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Week 7: Arts and Science Research Methods / Case Studies / Tutorials (ART723)

Updated: Apr 12, 2023

Week beginning: 14th March 2023

Arts and Science Research Methods/Case Studies and Tutorials

This weeks lectures explored the links between the Sciences and the Arts, it was interesting to explore these links in depth and discuss the advantages and disadvantages of each discipline when working together. The arts and sciences are not as far apart as we might think. Any field will have a mixture of creativity and discipline. In a way science and art are complementary.

Example: SEFT-1 Abandoned Railways Exploration Probe- Modern Ruins

Following the old abandonment of rail/ train lines and exploring where they go, via video and audio clips. Self- built car/craft designed to glide along these abandoned tracks. Data Mapping and coverage of growth. The crafting of the car and the concept of design and the legacy of the project, after it has finished all blur the line between art and science.


Four main features of research design, which are distinct, but closely related.

  • Ontology- How you, the researcher, view the world and the assumptions that you make about the nature of the world and of reality.

  • Epistemology- The assumptions that you make about the best way of investigating the world and about reality.

  • Methodology- The way you group together your research techniques.

  • Methods/Techniques- What you do in order to collect your data and carry out your investigations.


i.e. the way in which you choose to investigate the world.

Two main schools are positivism and social constructionism:

  • Positivists believe that the best way to investigate the world is through objective methods, such as observations. Positivism fits within a realist ontology.

  • Social constructionists believe that reality does not exist by itself. Instead, it is constructed and given meaning by people. Their focus is therefore on feelings, beliefs and thoughts, and how people communicate these. Social constructionism fits better with a relativist ontology.


Epistemology and ontology will have implications for your methodology

  • Realists tend to have positivist approach. They tend to gather quantitative sources of data

  • Relativists tend to have a social constructionist approach. They tend to gather qualitative sources of data

Remember these are not absolutes! People tend to work on a continuum. There is a role for mixed methods and approaches.

Also consider the role of the researcher*: internal/external; involved or detached?

1. Content Analysis

Here, you start with some ideas about hypotheses or themes that might emerge and look for them in the data that you have collected. You might, for example, use a colour-coding or numbering system to identify text about the different themes, grouping together ideas and gathering evidence about views on each theme.

2. Grounded Analysis

This is similar to content analysis, in that it uses similar techniques for coding. However, in grounded analysis, you do not start from a defined point, Instead, you allow the data to ‘speak for itself’, with themes emerging from the discussions and conversations. In practice, this may be much harder to achieve because it requires you to put aside what you have read and simply concentrate on the data. Some people, such as Myers-Briggs 'P' types, may find this form of analysis much easier to achieve than others.

3. Social Network Analysis

This form of analysis examines the links between individuals as a way of understanding what motivates behavior's. It has been used, for example, as a way of understanding why some people are more successful at work than others, and why some children were more likely to run away from home. This type of analysis may be most useful in combination with other methods, for example after some kind of content or grounded analysis to identify common themes about relationships. It’s often helpful to use a visual approach to this kind of analysis to generate a network diagram showing the relationships between members of a network.

4. Discourse Analysis

This approach not only analyses conversation, but also takes into account the social context in which the conversation occurs, including previous conversations, power relationships and the concept of individual identity. It may also include analysis of written sources, such as emails or letters, and body language to give a rich source of data surrounding the actual words used.

5. Narrative Analysis

This looks at the way in which stories are told within an organisation or society to try to understand more about the way in which people think and are organised within groups.

There are four main types of narrative:

  • Bureaucratic, which is highly structured and logical, and often about imposing control;

  • Quest, where the ambition is to have the most compelling story and lead others to success;

  • Chaos, where the story is lived, rather than told; and

  • Postmodern, which is rather like chaos narratives, in that it is lived, but where the ‘narrator’ is aware of the story and what they are trying to achieve.

6. Conversation Analysis

This is largely used in ethnographic research. It assumes that conversations are all governed by rules and patterns which remain the same whoever is talking. It also assumes that what is said can only be understood by looking at what went before and after.

Conversation analysis requires a detailed examination of the data, including exactly which words are used, in what order, whether speakers overlap their speech, and where the emphasis is placed. There are therefore detailed conventions used in transcribing for conversation analysis.

CASE STUDY PROPOSAL 2023 (Using Online sources & Materials)



Sampling designs are commonly used to estimate deforestation over large areas, but comparisons between different sampling strategies are required to gage the true scale of the damage. Using FCE deforestation data as a reference, deforestation in the region of Norfolk in England is evaluated using Landsat imagery, land surveys and a nearly synchronous MODIS dataset. MODIS: The Moderate Resolution Imaging Spectroradiometer is a satellite-based sensor used for earth and climate measurements. The MODIS-derived deforestation is used to assist in sampling and extrapolation. Three sampling designs are compared according to the estimated deforestation of the entire study area based on simple extrapolation and linear regression models.


Human-induced and natural forest disturbances change forest systems by influencing their composition, structure, and functional processes. Deforestation is the conversion of forested areas to non-forest land uses, such as arable land, urban areas, logged areas, or wasteland, and is important for forest resource management, biodiversity conservation, climate change, the global carbon cycle, and sustainability management. Research on the accurate monitoring of deforestation and its influence is a topic of considerable interest in the context of global warming.

Remotely sensed data with land set imagery and (MODIS), are commonly used over large areas, most research adopts sample-based methodologies to estimate deforestation with higher spatial resolution. Based on the estimated results from the sampling regions, the deforestation area or even the distribution of deforestation of the entire study area can be extrapolated. Sample-based methods that use a probability sampling design provide a quantitative measure of the precision of the uncertainty that is attributable to sampling and construct confidence bounds for the area of deforestation.

The precision depends on the number of samples and their locations. Sampling is a cost- and time-efficient alternative if the objective is to estimate the area of deforestation rather than to map deforestation. Sample-based methodologies can be classified as random sampling, stratified sampling, and systematic sampling. The random sampling method randomly selects complete images or several small blocks within the area of interest and then analyses the deforestation. This technique has been applied by lots of applications. Based on parameters such as biome, precipitation, elevation, dominant forest types, land cover types, disturbance degree, topography, and soil types, the stratified sampling method divides the study area into several strata and then selects the same number of samples or allocates more samples into the strata with greater expected levels of deforestation. The stratified sampling strategy is the common used method in areas such as humid tropical forests. Systematic sampling selects samples at a defined spatial interval and is easily performed compared to the two sampling methods described above. Systematic sampling designs have been adopted to monitoring deforestation over large areas by numerous researchers.

Comparisons between different sampling designs are required to further illustrate the adaptability of the sampling method to various spatial and temporal scales. Landsat imagery and a MODIS dataset from nearly the same period are used to identify areas of deforestation and select sample blocks within the forest cover regions.


The study area of Thetford Forest Norfolk, situated straddling the north of Suffolk and the south of Norfolk in England, is one of the locations that have been designated as part of the FCE deforestation watch. (Figure 1). Thetford Forests total land area is 19,000 ha (47,000 acres) of which up to 73.8% is of Site of Special Scientific Interest. Deforestation within this region has been monitored for more than two decades using a variety of satellite sensors. Deforestation data products have identified periods of increasing (2001–2004) and decreasing (2005–2007) deforestation rates Norfolk.

Figure 1: Ordnance Survey Map 2019


Landsat TM

Based on the characteristics of the study area, the high spatial resolution (35 m 40 m) multispectral satellite sensor Landsat Thematic Mapper (TM) is selected to assess the distribution of deforestation. This deforestation dataset is used as a reference for evaluating different sampling strategies. Ten Landsat TM scenes from paths 223 to 231 and rows 65 to 72 were obtained during dry periods (times of vegetation growth) from November 2022, to March, 2023. All of the TM image data were pre-processed with radiometric calibration, atmospheric correction based on the 6S radiant transfer model, geometric correction and registration, and image mosaicking.


Deforestation Detection from Landsat TM Data

The disturbance index (DI) is a simple and effective means of tracking vegetation disturbance across a variety of forest ecosystems and is especially useful for identifying complete forest canopy removal. The DI is based on the Tasselled-Cap data space. The DI algorithm assumes that a combination of the greenness and the wetness can highlight the spectral response characteristics of the vegetation, while the brightness can express the characteristics of non-vegetation areas. The brightness values of disturbed areas are higher than those of non-disturbed forest areas, while the greenness and wetness values are lower.

Sampling Strategies Design

Tucker and Townshend randomly sampled complete Landsat images and determined that using whole scenes will result in smaller standard errors and save little or nothing in acquisition costs. However, Duveiller et al suggested that the sampling efficiency can be increased significantly by using small image extracts as sampling units and having them systematically (rather than randomly) distributed over the forest domain. Even if the standard deviation of a small region is greater than that of a large region, more samples could compensate for the larger standard deviation, which means that more sampling units with small areas might achieve a higher precision.

Based on these analyses, we sampled large 35m × 45 m plots, and the entire study area was divided into 10 blocks. The area and proportion of deforestation in each block corresponding to the Landsat TM and MODIS results could then be calculated. Three sampling strategies were designed to select sample sites to estimate the deforestation areas in MT during the period from 2022 to 2023. The difference between our sampling designs and those in previously published studies is that the selected sample blocks must include some forest pixels based on the MODIS MCD12Q1 data.

1. Random Sampling

Two random sampling methods were used. First, we simply randomly selected samples within the forest cover region. However, because the deforestation regions usually distribute densely, sampling sites and the variation in densities could influence the precision of the results when using simple random sampling. Thus, we then selected samples within the regions where the proportion of deforestation from the MODIS dataset was greater than a threshold. Various deforestation proportion thresholds were used to further analyse the differences in the estimation results.

2. Stratified Sampling

The stratification was based on the MODIS-derived deforestation. The resulting low, medium, high, and very high deforestation strata were defined as MODIS-derived deforestation proportions of 0-1%, >1–5%, >5–8%, and >10% per block, respectively. The sample sites were allocated to the four strata using two different methods. First, the samples are proportionally distributed. And second, we selected samples based on Neyman optimal allocation [6]. The optimal allocation was determined using per stratum variances of the MODIS-derived percentage of deforestation for all blocks within each stratum.

3. Systematic Sampling

The systematic sampling design is based on the number of samples, and fixed intervals were used to obtain sample blocks. Furthermore, if there were no forest pixels for a certain selected block, we sampled the nearest block to the right or down as a substitute.


Using the FCE deforestation data as a reference, deforestation areas are detected from nearly synchronous TM imagery and MODIS datasets. Several sampling designs employing TM-derived deforestation were compared to estimate the deforestation across the study area from 2022 to 2023. In general, the sampling approaches merit consideration as timely and cost-effective components for monitoring deforestation over large areas. The complete coverage TM-derived deforestation provides a unique opportunity to assess different sampling designs because it allows comparisons that are based on wall-to-wall estimators and are not estimated from single samples. A stratified sampling method that included strata construction and sample allocation provided more precise estimates than both simple random sampling and systematic sampling. Moreover, regressions between the MODIS-derived and TM-derived deforestation results provide precise estimates of both the total deforestation area and the deforestation distribution in each plot.


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