Wednesday, 3 April 2013

Vegetation Classification - Coniferous & Deciduous Forest

High Resolution Imagery based Forest Classification – Woody 
(Coniferous & Deciduous)

Remote sensing has been recognized as an efficient tool for forest inventory. It allows us to observe accurate and up-to-date data from large forest areas. Imaging spectrometers allow the acquisition of images in hundreds of contiguous and very narrow bands. They provide a continuous and exact reflectance spectrum of the observed object. Usually, the object can be identified from its characteristic spectral reflectance. The reflectance spectrum of green vegetation in the visible region is controlled by contribution of chlorophyll. The spectrum in the near infrared region is controlled by water content and the contribution of other organic material.

However, different plant species usually have characteristic reflectance spectrum. It is obvious that water content and contribution of chlorophyll are not constants even in the same kind of plants and it causes variation in reflectance values. Besides, the reflectance values of two different plants are sometimes very similar. This leads to the problem that certain vegetation classes mix together. When looking at remote sensing images with high spatial resolution, it is possible to distinguish different age forest types. Even if reflectance of different age trees are very similar. This is mainly based on the texture of the forest. Taking texture into consideration we get more information about the forest. It was required to carry out satellite based study using High Resolution satellite Image to find out the different LU/LC (including detailed forest classification) classes in this case study.

Area Covered:
The LU/LC was to be created for more than 60 sq miles area of US.

Scope of Work:
Current case study required to carry out satellite based study using High Resolution Satellite Image to find out the different LU/LC classes. Hybrid classification techniques were used to extract the required information in raster format especially to distinguish the woody vegetation into coniferous and deciduous classes. Some of the classes classified were:

  • Non woody
  • Shrub
  • Deciduous
  • Coniferous
  • Mixed
  • Impervious
  • Water
  • Bare

Inputs Used:
The inputs that were used for this case study were:
·         Image type  -   High Resolution Satellite Images
·         Images Resolution      -           2.0 Ft
·         No. of bands used       -   03

Software Used:
The following software was used:
l  Erdas Imagine 9.1

The methodology followed in this project is
·         Image preparation (Sub-setting image as per area of interest)
·         Image classification and Compilation
·         Assessment (Quality Analysis/Error removing)
·         Final shipment

1.      Image Preparation

The images were given by the client as a high-resolution satellite images. ERDAS Imagine 9.1 software was used for extracting area of interest as well as for the entire classification.
2.      Image Classification and Compilation
The commonly used classification methods are Supervised and Unsupervised classification. Normally, multispectral data are used to perform the classification. The objective of image classification is to identify and portray, as a unique gray level (or color), the features occurring in an image in terms of the object or type of land cover these features actually represent on the ground.
Unsupervised classification is more computer-automated. It enables to specify some parameters that the computer uses to uncover statistical patterns that are inherent in the data. These patterns are simply clusters of pixels with similar spectral characteristics. In some cases, it may be more important to identify groups of pixels with similar spectral characteristics than it is to sort pixels into recognizable categories. This method is usually used when less is known about the data before classification. It is then the analyst’s responsibility, after classification, to attach meaning to the resulting classes. Unsupervised classification is useful only if the classes can be appropriately interpreted. Thus, with the help of ERDAS IMAGINE and its assistance in this process, various classes were classified. However, Luminous ETS has adopted an automated process of classification followed by manual cleaning. These automated processes include:

2.1 Hybrid Classification

Hybrid classification is the use of both supervised and unsupervised techniques to classify an image. Both methods, when taken singularly, have their drawbacks. Supervised classification requires knowledge of the area and/or detailed field data. Unsupervised classification requires minimum initial input from the analyst, but the output takes a significant amount of time to assign the computer-generated clusters to a known land cover.  Hybrid combines the benefits of both techniques.

2.2 Land Cover Mapping

The term land cover relates to the type of features present on the surface of the earth whereas the term land use relates to the human activity or economic function associated with the specific piece of land. Depending upon the level of mapping details, its land use can be described as in urban use, residential use or single-family residential use.
The number of classes which were classified is:
·         Non-woody: Grass, Turf, Non-woody wetland (Cattails, rushes, etc.)
·         Shrub: Woody vegetation < 15 ft in height
·         Deciduous: Trees which shed their leaves in the fall on an annual basis.
·         Coniferous: Cone shaped trees often found at high altitudes.
·         Mixed: Trees which may comprise of the above mentioned two classes.
·         Impervious: Roads, buildings, driveways, sidewalks, gravel and hard-packed dirt roads
·         Water: Swimming pools, open tanks, etc.
·         Bare: construction sites, mines, beaches, etc.
2.3 Generation of Land Cover Map

Geometrically and radiometrically corrected satellite images were used for classification. Classification was carried out using the unsupervised classification in which the number of classes, maximum iterations and convergence threshold were given first. Further, the classification was carried on by analyzing the spectral values of the pixels as well as its tone. Finally, the classes were finalized as mentioned by the client.
      At many times it becomes difficult to classify certain pixels especially the ones which fall in water and shadow areas. Therefore, to avoid such confusion neighborhood function was run in order to remove water and shadows. For it kernel filter of 3*3 (function: majority) was used. This was saved in a particular file for further use. Masking is an automated process which is performed to overall enhance the image. During the image classification process its obvious that many pixels can be left without having a proper class. Therefore, masking was done for such pixels. This process enables us to extract a particular class in which pixels are left unclassified.
After which very small pixels were clumped and eliminated. Clump performs a contiguity analysis on a single layer RASTER. Each separate raster region, or clump, is recoded to a separate class. The output is a single layer raster in which the contiguous areas are numbered sequentially. Then mosaicing of the extracted classes was done with the earlier saved file on which neighborhood function was run. Further, these classes were masked again so that more information could be extracted about the woody and impervious classes respectively. Once again the above mentioned steps were carried out.  Finally, both the images were mosaiced again for which neighborhood function was run. This time it was done in order to remove line feature of the class. Finally, it was mosaiced with water body, which was separately extracted.

·         Miss classification: The main problem that was faced during classification was of miss-classification by the automatic classification process. In this many of the pixels were not being correctly classified.
·         Shape: Another problem that was faced was in the shape of the feature which was not being properly identified. Especially the impervious features did not pick the exact shape.
·         Shadow: In automated classification the shadow of the trees were classified in Water due to the Black signature.
·         Other problems: Many times the pixels were not being identified either by its tone or by its texture. Thus, making it difficult to categorize them as per their respective class. Due to which some of the pixels belonging to one class were found in some other class.

·         Some of the classes were masked and extracted separately to pay attention on that particular class. It was further classified and picked accurately. When automated process and picking of the pixels completed then it was manually recoded with the help of AOI creation to fulfill the quality requirement of the client.
·         Whereas, at certain times the pixels of the particular class were clumped and then recoded in the defined class. Further viewing the quality of the images it was thought that the image should be masked and then recoded.
·         The general classification and processing techniques were not feasible to extract the features exactly in their proper shape. To overcome with these problems a semi automated classification technique was used which showed good results in a very less manual efforts and time.
·         Neighborhood function allows performing one of several analyses on class values in an .img file using a process similar to convolution filtering. They are specialized filtering functions that are designed for use on thematic layers. Each pixel was analyzed with the pixels in its neighborhood. The number and location of the pixels in the neighborhood were determined by the size and shape of the filter, which was defined. Further, each filtering function that resulted in the center pixel value was replaced by the result of the filtering function.
3.      Assessment(Quality Analysis/ Error Removing)

After the classification of the image was done, the final assessment was carried out which comprised of the manual editing along with the Quality Check/Quality Analysis, so as to remove the errors.

4.      Project Shipment

      The following final outputs were made-
i)                    Classified LU/LC Map
ii)                  Thematic Maps in Img/TIFF format

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