Mapping land development through periods of economic bubble and bust in Massachusetts using Landsat time series data

Figures & data

Figure 1. Real estate investment as percent of US GDP from 1947 to 2013. The second highest point of residential investment is in 2005, while the lowest is in 2010 (US Department of Commerce 2014).

Figure 2. Authorized new housing units in Massachusetts from 1995 to 2013 (US Census Bureau 2014).

Figure 3. Study area of central Massachusetts, USA, with major cities, highways (including Interstate-495 and 90), Wachusett Mountain, and water bodies.

Table 1. Data used in this study.

Data	Resolution	Range	Units	Source
ETM+ Bands 1–5, 7: 27 September 2000	30 m	0–1	Ρ	USGS: http://glovis.usgs.gov/
TM Bands 1–5, 7: 20 September 2006	30 m	0–1	Ρ	USGS: http://glovis.usgs.gov/
OLI Bands 2–7: 23 September 2013	30 m	0–1	Ρ	USGS: http://glovis.usgs.gov/
Massachusetts land-use map: 2000 and 2009	30 m	16 land-use categories	n/a	Clark University *HERO Program

Note: *http://www.clarku.edu/departments/hero/

Figure 4. Five endmember sets evaluated for use in spectral mixture analysis. The X-axis is the spectral range of Landsat-5 TM bands 1–5 and 7 (gray bars), and the Y-axis is mean proportional reflectance for each endmember in the set. Reflectance values were extracted from the 20 September 2006 Landsat-5 TM image. This figure was adapted from figure 3 in Small (2001).

Figure 5. Empirically derived hierarchical change thresholds developed to determine biophysically significant loss on undeveloped land cover for the bust period (2006–2013). Change detection was stratified per three aggregated undeveloped land-cover types (forest, urban forest, low vegetation) using a preexisting 2009 land cover map (MaFoMP 2011). The thresholds displayed are for the bust period; however, the same thresholds were applied to bubble period (2000–2006) using a 2000 land-cover map for stratification (MaFoMP 2011).

Figure 6. Example of change from to undeveloped to developed land cover isolated through change detection (a). Polygons of new developed land cover (b) are used to extract biophysical and socioeconomic variables specific to the time and place of that patch, e.g. town, area, mean vegetation fraction loss, mean slope, and residential tax rate. The polygon data is also utilized for validation of change detection in Google Earth^TM (bubble outlined in red, 2003: c and e to 2006: d and f; bust outlined in violet, 2006: g and i to 2013: h and j). For full color versions of the figures in this paper, please see the online version.

Table 2. Level I error matrix of bust and bubble period land-use change maps using Google Earth^TM data and 600 samples.

	Reference	Change	No change	Row total	Commission error
Bubble period
Classification	Change	259	41	300	0.14
	No change	13	287	300	0.04
	Column total	272	328	600
	Omission error	0.05	0.12		Overall accuracy 0.91
Bust period
Classification	Change	208	92	300	0.31
	No change	7	293	300	0.02
	Column total	215	385	600
	Omission error	0.03	0.24		Overall accuracy 0.84

Table 3. Level II area weighted error matrix of land-use change maps using Google Earth^TM data and 600 samples per land-use stratum: forest (orchard, hardwood, conifer, and mixed forest classes), urban forest (low- and high-density residential and commercial classes), and low vegetation (pasture/row crop, golf course, and grassland classes).

	Reference	F change	F no change	UF change	UF no change	LV change	LV no change	Samples	Map proportion
Bubble period (2000–2006)
Classification	F change	0.5386	0.0063	0.0564	0.0063	0.0125	0.0063	100	0.63
	F no change	0.0168	0.4595	0.0056	0.0729		0.0056	100	0.56
	UF change	0.0151		0.1942	0.0227	0.0101	0.0101	100	0.25
	UF no change	0.0083	0.0331	0.0055	0.2176		0.0110	100	0.28
	LV change	0.0012		0.0012		0.0887	0.0304	100	0.12
	LV no change	0.0033	0.0180		0.0443	0.0033	0.0952	100	0.16
	True proportion	0.58	0.52	0.26	0.36	0.11	0.16
	Omission error	0.07	0.12	0.25	0.40	0.19	0.40	0.20
Bust period (2006–2013)
Classification	F change	0.29027	0.01935	0.05418	0.00387	0.00774	0.01161	100	0.0011
	F no change		0.44048		0.10461	0.00551		100	0.5500
	UF change	0.03807	0.10152	0.19036	0.05499	0.03807		100	0.0012
	UF no change	0.00290	0.04345	0.00869	0.22016		0.01448	100	0.2888
	LV change	0.00570	0.00190	0.00190	0.00190	0.09498	0.08358	100	0.0006
	LV no change	0.00160	0.02396	0.00160	0.07028		0.06229	100	0.1593
	True proportion	0.34	0.63	0.26	0.46	0.15	0.17
	Omission error	0.14	0.30	0.26	0.52	0.35	0.64	0.35

Note: *F, forest; UF, urban forest; LV, low vegetation.

Table 4. Attributes and descriptive statistics of distribution of bubble and bust change maps at town and loss patch levels.

Mapping level	Minimum; maximum	Mean; median	Total	Global Moran’s I; Z-score
Bubble loss patch area (2000–2006)	1800 m^2; 428,400 m^2	4848 m^2; 2700 m^2	N = 7404	0.001; 1.90
Bust loss patch area (2006–2013)	1800 m^2; 230,400 m^2	4079 m^2; 1800 m^2	N = 7153	0.012; 1.88
Bubble (2000–2006) loss as percentage of town area	0.00%; 1.65%	0.33%; 0.28%	37.58 km^2	0.274*; 6.88
Bust (2006–2013) loss as percentage of town area	0.01%; 1.10%	0.27%; 0.23%	30.29 km^2	0.162*; 4.12

Note: *Significant at 0.01 level.

Figure 7. Map of towns with major loss of undeveloped land cover as percentage of town area (>0.85% of town area) during the bubble (a and b) and after the bubble burst (c and d). Major loss per town during the bubble was primarily attributed to large developments, while major loss per town in the bust period was primarily attributed to relatively smaller and more numerous developments.

Figure 8. Map displaying loss of undeveloped land cover per town as a percentage of the town’s total area during the bubble (a) and after the bubble burst (b). Plympton, Clinton, Northborough, and Danvers (displayed in Figure 7) are outlined in black.

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