High resolution wetland mapping I.

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1 High resolution wetland mapping I. Based on the teaching material developed by Steve Kas, GeoVille for WOIS Product Group #5 Dr. Zoltán Vekerdy and János Grósz

2 What shall we talk about? Objectives and methods Pixel-based classification Problems of pixel-based classification Mapping with unsupervised classification Mapping with supervised classification Hybrid approach 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 2

3 Objectives and methods Short review 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 3

Objectives These workflows will derive high

inventory providing essential information for

hazard exposure maps 4/18/2016 Nile Ecosystems

Training on EO Tools for Wetland Ecosystem

4 Objectives These workflows will derive high resolution land cover and land cover change inventory providing essential information for land cover monitoring, water demand maps and hazard exposure maps 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 4

5 Workflows Pixel-based classification (unsupervised K-Means and supervised Support Vector Machine (SVM) classification) Hybrid classification (Spatial statistic approach combining segmentation (object-based approach) with pixel-based classification) Land cover change detection (Multiplication of past land cover values by 100 and addition of recent land cover values to derive land cover change codes) Water demand evaluation (quantification of water demand (total m³) for a defined area of interest (e.g. catchments units) and land cover class of interest Hazard exposure analysis (exposure analysis for a defined area of interest (e.g. administrative units) while analysing a specific land cover class of interest with the extend of an hazard (e.g. flooded areas) 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 5

6 Image data (satellite images) Landsat 7, Landsat 8 and Sentinel-2 images about one test area, selected from: Lake Naivasha Lake Burullus Nakivubo wetland Dinder wetlands Mara wetlands (Minimum) 2 dates: one old and a recent Subset is made available Pre-processing made on the images: Geometric corrections Atmospheric corrections 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 6

7 Pixel-based classification 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 7

8 Image Space Multi-band Image

9 Multi-dimensional Feature Space feature vectors e.g. (34, 25, 117) (34, 24, 119)

10 Feature space (scatterplot) Low frequency Feature space Two/three dimensional graph or scattered diagram Formation of clusters of points representing DN values in two/three spectral bands Each cluster of points corresponds to a certain cover type on ground (theoretically) High frequency 1D

11 Distances and clusters in feature space band y (units of 5 DN).. (0,0) band x (units of 5 DN) Max y Min y Euclidian distance (0,0) Min x Max x Cluster

12 Supervised vs. unsupervised classification UNSUPERVISED APPROACH Considers only spectral distance measures Minimum user interaction Requires interpretation after classification Based on spectral groupings SUPERVISED APPROACH Incorporates prior knowledge Requires a training set (samples) Based on spectral groupings More extensive user interaction

13 We shall use the following methods Unsupervised (K-Means) classification Supervised SVM (Support Vector Machine) classification 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 13

14 Problems of pixel-based classification What do we have to pay attention to? 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 14

.. Single class label per pixel Mixed pixels Spectral overlap

15 Pixel based problems No use of other characteristics location, orientation, pattern, texture... Single class label per pixel Mixed pixels Spectral overlap Different classes with similar spectral characteristics Class definition Land Use / Land Cover

16 PROBLEMS LAND COVER/LAND USE Constraints of pixel based image classification it results in spectral classes each pixel is assigned to one class only Land use Land cover Sport Grass Training samples Spectral classes Meadow Spectral bands - Spectral classes - Land cover - Land use

17 Spectral Class Land Cover Class Land Use Class water water shrimp cultivation grass1 grass2 grass3 bare soil grass grass grass bare soil nature reserve nature reserve nature reserve nature reserve trees1 trees2 trees3 forest forest forest nature reserve production forest city park 1-n and n-1 relationships can exist between land cover and land use classes PROBLEMS LAND COVER/LAND USE DEM or other additional data can help improve a classification

18 Objects smaller than a pixel Mixtures Boundaries between objects Transitions PROBLEMS MIXED PIXELS

19 PROBLEMS SPATIAL RESOLUTION Resolution dependency Each pixel contains approximately the same mixture Distinct reflection measurement Regular, repetition of spatial pattern Large cluster in the feature space Spectral overlap with other classes

20 Mapping with unsupervised classification Starting from the spectral characteristics of the image 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 20

21 1-D unsupervised classification (slicing) Histogram Input layer (single)? Reality: spectrally fuzzy classes Classified image Histogram unsupervised classification Ideal case: Distinction between classes

22 Unsupervised K-means classification Algorithms that separate pixels into natural groupings or clusters present in the image Basic Premise: pixels of a given spectral (cluster) class are likely to be members of the same cover type class Or, conversely pixels of a given cover class are more likely to be spectrally similar than pixels of different classes 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 22

Unsupervised classification: clustering (in 2-D) Clustering algorithm User defined cluster parameters Class mean vectors are arbitrarily set by algorithm (iteration 0) Class allocation of feature

23 Unsupervised classification: clustering (in 2-D) Clustering algorithm User defined cluster parameters Class mean vectors are arbitrarily set by algorithm (iteration 0) Class allocation of feature vectors Compute new class mean vectors Class allocation (iteration 2) Re-compute class mean vectors Iterations continue until convergence threshold has been reached Final class allocation Cluster statistics reporting Recode/group them into sensible classes e.g. 2, 3, 4 and 5 make one class Feature spaces!

24 When is the process terminated? When a fixed number of iterations i has been completed When centroids do not change between iterations This criterion ensures that the clustering is of a desired quality after termination. In practice, it needs to be combined with a bound on the number of iterations to guarantee termination. Terminate when the change between iterations are below a threshold t For small t, this indicates that we are close to convergence. Again, it needs to be combined with a bound on the number of iterations to prevent very long runtimes. 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 24

Unsupervised K-Means classifcation - Reclassification Done by analyst (subjective) Use of reference material (maps, air-photos, GPS locations etc.

25 Unsupervised K-Means classifcation - Reclassification Done by analyst (subjective) Use of reference material (maps, air-photos, GPS locations etc.) 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 25

26 Steps shown on images K-Means clustering Reclassification 4/18/2016 Nile Ecosystems Valuation for Wise Use Project (Nile Eco-VWU) Training on EO Tools for Wetland Ecosystem Management and Valuation APRIL, 2016, Nairobi, Kenya 26

Clustering. Introduction to Data Science. Jordan Boyd-Graber and Michael Paul SLIDES ADAPTED FROM LAUREN HANNAH

Clustering. Introduction to Data Science. Jordan Boyd-Graber and Michael Paul SLIDES ADAPTED FROM LAUREN HANNAH Clustering Introduction to Data Science Jordan Boyd-Graber and Michael Paul SLIDES ADAPTED FROM LAUREN HANNAH Slides adapted from Tom Mitchell, Eric Xing, and Lauren Hannah Introduction to Data Science