Projecting future land use/land cover by integrating drivers and plan prescriptions: the case for watershed applications

Figures & data

Figure 1. Map of the study area: Lower Chippewa River watershed (a), Wisconsin (b), USA (c).

Table 1. Land change drivers & constraints.

Categories	Initial consideration	Final selection
Socio-economic	Housing density	o
	Population density	Y
	Median household income	Y
	Total manuf. & const. employees	o
	Total agricultural employees	o
	Total wholesale & retail employees	o
	Total service industry employees	o
	Assessed land value	Y
Proximity	Distance to major highways	Y
	Distance to major cities	Y
Biophysical	Slope	o
	Aspect	o
	Soil	o
	Elevation	o
Probability	Evidence likelihood	Y
Policy	Forest land constraint	Y
	Agricultural land constraint	Y
	Urban growth boundary constrain	Y
	Riparian vegetation constrain	Y
	Non-zoned	Y
	Zoned	Y

Note: Y included in projection, o omitted from projection,

Manuf. manufacturing, const. construction

Table 2. LULC trajectory used in creating probability/transition potential Surfaces.

		To
		water	Commercial	Forest	GV/S	Residential	Institutional	Agriculture	Industrial
From	Water	–	Y	N	Y	Y	Y	Y	Y
	Commercial	N	–	N	N	Y	Y	N	Y
	Forest	Y	Y	–	Y	Y	Y	Y	Y
	GV/S	Y	Y	N	–	Y	Y	Y	Y
	Residential	N	N	N	N	–	N	N	N
	Institutional	N	N	N	N	N	–	N	N
	Agriculture	N	Y	Y	Y	Y	Y	–	Y
	Industrial	Y	Y	N	Y	Y	Y	Y	–

Note: Y included, N not included, – not applicable, GV/S green vegetation/shrub.

Figure 2. Projected land use data model construction.

Note: D_se socioeconomic drivers, D_pr proximity drivers, D_pb probability driver, 1–3 perceptron, w weights, C_i first constraint (e.g. forest), C_n last constraint (e.g. urban growth boundary).

Figure 3. Comparison of overall accuracy for historical and contemporary images.

Table 3. Producers and users accuracy for pixel-based hybrid classified images.

		Water	Comm	Forest	GV/S	Residential	Inst	Agric	Indst
1990	PA(%)	98	77	92	73	75	88	79	81
	UA(%)	95	94	83	95	86	97	95	91
2000	PA(%)	94	76	89	63	78	67	75	89
	UA(%)	93	100	82	91	85	96	96	79
2011	PA(%)	98	89	98	71	89	67	72	92
	UA(%)	99	97	80	84	94	100	86	80

Note: comm commercial, GV/S green vegetation/shrub, Inst institutional, Agric agriculture, Indst industrial, PA producer’s accuracy, UA user’s accuracy.

Table 4. Producers and users accuracy for object-based hybrid classified images.

		Water	comm	Forest	GV/S	Residential	Inst	Agric	Indst
1990	PA(%)	98	81	91	77	83	86	87	85
	UA(%)	96	91	87	86	86	92	93	91
2000	PA(%)	96	86	92	73	84	84	84	79
	UA(%)	94	84	85	82	86	92	90	89
2011	PA(%)	98	90	94	81	92	82	85	84
	UA(%)	96	84	86	87	95	95	91	87

Note: comm commercial, GV/S green vegetation/shrub, Inst institutional, Agric agriculture, Indst industrial, PA producer’s accuracy, UA user’s accuracy.

Table 5. Land use/land cover net gain transition matrix (hectares).

		To 2011
		water	Commercial	Forest	GV/S	Residential	Institutional	Agriculture	Industrial
From 1990	Water	–	17	0	0	231	10	12	3
	Commercial	0	–	0	0	0	0	0	0
	Forest	502	283	–	98	5882	78	9026	496
	GV/S	100	18	53	–	286	13	425	17
	Residential	0	0	0	0	–	0	0	0
	Institutional	0	0	0	0	7	–	0	0
	Agriculture	0	426	30	18	6789	301	–	475
	Industrial	10	0	0	7	21	11	15	–

Note: – not applicable, GV/S green vegetation/shrub.

Figure 4. Historical and contemporary object-based hybrid classified images for 1990 (a), 2000 (b), and 2011 (c).

Figure 5. Percentage change in LULC, 1990–2011.

Note: GV green vegetation.

Figure 6. Snippet of 2011 pixel-based hybrid projected image (a) compared to object-based hybrid (b).

Figure 7. Average ROC performance of model for all projected 2011 LULC classes.

Note: AUC_O is object-based hybrid, AUC_P is pixel-based hybrid.

Figure 8. ROC performance of models per projected 2011 LULC class.

Note: AUC_O is object-based hybrid

, AUC_P is pixel-based hybrid.

Figure 9. Contemporary and projected images for 2011 (a), 2030 (b), and 2050 (c).

Figure 10. Potential percentage change in LULC, 2011–2050.

Note: GV green vegetation.