  Accurate estimation of the biomass of roadside grasses plays a significant
role in applications such as fire-prone region identification. Current
solutions heavily depend on field surveys, remote sensing measurements and
image processing using reference markers, which often demand big investments of
time, effort and cost. This paper proposes Density Weighted Connectivity of
Grass Pixels (DWCGP) to automatically estimate grass biomass from roadside
image data. The DWCGP calculates the length of continuously connected grass
pixels along a vertical orientation in each image column, and then weights the
length by the grass density in a surrounding region of the column. Grass pixels
are classified using feedforward artificial neural networks and the dominant
texture orientation at every pixel is computed using multi-orientation Gabor
wavelet filter vote. Evaluations on a field survey dataset show that the DWCGP
reduces Root-Mean-Square Error from 5.84 to 5.52 by additionally considering
grass density on top of grass height. The DWCGP shows robustness to
non-vertical grass stems and to changes of both Gabor filter parameters and
surrounding region widths. It also has performance close to human observation
and higher than eight baseline approaches, as well as promising results for
classifying low vs. high fire risk and identifying fire-prone road regions.
