Urban Tree Canopy Detection Using High Resolution Satellite Images

Dr. Imdad Rizvi – PhD (IIT Bombay)

Terna Engineering College, University of Mumbai, INDIA imdadrizvi@ternaengg.ac.in

UTC

• Urban tree canopy (UTC) is the layer of leaves, branches, and stems of trees that cover the ground when viewed from above.

• A canopy of urban trees includes Public (parks, streets, riparian corridors and neighbourhood)

• And private residential, commercial, industrial areas, etc.

Benefits

• The canopy provides shade and reduces the urban heating.

• It reduces pollution, provide beauty and provide habitats for wildlife.

• Trees reduce the amount of storm water runoff, which reduces erosion and pollution in our waterways and may reduce the effects of flooding.

• Provides fresh air

Aim of this Workshop

• With the help of High Resolution Satellite Images find out the trees present in area

• Tree planting programs can be plan in specific areas

• Municipalities need accurate and updated inventories of urban vegetation

Pervious work • Using ground survey techniques by means of

tools and techniques implemented in forest plantation inventories

• “a wide range of methods have been developed, including Global Positioning System (GPS), hand held data recorders, bar code readers, and advanced surveying instruments”

• The combination of spectral classifiers and local anomaly detectors in image

• Pixel based Image analysis. • Geographic information systems (GIS) and

remote sensing techniques

OBIA

• OBIA subdivides image into group of pixels (homogeneous) that form an ‘object’

• It focus not only on spectral properties but also on shape, texture, size and other topological features

• It is widely used with high resolution images

• object based approaches do not operate directly on individual pixels but on object.

OBIA

• OBIA approach that considers the spectral, spatial and contextual characteristics of tree objects

• The OBIA greatly reduces the salt-and-pepper effect in the classified image without adversely affecting the classified image accuracy.

Exercise 1

To import the image into eCognition

To import the image into eCognition

Different layers can be added in Projects

For vegetation analysis False Color Composite image (FCC) is more useful as vegetation has strong reflectance in NIR band. Edit Image Layer Mixing window is used to select required bands of Multispectral Image.

False color composite

True color image

To strengthen the spectral power of imagery for vegetation extraction NDVI (Normalized

Difference Vegetation Index) is computed as a separate band.

Along with 8 multispectral bands NDVI band is also included in the imagery.

Fig. (a) FCC Image (b) Green Band (c) NIR Band (d) NDVI band

(a) (c)

(b) (d)

Exercise 2

Segmentation Process

Segmentation Process

• A prerequisite to classification is image segmentation.

• Image objects are building blocks for further classification or other segmentation processes.

• There are two basic segmentation principles

1. Top-down Segmentation

(Chessboard Segmentation, Quadtree-Based Segmentation, Contrast Split Segmentation)

2. Bottom-up Segmentation

(Multiresolution Segmentation, Spectral Difference Segmentation)

Multiresolution segmentation

20

RGB image segmented with scale parameter 125 and 200 respectively

Multiresolution segmentation

21 RGB image segmented with scale parameter 25, 50 and 75 respectively

Example

22

Multiresolution then contrast split segmentation

A result of the quadtree-based segmentation process

Source: Xiaoxiao Li and Guofan Shao, "Object-based urban vegetation mapping with high-resolution aerial photography as a single data source," International Journal of Remote Sensing, Vol. 34, No. 3, pp. 771–789, 10 February 2013.

Exercise 3

Classify the urban landscape in different vegetation areas

Contrast split segmentation

• NDVI layer is selected for contrast split segmentation algorithm.

Contrast split segmentation result

Hence isolated trees having contrast with background can be easily identified. Area and elliptic fit are the parameters used for classification.

In class of Small isolated trees some other vegetation area is included that is removed with standard deviation value of layer 7 i.e NIR 1.

Multi-Level Analysis

In object oriented paradigm multiple levels can be created and various segmentation processes can be employed to get image object features at multiple levels. Multiresolution segmentation (MRS) is applied on remaining vegetation area to separate grass and tree clusters at level 2.

Copy Image Object Level

Multiresolution segmentation

Feature selection

On this segmentation result different feature values are calculated for image objects. The features which are providing best separation between classes are used for assigning class. Rare grass is not chlorophyll rich area also soil particles are present within that area. Mean of red band, NDVI, area are the features used for separating rare grass class.

Assigning Membership value to Features

Canopy content chlorophyll index (CCCI) is used to separate out dense grass from vegetation class.

Comparing Features

• To compare samples or layer histograms of two classes, select the classes or the levels you want to compare in the Active Class and Compare Class lists.

• Values of the active class are displayed in black in the diagram, the values of the compared class in blue.

• The mean value of band (range) and standard deviation of the samples are displayed on the right-hand side.

Morphology Operations

After grassland separation vegetation class is merged. From vegetation class small tree clusters are separated and morphological operations are applied on them. Morphology operations are used to smooth features.

vegetation area below shadow

• One more advantage of creating NDVI as separate band is that the vegetation area below shadow is also visible.

• This is however not possible with false color composite imagery.

• To do the analysis on vegetation area covered by shadow, preliminary requirement is detection of shadow.

Combination of GRVI (Green Red Vegetation Index) and NDVI feature is used to classify the shadow area into vegetation below shadow which is visible only by NDVI band.

Creating separate region for complex areas • In the imagery a few regions have very low contrast

between tree canopy and other vegetation. Such a specific region is selected and created a separate map for that region.

• Chessboard segmentation with object size 1 is applied on map. To extract the tree canopy seed pixels are selected first followed by region growing algorithm.

• In the newly created map seed pixels are those having maximum value of NIR1. This is done by find-domain-extrema algorithm. These seed pixels are grown into surrounding if difference between the parent and child is less than threshold value. So tree clusters are separated from other vegetation area having similar spectral properties. This new map is placed back on main project map by synchronize-map algorithm.

Create Separate Map

Tree clusters are classified from backgound

Synchronize the result back to main map with synchronize map algorithm. With the help of level and

class filter parameter results obtained on map 2 are place back to main map.

Exercise 4

Classify the urban landscape and water

The imagery comprise of landscape as well as water area. First we are separating water and land. Land and Water Mask index is used for classification.

Customized Feature

Contrast split to get Shadow area

This exercise shows the repeated use of contrast split segmentation algorithm but on different image layers and hence every time gives significant results.

Exercise 5

Nearest Neighbor Supervised Classification

Nearest neighbor is one of the most popular supervised classification approach commonly present in all image analysis software (Zhang and Feng. 2005). In this method samples for each class are defined and other unknown image objects are assigned to the nearest neighbor in terms of features and observations within training samples.

Feature space optimization is one more popular approach which provides the most suitable combination of features for separating classes, in conjunction with a NN classifier. After segmentation process different features are obtained for image objects. On the set of such features feature space optimization is applied to select the best features for separating the classes.

Selection of classes Standard Nearest Neighbor : Feature Space is valid for all the classes within the Project. Nearest Neighbor : Feature Space can be defined separately for each class by editing class description.

Selection of Feature Space

Class Separation Distance Matrix

Exercise 6

Accuracy Assessment

Accuracy Assessment

• Quality of classification has to be accessed because up to date information comes from satellite images also this information can be input for decision making purposes.

• For accuracy assessment eCognition has built-in functionality.

• These tools produce statistical and graphical outputs that can be used to check the quality of classification results.

• The tables from statistical result are saved in comm-separated ASCII text files.

Accuracy Assessment

• Test samples are selected by random sampling method where each item in population has an equal chance of being included in the sample. For measuring the accuracy of classified image following important parameters are available.

• Error (confusion) Matrix • Producer’s Accuracy • User’s Accuracy • Overall Accuracy • Kappa Coefficient

Accuracy Assessment

Select Classes for Statistics

Error Matrix

Classification Result

• Along with error matrix classified image can also exported with algorithm “Export Classification View”.

• Test samples can also bring in sample space through TTA mask and then Accuracy assessment done through Error matrix based on TTA mask.