emergy synthesis 4 - cep.ees.ufl.educhapter 7. landscape development intensity index -7.2- when the...

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EMERGY SYNTHESIS 4: Theory and Applications of the Emergy Methodology

Proceedings from the Fourth Biennial Emergy Conference,

Gainesville, Florida

Edited by Mark T. Brown

University of Florida Gainesville, Florida

Managing Editor Eliana Bardi

Alachua County EPD, Gainesville, Florida

Associate Editors

Daniel E. Campbell US EPA

Narragansett, Rhode Island

Shu-Li Haung National Taipei University

Taipei, Taiwan

Enrique Ortega Centre for Sustainable Agriculture

Uppsala, Sweden

Torbjorn Rydberg Centre for Sustainable Agriculture

Uppsala, Sweden

David Tilley University of Maryland College Park, Maryland

Sergio Ulgiati University of Siena

Siena, Italy

December 2007

The Center for Environmental Policy Department of Environmental Engineering Sciences

University of Florida Gainesville, FL

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7

Landscape Development Intensity Index

Mark T. Brown and Benjamin Vivas

ABSTRACT

The Landscape Development Intensity (LDI) index is a measure of the human disturbance gradient applied to landscape units (Brown and Vivas, 2004). In this paper we introduce a new method for calculating the LDI based on log scale of the ratio of nonrenewable areal empower density to the average renewable areal empower density of the system of interest. Empower is emergy per unit time, (its units are sej*yr-1). Areal empower density is defined as empower per unit area (its units are sej*yr-1*ha-1). The new method enables the calculation of LDIs for areas, using the total nonrenewable areal empower density and the background renewable areal empower density. With the new method, there is no maximum value and the minimum value is zero, when there is no nonrenewable emergy. The new LDI is applied at various spatial scales and related to measures of ecosystem health, water quality, sound and spectral reflectance on satellite images.

INTRODUCTION

An index of Landscape Development Intensity (LDI) was proposed by Brown and Vivas (2004) following on earlier work of Brown (1980) and evaluation of relationship of development intensity to water quality in the St. Marks Watershed in Florida (Brown, et. al , 1998; Parker, 1998). The LDI is an index based on nonrenewable areal empower density of land uses. The LDI has been used as a human disturbance gradient in developing wetland bio-indicators of ecosystem health (Lane and Brown, 2006; Reiss and Brown, 2006) and in developing a Stream Condition Index (Fore et.al 2007). Recently the LDI was tested as a indicator of human disturbance against a large wetland data set in Ohio (Mack, 2006). Here we propose a new method for calculating the LDI of a landscape unit based on log10 scale of the ratio of the nonrenewable areal empower density of the landscape unit to an areal empower density of the environmental baseline of the landscape unit. The environmental baseline is the average renewable areal empower density.

Emergy, Time, and Area

Emergy is defined as the amount of energy of one type (usually solar) that is directly or indirectly required to provide a given flow or storage of energy or matter. The units of emergy are emjoules (abbreviated eJ) to distinguish them from energy joules (abbreviated J). We propose that the Greek letter epsilon (εεεε) be used for emergy in equations. Solar emergy is expressed in solar emergy joules (seJ, or solar emjoules). Emergy per unit time is empower, in units of emjoules per time. Solar empower is solar emjoules per time (e.g. seJ/time). We propose that the Greek letter omega, (ωωωω) be used for empower in equations.

Chapter 7. Landscape Development Intensity Index

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When the emergy required to make something is expressed as a ratio to the available energy of the product, the resulting ratio is called a transformity. The solar emergy required to produce a unit flow or storage of available energy is called solar transformity and is expressed in solar emergy joules per joule of output flow (seJ/J). The transformity of solar radiation is assumed equal to one (1.0 seJ/J). We propose that the Greek letter tau (ττττ) be used for transformity in equations.

Specific emergy is the unit emergy value of matter defined as the emergy per mass, usually expressed as solar emergy per gram (seJ/g). Solids may be evaluated best with data on emergy per unit mass for its concentration. Because energy is required to concentrate materials, the unit emergy value of any substance increases with concentration. Elements and compounds not abundant in nature therefore have higher emergy/mass ratios when found in concentrated form since more work was required to concentrate them, both spatially and chemically. We propose that the Greek letter sigma (σσσσ) be used for specific emergy in equations.

Areal Empower Intensity

The following paragraphs provide background on our choice of the terminology for emergy per unit time per unit area, areal empower intensity. In the past we have proposed the terms areal empower density to describe emergy per unit time per unit area, however in light of our need to define a new concept of emergy per unit time per unit volume, we suggest differentiating between intensity and density following the lead of physics.

In physics (especially related to sound) intensity is a measure of the time-averaged energy flux. In other words, intensity is the amount of energy which is transported past a given area of a medium per unit of time. Intensity is the energy/time/area (energy*time-1*area-1); and since the energy/time ratio is equivalent to the quantity power, intensity is simply the power/area. Emergy intensity, then, is the emergy/time/area, and since emergy/time is empower, emergy intensity is empower/area.

It should be noted, that Energy Intensity as used in economics, is defined as the measure of the energy efficiency of a nation's economy. It is calculated as units of energy per unit of GDP.

We suggest that the terminology, Areal Empower Intensity, be used to describe emergy per unit time per unit area and that we use the Greek letters alpha, omega, iota (ααααωωωωιιιι) to denote it in equations.

Environmental Emergy Density

In physics density is defined as the ratio of the mass of any substance to the volume occupied by it (usually expressed in kilograms per cubic meter). Energy density is usually defined as the amount of energy stored per unit volume, or per unit mass, depending on the context. Its units are J/g or J/L. When considering concentrations of pollutants in environmental systems, it is often appropriate to express them as concentrations (i.e. mg/L, µg/L, ppm, ppb). Since pollutants can be expressed as emergy using their specific emergy (sej/g) then concentrations of pollutants in the environment, especially in aqueous environments can be expresses as emergy density (ie sej/m3).

We propose that the terminology Emergy Density be used to describe emergy per unit volume in environmental systems and that the Greek letters εεεεδδδδ (epsilon delta) be used to denote it in equations.

Environmental Empower Density

In engineering, the term power density refers to power per unit volume. It is often used to describe the amount of power delivered by an energy source, divided by some measure of the source's


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size or mass. In the environment, when pollutants are released over time, their emergy per unit time per unit volume can be calculated from the pollutant’s specific emergy and the quantities released.

We have used the terminology empower density and more recently, areal empower density to describe emergy per unit time per unit area. However, in keeping with engineering and physics definitions of density, we suggest the term empower density be used to describe emergy per unit time per unit volume and that the Greek letters ϖϖϖϖδδδδ (pi delta) be used to denote it in equations.

Landscape Development Intensity (LDI)

This method facilitates the calculation of LDIs for any area, using the areal empower intensity of land uses. The new LDI scale begins with zero (i.e. equal to average renewable empower of the landscape unit) and there is no upper limit.

The calculation of a landscape, basin, or watershed LDI, requires a land use / land cover map of the landscape unit of interest, areal empower density multipliers for land use types (Table 1 is an example for Florida land uses), and the ability to calculate areas of land use within the landscape unit. The step-by-step procedure is as follows:

First, areas of each land use type within the landscape unit are summed and expressed as percent of total area. Second, percent of land use types are multiplied by the nonrenewable areal empower intensity of each type (Table1) and summed. Then the following equations are applied: LDI = 10 * log (αϖιTotal /αϖιRef) (Eq. 1) Where:

LDI = Landscape Development Intensity index for a given landscape unit; αϖιTotal = total areal empower intensity (Sum of renewable background areal empower

intensity and nonrenewable areal empower density of land uses; and αϖιRef = renewable areal empower intensity of the background environment (Florida =1.97

E15 sej*ha-1*yr -1; Vivas, 2006). The total areal empower intensity (αϖιTotal) is calculated as follows: αϖιtotal = αϖιRef + ∑ ( %LUi * αϖιni ) (Eq. 2)

Where:

%LUi = percent of the total area in land use i; and αϖιi is the nonrenewable empower intensity for land use i.

Table 1 lists common land use types found in the Florida landscape. The second column lists

typical nonrenewable areal empower intensities for land uses. The third column in Table 1 lists LDI’s for 1 hectare of the various land use types calculated using Equations 1 and 2 and the Florida baseline (1.97 E15 sej*ha-1*yr -1).


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Table 1. LDI Coefficients for Typical Land Uses.

Non-Renewable

Notes Land Use Areal Empower Intensity LDIFl*

(E15 sej-1*ha-1*yr -1)

1 Natural Land / Open Water 0.0 0.00

2 Pine Plantation 0.5 1.00

3 Low Intensity Open Space / Recreational 0.5 1.02

4 Unimproved Pastureland (with livestock) 0.5 1.04

5 Improved Pasture (no livestock) 2.0 3.07

6 Low Intensity Pasture (with livestock) 3.4 4.34

7 High Intensity Pasture (with livestock) 5.9 6.03

8 Medium Intensity Open Space / Recreational 6.1 6.10

9 Citrus 7.8 6.94

10 General Agriculture 15.1 9.38

11 Row crops 20.3 10.53

12 High Intensity Agriculture (dairy farm) 50.4 14.25

13 Recreational / Open Space (High-intensity) 123.0 18.02

14 Single Family Residential (Low-density) 197.5 20.05

15 Transportation- 2 lanes highway 308.0 21.97

16 Single Family Residential (Med-density) 658.3 25.25

17 Single Family Residential (High-density) 921.7 26.71

18 Transportation 4 lane Highway - Low Intensity 2533.7 31.10

19 Multi-family residential (Low density) 4213.3 33.30

20 Institutional 4042.2 33.12

21 Transportation 4 lane Highway - High Intensity 5020.0 34.06

22 Low Intensity Commercial (Comm Strip) 5173.4 34.19

23 Industrial 5210.6 34.23

24 High intensity commercial (Mall) 8372.4 36.28

25 Multi-family residential (High rise) 12771.7 38.12

26 Central Business District (Avg 2 stories) 16150.3 39.14

27 Central Business District (Avg 4 stories) 29401.3 41.74 * LDI = 10 * log [(αειi + αει ref)/ αειref]

Where: αειi = nonrenewable areal empower intensity of Land Use i αειref = areal empower intensity of background environment; Florida = 1.97E+15 sej-1*ha-1*yr -1

Notes to Table 1: 1 Non-renewable empower intensity for natural systems = 0 2 Doherty (1995) 3 Average of empower densities of 2 and 4 4 Based on 0.09 cows/ha/yr (27 acres/animal) Kalmbacher and Ezenwa (2006) Empower intensity to support 0.09 cows: 0.53 E15 sej/ha/yr

5 Brandt-Williams (2001)


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Table 1, notes. (continued)

6 Based on 0.57 steer/ha/yr (1.76 ha/animal) Arthington et.al (2007) Empower intensity to support 0.57 steer: 3.38 E15 sej/ha/yr = Improved Pasture (5) + 1.61 E15 sej/ha/yr

7 Based on 2 steer/ha/yr Brandt-Williams (2001) Empower intensity to support two steer: 5.93 E15 sej/ha/yr

8 Assume three times intensity of improved pasture. In an urban landscape applies generally to grassy lawns Falk (1976)

9 Brandt-Williams (2001) 10 Average of all crops Brandt-Williams, (2001) 11 Average of empower intensities for 6 row crops Brandt-Williams, (2001) 12 Brandt-Williams (2001) 13 Based on the emergy evaluation for a golf course Behrend (2000) 14 Parker (1998) and Brown (1980). Assume 1.5 units per hectare 15 Parker (1998) 16 Parker (1998) and Brown (1980). Assume 5 units per hectare 17 Based on Brown (1980). Assumes 7 units per hectare 18 Brown and Vivas (2007) 19 Parker (1998) and Brown (1980). Assumes 32 units per hectare 20 Brown (1980) 21 Vivas and Brown(2007) 22 Vivas and Brown(2007) 23 Parker (1998) and Brown (1980). 24 Vivas and Brown(2007) 25 Parker (1998) and Brown (1980). Assumes 97 units per hectare 26 Brown (1980) 27 Brown (1980)

RESULTS

CASE STUDY 1: Floodplain Wetlands of the Bayou Meto Watershed, Arkansas

The BAYOU METO WATERSHED (BMW) is located in eastern Arkansas between the Arkansas River and the White River (Figure 1) and almost wholly within the Mississippi Alluvial Plain eco-region (Omermik, 1987). Once rich in forests and wetlands, agriculture is currently the predominant land use within the BMW. Only 25% of the BMW is forested, while urban land uses account for only 3% of the total landscape. Twenty-nine study wetlands were selected a priori to ensure that their surrounding lands provided a range of landscapes that represented a gradient from undeveloped to highly developed lands (reference, rural, and urban). Twelve reference sites were selected from within the few forested areas in the watershed with some level of protection from within the floodplain of the Bayou where it was wide enough to be surrounded mostly by forested land.


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LDI and Wetland Condition

Spearman’s rank order correlation, the non-parametric measure of correlation (Dytham, 1999), was used to assess the association between the LDI and three independent measures of wetland condition (Level 2): WRAP (Miller and Boyd, 1999), UMAM (62-345.100(6), Florida Administrative Code), and HGM procedure (Brinson, 1993). Measures of wetland condition were field-scored by a research team of the Arkansas Multi-Agency Wetland Planning Team (MAWPT) between September and November of 2005.

The WRAP is a rapid assessment procedure consisting of a rating index that can be used to evaluate wetland condition based on six variables: wildlife utilization, wetland overstory/shrub canopy, wetland vegetative ground cover, adjacent upland support/wetland buffer, field indicators of wetland hydrology, and water quality input and treatment systems. UMAM, developed by the Florida Department of Environmental Protection, provides a standardized procedure for assessing the functions provided by wetlands and other protected waters of the state, the amount those functions are reduced by proposed impacts, and the amount of mitigation necessary to offset that loss. It allows a qualitative description and a quantitative evaluation of the assessment area. The HGM, a procedure for measuring wetland functional capacity (Brinson, 1993) is based on three fundamental factors that influence wetland function: the position of the wetland in the landscape (geomorphic setting), the water source (hydrology), and the flow and fluctuation of the water within the wetland (hydrodynamics).

Figure 1. The Bayou Meto Watershed in eastern Arkansas. The watershed is almost entirely dominated by agriculture lands with urban dominating the northern most areas of the watershed.


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Relationship Between the LDI and Wetland Condition

All correlations between the LDI and wetland condition assessment methods (WRAP, HGM, and UMAM) were statistically significant. The correlation between the LDI and the WRAP was strongest (Spearman’s r = -0.81, P < 0.001) (Figure 2). Level of impairment, evaluated by means of the WRAP, increased as the development intensity of the surrounding landscape increased. Correlations between the UMAM and the LDI were not as strong as with the WRAP scores (Spearman’s r = -0.50, P < 0.001) but still significant. As with WRAP, wetland condition as assessed by the UMAM decreased with increasing landscape development intensity. Among the three categories of the HGM evaluated, the habitat component of the HGM had the highest correlations with LDI. (Spearman’s r = -0.73, P < 0.001). Similarly, the hydrological component of the HGM exhibited strong association with the LDI (Spearman’s r = -0.57, P < 0.001). The biogeochemical component of HGM also exhibited strong relationship with the LDI (Spearman’s r = -0.65, P < 0.001).

CASE STUDY 2: LDI and Sound

There are many similarities between sound and areal empower intensity. As a sound wave carries its energy through a two-dimensional or three-dimensional medium, the intensity of the sound wave decreases with increasing distance from the source. The decrease in intensity with increasing distance is explained by the fact that the wave is spreading out over a circular (2 dimensions) or spherical (3 dimensions) surface and thus the energy of the sound wave is being distributed over a greater surface area. Since energy is conserved and the area through which this energy is transported is increasing, the power (being a quantity which is measured on a per area basis) must decrease. The mathematical relationship between intensity and distance is sometimes referred to as an inverse square relationship.

The impact of sound in a complex sound environment depends on many factors. If all the sound components are sufficiently low level, they combine into a composite background sound called "ambience". If two sounds are in approximately the same frequency region but one is much louder, the quieter sound will be "masked". The impact of anthropogenic, high noise environments on wildlife has been little studied although recently, Swaddle and Page (2007) found that high levels of environmental noise eroded pair preferences in zebra finches, A thorough review of the literature on the effects of noise on wildlife for the US Army Corps of Engineers (Larkin et al 1996) concluded noise affects on wildlife vary from serious to nonexistent in different species and situations. Behavioral effects that might decrease chances of surviving and reproducing include retreat from favorable habitat near noise sources and reduction of time spent feeding, with resulting energy depletion. Serious effects such as decreased reproductive success have been documented in some studies and documented to be lacking in other studies on other species. Decreased responsiveness after repeated noises is frequently observed and usually attributed to habituation. Finally they conclude that research is hampered by a preponderance of small, disconnected, and anecdotal studies and coherent programs of controlled experiments are needed.

Krause, 1993 suggested that animal and insect vocalizations tended to occupy small bands of frequencies leaving "spectral niches" (bands of little or no energy) into which the vocalizations (fundamental and formants) of other animals, birds or insects can fit. Further, he proposed that as urban areas spread, the accompanying noise might "block" or "mask" spectral niches and, affecting mating and potentially causing extinction of species.


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Solid dots = reference wetlands; triangles = rural wetlands; diamonds = urban wetlands.

Figure 2. Relationship between the LDI and the (a) WRAP, (b) UMAM, (c) HGM – habitat category, (d) HGM – hydrological category, and (e) HGM – biogeochemical category. Different levels correspond to the most significant areas of influence. Data on both axes are shown as ranked scores.


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Relationship Between LDI and Sound

Measurement of sound pressure was conducted with digital sound level meter. Several land use types were selected and sound pressure measured for 5 minutes. For the purposes of this study, sound pressure was averaged for the 5 minute period. Table 2 lists the average sound levels for several land use types and their corresponding LDI values. Figure 3 graphs sound level against LDI. Sound level increases with increasing LDI.

The graphs in Figure 4 show the sound decibel recordings for three land use types. Notice that not only does the sound level increase, but the variation and amplitude of sound levels increases with increasing LDI from the top graph to the bottom graph. While sound level and frequency of the sound level may be important, the amplitude and modulation of amplitude may be just as important in masking wildlife sound information. Rose and Capranica (1983) and Allan and Simmons (1994) have shown that the information in amplitude modulation is used by some species of anurans. Because amplitude of sounds is discriminated more coarsely by vertebrate ears than frequency and because noises can additively modify amplitudes of sounds with which they share spectral components, noise might mask the information in amplitude more than frequency information. Communication systems that rely on amplitude information might be more sensitive to interference by noise or to acoustic impairment than a system relying on frequency alone.

Table 2. LDI and Sound Intensity for Different Land Uses.

Land Use LDIFR Sound (dB)

Natural Land / Open Water 0.0 25 Pine Plantation 1.0 25 Low Intensity Open Space/Recreational 1.0 28 Unimproved Pastureland (with livestock) 1.0 25 Improved Pasture (no livestock) 3.1 30 Low Intensity Pasture (with livestock) 4.3 30 High Intensity Pasture (with livestock) 6.0 34 Medium Intensity Open Space/Recreational 6.1 37 Citrus 6.9 31 General Agriculture 9.4 35 Row crops 10.5 High Intensity Agriculture (dairy farm) 14.3 Recreational/Open Space (High-intensity) 18.0 40 Single Family Residential (Low-density) 20.1 41 Low Intensity Transportation 22.0 Single Family Residential (Med-density) 25.3 45 Single Family Residential (High-density) 26.7 Highway (4 lane) - Low Intensity 31.1 Institutional 33.1 54 Multi-family residential (Low rise) 33.3 65 Highway (4 lane) - High Intensity 34.1 77 Low Intensity commercial (Comm Strip) 34.2 52 Industrial 34.2 78 High intensity commercial (Mall) 36.3 Multi-family residential (High rise) 38.1 Central Business District (Avg 2 stories) 39.1 65 Central Business District (Avg 4 stories) 41.7


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Figure 3. Sound pressure verses LDI

CONCLUSIONS

The LDI is a quantitative measure of the intensity of human use of landscapes. It is based on the use of energy per unit area converted to energy of one type (solar emergy). The LDI may capture in one index the combined action of various factors that result from human activity that influence ecosystem structure and function. Applications of the LDI have provided evidence that the index is a useful landscape-scale assessment tool of wetland condition. In the Arkansas study, the LDI exhibited strong correlations with three independent measures of wetlands condition with the highest correlations reported with the WRAP and the habitat category of the HGM. Similar results were show in earlier studies in Florida (Reiss and Brown, 2007)

The LDI was related to sound levels and another measure of potential impacts from developed lands. Sound levels increased with LDI as did amplitude. It is suggested that sound from urban lands may interfere with wildlife communication in lands surrounding urban development. Further research is needed to explore the relationships between LDI sound and potential interference in sound information in ecosystems.

Sound (dB) ~ LDI

0

10

20

30

40

50

60

70

80

90

0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0

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Figure 4. Sound decibel level recording for single family residential land use (top), Shopping center (middle), and intersection of two 4-lane urban streets.

SF Residential

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55

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52:03.0 52:53.0 53:43.0 54:33.0

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11:40:39 11:41:29 11:42:19 11:43:09 11:43:59 11:44:49

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4 Lane Intersection

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455055

606570

7580

36:21.0 36:41.0 37:01.0 37:21.0 37:41.0 38:01.0 38:21.0

Time


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Vivas. M. B. 2007. Development of an index of landscape development intensity for predicting the ecological condition of aquatic and small isolated palustrine wetland systems in Florida. PhD. Dissertation. Department of Environmental Engineering Sciences, University of Florida, Gainesville.

Vivas,B. and M.T. Brown 2007. Landscape Development Intensity (LDI) Coefficients for Land Use Classes of the Little Bayou Meto Watershed, Arkansas. Report Submitted to the Arkansas Soil and Water Conservation Commission Under the Sub-grant Agreement SGA 104. Center for Environmental Policy, University of Florida, Gainesville.


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