ASPECTOS ANALÍTICOS

In order to improve manual habitat classification, there are several automatic habi- tat classification methods that have been developed for different classification schemes. Examples of these include [35, 54]. It is interesting to notice that, to our knowledge, no previous work has been done for the automation of Phase 1 habitat classification in particular. Consequently, the approaches reviewed in this section use other classi- fication schemes. Moreover, none of the automatic approaches developed to date use ground-taken photographs as the main source of data.

An automatic habitat classification approach, like the one proposed in this thesis and those reviewed here, could ideally eliminate the disadvantages presented in the previous section regarding manual habitat classification. Users would need no additional training to use the automatic system and they might not even need to be ecologists. For example,

the EUNIS framework enables non-expert users to search for habitat information by geographical site or by species [51].

Moreover, these users would not have to have previous knowledge about the site they want to classify. In fact, this external knowledge could be implemented into the au- tomatic system in different forms. For example, [38] combined Light Detection And Ranging (LiDAR) height and intensity information with multi-spectral imagery to map coastal habitats in the Basque Country. Moreover, [163] combined Shuttle Radar Topog- raphy Mission (SRTM) data and Landsat TM imagery to classify habitats in neotropical environments. In our case, our framework could ultimately combine multiple sources of information such as aerial imagery and ground-taken photographs. As we will show in Chapter8, contextual information could also be taken into consideration in the classifi- cation process and could even be used as part of the input. For example, if imagery was used as input, information such as the time of the year in which the image was taken, the geographical location of the site and even past results from other surveys from the same area could be added automatically. In particular, in our work, we have combined low-level visual features, medium-level contextual features and geographical location in the classification process.

Additionally, there would be no need to transport any humans to these sites which would save time, labour and money. The outputs would be already digitised and human errors would not be introduced during this process. Moreover, the system’s decision making process would be uniform. Consequently, the classification would be equally uniform and there would not be any subjectivity involved in the process. Finally, the output statistics needed could be easily calculated using a computer and the already digitised information.

Most of the automatic approaches proposed in the literature use remote sensing imagery [54]. Consequently, remote sensing is defined as “the science and art of obtaining infor- mation about an object, area, or phenomenon through the analysis of data acquired by a device that is not in contact with the object, area, or phenomenon under investiga- tion” [117]. Remote sensing data is data that has been obtained using remote sensing methods. Common types of remote sensing imagery that has been used for habitat and species classification include: aerial imagery [45], satellite photography [111], LiDAR [38] and hyperspectral imagery [212].

The use of remote sensing imagery has several advantages over manual approaches: they are more exhaustive, data can be periodically recorded and they can be used to record spectral information in non-visible regions of the electromagnetic spectrum [54]. Moreover, while the data collection phase might be time consuming and requires specialised equipment, the classification process is faster and more efficient.

2.1.3.1 Pixel-Oriented and Object-Oriented Classification Methods

Classification methods using remote sensing can be divided into two classes: pixel- oriented methods and object-oriented methods. In pixel-oriented methods, each pixel is classified individually and independently of the other pixels within the image. This type of classification is referred to as spectral pattern recognition. On the other hand, object- oriented classifiers use spectral and spatial pattern recognition. This means that when classifying a pixel, its surroundings and how the same pixel’s value change over time are taken into consideration during the classification process. Object-oriented method- s normally involve two steps: first, the image is segmented into discrete objects and then each object is classified. These methods approach classification in a way similar to how humans approach digital imagery interpretation, which uses different types of in- formation, such as colour, shape, size, texture, pattern and context to group pixels into meaningful objects [117]. Pixel-oriented methods frequently obtain less accurate results and this can be attributed to the fact that, by taking only one pixel into consideration, there is a lot of spatial, temporal and contextual information that is being ignored in the classification process.

Object-oriented methods usually obtain more accurate results. Consequently, they are more popular when developing automatic habitat classification systems [117]. For ex- ample, in [54], D´ıaz Varela el al. used satellite imagery to classify habitats in the western end of the Cantabrian Coast, in Spain. They compared the performance of a Nearest-Neighbour and a maximum likelihood classifiers using an object-oriented and pixel-oriented approach and object-oriented methods outperformed pixel-oriented meth- ods. [37] also followed an object-oriented methodology, using multi-temporal satellite imagery as part of their system for detecting shoreline changes for tideland areas and obtain an error rate of less that 15.5%. Moreover, [111] used a hierarchical induc- tive classification of satellite imagery to identify native grasslands in eastern Kansas. They used discriminant analysis of ground occurrence data that was extrapolated to distinguish high-quality from low-quality grasslands. [212] also followed an object-based methodology, by extracting texture measures from hyperspectral imagery and using a neural-network approach. They successfully applied it to map vegetation Everglades and obtained a 94% accuracy. In [5] coastal and marine ecological classification standard us- ing satellite-derived and modeled data products for pelagic habitats in the Northern Gulf of Mexico. And [45] used aerial images to classify wetlands and deep-water habitats of the United States.

2.1.3.2 Limitations of Remote Sensing Data and Methods

Currently, most remote sensing methods focus on land cover or land use over habitat classification [7,79,81,177]. While land cover and land use share some similarities with habitat classification, the classification schemes used and the applications are extremely different. For example land cover and land use classifications collect very little to none biodiversity or species information [179]. Consequently, they cannot be applied to habi- tat monitoring or rare species monitoring and conservation. Research has been done on how to translate between land cover methodologies and habitat classification [145,179].

Those methods that focus on habitat classification tend to either focus on mapping particular habitat species instead of using complete schemes, such as [176, 212], or to create their own classifications depending on the site to map, such as [54, 111]. The former leads to relative or incomplete results [4] and the latter leads to results which are very dependant on the site and not easily comparable with other classifiers. For example, in [54], instead of using a standardised habitat scheme, the authors developed their own hierarchical classification with fifteen first-tier classes according to the geographical characteristics of the particular site they were classifying. While their results were very promising, their classification was tied to the particular site they were mapping. The same problem arises from [124], in which the authors combined aerial photography, CASI and HyMap data to classify forests in Australia. Once again, the authors created a new scheme instead of using an standardised classification.

Moreover, the use of remote sensing imagery to classify habitats is frequently hampered by the presence of complex habitats, such as mosaics with combinations of different habitats, complex canopy structures and transitions of vegetation types [67,187]. These problems are very common in mountain areas, fragmented ecosystems, tropical environ- ments or fine patterned landscapes [34,99].

Additionally, in the specific case of Phase 1 classification, the use of remote-sensed imagery to categorise habitats presents additional disadvantages. Table 2.5summarises the limitations of aerial and satellite images in comparison with manual Phase 1 habitat classification [102].

In conclusion, several automatic habitat classification methods have been developed to date. However, no automatic Phase 1 habitat classification system has been created. Additionally, most of the automatic systems use remote-sensed imagery, such as satellite or aerial imagery. Remote-sensed imagery on its own has several limitations, specially for the case of Phase 1, which requires a large level of detail. Moreover, it can be difficult to obtain. These are the reasons why we have created an automatic framework

te r 2. L iter a tu re R evi ew 21

Classification Methods Manual survey Aerial Photography Aerial Satellite Photography

Data Coverage Complete ground cover possi-

ble

Incomplete for some dates. Variable quality

Complete cover but data can be obscured by clouds

Data Collection Direct recording in the field

by humans

Relies on tone and pattern of spectral reflectance

More limited range of tones but greater contrast than aeri- al photography

Equipment No sophisticated or expensive

equipment required

Needs complicated and expen- sive equipment

Needs complicated and expen- sive equipment

Accuracy Accuracy depends on field

surveyors Images are accurate Images are accurate

Interpretation _{Interpretation problems Interpretation can be difficult Interpretation can be difficult}

Habitat Coverage Yields complete set of Phase 1

habitat categories

Yields limited set of habitat categories

Yields limited set of habitat categories

Canopy Information Gives information on canopy

and groundlayer

Information on canopy only (unless repeated at different seasons)

Information on canopy only (unless repeated at different seasons)

Species Information Gives information on domi-

nant and other plant species Little species information Very little species information

Conservation Evaluation Can be used for conservation

evaluation

Limited used for conservation evaluation

Limited use for conservation evaluation

for habitat classification and why we will be working with ground-taken images for the automatic classification of Phase 1 habitats in this thesis.