Toward the automatic detection of effective gerbil holes in desert grasslands through unmanned aerial vehicle imagery

Figures & data

Figure 1. Location of the study area on Ordos Plateau, China.

Table 1. Sample plots distribution and the numbers of gerbil holes.

Sample plot number	Longitude (/°)	Latitude (/°)	Number of effective gerbil hole
1	107.9353338	39.37637958	78
2	107.3285293	39.74653606	105
3	107.3461522	39.67427311	71
4	107.4647808	38.68700939	25
5	107.5344532	39.24878197	112

Table 2. Image object information on brightness, spectral and geometric features.

Characteristic category	Main content	Main application
Spectral	Mean blue, mean green, mean red, max difference, Standard deviation (std. dev.) blue, std. dev. green, std. dev. red, brightness, EXG (Vegetation indices of excess green index)	Spectral features were the most basic features for evaluating an image object’s pixel value.
Texture	GLCM (Gray-Level Co-occurrence Matrix) homogeneity in all directions, GLCM in the 0° direction, GLCM in the 45° direction, GLCM in the 90° direction, GLCM in the 135° direction	Texture features are derived from texture after Haralick based on the Gray-Level Co-occurrence Matrix or Gray-Level Difference Vector.
Geometric	Area, Width, border length, length/thickness, length/width, number of pixels, thickness, volume, width, asymmetry, border index, compactness, density, elliptic fit, roundness, shape index	Geometric features refer to the geometric information of meaningful objects, which is crucial to the classification effect when the target object has a regular geometry.

Figure 2. Process of gerbil hole identification and optimization based on UAV imagery.

Figure 3. ESP tool outputs for the UAV imagery Graphs depict changes in local variance (LV) (solid black) and rate of change (ROC) (solid blue) with increasing scale parameter.

Figure 4. Segmentation results of UAV imagery under different scale parameters.

Figure 5. Segmentation results of gerbil holes under different shape factors.

Figure 6. Segmentation results of gerbil hole under different compactness values.

Table 3. Correlation-based forward search process.

Feature subset	k	$\overline{r_{cf}}$	$\overline{r_{ff}}$	Merit
[ ]	0	N/A	N/A	0.0
[EXG]	1	0.751	1.000	0.751
[Brightness]	1	0.542	1.000	0.542
[GLCM]	1	0.420	1.00	0.420
[Roundness]	1	0.454	1.00	0.445
……	1	……	……	……
Standard_d3	1	0.023	1.00	0.023
[EXG, Brightness]	2	0.647	0.460	0.758
[EXG, Roundness]	2	0.587	0.393	0.704
[EXG, GLCM]	2	0.598	0.07	0.819
……	2	……	…….	…….
[EXG, GLCM, Roundness]	3	0.579	0.249	0.820
[EXG, GLCM, Standard]	3	0.539	0.308	0.73800
[EXG, GLCM, Shape_inde]	3	0.536	0.312	0.731
……	……	……	…….	……
[EXG, Brightness, Roundness, GLCM]	4	0.539	0.397	0.728
……	k	……	……	$\mathit{Merit}=\frac{k\overline{r_{cf}}}{\sqrt{k+k(k-1)\overline{r_{ff}}}}$

Figure 7. Scatter plot of the distribution between optimal feature subset.

Figure 8. Classification results of UAV imagery based on spectral features, texture features and geometric features.

Table 4. Recommended parameters for identifying effective gerbil holes in OBIA.

	Parameters or features	Values or threshold
Segmentation parameters in multiresolution segmentation	Scale parameter	170
	Shape factor	0.2
	Compactness value	0.3
Classification feature in OBIA	Spectral feature (EXG)	–5
	Texture feature (GLCM)	0.120–0.145
	Geometric feature (Roundness)	0.9

Figure 9. Maximum likelihood classification result with post classification.

Figure 10. UAV imagery and partly enlarged detail of classification results. (a) UAV imagery of an entire sample plot, (b) Subset of UAV imagery, (c) OBIA result of subset, (d) ML classification result of subset, (e) OBIA with post classification and (f) MLC with post classification

Table 5. Accuracy assessments of two methods.

Classification method		Grassland	Gerbil hole	Bare soil	User’s accuracy (%)
OBIA	Grassland	1152	31	58	92.82
	Gerbil hole	24	209	23	81.64
	Bare soil	91	47	915	86.89
	Producer’s accuracy (%)	90.92	72.82	91.86
	Overall accuracy (%)	88.25	Kappa coefficients= 0.82
ML classification	Grassland	1012	67	162	81.54
	Gerbil hole	11	227	18	88.67
	Bare soil	31	51	971	92.21
	Producer’s accuracy (%)	96.01	65.79	84.36
	Overall accuracy (%)	86.67	Kappa coefficients = 0.77

Figure 11. Relationship between grass coverage and area of effective gerbil holes. (a) Area of gerbil holes and (b) Mean area.

Data availability statement

The data that support this study was shared on ScienceDB (scidb.cn).