The primary objective of green infrastructure planning is to support the green spaces that provide critical ecosystem services. The clearest measure of county green infrastructure planning success then, is a county’s ability to a) retain, b) connect, and c) protect green infrastructure over time. This study examines ‘a’ and ‘b’ through GIS analysis and ‘c’ through GIS analysis and supplemental information provided by the counties and states themselves.
Retaining Green Infrastructure Over Time
The first step in assessing green infrastructure planning success is to understand on-the- ground gain or loss of green infrastructure over the 2000 to 2010 study period. This study uses remote sensing and GIS analysis of Global Land Survey Data through the following steps:
1. Downloading GLS 2000 and GLS 2010 Data
2. Classifying Land Use/Land Cover using three vegetation indices: NDVI, NDBI, and WET
3. Assessing Gain/Loss of Natural and Agricultural Land between 2000 and 2010 The USGIS Global Land Survey (GLS) 2000 and 2010 datasets are comprised of
orthorectified leaf-on 30m Landsat TM and ETM+ satellite images taken within a year of the 2000 and 2010 study dates. The USGS processed all eighteen Landsat scenes used in this research to Standard Terrain Correction, the highest level widely available. The correction uses ground control points for radiometric and geometric accuracy and a DEM (Digital Elevation Model) for topographic accuracy (USGS 2012).
GLS scenes use the Landsat Path/Row system. For this research, the following scenes were downloaded and clipped to county boundaries. Where two scenes are listed, they
were combined in a two-image mosaic to cover the county, with the scene that best matched the other two counties in the state as the dominant image.
County, State Path/Row 2000 Image Date 2010 Image Date
Anne Arundel, Maryland
15/33 5 Oct 2001 29 Jun 2009
Baltimore, Maryland 15/33 5 Oct 2001 29 Jun 2009
Charles, Maryland 15/33 5 Oct 2001 29 Jun 2009
Alachua, Florida 17/39 16 Oct 2000 4 Oct 2010
Leon, Florida 18/39 6 Nov 1999 9 Jan 2009
Marion, Florida 17/40, 16/40 1 Dec 1999, 23 Oct 1999 8 Apr 2009, 15 Feb 2010
Adams, Colorado 33/32 20 Sep 2002 15 Jul 2010
Arapahoe, Colorado 33/32 20 Sep 2002 15 Jul 2010
Boulder, Colorado 33/32, 34/32 20 Sep 2002, 24 Sep 2001 15 Jul 2010, 21 Aug 2009
After data acquisition, three different vegetation indices were used to classify the images into a simple four-category land use/landcover map. Green vegetation, soil, water, and impervious surface absorb and reflect light differently, and within different spectral bands. Landsat images include 8 bands. When they are combined mathematically, in different combinations, they create values that indicate on-the-ground conditions and are useful for land use classification. This study uses three known indexes, the Normalized Difference Vegetation Index (NDVI), Normalized Built-Up Index (NDBI) (Waqar, Mirza et al. 2012), and a third unnamed combination of Bands 5 and 7, referred to here as WET (Ozesmi and Bauer 2002).
The Normalized Difference Vegetation Index (NDVI) is a popular vegetation index for long-term vegetation change studies. It is simple, largely insensitive to atmospheric and topographic effects and differences in solar illumination (Kumar 2007), and provides good estimates of green vegetation status (McCloy 2006). The chlorophyll in live green plants absorbs visible (red) light (0.4µm to 0.7µm) and reflects near-infrared light (from 0.7 to 1.1 µm). Since these are two of the eight bands included in a Landsat image, a simple ratio of the two provides an index of green vegetation. The index runs from 1 to - 1 with healthy green vegetation at one end and bare ground and water at the other (ibid).
Generally, cells with an NDVI greater than one have some vegetation while cells with an NDVI less than one are fallow, bare, paved, or water. On the vegetated side of NDVI, higher values are forested while lower values are scrubby vegetation or low agriculture. Classifying land cover using NDVI requires an understanding of the index ranges for ! NDVI= (NIR"Red) (NIR+Red)!!!"!!! ! (Band4"Band3) (Band4+Band3)
each category of interest (forest, agriculture, developed, and water). This study obtained the ranges by comparing the NDVI map to a reference aerial image from the same time. Taking the NDVI values of 30 to 50 cells known to be forest and wetlands, agriculture, developed (including bare ground), and water, respectively, creates a starting range of for each. Ranges were refined further to create a picture that matched a reference image from the same date as closely as possible.
Use of NDVI presents two challenges. First, on the high end of the NDVI spectrum, values for certain types of agriculture are the same as for forested land. Second, on the lower end of the NDVI spectrum, values for fallow agricultural fields are similar to those for developed areas such as bare ground and yards. The similarity causes fallow fields to present as subdivisions and green fields to present as forests. Separating the land uses requires more spectral information than is included in the NDVI. While NDVI is the most popular, there are a variety of vegetation indices that employ different spectral bands and combine them in different ratios. These include the Soil Adjusted Vegetation Index, Green Vegetation Index, Enhanced Vegetation Index, Normalized Difference Infrared Index, Bare Soil Index, New Built-Up Index, Normalized Built-Up Area Index, Soil Index, and two unnamed spectral combinations aimed at separating water and wetlands from urban area, among others.
To find the best for separating forest from farmland and bare fields from developed areas, this study employed a simple exploratory approach. Each index was applied to Charles County, Maryland, and examined for a) success in separation between forest and green farmland, and b) success in separation between fallow fields and developed areas. Only three of the indices created more distinct separation than NDVI: New Built-Up Index (NBI), Normalized Built-Up Index (NDBI), and WET (a ratio of bands 5 and 7). All three provided distinction between forest and green farmland, with WET, due to addition of band 7 and orientation toward separating wetlands from urban lands, providing the clearest distinction. NDBI was selected as providing the clearest separation of fallow farmlands.
The main vegetation index layers included in this analysis are: following table describes the creation of each of the four land use layers used in this analysis.
Land Use/Land Cover Category Source Vegetation Index
Water WET (water spectral range)
Forest WET (forest spectral range)
Grassland (Colorado Only) NDBI (grassland spectral range Bare Ground/Cleared Cropland NDBI (fallow spectral range) Developed Land and Cleared
Cropland
NDVI (developed/fallow spectral range)
!
NBI=(TM3*TM5) TM4 !!"!
!
NDBI=(Band5"Band4)
(Band5+Band4)!!"!!
!
WET= (Band5 *Band7) (Band5+Band7)
Additional editing and map algebra was required to clean up layers, confirm accuracy, and minimize seasonal differences between 2000 and 2010 classified images prior to change analysis.
The resulting four data layers: Water, Forest, Developed, and Agriculture were reclassified as follows then added to create a single land use change dataset. Note that water was reclassified to zero to remove it from the analysis.
Land Use/Land Cover Category Reclassified Value
2000 Forest 100,000 2010 Forest 10,000 2000 Agriculture 1,000 2010 Agriculture 100 2000 Development 10 2010 Development 1 2000 Water 0 2010 Water 0
The change matrix yields nine values:
Status of 30m by 30m Cell Change Matrix Value
Water 0
Stays Development 11
Changes from Agriculture to Development 1001
Stays Agriculture 1100
Changes from Development to Forest/Grassland*
10010 Changes from Agriculture to
Forest/Grassland
11000 Changes from Forest/Grassland to
Development
100001 Changes from Forest/Grassland to
Agriculture
100100
Stays Forest/Grassland 110000
* Rare
Results are presented and analyzed in chapters 4, 5, and 6. Connectivity of Green Infrastructure
Fragmentation is one of the greatest threats to the health and integrity of green infrastructure. As forested areas are fragmented by development they suffer from
increasing edge effects, become more vulnerable to disease and invasive species, and are less effective in providing ecosystem services such as wildlife habitat and water
purification. Agricultural areas are similarly vulnerable. The economic success, and therefore perpetuation, of farming in a community is dependent upon agricultural areas retaining a critical mass. There must be enough farming activity to support a local farm economy including available land and agricultural businesses (Daniels and Bowers 1997). Additionally, contact between forestry or agricultural lands and their new neighbors is not always pleasant. Residents may be concerned about the noise or smell associated with farm activities, the cutting of trees in their area, or potential for wild animals - or unknown hikers - in their backyard.
While the needs and services of forestland and farmland differ, both benefit from
connectivity. A robust green infrastructure network is interconnected, particularly within individual land use types. This study examines the connectivity through the lens of landscape ecology. Landscape ecology envisions landscapes as series of hubs and linkages. Hubs are contiguous, high quality areas that are largely undeveloped and allow for undisturbed ecological processes. Links connect hubs through corridors of
compatible land uses to create a function system of green space. Notably, agricultural lands have largely been rejected as green infrastructure network hubs, due to their
incompatibility with many ecological functions and high degree of disturbance (Benedict and McMahon 2006). However, since critical mass is a main objective of farmland protection, and food production an important ecosystem service, the connectivity and contiguity of agricultural areas remains essential.
From an assessment perspective, landscapes are comprised of a series of patches, contiguous areas of the same land use/land cover type. This study includes three patch types, also called classes - developed, forest, and agriculture - that are spread throughout the landscape. Landscape ecologists have created a variety of metrics to measure the spatial arrangement of these landscapes elements. They can be divided, broadly, into two types, composition and configuration. Composition metrics examine the number and diversity of classes and patches. Configuration metrics assess the position and configuration of patches and classes within a landscape (Leitao, Miller et al. 2006). There is a robust literature surrounding the selection of composition and configuration metrics, but the overwhelming consensus is that metrics must be carefully selected to align with the landscape processes being studied (Li and Wu 2004). For example, core- to-edge ratio is a commonly used landscape metric, but the edge depth used in the calculation must be calibrated to the species under examination. Since this study has no particular focal species, and associated edge depth/core distance metric, core to edge ratio calculations would be meaningless. However, there are several landscape metrics that provide more general composition and connectivity information and are supported broadly by landscape assessment literature (Riiters, O'Neill et al. 1995; Hargis,
Bissonette et al. 1998; Li and Wu 2004; Leitao, Miller et al. 2006). The list of selected metrics (shown below) emphasizes measures which are known to be useful for planning applications (Leitao, Miller et al. 2006). As the emphasis of this study is the connectivity of individual land use classes, all metrics of interest are at the class level, which usually involves averaging values for all patches of a certain class:
Area-Edge Metrics
- Mean Radius of Gyration- Measures the mean patch extensiveness for each class Shape Metrics
- Mean Shape – Measures the mean geometric complexity of patches in each class Aggregation Metrics
- Clumpiness Index – Determines whether the ‘clumpiness’ of a class is greater than would occur under random conditions
- Mean Euclidian Nearest Neighbor – Average distance between a patch of a certain class and its nearest neighbor of the same class
- Proximity – Measure of patch isolation based upon the size and distance of like patches (defined search radius of 1000m)
Each of these metrics is calculated using FRAGSTATS, a spatial pattern analysis program. The program is oriented toward landscape ecology, but can be used to assess any spatial phenomenon. For input into FRAGSTATS, 2000 and 2010 land use
classifications for each county were reformatted into simple three-category rasters (developed, forest, agriculture), with water included with the background class. Each of the six grids was added to the FRAGSTATS program and analyzed on the basis of the five metrics described above.
Results are presented and analyzed in chapters 4, 5, and 6. Protection/Development of Green Infrastructure
One of the more important aspects of local green infrastructure planning - and land use and environmental planning more broadly - is the balance between land that is preserved and land that is developed. Some communities directly compare preserved acres with converted acres, with the understanding that the two should proceed at a rate that ensures new residents will have equal access to quality open space and natural areas. But the most popular land preservation metrics are, colloquially, ‘bucks and acres,’ or the funds spent on land preservation and the number of acres acquired for that expenditure. These
measures do not speak to the quality, size, or, configuration of lands that are preserved or developed (Sawhill and Williamson 2001).
One objective of green infrastructure planning is to protect and retain the high quality resource lands that most effectively support ecosystem services. This study goes beyond expenditures and acres to understand the success of counties in meeting this key green infrastructure planning goal. A previous section described the procedure for identifying lands that were converted from ‘natural’ or 'agriculture’ to ‘developed’ over the ten-year study period. In this section, a GIS-based analysis examines the mean ecological value of these converted lands and compares it to that of protected areas in the same county. If developed lands have a low average ecological value, as compared to the value of protected areas, a county has been effective at steering development away from critical ecosystem service areas and toward more marginal lands.
The metric ‘difference in quality between protected and developed land’ is unique to this study. While areas of a state may be similar, and many of the counties used in this analysis are located in the same region (i.e. the along the Front Range, coastal Maryland, inland Florida), no two counties have the same environment. Natural systems are
complex and naturally variable. The difference in quality metric accounts for some of that variability by comparing ecological qualities within each county, rather than simply between counties. Counties with many sensitive and important ecosystems and low levels of development will have a high overall average ecological value, which inflates the value of both protected and developed lands above that of counties with less diverse and more fragmented landscapes. In such a case, comparing the ecological quality of
protected and developed lands in one county to that of another county does not show relative environmental planning success, it reflects the background environmental quality within each jurisdiction. But examining the difference in quality between protected and developed lands within a county and then comparing the magnitude of the difference accounts for much of that variability.
The most effective way to understand the quality of protected or developed land is to evaluate and classify lands according to their relative ecological importance. In doing so, Ian McHarg’s “ecological determinism” (1969) and the Natural Resource Conservation Service’s “Land Evaluation and Site Assessment System” (1983) are useful models (Pease and Coughlin 2001). McHarg used overlays of natural resource attributes to understand the suitability of land for development, from a practical and environmental perspective. This work does the reverse. It uses natural resources information to understand the value of land for ecosystem services. In this, the process more closely resembles the Land Evaluation and Site Assessment System (LESA). LESA uses soil and other natural resource data to score sites on the basis of their agricultural value. Each parameter (e.g. soil potential, size, scenic quality) receives a score and a weight, after which sites are ranked by their relative agricultural importance. This research uses a similar process to rank 30m by 30m cells by their relative ecological importance. Planning and natural resources departments in Maryland, Colorado, and Florida make available a variety of geographically-referenced natural resource information that can be combined into an ‘ecological value’ GIS layer. In Maryland, such an index already exists. The Maryland Department of Planning created a GIS layer, called ‘green
infrastructure ecovalue,’ as part of the state’s 2001 Green Infrastructure Assessment. It is comprised of high-value and sensitive areas such as interior forest, wetlands and stream valleys, in addition to key green infrastructure hubs and links, combined and scaled from 0 to 100. The data is available through the agency’s website and provides information on the relative ecological importance of each 30m by 30m cell in the Maryland grid (See Table 3-4).
While Maryland has an existing ecological value (ecovalue) layer, Florida and Colorado do not. Creating ecovalue layers for Florida and Colorado that are identical to
physiographic regions, ecosystems, species, programs, and interests. Consequently, each collects information on – and values – environmental systems differently. In creating ecovalue layers for Florida and Colorado, this study follows, but does not entirely replicate Maryland’s strategy. The following section describes the components and procedure of Maryland’s ecovalue layer and the process of creating similar datasets for Florida and Colorado.
Table 3-4. Parameters used in fine scale ecological ranking for Maryland (Maryland DNR 2001)
Parameters Weight Max Score
Rare Plant and Animal Element Occurrences 4 200
Delmarva Fox Squirrel Habitat 6 60
Proximity to Natural Heritage or other heritage areas
3-5 100
Land Cover 4 40
Proximity to Development 4 40
Distance to Nearest Road, Weighted by Road Type
2-4 40
Highly Erodible Soils 2 20
Proximity to Unmodified Wetlands 4 40
Interior Forest 4 40
Proximity to Streams 2-6 60
Proximity to Stream Nodes 1 10
From Table 8-4. Local ecological parameters and weighting (Maryland DNR 2003) Maryland DNR combined the scores for each parameter and rescaled the result to 100. They also identified green infrastructure hubs and corridors, ranked them by their ecological integrity and importance for connectivity, and scaled that result to 100. The agency then combined the fine scale and hub/corridor datasets and rescaled to 100 a final time to yield the final Maryland ecovalue layer.
While Florida does not have precisely the same datasets as Maryland, the state has collected a variety of natural resource data through the Florida Natural Areas Inventory (FNAI), a non-profit organization administered by Florida State University (See Table 3- 5). Portions of the data were updated regularly since the project began in the mid-2000s, but updates were not comprehensive and their impact on the data’s appropriateness for this work is negligible. Additionally, any updates during the time period would serve to make the analysis more conservative, as newly developed areas receive ecological downgrades.
Table 3-5. Parameters used in fine scale ecological ranking for Florida (by Author)
Parameters Weight Max Score
Listed Species Locations & Species Richness
(Original Data Source: Florida Natural Areas Inventory)
5 200
Underrepresented Natural Areas
(ODS: Florida Natural Areas Inventory)
6 60
Aquifer Recharge Areas & Strategic Habitat Conservation Areas
(ODS: Florida Natural Areas Inventory)
10 100
Proximity to Development
(ODS: National Land Cover Database 2001)
4 40
Distance to Nearest road, Weighted by Road Type (ODS: ESRI Layer)
1 40
Proximity to Wetlands, Weighted by Type
(ODS: National Wetland Inventory – based upon 1980s imagery)
4 40
Interior Forest
(ODS: National Land Cover Database 2001)
4 40
Proximity to Streams
(ODS: Florida Natural Areas Inventory)
2 60
Farmland Quality, Weighted by Type
(ODS: NRCS Soil Survey Geographic Database)
4 40
Scores for each parameter were combined and rescaled to 100 to yield a fine scale ecological value dataset. Since there is no consistent green infrastructure hub/corridor or greenway data available for Florida, the ecovalue dataset includes only fine scale data. Colorado has the least available data of the three counties examined in this analysis.