Although there are better ways of analysing these data (for example, using NumPy), one pure-Python solution is as follows:

data_file = 'redwood-data.txt'

# Indexes of the fields in each record:
NAME, LOCATION, DIAMETER, HEIGHT = 0, 1, 2, 3

f = open(data_file)
# Skip the header lines
f.readline()
f.readline()

tree_data = []
max_height, max_diameter = 0, 0
for i, line in enumerate(f.readlines()):
    records = line.split('\t')
    diameter = records[DIAMETER] = float(records[DIAMETER])
    height = records[HEIGHT] = float(records[HEIGHT])
    if diameter > max_diameter:
        max_diameter, max_diameter_index = diameter, i
    if height > max_height:
        max_height, max_height_index = height, i
    tree_data.append(records)

highest_tree = tree_data[max_height_index]
print('The highest tree: {}, {} m in {}'.format(highest_tree[NAME],
                            highest_tree[HEIGHT], highest_tree[LOCATION]))

widest_tree = tree_data[max_diameter_index]
print('The widest tree: {}, with diameter {} m in {}'.format(widest_tree[NAME],
                            widest_tree[DIAMETER], widest_tree[LOCATION]))

The output is:

The highest tree: Hyperion, 115.61 m in Redwood National Park
The widest tree: Stratosphere Giant, with diameter 5.18 m in Humboldt
Redwoods State Park