Jason Parent

The fragmentation of urban landscapes: global evidence of a key attribute of the spatial structure of cities, 1990–2000

The fragmentation of urban landscapes – or the inter-penetration of the built-up areas of cities and the open spaces in and around them – is a key attribute of their spatial structure. Analyzing satellite images for 1990 and 2000 for a global sample of 120 cities, we find that cities typically contain or disturb vast quantities of open spaces equal in area, on average, to their built-up areas. We also find that fragmentation, defined as the relative share of open space in the urban landscape, is now in decline. Using multiple regression models, we find that larger cities are less fragmented, that higher-income cities are more fragmented, that cities with higher levels of car ownership are less fragmented, and that cities that constrain urban development are less fragmented. We recommend that making room for urban expansion in rapidly growing cities should take into account their expected fragmentation levels.

KH-series satellite imagery and Landsat MSS data fusion in support of assessing urban land use growth

Multi-temporal land use data, circa 1990 and 2000, have been analyzed an our urban growth model which identifies three levels of the urban extent - the impervious surface, the urbanized area, and the urban footprint - to account for the differing degrees of open space degradation associated with the city. The model also generates metrics such as cohesion, proximity, population densities, average openness, open space contiguity, and depth which quantify spatial characteristics that are indicative of urban sprawl. We plan on expanding this time-series further, and for additional cities, with mid-decadal, gap-filled Landsat ETM data, as well as resolution-enhanced Landsat MSS data from the 19070s. The cities used in this pilot project consisted of: (a) Kigali, Rwanda; (b) Portland, Oregon; (c) Tacoma, Washington; and (d) Plock, Poland. Based on research done in this project, complemented by results from other efforts, the Ehlers data fusion approach was used in the resolution enhancement of Landsat MSS imagery. In this paper, using Portland and Kigali as the principal examples, we discuss the procedures by which (a) the KH-series declassified military intelligence imagery was geometrically-corrected and registered to Landsat data, (b) the Ehlers Fusion of the KH-data with Landsat MSS, (c) the derivation of 1970s urban land use information, and (d) the calculation of select urban growth metrics. This paper illustrates the power of leveraging the high resolution of the military reconnaissance imagery with the multispectral information contained in the vintage Landsat MSS data in historical land use analyses.

Assessing species-level biases in tree heights estimated from terrain-optimized leaf-off airborne laser scanner ALS data

Canopy height is an important metric in forest research and management with uses that include estimating stand volume, scheduling silvicultural treatments, and inferring site quality. In recent years, airborne laser scanner (ALS) data have been frequently used to model canopy height continuously and remotely across large areas. A number of studies have demonstrated that ALS is effective in this regard when collected during leaf-on conditions; however, relatively few studies have investigated the accuracy of leaf-off ALS in modelling canopy height. In this article, we assessed species-level biases in heights estimated from terrain-optimized leaf-off ALS data (1.5 points m–2). We focused on several deciduous and coniferous species common to the forests of the northeastern USA. Our study area included 13 sites located in the temperate deciduous forests of eastern Connecticut. Tree heights were measured in the field for 1192 trees which included 17 deciduous and 2 coniferous species. For one site, terrestrial laser scanner (TLS) data were collected and used to estimate tree heights. The ALS data were used to create a 1 m resolution canopy height model (CHM)ALS in which cell values corresponded to the heights of the highest returns. The (CHM)ALS underestimated tree heights with a median difference of approximately 1.3 m when compared to field-based measurements. Height biases ranged from approximately 0.1 to 2.1 m with the smallest bias for black cherry, red maple, shagbark hickory, and black oak and the largest for white ash, red oak, and white oak. We found no significant differences in bias corresponding to species’ leaf-types (i.e. simple, compound, needle). Biases in tree height estimates increased substantially as the (CHM)ALS cell size increased above 1 m. Our study suggests that leaf-off ALS data with a density > 1 point/m2 can be used to estimate tree heights with relatively small bias regardless of the species type.

Estimating percent impervious cover from Landsat-based land cover with a simple and transferable regression model

ABSTRACT Percent impervious cover (PIC) is a widely used metric in ecological and hydrological analyses because it is highly correlated with pollutant and storm water run-off. The moderate-resolution satellite data (e.g. Landsat), that are typically used to calculate PIC, tend to overestimate PIC for all but very rural and very urban landscapes. Existing models for calibrating PIC estimates (e.g. ISAT, ETIS) are limited in that they are applicable only for specific land cover datasets and may also require population data; furthermore, these models have not been tested for performance outside of the geographic locations in which they were developed. The goal of this study was to explore simple but widely applicable regression models as tools for calibrating PIC estimates based on moderate resolution satellite data. The regression models used impervious land cover, from Landsat-based datasets, as the sole predictor of actual PIC. PIC was calculated for analysis units, ranging in size from 2.25 ha to ≥100 ha, for locations in Connecticut, Massachusetts, and Ohio in the United States. Regression models were fit for each size class of analysis unit at each study location; generalized versions of the models were created by fitting a regression to all size classes of analysis units at a given study location. Calibrated PIC estimates had root mean square error (RMSE) values that ranged from 1.5–10.7%; these values were considerably better than RMSE values for uncalibrated PIC estimates which ranged from 10.1–23.3%. For both calibrated and uncalibrated PIC, the accuracy of the estimates improved with the increasing size of the analysis units. Model regression coefficients were similar regardless of the analysis unit size, geographic location, or land cover dataset; model performance declined only slightly when applied outside the area in which it was developed. The simple regression models developed in this study had similar performance to previous calibration models (i.e. ISAT, ETIS) but are easier to apply and more widely applicable.