UTM), Zone 18 South on the
World Geodetic System (WGS) 1984 Horizontal Datum
No atmospheric correction was applied to either image Landsat image. Most
remote sensing studies involving imagery of a single date forego this procedure
as it is considered unnecessary (Song et al., 2001)
Landsat Imagery - Preprocessing

18. The Iterative Self-Organizing Data Analysis Technique (ISODATA) clustering
algorithm was used to stratify the Landsat image into a set of 200 clusters:
ISODATA changes the number of clusters by merging, splitting, and deleting as it
passes (or iterates) through the RS data. With each iteration, the algorithm evaluates
the statistics of the clusters. It will merge two clusters if the distance between their
mean points is less than a predefined minimum distance. It will split a single cluster if
its standard deviation is greater than a predefined maximum value. Or, if a cluster
has fewer than the minimum specified number of pixels, it will be deleted. This is
repeated to cluster sets, until either no significant change in the cluster statistics
exists, or it has reached the maximum number of iterations (Lillesand et al., 2004)
After masking pixels falling outside of the urban areas, the remaining
clusters were manually collapsed into five nominal urban / built-up intensity
levels.
Urban / Built-up Intensity Map

19. 200 spectral clusters
Reduced to 5 urban classes

20. Stratified random sampling - sampling procedure for testing an area that has
been subdivided into land-cover strata through an image classification scheme.
It assigns a minimum number of sample points to each land-cover category so
that the size of each category is the same regardless of its areal extent in
proportion to total size of the study area (Jensen, 2005).
36 sample points assigned per strata (180 sample points) based on the
estimated time and cost required in referencing each point.
Some points might fall in areas inaccessible for in situ referencing, so that up to
six points per category could be left unsampled without compromising the
statistical validity of the model.
Sampling Design

21. Pucallpa field guide from
2004 Google Earth imagery
Other field tools:
1:65000 planning map provided by the Gobierno Regional de Ucayali
Handheld GPS for in situ referencing of the sample points
In Situ Referencing

22. IN SITU REFERENCING

23. IN SITU REFERENCING
Moto-taxi: traditional mode of transportation used for in situ referencing

24. IN SITU REFERENCING - PUCALLPA
Central Pucallpa with paved roads and concrete structures

25. IN SITU REFERENCING - PUCALLPA
Areas prone to flooding

26. IN SITU REFERENCING - PUCALLPA
Central, heavily built region of Pucallpa

27. IN SITU REFERENCING - PUCALLPA
Northern fringe region with hastily built wooden structures

28. IN SITU REFERENCING - PUCALLPA
Informal settlement with hastily built structures in the southern fringe region

29. IN SITU REFERENCING - PUCALLPA
Predominantly residential area with concrete structures

30. IN SITU REFERENCING - PUCALLPA
Southern fringe region with open fields and scattered wooden structures

31. IN SITU REFERENCING - PUCALLPA
Central, heavily built areas

32. IN SITU REFERENCING - PUCALLPA
Recently-settled area in western fringe region

33. IN SITU REFERENCING - PUCALLPA
Scattered development in western fringe

34. IN SITU REFERENCING - PUCALLPA
Eastern extent of old-city Pucallpa along Ucayali River

35. IN SITU REFERENCING - PUCALLPA
Recent settlement in northwestern region

36. IN SITU REFERENCING - PUCALLPA
Large informal settlement along the extreme southern fringe

37. IN SITU REFERENCING - PUCALLPA
Aerial view of Pucallpa heading northeast

38. IN SITU REFERENCING
158 points referenced in situ.
Remaining 22 points not referenced due to inaccessibility, i.e., located in
marshland, jungle, deep within private property, etc.

39. Meeting with officials from the National Institute of Statistics and Informatics
(INEI) to validate the referenced sample points .
Due to time constraints, 110 total points were validated using a municipal
planning map.
Each point was assessed according to its land use and approximate population
density per hectare (city block).
From this a new schema was derived: 5 population density categories
Expert Validation

40. First component for building a regression model: amount (%) of built-up land
cover (buildings, roads, and bare soil ) at each reference location
30-meter buffer around each sample point: account for possible GPS positional
errors and the 30 × 30 m resolution of Landsat imagery
Attempt was made to register subsets
of the air photo mosaic then digitize
polygons representing built-up features
This was too time intensive due to image
distortion and without a planimetric map
Source: Fuerza Aérea del Perú, 2005
Data Processing

41. DATA PROCESSING – BUILT-UP LAND COVER
Second attempt successful using Google Earth
Sample points dropped onto the GE scene of Pucallpa and a screenshot was
then acquired at each point
Corner coordinates for each screenshot recorder for image-to-image
rectification by coordinates to Landsat imagery
x, y -adjustment was necessary due to GE’s Simple Cylindrical projection

42. DATA PROCESSING – BUILT-UP LAND COVER
30-meter buffer around point BU areas manually digitized

43. NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI)
Two spectral bands, Red and Near Infrared (NIR), to estimate the presence and
health of vegetation within a given area.
Exploits the typical spectral behavior of healthy green vegetation, with an
absorption feature on the red portion of the electromagnetic spectrum due to
photosynthetic pigments, and high reflectance in the NIR region due to the
spongy mesophyll (Rouse et al. 1974).
Range of values between -1.0 and 1.0. Highly vegetated areas will typically
have NDVI values greater than 0.4.
NDVI = (NIR - Red) / (NIR + Red)
At 30 x 30 m, NDVI data is sensitive to contextural information, such as small
areas of water – these might skew the results and cause inaccuracies in the
regression model.
To minimize these effects, a 3 x 3 low-pass filter was applied to ‘smooth’ and
generalize the data.
DATA PROCESSING

44. 2002 NDVI
Darkest areas represent least vegetated regions

45. 2002 NDVI
After data ‘smoothing’ with 3 x 3 Low-Pass Filter

46. BU % = a + b × NDVI + e
Intercept a is the point where the line intersects the vertical y-axis.
Slope b represents the change in the dependent variable expected from a unit
change in the independent variable.
Residual term e indicates that there is a difference between the predicted y
and observed y in the paired data (Rogerson, 2001).
NDVI values were extracted at each of the sample points in ArcMap.
Sample data split into two groups:
Calibration set 75% / Validation set 25%
Regression Analysis

47. Of 82 original training points, one was discarded from the model due possibly
to exaggerated effects on the NDVI from nearby water, particularly after
applying a low pass filter. NDVI at this location was relatively low at 0.0989;
however, predicted BU % was very high at over 95%.
INITIAL TEST - EXTREME OUTLIER

48. Predicted BU % = 84.141 - 229.581 × NDVI
The slope in this equation is -229.581 shows that an increase in NDVI value of
0.01 will result in an average 2.30% decrease of built-up land cover.
The intercept of this equation, 84.141 indicates that on average, an NDVI value
of 0.0 will result in a predicted BU% of 84.14%.
When NDVI is 1.0, predicted BU% will be 0.0.
The Regression Model

49. RESIDUALS – DETECTING OUTLIERS
Residuals: the difference between a value predicted by the regression line and the
observed value for the dependent variable.
Points should be homogenously distributed along the curve (above and below)

50. REGRESSION ANALYSIS
Regression model re-run using 81 of the 82 training points

51. REGRESSION ANALYSIS
0.500000000000.400000000000.300000000000.200000000000.100000000000.00000000000-0.10000000000
NDVI3X3
100.000000000000
80.000000000000
60.000000000000
40.000000000000
20.000000000000
0.000000000000
%BuiltUp
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R Sq Linear = 0.776
Regression model re-run using 81 of the 82 training points

52. OUTLIERS
This location was observed and digitized as 95.97% built-up;
whereas the model predicted it as 61.42% built-up

53. VALIDATION OF REGRESSION MODEL
Testing the model using the remaining 28 validation points revealed a mean
predictive error of 7.6% and a standard deviation of 27.02

54. MINIMUM ERROR
Observed built-up land cover was 5.7%; the model predicted 76.34%

55. MAXIMUM ERROR
Observed built-up land cover was 67.45%; the model predicted 0% built-up

56. URBAN BUILT-UP INTENSITY MAP

57. URBAN BUILT-UP INTENSITY MAP

58. URBAN BUILT-UP INTENSITY MAP

59. NDVI MAP

60. NDVI MAP

61. NDVI MAP

62. Evaluation of Google Earth
Served as guide for compiling an initial urban density map and for extracting
built-up land cover information.
Contains a wealth of satellite and aerial imagery availabile at no cost
anywhere a connection with the Internet can be established.
Much of the imagery is available at high (approximately 1 meter) spatial
resolution.
Google Earth imagery is georeferenced; it can substitute in some cases for
digital planimetric maps.
Much of its imagery is at least two years old; imagery of Pucallpa was three
years old.
Google Earth is still preferable to relying on aerial imagery 10 years or more
out of date, or no imagery at all.

63. Strong relationship between these variables - thus the null hypothesis of no
relation was rejected – this makes possible the use of a regression model to
make rapid assessments of built-up land cover in Pucallpa and other places
similar to it.
Moderate-resolution imagery is considered best suited for urban analysis at a
regional rather than local scale (Gluch, 2006). However, when combined with
high-resolution imagery, such as provided by Google Earth when available, the
potential uses of moderate-resolution imagery are multiplied.
Conclusions & Recommendations

64. STATISTICAL ROBUSTNESS:
Regression model successfully explained 77.4% (R² = .774) of the
variability observed in the %BU land cover.
COSTS:
Cost of Imagery: For this project, imagery cost was $0.00. A planning
agency will need to factor at least $600.00 for a Landsat image
Cost in Time: In situ referencing of 158 points (4-5 days)
Capturing and georectifying 158 GE screenshots (3 days)
Expensive Software used: ERDAS Imagine, ESRI ArcMap
COMPLEXITY:
Basic image processing and GIS procedures used throughout
Conclusions & Recommendations
ASSESSMENT OF METHODOLOGY

65. Conclusions & Recommendations
QUESTIONS:
How will this model perform in other parts of the Amazon region and in other
world regions?
What will be the seasonal effects of vegetation on the model?
How can this model be adapted to derive more detailed information about the
human populations found within built-up regions?
What are the tradeoffs of adding additional variables to the model to increase
its predictive capabilities?

66. Future Work
LandScan: a global population database of the United States Department of
Energy’s (USDOE) Oak Ridge National Laboratory (ORNL) Global Population
Project (land cover, roads, slope, and night time lights )
Peru recently conducted its first national census since 1993 – results should be
used for further study into Pucallpa’s population dynamics
Other low-cost imagery sources (ASTER, ALI) should be considered as
alternatives to imagery produced by the aging Landsat constellation.
Free or low-cost GIS systems, such as SPRING of Brazil’s National Institute for
Space Research and IDRISI, should be used to further enhance overall
practicality of these methods for use by agencies in LDCs

67. The End