quantifying the changes in land use in developing countries using remote sensing: challenges and...

Quantifying the changes in land use in developing countries using remote sensing: challenges and solutionsAlfred Stein, Gao Wenxiu, Salma Anwar

Alfred Stein, [email protected]

Salma Anwar, [email protected]

This presentation

Developing countries have specific problems Data availability can be poor, the areas are big but sometimes

inaccessible There is much to be gained from earth observation satellites Problems can be specific Solutions can be drawn from spatial statistics

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Le menu du jour

Use of modern techniques leads to novel ways of mapping

Differences with existing methods can be big Automatic procedures may lead to odd

situations that have to be resolved Spatial statistics may lead to tools and

methods that can be of use to improve automation

There is the common story:

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The common story

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Mathematics

Statistics

Problem

Data

Solution

Reporting

Premier platLanduse change in china

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Land use change in China

In China there are different classification systems for land use

There is land owned by many owners A main concern is the updating of existing maps Classifications may have changed: object oriented

classification in stead of pixel based classification Increasingly satellite images are used for the purpose

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Land use in ChinaLevel 1 Level 2 Level 3

Agriculture Land (1) Arable land (11) Irrigable land (111)

Dry land (114)

Garden plot (12) Orchard (121)

Tea plantation (123)

Wood lnd (13) Woodland (131)

Sparsely forested woodland (133)

Other land (15) Pond for irrigation (154)

Pond for vegetation (155)

Construction land (2) Building area (20) Residential in rural areas (203)

Industrial and mining land (204)

Unused land (3) Unused land (31) Wasted land (311)

Exposed rock and shingle land (316)

1

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Two Landuse Maps

A traditional land-use map An image-derived land-use map

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Improving Representation of Land-use Maps Derived from Object-oriented Image Classification

Intention: derive the vector landuse map from image with OO image

Problems: For individual polygons: small, congested and

twisted polygons exist with step-like boundaries. For a group of polygons: geometric conflicts between

polygons (e.g. unreadable small areas and narrow corridors)

Unclassified polygons Methodology:

Map generalization combining with a polygon similarity model and spectral information from images.

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Problems in an OO image-derived Landuse Map (1)

Individual polygons: Congested polygons Twisted polygons Narrow corridors Step-like boundaries.

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Problems in OO image-derived Landuse Map (2) A group of polygons:

Geometric conflicts Unreadable small

areas Narrow corridors

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Methodology

A framework for improving representation of OO image-derived land-use maps.

Polygon similarity model Outward-inward-buffering Elimination of small polygons

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The Framework

Resolve problematic polygons

Final land-use map

Manipulate unclassified polygons

Original image-derived land-use map

Resolve geometric conflicts- Eliminate small polygons- Resolve narrow-corridor conflicts- Smoothen boundaries of polygons

Evaluate optimized output

Preliminary optimized output

Detect problematic polygons

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Spectral similarity

Spectral similarity (SP) quantifies the degree of resemblance in spectral characteristics of Pi and Pk and is calculated as the difference between their spectral values.

The spectral values are described as the standard deviation of DN values the pixels covered by a polygon (brightness). Brightness contains the spectral characteristics of different layers of the image.

A lower SP value corresponds with more similar spectral characteristics of two polygons.

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Semantic similarity Semantic similarity (SE) measures the equivalence in land-use of

Pi and Pk

It is determined by the relationship between land-use classes of Pi and Pk based on a hierarchical land-use classification system:

n: nr of class levels in the land-use classification system. Vl = 1 if Pi and Pk belong to the same land-use class at the lth

level, and 0 otherwise. If Vl = 1 and l > 1, then V1 =…= Vl-1 = Vl =1 and Vl+1 = …=Vn =0.

A larger SE value corresponds with a closer semantic relationship.

n

l

lik n

VlSE

1

*

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Semantic similarity: some cases

The land-use classes of Pi and Pk are identical at l = 3, e.g. the both polygons belong to Class I. Then V1 = V2 = V3 = 1, and thus

SE = 2. The land-use classes of Pi and Pk are different at l = 3, e.g. Pi

belongs to Class 1 and Pk belongs to Class 2, but they belong to the same class A at Level 2. Then V1 = V2 = 1, V3 = 0, and thus SE

= 1. The land-use classes of Pi and Pk differ at Levels 2 and 3, e.g. Pi

belongs to Class A and Pk belongs to Class B, but they belong to the same class (e.g. Class II) at Level 1. Then V1 = 1 and V2 =V3 =

0, and thus SE = 1/3. The land-use classes of Pi and Pk are different at all levels, e.g. Pi

belongs to Class I and Pk one belongs to Class X. Then SE = 0.

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Geometric similarity

Geometric similarity (GE) measures the resemblance in shape (size, perimeter) characteristics SIi of Pi and SIk of Pk.

For eliminating a small polygon Pi, GE equals the ratio of the length of the sharing boundaries Pi with its neighbor polygon Pk to its perimeter. This

shape index quantifies the difference in shape between a polygon and the circle with the same area.

The small polygon is merged with its neighbor with the largest GE value. Thus the possibility is eliminated of introducing new narrow-corridor conflicts when eliminating the small polygon.

For unclassified polygons, GE adopts the difference in the shape index of two polygons as

ikiik SISISIGE /)(

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Polygon similarity model

Polygon similarity (S) is defined as the degree of similarity of two polygons depending on their contextual characteristics. Spectral characteristics (SP) Semantic characteristics (SE) geometric characteristics (GE)

ikikikik GESESPS 321 )1(

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Eliminate small polygons

Basic solution: merged with the neighbor with the highest polygon similarity

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Outward-inward-buffering

To resolve narrow-corridor conflicts existing in polygons.

Basic rationale: an outward-buffering process (dilation process) + an inward-buffering process (erosion process)

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Improved Map

image-derived land-use map at 1:10000

image-derived land-use map at 1:50000

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We notice…

Well developed spatial statistical techniques are able to resolve emerging problems in new classification procedures

Further optimization is to be done Automating updating steps is receiving a new flavor There is room for a further (probabilistic) approach

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Seconde platDeforestation in the Amazonian

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Selective Logging

In the Brazilian Amazonia, selective logging is a major source of forest degradation

Detection and analysis of selective logging is an important challenge to forest researchers

Log-landing sites serve as proxy for selective logging activities Spatial point pattern statistics may serve as an important tool for

analyzing patterns of log-landing sites

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Landsat Image

ISODATA Classification

Linear Spectra Unmixing

Forest/Non Forest Mask

Soil Fraction Image

Forest Soil Fraction Image

Binary Classification

Log Landing Locations

Selectively Logged Forest Map

Buffer application

Manual Editing

0, soil fraction < 20%1, soil fraction > 20%

Grouping of 1-4 pixels

Selective Logging Detection

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Study area

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Map of log-landings (2000)

650 locations13Nov12

Point pattern statistics

First order characteristics

where dx is a small region located at x of the log-landing pattern X, |dx| being its area and N(dx) is the number of log-landings in dx

Second order characteristics

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Distance summary functions Nearest neighbor distance distribution function

Empty space distance distribution function

The J-function

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Stationarity: all properties of a pattern remain invariant under translation (constant density)

Non-stationarity: configuration of the pattern depends on the locations (variable density) variability due to environmental heterogeneity interactions between the points

In case of non-stationarity: Markov Chain Monte Carlo methods (MCMC) become computationally extensive

Stationarity vd. Non-stationarity

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Estimation of the intensity function and choice of the kernel bandwidth

Intensity function is generally unknown and estimated non-parametrically using kernel smoothing

Suitable choice of kernel bandwidth is the main challenge in estimation of the intensity function

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kernel size=10kernel size=30kernel size=40kernel size=50Kernel density estimate with kernel size=10 km

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Kernel density estimate with kernel size=20 km

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Observations

A larger value of kernel bandwidth r reduces the interaction distance between the log-landing sites, thus reducing the effective range of interaction distance r over which the J-function is calculated.

As the value of r increases beyond its effective range, the simulated envelopes span over wider range and relative noise in the simulated envelopes also increases.

Relative noise in the calculated J-function also increases beyond the effective range of r

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Map of loglandings (2001)

917 locations13Nov12

kernel size=10kernel size=20kernel size=30kernel size=40Kernel density estimate with kernel size=20 km

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To summarize

The presented visual and graphical methods provide a useful tool to get an insight into the spatial characteristics of log-landings distribution.

Spatial statistics was useful for analysis and interpretation of the pattern of log-landing sites.

The inhomogeneous J-functions helps to infer the type and ranges of interaction using non-parametric form of intensity.

The selective logging operations are strongly aggregated with in the study area

The appropriates bandwidth increased from 20 to 30 km within a single year, indicating an increase in the extent of the clustering of log-landing sites.

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Further work

Fitting a spatial point pattern model to explain the clustered pattern of log-landing sites in terms of related environmental and geographic factors

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Le desertA new scientific journal

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A new journal

ees.elsevier.com\spasta

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The history

First ideas date back from 2007

Aims and scope were defined

A key word analysis was done

2007 – 2010: discussing it @ Elsevier

Reluctance because of the economic crisis

Reluctance because of increasing e-journals and internet

There was a recent journal in a related area: Spatial and Spatio-Temporal Epidemiology, Andrew Lawson editor in chief

No new journals

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Then, in 2010…

We had the idea for a conference to check the support

Elsevier organized the meeting

Conference took place in Enschede, in 2011

It was a great success (> 300 participants)

This convinced Elsevier that it was a good idea to continue

I was formally invited to become the ed-in-chief

The first issue appeared in 2012, containing a wide range of publications

The second issue is in press

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Aims and scope (1)

The aim of the journal is to be the leading

journal in the field of spatial statistics.

It publishes articles at the highest scientific level

concerning important and timely developments in

the theory and applications of spatial and spatio-temporal

statistics.

It favors manuscripts that present theory generated by new applications, or where new theory is applied to an important spatial problem.

A purely theoretical study will only rarely be acceptable without a proper application, whereas a single case study is not acceptable for publication.

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Aims and scope (2)

Spatial statistics concerns the quantitative analysis of

spatial data, including their dependencies and uncertainties.

The extension to spatio-temporal statistics includes the

time dimension as well.

The three major groups of data are covered:

lattice data that are collected on a predefined lattice

geostatistical data that represent continuous spatial variation

spatial point data that are observed at random locations.

These types of data have their logical extension into the space-time domain, where the relations remain similar, but estimation may be different.

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Aims and scope (3)

Methodology for spatial statistics is found in probability,

stochastics and mathematical statistics as well as in

information science.

Typical applications are mapping of the data,

issues of spatial data quality,

modeling the dependency structure and

drawing valid inference on the basis of a limited set of data.

Applications of spatial statistics occur in a broad range of disciplines: agriculture, geology, soils, hydrology, the environment, ecology, mining, oceanography, forestry, air quality, remote sensing, but also in social/economic fields like spatial econometrics, epidemiology and disease mapping.

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The future (4)

We are looking for good papers!

To report your science

To communicate your findings

To have feedback from colleagues

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The end

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quantifying the changes in land use in developing countries using remote sensing: challenges and...

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