Geographical Fieldwork

Aim: to investigate the effects of vegetation on microclimatic variables, such as humidity, light intensity, temperature and wind speed.

Theory: suggests that in woodlands the wind speeds, light intensity and emperature will be lower but it will have high humidity in the day. Comparing to the grassland which during the day will have higher wind speeds, light intensity and temperature but a low humidity. WHereas at night the theory suggests that the woodland due to insulation from the high foliage and trees will have a warmer temperature compared to the exposed grassland.

Hypotheses:

H1 NULL: there will be significant difference in humidity levels between woodland and grassland.

H2 Null: There will be no significance between the temperature between woodland and grassland.

H3 Null: There will be no correlation between vegitation height and wind speed. 

Stanmer Park is in East Sussex, Brighton. 

Why is it appropriate:

Only 5km from school, Has toilets, has two types of micro climates, A cafe.

Risks i managed: Animals, The weather- looket at forcast, Trips and falls, stranger danger, loss of group.

Anemometer- assess windspeed

Whriling hygrometer- assess relitive humidity

Thermometer- temperature

A subjective ten point scoring scale- light intensity

Tape measure: for transect 

Ruler: measure vegetation height.

I used a systematic sample for data collection, i laid out a 50 metre tape measure and measured every 5 metres the wind speed at shoulder height, used the whirling hygrometer for 30 seconds at shoulder height.

I presented the data i collected in different ways: An ArchGis map with proportional symbols of the wind speed and vegitation height, and did a scattergraph with this, i used a line graph for temperature and a bar graph for light intensity.

H Null: there will be no significant difference within the humidity in the woodland or grassland.

H: there will be a significant difference"".

Using chi squared and my 10 data sets for each microclimate, I found a calculated value of 9.49, which was higher than the critical value at p= 0.05.

This means that i reject my null hypothesis and accept my alternative. 

This realtes and helps prove the theory which stated that the woodland would be 10% higher in humidity.

Mann Whitney determines whether there is a significant different between the medians of 2 groups of data. 

H Null: There is no significance difference in temperature found in the woodland or grassland.

H: there will be a significant difference.

I had 10 sets of data for each microclimate and calculated a value of 42.5 as my smallest u value, this was larger than my critical value of 23 at p = 0.05.

So i rejected the alternative and accepted the null as not significant because the u value was bigger.

I used spearmans because i wanted to see if there was a correlation between wind speed and vegetation height, as i had drawn a scatter graph which looked as if there may have been a correlation within the data so i did this stats test. 

I calculated the value of 0.825 which was bigger than the critical value at p = 0.05 and 1o degrees of freedom which was 0.6. 

Therefore i rejected null hypothesis as there was a significant difference.

Limitations of the study?: not many data points, subjective way of mesauring large vegitation height, and light intensity.

How to improve: use a clinometre, use a lazer thermometre to test ground temperature instead of air. Use a light metre to measure light in an objective way.

How did results help aim: showed could reject null hypothesis for humidity and wind speed but accept null for temperature. So proved some of the thoery/ aim and showed that sometimes= no effect. was effective as results were accurate most of time. but could have used more accurate ways of data collection. also limited no of data points.

Scatter graph, for wind speed. 

Effectiveness: Clear visual representation of relationship between 2 variables

Data continuous – needed scatter graph

The need to see relationship for these 2 variables to meet aim influenced choice

Allows for line of best fit – could see needed statistical test as line hard to draw

Effective at communicating main info necessary to reach conclusion for aim

Anomalies easily identifiable - useful

Useful for easy data extraction

Limitations: Could only compare the 2 variables, Not statistically verifying – may see correlation, but need statistical test to confirm