Quantification and mapping of the spatial landscape pattern and its planning and management implications a case study in Addis Ababa and the surrounding area, Ethiopia

Figures & data

Figure 1. Study area map of Addis Ababa and the surrounding Oromiya

Table 1. The Landscape metrics selected for detailed landscape- and class-level analysis

Metrics	Acronyms	Descriptions
Area Metrics
Total Landscape Area	TLA	Sum of areas of all patches in the landscape. Example: Landscape Area (TLA) = 46,872.719 + 359,047.844 + … + 62,423.574, TLA = 184.11 hectares
Patch Density & Size Metrics
Mean Patch Size	MPS	Example: Mean Patch Size of Conifer Patches (Class Level), MPS = (359,047.844 + 139,531.484 … + 65,819.984)/4, MPS = 17.42 hectares Example: Mean Patch Size of Patches (Landscape Level), MPS = (46,872.719 + 359,047.844 + … + 62,432.574)/14, MPS = 13.15 hectares
Patch Size Coefficient of Variance	PSCoV	Example: Coefficient of Variation of Conifer patches (Class Level) PSCoV = PSSD/MPS = (11.05 hectares/17.42 hectares) *100 = 63 Example: Coefficient of Variation of all patches (Landscape Level) PSCoV = (9.51 hectares/13.15 hectares)*100 = 72
Patch Size Standard Deviation	PSSD	Example: Patch Size Standard Deviation of Conifer Patches (Class Level) PSSD = 11.05 hectares Example: Patch Size Standard Deviation of all patches (Landscape Level) PSSD = 9.51 hectares
Edge Metrics
Total Edge	TE	Example: Total Edge Conifer (Class Level) TE = Sum of perimeter of all conifer patches. TE = 10,858.88 meters Units are expressed in native maps units. Example: Total Edge all patches (Landscape Level) TE = Sum of perimeter of all patches TE = 28,607.27 meters
Shape Metrics
Mean Shape Index	MSI	MSI is equal to 1 when all patches are circular (for polygons) or square (for rasters (grids)) and it increases with increasing patch shape irregularity. MSI = sum of each patch’s perimeter divided by the square root of patch area (in hectares) for each class (when analyzing by class) or all patches (when analyzing by landscape), and adjusted for circular standard (for polygons), or square standard (for raster (grids)), divided by the number of patches.
Area Weighted Mean Shape Index	AWMSI	AWMSI is equal to 1 when all patches are circular (for polygons) or square (for rasters (grids)) and it increases with increasing patch shape irregularity. AWMSI equals the sum of each patch’s perimeter, divided by the square root of patch
Mean Patch Fractal Dimension	MPFD	Mean patch fractal dimension (MPFD) is another measure of shape complexity. Mean fractal dimension approaches one for shapes with simple perimeters and approaches two when shapes are more complex.
Diversity & Interspersion Metrics
Mean Proximity Index	MPI	Mean proximity index is a measure of the degree of isolation and fragmentation of a patch. MPI uses the nearest neighbor statistic. The distance threshold default is 1,000,000. If MPI is required at specific distances, select Set MPI Threshold from the main Patch pull-down menu and enter a threshold distance. Both MNN and MPI use the nearest neighbor statistic of similar polygons in their algorithm.
Interspersion Juxtaposition Index	IJI	Measure of evenness of patch adjacencies, equals 100 for even and approaches 0 for uneven adjacencies

Table 2. Accuracy assessment result by study period

	2015
LULC Type	PA	UA
Water	100	100
Forest	95	96.6
Mixed Woodland	83.3	84.7
Cultivated Land	81.7	80.3
Bareland	78.3	82.5
Builtup	91.7	91.7
Overall accuracy	89
Overall kappa statistics	87

Figure 2. The Land use-land use/land cover map of the study area

Table 3. Summary statistics of 39 computed metrics for hexagonal regions at landscape and class levels

	Landscape Level Summary statistics			Forest Class Level summary statistics
Metrics	Mean	SD	CoV	Mean	SD	CoV
MPI	520.4	235.4	45.2	1800.7	2395.9	133.1
MNN	44.8	10.1	22.6	60.9	50.9	83.7
IJI	76	14.9	19.6	52.8	21.1	40
TLA	2492.2	13.3	0.5	2498.3	6.2	0.2
NumP	1336.2	358.5	26.8	34.1	44.7	131
MPS	2	0.8	37.6	29.4	41.6	141.4
PSCoV	1600.8	575	35.9	325.2	193.4	59.5
PSSD	33.1	17.8	53.8	88.2	104.2	118.1
TE	493,516.2	123,886.7	25.1	55,856.4	45,066.5	80.7
ED	198.1	50.2	25.3	22.4	18	80.6
MSI	1.4	0.1	5.3	1.4	0.2	13.2
AWMSI	9.5	3.7	39.3	3.8	2	52.4
MPFD	1.1	0	0.7	1.1	0	1.8
AWMPFD	1.3	0	2.4	1.2	0.1	5.3
SDI	1.1	0.2	19.4
SEI	0.8	0.2	18.6
TCA	1037.8	343.7	33.1	373.5	381.1	102
CAD	19.4	7.2	36.9	0.9	0.9	103.8
MCA	3.2	4.3	133.7	44.4	98.5	221.9
CASD	39.2	37.5	95.7	81.6	109	133.5
CACoV	4000.3	1683.4	42.1	283.5	188.6	66.5
TCAI	41.6	13.7	32.9	63.9	25.6	40.2
CACV1	2267.6	747.8	33	353.4	210.7	59.6
CASD1	21.4	16.7	78.1	74.7	95.8	128.3
LPI	41.9	20.4	48.7	15.6	17.1	109.4
LSI	24.7	6.2	25.2	3.8	2.1	55.2
MCAI	1.9	0.6	31.5	12.5	11	87.9
MCA1	0.9	0.7	79.6	24.7	38.9	157.5
NCA	482.9	177.9	36.8	22	22.9	103.9
MSIDI	1	0.3	26.7
PR	3.9	0.8	19.8
PRD	0.2	0	19.8
SHEI	0.8	0.2	18.6
SIEI	0.8	0.2	18.5
MSIEI	0.7	0.2	27.1
CA				480.5	436	90.8
%LAND				19.2	17.4	90.7
C%LAND				14.9	15.2	102
DLFD				1.3	0.1	9.4

Figure 3. Correlation coefficient of all computed landscape metrics (a) landscape-level correlation matrix and (b) forest class-level correlation matrix

Table 4. Factor varimax rotated matrix for the selected class- and landscape-level metrics

	Landscape level metrics					Forest class level metrics
	F1	F2	F3	F4		F1	F2	F3	F4
					MPI	0.846	0.361
MPI	0.325	−0.425	0.475		MNN
MNN	0.772		−0.537		IJI
IJI				0.915	TLA
TLA	0.331	0.542			NumP		0.31		0.919
NumP	−0.855	−0.367			MPS	0.896	−0.342
MPS	0.929				PSCoV		0.731		0.341
PSCoV	0.36	−0.862			PSSD		0.997
PSSD	0.937				TE		0.769		0.533
TE	−0.839		0.385		MSI				0.979
MSI		0.341	0.908		AWMSI		0.865
AWMSI		−0.911			MPFD			0.881
MPFD	−0.314		0.842		TCA	0.868	0.364
SDI	−0.672	0.564			MCA	0.683	−0.425
SEI	−0.715	0.337		0.462	TCAI	0.628
MCAI		0.799			MCAI		−0.351	0.704
PR		0.31	−0.417	−0.757	DLFD		0.596

^aF = factor loadings.

^bFactor loadings smaller than 0.3 have been removed.

Figure 4. Landscape-level landscape metrics pattern

Figure 5. Forest class-level landscape metrics pattern