Evaluating post-disaster ecosystem resilience using MODIS GPP data

International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

Contents lists available at SciVerse ScienceDirect

International Journal of Applied Earth Observation and Geoinformation journal homepage: www.elsevier.com/locate/jag

Evaluating post-disaster ecosystem resilience using MODIS GPP data Amy E. Frazier a,∗ , Chris S. Renschler a,b , Scott B. Miles c a National Center for Geographic Information and Analysis (NCGIA), Department of Geography, 105 Wilkeson Quad, University at Buffalo – The State University of New York (SUNY), Buffalo, NY 14261, USA b MCEER-Earthquake Engineering to Extreme Events, 133A Ketter Hall, University at Buffalo – The State University of New York (SUNY), Buffalo, NY 14260, USA c Resilience Institute, Department of Environmental Studies, Western Washington University, Bellingham, WA 98225, USA

a r t i c l e

i n f o

Article history: Received 7 September 2011 Accepted 23 July 2012 Keywords: Ecosystem resilience Ecological capital MODIS GPP ResilUS

a b s t r a c t An integrated community resilience index (CRI) quantifies the status, exposure, and recovery of the physical, economic, and socio-cultural capital for a specific target community. However, most CRIs do not account for the recovery of ecosystem functioning after extreme events, even though many aspects of a community depend on the services provided by the natural environment. The primary goal of this study was to monitor the recovery of ecosystem functionality (ecological capital) using remote sensing-derived gross primary production (GPP) as an indicator of ‘ecosystem-wellness’ and assess the effect of resilience of ecological capital on the recovery of a community via an integrated CRI. We developed a measure of ecosystem resilience using remotely sensed GPP data and applied the modeling prototype ResilUS in a pilot study for a four-parish coastal community in southwestern Louisiana, USA that was impacted by Hurricane Rita in 2005. The results illustrate that after such an extreme event, the recovery of ecological capital varies according to land use type and may take many months to return to full functionality. This variable recovery can potentially impact the recovery of certain businesses that rely heavily on ecosystem services such as agriculture, forestry, fisheries, and tourism. © 2012 Elsevier B.V. All rights reserved.

1. Introduction A resilient community is able to minimize the effects of extreme events and ‘bounce back’ quickly by maintaining critical services, carrying out recovery activities in a minimally disruptive manner, and mitigating the effects of future disasters (Bruneau et al., 2003; Paton et al., 2001; Renschler et al., 2010; Tobin, 1999). If certain critical services are degraded or fail as the result of a disaster, a resilient community is able to recover to a level of service similar to, or better than, the original state in a reasonable amount of time (Cimellaro et al., 2009). A resilient community is also able to reorganize, change, and learn from those setbacks during the postevent adaptive process (Cutter et al., 2008). A community that is not resilient in the face of a severe disturbance is likely to face serious impairment of personal livelihoods where significant efforts must be directed toward recovery of services, thereby increasing the time to return to a pre-event state. Since Holling (1973) first introduced the term resilience, the frequency of natural and man-made disasters has created a need to define and quantify resilience at the community level in order

∗ Corresponding author. E-mail address: [email protected] (A.E. Frazier). 0303-2434/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.jag.2012.07.019

to increase preparedness and decrease recovery time. Efforts have mainly focused on developing indices to measure the response of variables to perturbation because they provide a relative quantitative measure of resistance and allow comparisons between systems. Indices have been used to quantify resilience of the natural environment, including soil (Maul et al., 1999), soil biota (Orwin and Wardle, 2004), lakes (Cottingham and Carpenter, 1994), vegetation cover (Simoniello et al., 2008), and vegetation after fire (Bisson et al., 2008; Diaz-Delgado et al., 2002). Indices have also been used to quantify resilience in the built environment (AttohOkine et al., 2009; Cimellaro et al., 2009, 2010b), and measure the recovery of social components, such as behavioral health and quality of life (Cutter et al., 2003; King, 2001; Paton et al., 2001). A community resilience index (CRI) links indices from many parts of the community system (i.e., infrastructure, transportation, healthcare systems, and environment) into a single measure of the community’s ability to resist and recover from disaster (Cimellaro et al., 2010a). In the aftermath of a disaster, the devastation of the physical infrastructure (i.e., housing, transportation, communication networks, etc.) is often apparent (Renschler et al., 2010), and much focus is given to modeling the resilience of these systems (see Bruneau and Reinhorn, 2007; Cimellaro et al., 2009, 2010b; Fisher and Norman, 2010; Rodriguez and Aguirre, 2006). Not surprisingly, CRIs typically target these aspects and do not consider

44

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

ecosystem services, which are an equally integral part of community resilience. Ecosystem services are the benefits provided to a community through the resources and processes supplied by the natural environment. Ecosystem services provide energy and materials for production of economic goods as well as services such as climate regulation, water regeneration, and erosion control (Gomez-Baggethun and de Groot, 2010). We consider these resources and processes ‘natural capital’ or ‘ecological capital’ because of the benefits they provide. Monitoring the resilience of ecosystem services and assessing their impact on the recovery of industries is a very complex process. However, it is increasingly necessary to quantify and incorporate these services into measures of overall community resilience given the escalating occurrence of hazards worldwide. With the record of developing indices for quantifying resilience in natural systems as well as the foundations for developing robust models of community recovery (Miles and Chang, 2004, 2006), developing and incorporating an ecosystem service index into an overall CRI should be feasible. Using globally available remotely sensed primary production data, this study aims to develop an index of ecosystem resilience through assessing the recovery of vegetative biomass. Geospatial technologies are increasingly being applied to disaster and emergency management studies because of their accessibility, reliability, and effectiveness for providing accurate representations of real world phenomena (Abdalla and Li, 2010). The ecosystem resilience index is then incorporated into ResilUS, a computer simulation model of community disaster resilience (Miles and Chang, 2011), in order to analyze the impact of ecosystem resilience on overall community resilience. The specific objectives of the study are to: (1) assess the recovery of biomass primary production of various land use and land cover (LULC) types in the aftermath of an extreme event, (2) develop an ecosystem resilience index computed at the neighborhood scale to assess ecosystem wellness, and (3) integrate the ecosystem service resilience index into an overall model of community resilience to determine the impact of ecological, or natural capital on businesses dependent on those ecosystem services through implementation of the ResilUS model.

2. Remote sensing for ecosystem resilience Remote sensing is commonly used to assess ecosystem function and has been employed to model the susceptibility of environments (Leuven and Poudevigne, 2002; Maina et al., 2008) and develop resilience indices and frameworks (Ares et al., 2001; Forbes et al., 2009). In terms of disaster preparedness, remote sensing has the potential to provide basic support for response and monitoring during a disaster and post-disaster reconstruction (Jayaraman et al., 1997) but has been used only sparingly to assess ecological resilience to hazards (Liu et al., 2010; Washington-Allen et al., 2008). This study utilizes remotely sensed indicators of primary production to quantify ecosystem resilience. Gross primary production (GPP) is the rate at which plant biomass is captured and stored from photosynthesis while net primary production (NPP) accounts for the rate of energy use through respiration. The Moderateresolution Imaging Spectroradiometer (MODIS) is an instrument onboard NASA’s Terra and Aqua satellites that collects global terrestrial GPP and NPP measurements at 1 km spatial resolution. The MODIS GPP and NPP products are the first continuous, satellitedriven dataset monitoring vegetation primary production (Running et al., 2004; Zhao et al., 2005) and have been operational for over a decade, yet are rarely used to assess ecological resilience (but see Liu et al., 2010; Poulter et al., 2009). The MODIS GPP product (MOD17A2) provides a measure of terrestrial vegetation growth and is intended for monitoring seasonal and spatial patterns of

Fig. 1. Four-parish study area in southwestern Louisiana, USA.

photosynthetic activity (Turner et al., 2006; Wu et al., 2010), and was therefore selected for this study. MODIS GPP data are composited over an 8-day temporal resolution (46 datasets per year) and are calculated using Eq. (1) (Running et al., 2004): GPP = ε × FPAR × PAR ≈ ε × NDVI × PAR

(1)

where FPAR is the fraction of photosynthetically active radiation; PAR is the incident radiation in photosynthetic wavelengths; NDVI is the normalized difference vegetation index; and ε is the PAR conversion efficiency, which varies with vegetation type. 3. Study site and data The study area encompasses a four-parish region in southwestern Louisiana, USA (Fig. 1). The climate is humid subtropical with hot, humid summers and short, mild winters. Temperatures range from winter averages of 19 ◦ C to summer maximums of 38 ◦ C. Topography consists of alluvial lowlands with marshes, bayous, and wetland inlets. The region is frequently subjected to tropical cyclones and major hurricanes. The summer of 2005 was a particularly active storm season, and Hurricane Rita, the third most intense hurricane ever recorded in the Atlantic Basin, made landfall early on September 24, 2005. The entire Gulf coast was subjected to high winds, heavy rains, and widespread flooding that resulted in destruction of buildings and infrastructure, loss of life, and significant alteration of the environment. Pre-event MODIS GPP measurements from 1 January 2000 to 23 September 2005 (the day before the event) were used to determine baseline ecosystem functioning. One year of post-event data were acquired to assess the resilience and recovery of GPP in the aftermath of the event. GIS shapefiles for the 32 zip codes form the neighborhood mapping and analysis units for the study in order to meet ResilUS input requirements. Since GPP is closely tied to the amount of absorbed radiation (Running et al., 2004), which varies with vegetation cover, resilience was computed according to LULC type. LULC information was obtained from the USGS 2001 National Land Cover Database (NLCD), which provides land cover mapping at 30 m resolution using a 21-class classification scheme. Only the 2001 NLCD is available for the time period of the study, therefore, it was assumed that land covers remained constant over the course of the study. Of the 21 classes, 15 are present within the study area. Two additional classes were omitted due to lack of GPP (developed high intensity and open water). The 13 remaining classes are listed in Table 1. In general the study area is dominated in the south by

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52 Table 1 Land use land cover (LULC) types and descriptions for the 13 classes included in the study. Classes and descriptions are from the 2001 National Land Cover Database (NLCD; Multi-Resolution Land Characteristics Consortium, www.mrlc.gov). LULC

Description

Deciduous forest

Areas dominated by trees taller than 5 m constituting more than 20% of total vegetation cover. More than 75% shed foliage simultaneously in response to seasonal change.

Evergreen forest

Areas dominated by trees taller than 5 m constituting greater than 20% of total vegetation cover. More than 75% maintain leaves all year, canopy is never without green foliage.

Mixed forest

Areas dominated by trees generally greater than 5 m tall constituting greater than 20% total vegetation cover.

Pasture/hay

Areas of grasses, legumes, or grass-legume mixtures planted for livestock grazing or the production of seed or hay crops, typically on a perennial cycle.

Cultivated crops

Areas used for the production of annual crops such as corn, soybeans, vegetables, tobacco, and cotton, and also perennial woody crops such as orchards and vineyards.

Shrub/scrub

Areas dominated by shrubs less than 5 m tall with shrub canopy typically constituting greater than 20% of total vegetation.

Grassland/ herbaceous

Areas dominated by grammanoid or herbaceous vegetation, generally constituting greater than 80% of vegetation.

Woody wetlands

Areas where forest or shrubland vegetation accounts for greater than 20% of vegetative cover and the soil or substrate is periodically saturated or covered with water.

Emergent herbaceous wetlands

Areas where the perennial herbaceous vegetation accounts for greater than 80% of vegetative cover and the soil or substrate is periodically saturated or covered with water.

Developed open space

Areas with a mixture of some constructed materials and vegetation, but mostly vegetation. Impervious surfaces account for less than 20% of total land cover.

Developed low intensity

Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 20–49% of total land cover.

Developed medium intensity

Areas with a mixture of constructed materials and vegetation. Impervious surfaces account for 50–79% of total land cover.

Barren land

Barren areas of bedrock, desert pavement, scarps, talus, slides, volcanic material, glacial debris, sand dunes, strip mines, gravel pits, and other accumulations of earthen material. Generally, vegetation accounts for less than 15% of total cover.

wetlands and croplands and in the north by forest and grassland (Fig. 2). 4. Methods The methodology is implemented in two stages. First, resilience values (RVs) are calculated to assess the recovery of each LULC after the event and integrated into a comprehensive ecosystem resilience index (ERI) for each zip code. Second, ERI values are incorporated into ResilUS to model the effect of ecosystem resilience on an integrated community resilience index and assess the results. 4.1. Ecosystem resilience index (ERI) ERI is a measure of the ecosystem’s ability to maintain its state of equilibrium in the aftermath of an extreme event and is

45

calculated from the RVs for each LULC. RV was assessed by comparing post-event GPP measurements to a baseline value. The NLCD was overlain on the MODIS image, and MODIS pixels representing homogeneous land covers were extracted to serve as indicator pixels for each LULC. When multiple homogeneous pixels could be identified for a single land cover, GPP values were averaged. If a single, homogenous pixel could not be identified, a pixel containing a majority of the LULC was selected. Pre-event baseline GPP for each LULC was then established by averaging GPP values from equivalent collection periods between January 1, 2000 and September 23, 2005. A second major hurricane (Katrina) occurred in the Gulf coast region one month prior to Hurricane Rita. Katrina made landfall approximately 250 miles east of the study region, and although it brought rain and high winds to the area, ecosystem destruction was not great enough to warrant omission of those data from baseline measurements. Post-event GPP data were compiled for 12 months after the event and compared to baseline readings. Each period where postevent GPP was above the baseline was assigned a value of one; each instance where post-event GPP fell below the baseline was assigned zero. If GPP remained above the baseline for consecutive periods, each period was counted as a separate instance. We used a binary measure rather than actual GPP measurements since the purpose of this study is to measure the ability of the system to recover, not the degree to which it recovered. Since the study area is affected by seasonality, RVs were calculated monthly for each LULC using the following equation:

 n  i

RV =

xi

n

(2)

where xi denotes the ecosystem functionality for each LULC, and is equal to 1 if post-event GPP is above the baseline and 0 if GPP is below; n is the number of MODIS collection periods in the month. Monthly ERIs for each zip code were calculated by standardizing RV by the percentage of LULC present within each zip code, summing across all LULCs, and dividing by the total amount of land in each zip code (Eq. (3)). ERI values range from zero to one, with one signifying complete resilience (i.e., GPP never fell below the baseline) and zero indicating complete failure of the ecosystem to recover biomass production to normal levels in the aftermath of the event.



ERI =

i

RV i × A T

(3)

where RVi is the resilience value (Eq. (2)) for a given LULC type i; A is the amount of the land cover class within the zip code; T is the total amount of land in the zip code; and c is the LULC type. 4.2. Integration into ResilUS The second part of the methodology incorporates the ERI into ResilUS (Miles and Chang, 2011), a computer model that simulates the recovery dynamics of a community following a disaster and quantifies an integrated CRI. ResilUS is based on a robust and detailed conceptual model that facilitates application to multiple hazards. For a complete exposition of the model and all variables, see Miles and Chang (2011). The ResilUS model assesses resilience according to forms of capital, and was specifically selected for its ability to incorporate ecological, or natural, capital into its other aggregate elements of community capital: (1) physical capital, (2) economic capital, (3) social capital, and (4) human capital. Physical capital refers to infrastructure including roads, buildings, utilities, etc. Economic capital refers to businesses including sector, size, and financial resources. Social capital refers to networks of relationships between individuals, groups, and entities (e.g., alumni of a

46

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

Fig. 2. Land use/land cover (LULC) types for the four-parish study area in southwestern Louisiana, USA.

college or neighborhood groups). Human capital refers to the economic value of a person’s labor skills. The methodology developed here can be implemented in any type of resilience, recovery, or restoration model. To illustrate the linkages between types of capital, we provide an example of sugar cane, a major agricultural export crop in Louisiana. In the event of a natural disaster, destruction of sugar cane plantations will most immediately affect economic capital through loss of export products and loss of revenue. If plantations are not able to recover quickly, their lack of resilience will affect human capital by reducing the need for farm laborers. Laborers may eventually be forced to emigrate from the area to find jobs, and the loss of this group will impact the labor force in the region. Without sugar to export, former buyers will turn to other regions/growers to fill orders. This severing of market ties compromises business relationships and networks built on sugar cane, thereby affecting the social capital of the region. Lastly, if the sugar cane industry is unable to recover from disaster, the infrastructure (e.g., processing plants, buildings, machinery, etc.) may be abandoned or ill-maintained, thus affecting physical capital. Two separate ResilUS model runs were completed to simulate five output measures of business recovery: failed businesses, employment, demand recovery time, business debt index, and business productivity index (Table 2). During the first run, natural capital was not included in the model, and the results provide a baseline from which to compare the impact of ecosystem services. In the second run, natural capital was included to assess its influence on sectors that are dependent on ecosystem services. In the case where a business was deemed dependent on ecological capital (e.g., agriculture, forestry, tourism, etc.), ERI values were used as a recovery probability in the model. For a complete description of how indicators are assessed in the model, see Miles and Chang (2011). 5. Results and discussion 5.1. GPP as a measure of ecosystem wellness Results showing the comparison of post-event GPP and preevent baseline (Fig. 3) are displayed in chronological order, starting

with the first MODIS GPP acquisition after the event (September 30, 2005). Results have been smoothed using a 3-cell moving window to remove the variations typical in the GPP product due to variations in meteorological conditions, FPAR, and other sensor inputs and allow visual comparison. At the start, ten of the 13 LULCs are above the baseline. Only evergreen forest, pasture/hay, and woody wetlands are below their baseline GPP values. The storm resulted in significant forest damage and tree blowdown (Phillips and Park, 2009), which explains the poor recovery of evergreen forest GPP. A study of the impacts of Hurricane Katrina on trees found that wind damage varied with species and forest type (Chapman et al., 2008), which may explain why the softwood evergreens suffered more damage than the deciduous hardwood forests. Woody wetlands are mainly located along the Louisiana-Texas border, directly in the path of the storm, and likely suffered the most severe wind and rain conditions, leading to their poor initial resilience. Over the next two months, GPP for most LULCs remains slightly above or below the baseline, indicating relatively good short-term resilience. Pasture/hay and woody wetlands were able to recover from their initially low GPP values and are both trending toward to the baseline by Day 65. Evergreen forest GPP stayed well below the baseline, and developed medium intensity was unable to sustain its high resilience after the first few weeks and fell below the baseline by Day 65. Between Day 65 and Day 129, almost all LULCs remain above the baseline. It is thought that thousands of acres of coastal marsh were inundated with saltwater storm surges (Lance et al., 2010) that were detrimental to the wetlands. However, our results show that these regions, which are mostly comprised of emergent herbaceous wetlands, displayed strong resilience through the first six months after the storm (Fig. 3i) and were able to continue producing biomass. Despite widespread coastal flooding, there was no extensive river flooding (Phillips and Park, 2009), which helps explain the high resilience of deciduous and mixed forests. Trees that were not destroyed by the initial storm effects were able to rebound quickly in the absence of secondary flood effects. In the second half of the study period (Day 193 to the end), the situation changes and overall resilience decreases. Most LULCs fall well below the baseline, notably near the growing season peak from June to August (Day 257 to Day 321). During this time, only cultivated crops, shrub/scrub, and grassland/herbaceous remain

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

47

Table 2 Ecosystem resilience index (ERI) values for each zip code for the 12 months following Hurricane Rita. Zip codes are listed in order of proximity to the Gulf of Mexico. Values equal and greater than 0.40 are classified in gray intensity scale classes. Extreme low values are struck through. Locations of zip codes are shown in Fig. 4.

consistently above their baselines. This decline in resilience can be explained by other environmental effects. In the months following the storm, the region experienced a prolonged drought with the lowest recorded rainfall in 111 years (Lance et al., 2010). The drought was then followed by heavy spring rains, which delayed planting. Both factors may be contributing to the decrease in resilience over the long term. Almost all LULCs rebound at the end of the reporting year and finish with GPP values above the baseline. Interestingly, shrub/scrub and barren land (Fig. 3f and m) were the only LULCs with GPP consistently above the baseline. Shrub/scrub had the lowest severity of damage of forest types during Hurricane Katrina (Wang and Xu, 2009), and our results agree that it also fared best during Hurricane Rita. The slightly elevated GPP levels in barren lands may be due to vegetation deposited here by flood waters. Vegetation can continue to absorb PAR for a short time after being uprooted, provided the root system maintains contact with water. It is also conceivable that the flood disturbance promoted establishment of new vegetation, but after normal activities resumed on the land (e.g., mining), new vegetation growth was eliminated, explaining the return to baseline values in the later months. Our findings are consistent with early work by Forman and Godron (1986, p. 28), which found that systems with low levels of biomass have little resistance to change but can recover rapidly from disturbance. Overall, our GPP results indicate that in the aftermath of Hurricane Rita, most LULCs were highly resilient with respect to biomass. The shrub/scrub land use type demonstrated the highest resilience, and evergreen forest demonstrated the lowest resilience. While most LULCs were resilient during the first six months, we found

those results varied during the longer term, thus supporting the need for long-term ecosystem resilience studies. 5.2. Monthly ecosystem resilience The 12-month average ERIs for each zip code (Fig. 4) show that resilience is higher in the southern areas of the study region dominated by wetlands, cultivated crops, and pasture/hay. Resilience is lower in the northern areas dominated by forest and grassland (see Fig. 2). These results are consistent with the LULC findings (see Fig. 3) where evergreen forest did not recovery robustly, but emergent herbaceous wetlands along coastal areas were resilient. The relatively high resilience of these wetlands is an encouraging finding, and indicates the coastal areas, which provide fundamental ecosystem services to the region such as fisheries, water filtration, and biodiversity, were able to recover to a high level of functioning in a relatively short time period following the hurricane. The rapid recovery for cultivated lands is likely due to active agricultural replanting and quick re-growth during the study period. Cultivated lands have relatively lower levels of biomass compared to forests, and therefore should recover more rapidly (Forman and Godron, 1986). Forested ecosystems are less likely to have been actively restored, and are not able to mature as quickly as crops. Monthly ERI values for each zip code (Table 3) indicate that temporally, the highest ERIs occur in January and April for zip codes nearest to the coast. High resilience in January is likely due to generally low GPP during this time of year. The growing season in southern Louisiana ends in early-December and does not begin again until late-February, which means that productivity is at its

48

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

Fig. 3. Baseline and post-event GPP results for the 13 land use land cover (LULC) types. Several calendar dates corresponding to the post-event day are listed for reference.

lowest during January. Even small increases in GPP at this time would manifest as high resilience. High resilience in April can be attributed to an increase in crop plantings to compensate for losses the previous year caused by Hurricane Rita. The lowest ERI values occur in March, which can be attributed to a late start to the growing season in 2006 due to spring rains that delayed planting, and July, which is during the height of the growing season and gives a

strong indication that ecosystem services have not rebounded to their pre-event levels. Average monthly ERI values for all 32 zip codes (Fig. 5) were low during the first three months, which is likely due to the damaged caused to late-harvest crops (Schnepf and Chite, 2005). The destruction of this crop biomass reduced GPP and decreased ERIs for the autumn months after the storm. ERI peaks in January, but as

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52 Table 3 Selected ResilUS model outputs and descriptions. Output [dimension]

Description

Failed businesses Employment

Occurrence of business failure Probability that employment is available Time for demand for a business’s products or services to return to normal levels Normalized level of debt Probability a business is at pre-event production levels

Demand recovery time [weeks]

Business debt index Business productivity index

49

reveal that while the system may appear to be resistant to disaster and recover in the short term, high resilience is not necessarily sustained for longer time periods. Initial peaks in resilience followed by declines may be due to ‘false positives’ from remnant vegetation or a lag between ecosystem damage and the manifestation of that damage in measurable variables. When using a biomass index, high resilience values can also manifest during normal winter periods of low GPP since even a poorly functioning system would compare well to an essentially non-productive system. These findings all confirm that the true resilience of a system may not be exposed or be perceptible for many months, or even years, after an event, thus supporting the need for longer-term studies. 5.3. Impact of ecological capital on business recovery

Fig. 4. 12-Month averaged ecosystem resilience index (ERI) values for each zip code.

discussed above, this peak is not necessarily indicative of a strong recovery. ERI declines steadily through March due to the delayed planting season as a result of spring rains. With the resumption of agricultural activities, resilience peaks again in April and then declines drastically through July. The April peak coincides with leafon season for deciduous trees, which may have added a spike of biomass production to the ecosystem, but the decline in resilience after April suggests that as harvesting and planting cycles returned to normal, the system stabilized below normal. A final ER peak occurs in August before eventually declining in September to finish the year below its level initially following the event. The region is heavily influenced by agricultural activities such as crop cultivation and pasture/hay. These land uses have little resistance to change, but recover rapidly from disturbance (Forman and Godron, 1986). The results from the ERI demonstrate that resilience is closely tied to LULC activities and varies according to LULC and the time of year when recovery is measured. The findings support the need for longer term (>1 year) study as they

Fig. 5. Monthly average ecosystem resilience index (ERI) for all zip codes. Error bars show one standard deviation.

Simulated recovery results for the five output measures of business recovery one year after Hurricane Rita are described below. Since we assumed only export-oriented businesses are affected by ecological capital, the results of the second scenario are a validation of the influence of ERI on ecological capital-dependent businesses and also measure the effect of ecosystem resilience on an overall community resilience index. The results (Fig. 6) show the change in value between the first and second model runs and indicate the impacts of ecological capital on overall community resilience. Values have been normalized between negative one and one to allow comparisons between the measures. Positive change values indicate that overall resilience increases when the effects of ecological capital are included. Negative change values indicate that overall resilience decreases when ecological capital is considered. Failed businesses (Fig. 6a) experienced the greatest decline in resilience when ecological capital was included. This negative change is likely due to the strong ties between the economy and environmental services in the region (e.g., agriculture, fisheries, forestry, etc.) and the large number of businesses that depend on these services. Similarly, business productivity (Fig. 6e) also experienced declines in resilience across the study area. The business debt index (Fig. 6d) did not experience considerable changes in either direction, and results vary according to zip code with no clear spatial trends. Since debt levels are strongly influenced by a number of other factors (e.g., number of employees, size and number of loans, etc.), we did not expect this output to be strongly influenced by ecological capital inputs. Employment also varied, but most zip codes increased their resilience when ecological capital was included, probably due to the relatively high ecosystem resilience in the wetland and agricultural areas, and the large labor force needed in these areas. Demand recovery time (Fig. 6c) remained mostly unchanged between the two model runs. Portions of the northwestern and southwestern corners of the study area increased resilience for demand recovery time, which supports validation of the model since demand for the products and services of exportoriented business originate outside the community and should not be highly dependent on the recovery of ecological capital within the community. Linkages between the five outputs also support the ERI results. For example, business productivity and employment are influenced by the demand for services and products. Since demand recovery time did not change drastically, provided a business still had access to the goods and services it was exporting, its resilience would remain high. Those export-oriented businesses located in regions of high ecosystem resilience (see Fig. 4) had higher overall resilience as a result of their location. Through quantifying the change between the two model scenarios and comparing the impact of natural capital to ERI, it is evident that ecosystem recovery, established through the ERI, influences overall community resilience.

50

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

Fig. 6. Difference in ResilUS model outputs between the first (no natural capital dependence) and second (natural capital dependence for export-oriented businesses) model runs. Results have been normalized between negative one and one for comparison. Blue values indicate an increase in overall resilience when natural capital is included, red values indicate a decrease. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

5.4. Limitations of the MODIS-derived GPP product for ERI MODIS provides state-of-the-art, freely available, global satellite products (Reeves et al., 2005), but there are several limitations of the MODIS GPP algorithm as well as sensor capabilities that must be addressed in order to use the data for the purposes of this study. The most significant limitations of the GPP algorithm stem from assumptions made for certain input parameters. MODIS GPP requires daily, gridded meteorological input data, and inconsistencies in the spatial resolution between these data and MODIS pixels can introduce considerable errors. These inconsistencies were improved in the updated Collection 5 product (Zhao et al., 2005), which was used in this study, but continued efforts are still needed to further improve the product (Heinsch et al., 2006). Uncertainties for certain biomes, particularly tropical regions (Zhao et al., 2006) remain, but the overall quality of the product has been found to be suitable for terrestrial ecosystem analysis (Heinsch et al., 2006). The GPP algorithm is also impacted by assumptions that FPAR input remains constant across the 8-day time span of data compilation (Reeves et al., 2005) and cloud-contaminated retrievals can introduce considerable errors (Zhao et al., 2005). Strict quality controls of the FPAR product have eliminated potential contamination in areas not affected by snow and ice in the winter (Heinsch et al., 2006), such as southwestern Louisiana.

The data collection capabilities of the MODIS sensor also impact the study. The 1 km spatial resolution of the GPP product is intended for regional and global studies, and our study area is best characterized as a small region. LULCs were often mixed in the study area at 1 km resolution and limited our ability to locate MODIS pixels comprising pure, homogeneous land uses, for certain land covers (e.g., barren land). In these instances, we assumed a pixel containing a land cover majority could be assigned to a single LULC. In order to account for the uncertainty of the GPP product and the spatial resolution within the ResilUS model, we selected a spatial mapping unit that was coarse enough to include a significant number of MODIS pixels as well as a wide variety of land covers. This prevented any single unit from being overly influenced by GPP uncertainty for a single LULC. We also accounted for uncertainty by compiling results at monthly time periods instead of using the 8-day resolution of the MODIS sensor. ResilUS can compute recovery for very short time intervals (e.g., daily, weekly, bi-weekly, etc.), but we used a coarser temporal scale to account for the above-mentioned uncertainties that might affect a single MODIS collection date. The lack of GPP data for certain land uses also impacted our study. We could not compute an ERI for ‘Developed High Intensity’ areas despite its presence in the study site (see Fig. 2). While these areas comprise mostly impervious surfaces, micro-scale ecosystem services can exist in rooftop gardens, median planting strips,

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

or small urban agricultural plots. Detecting productivity globally in urban areas will require significant spatial resolution improvements of the MODIS sensor (e.g., 5–10 m resolution), which are not anticipated in the near future. The MODIS GPP product is also limited to terrestrial ecosystems and does not assess primary productivity in water. In coastal regions, oceanic primary production may play a vital role in the recovery of carbon storage and other ecosystem services. Studies have explored the possibilities of quantifying oceanic primary productivity remotely (Balch et al., 1992; Delu et al., 2005; Longhurst et al., 1995), but it will likely be some time before it is operational on an instrument such as MODIS. Our MODIS-based approach also limited our ability to assess the recovery of certain industries that are prevalent in southwestern Louisiana, such as fisheries and oil exploration, because they are not impacted by changes in terrestrial biomass. Despite the limitations of MODIS’s data acquisition capabilities and the assumptions of the GPP algorithm, the MODIS GPP product remains the only publicly available global GPP data. Ongoing efforts to test the validity of the product (Heinsch et al., 2006; Plummer, 2006) as well as improve the accuracy (Zhao et al., 2005, 2006), will continue to enhance the usefulness of the product. While challenges still remain for certain biomes, overall the algorithm captures the seasonality of GPP well across a wide array of biomes and climate regimes (Heinsch et al., 2006; Plummer, 2006; Running et al., 2004). The general agreement is that the product performs well, and it is expected that it will continue to provide a key component for global terrestrial ecosystem studies. 6. Conclusions This study proposed a new approach to assess the recovery of ecological capital in a community impacted by an extreme event through remote sensing-based monitoring of GPP produced globally for terrestrial environments by the MODIS satellite. An index of ecosystem resilience was developed from GPP data and incorporated into a revised implementation of the ResilUS model as measures of ecological, or natural capital, for the coastal parishes of southwest Louisiana, which were heavily impacted by Hurricane Rita in September 2005. Several key findings emerge from this research. First, operational GPP measurements from the MODIS satellite are a reliable parameter for determining the recovery of biomass in an ecosystem in the aftermath of an extreme event. Overall we found that in the aftermath of Hurricane Rita, most LULCs were highly resilient with respect to biomass, but those results varied during the longer term, thus supporting the need for long-term ecosystem resilience studies. Second, we found that biomass productivity can successfully be incorporated into a resilience index for any desirable mapping scale based on the specific LULCs within the region. Lastly, when remote-sensing derived resilience indices were integrated into the ResilUS model, it was found that the model was successfully able to model ecologically dependent business recovery from the inputs. Future work should focus on the development of additional proxies for ecosystem wellbeing from remotely sensed data. Satellite-driven datasets are becoming increasingly available and are a powerful tool for global monitoring. Additionally, next steps involve implementation of monitoring plans that enable local and regional stakeholders to enhance their resilience against episodic and slow-onset coastal hazards. Acknowledgements The authors would like to thank the National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center (CSC) for sponsoring this research (NA07NOS4730146). We also would

51

like to acknowledge the other research team members – Ronald T. Eguchi, Beverley Adams and John Bevington (ImageCat Inc.), Stephanie Chang (University of British Columbia), John C. Pine (Appalachian State University), Adam Rose (University of Southern California) as well as Sue Roussie and Graham Hayes (University at Buffalo) – for providing input to the ResilUS conceptual model and baseline simulation. References Abdalla, R., Li, J., 2010. Towards effective application of geospatial technologies for disaster management. International Journal of Applied Earth Observation and Geoinformation 12, 405–407. Ares, J., Bertiller, M., del Valle, H., 2001. Functional and structural landscape indicators of intensification, resilience and resistance in agroecosystems in southern Argentina based on remotely sensed data. Landscape Ecology 16 (3), 221–234. Attoh-Okine, N.O., Cooper, A.T., Mensah, S.A., 2009. Formulation of resilience index of urban infrastructure using belief functions. IEEE Systems Journal 3 (2), 147–153. Balch, W., Evans, R., Brown, J., Feldman, G., McClain, C., Esaias, W., 1992. The remote-sensing of ocean primary productivity – use of a new data compilation to test satellite algorithms. Journal of Geophysical Research: Oceans 97 (C2), 2279–2293. Bisson, M., Fornaciai, A., Coli, A., Mazzarini, F., Pareschi, M.T., 2008. The Vegetation Resilience After Fire (VRAF) index: development, implementation and an illustration from central Italy. International Journal of Applied Earth Observation 10, 312–329. Bruneau, M., Chang, S., Eguchi, R., Lee, G., O’Rourke, T., Reinhorn, A., et al., 2003. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra 19, 733–752. Bruneau, M., Reinhorn, A.M., 2007. Exploring the concept of seismic resilience for acute care facilities. Earthquake Spectra 23 (1), 41–62. Chapman, E.L., Chambers, J.Q., Ribbeck, K.F., Baker, D.B., Tobler, M.A., Zeng, H., White, D.A., 2008. Hurricane Katrina impacts on forest trees of Louisiana’s Pearl River basin. Forest Ecology and Management 256, 883–889. Cimellaro, G.P., Fumo, C., Reinhorn, A.M., Bruneau, M., 2009. Quantification of Seismic Resilience of Health Care Facilities. MCEER Technical Report-MCEER09-0009. Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY. Cimellaro, G.P., Reinhorn, A.M., Bruneau, M., 2010a. Framework for analytical quantification of disaster resilience. Engineering Structures 32, 3639–3649. Cimellaro, G.P., Reinhorn, A.M., Bruneau, M., 2010b. Seismic resilience of a hospital system. Structure and Infrastructure Engineering 6 (1–2), 127–144. Cottingham, K.L., Carpenter, S.R., 1994. Predictive indices of ecosystem resilience in models of north temperate lakes. Ecology 75 (7), 2127–2138. Cutter, S.L., Boruff, B.J., Shirley, W.L., 2003. Social vulnerability to environmental hazards. Social Science Quarterly 84 (2), 242–261. Cutter, S.L., Barnes, L., Berry, M., Burton, C., Evans, E., Tate, E., Webb, J., 2008. A place-based model for understanding community resilience to natural disasters. Global Environmental Change 18, 598–606. Delu, P., Wenjiang, G., Yan, B., Haiqing, H., 2005. Ocean primary productivity estimation of China Sea by remote sensing. Progress in Natural Science 15 (7), 627–632. Diaz-Delgado, R., Lloret, F., Pons, Z., Terradas, J., 2002. Satellite evidence of decreasing resilience in Mediterranean plant communities after recurrent wildfires. Ecology 83 (8), 2293–2303. Fisher, R.E., Norman, M., 2010. Developing measurement indices to enhance protection of critical infrastructure and key resources. Journal of Business Continuity & Emergency Planning 4 (3), 191–206. Forman, R.T.T., Godron, M., 1986. Landscape Ecology. John Wiley & Sons, New York. Forbes, B.C., Stammler, F., Kumpula, T., Meschtyb, N., Pajunen, A., Kaarlejarvi, E., 2009. High resilience in the Yamal-Nenets social-ecological system, West Siberian Arctic, Russia. Proceedings of the National Academy of Sciences of the United States of America 106 (52), 22041–22048. Gomez-Baggethun, E., de Groot, R., 2010. Natural capital and ecosystem services: the ecological foundation of human society. In: Hester, R.E., Harrison, R.M. (Eds.), Ecosystem Services: Issues in Environmental Science and Technology. Royal Society of Chemistry, Cambridge. Heinsch, F.A., Zhao, M., Running, S.W., et al., 2006. Evaluation of remote sensing based terrestrial productivity from MODIS using regional tower Eddy flux network observations. IEEE Transactions on Geoscience and Remote Sensing 44 (7), 1908–1925. Holling, C.S., 1973. Resilience and stability of ecological systems. Annual Review of Ecology and Systematics 4, 1–23. Jayaraman, V., Chandrasekhar, M.G., Rao, U.R., 1997. Managing the natural disasters from space technology inputs. Acta Astronomica 40 (2–8), 291–325. King, D., 2001. Uses and limitations of socioeconomic indicators of community vulnerability to natural hazards: data and disasters in Northern Australia. Natural Hazards 24, 147–156. Lance, V.A., Elsey, R.M., Butterstein, G., Trosclair, P.L., Merchant, M., 2010. The effects of Hurricane Rita and subsequent drought on alligators in southwest Louisiana. Journal of Experimental Zoology 313A, 106–113. Leuven, R.S., Poudevigne, I., 2002. Riverine landscape dynamics and ecological risk assessment. Freshwater Biology 47 (4), 845–865.

52

A.E. Frazier et al. / International Journal of Applied Earth Observation and Geoinformation 21 (2013) 43–52

Liu, Y., Ronggao, L., Quansheng, G., 2010. Evaluating the vegetation destruction and recovery of Wenchuan earthquake using MODIS data. Natural Hazards 54, 851–862. Longhurst, A., Sathyendranath, S., Platt, T., Caverhill, C., 1995. An estimate of global primary production in the ocean from satellite radiometer data. Journal of Plankton Research 17 (6), 1245–1271. Maina, J., Venus, V., McClanahan, M.R., Ateweberhan, M., 2008. Modelling susceptibility of coral reefs to environmental stress using remote sensing data and GIS models. Ecological Modelling 212 (3–4), 180–199. Maul, R.S., Holland, M.M., Mikell, A.T., Cooper, C.M., 1999. Resilience of forested wetlands located in the southeastern United States: demonstration of a soil perturbation index. Wetlands 19 (1), 288–295. Miles, S.B., Chang, S.E., 2004. The dynamics of recovery: a framework. In: Okuyama, Y., Chang, S.E. (Eds.), Modeling the Spatial Economic Impact of Disasters. Springer-Verlag, Berlin. Miles, S.B., Chang, S.E., 2006. Modeling community recovery from earthquakes. Earthquake Spectra 22 (2), 439–458. Miles, S.B., Chang, S.E., 2011. ResilUS: a community based disaster resilience model. Cartography and Geographic Information Science 38 (1), 36–51. Orwin, K.H., Wardle, D.A., 2004. New indices for quantifying the resistance and resilience of soil biota to exogenous disturbances. Soil Biology & Biochemistry 36, 1907–1912. Paton, D., Millar, M., Johnston, D., 2001. Community resilience to volcanic hazard consequences. Natural Hazards 24, 157–169. Phillips, J.D., Park, L., 2009. Forest blowdown impacts of Hurricane Rita on fluvial systems. Earth Surface Processes and Landforms 34, 1069–1081. Plummer, S., 2006. On validation of the MODIS gross primary production product. IEEE Transactions on Geoscience and Remote Sensing 44 (7), 1936–1938. Poulter, B., Heyder, U., Cramer, W., 2009. Modeling the sensitivity of the seasonal cycle of GPP to dynamic LAI and soil depths in tropical rainforests. Ecosystems 12, 517–533. Reeves, M.C., Zhao, M., Running, S.W., 2005. Usefulness and limits of MODIS GPP for estimating wheat yield. International Journal of Remote Sensing 26 (7), 1403–1421. Renschler, C.S., Frazier, A.E., Arendt, L.A., Cimellaro, G.P., Reinhorn, A.M., Bruneau, M., 2010. A Framework for Defining and Measuring Resilience at the Community Scale: The PEOPLES Resilience Framework, MCEER Technical

Report-MCEER-10-0006. Multidisciplinary Center for Earthquake Engineering Research, Buffalo, NY. Rodriguez, H., Aguirre, B.E., 2006. Hurricane Katrina and the healthcare infrastructure: a focus on disaster preparedness, response, and resiliency. Frontiers of Health Services Management 23 (1), 13–23. Running, S., Nemani, R.R., Heinsch, F.A., Zhao, M., Reeves, M., Hashimoto, H., 2004. A continuous satellite-derived measure of global terrestrial primary production. Bioscience 54 (6), 547–560. Schnepf, R., Chite, R.M., 2005. U.S. Agriculture after Hurricanes Katrina and Rita: Status and Issues. Congressional Research Service Report for Congress. The Library of Congress. Simoniello, T., Lanfredi, M., Liberti, M., Coppola, R., Macchiato, M., 2008. Estimation of vegetation cover resilience from satellite time series. Hydrology and Earth System Sciences 12, 1053–1064. Tobin, G.A., 1999. Sustainability and community resilience: the holy grail of hazards planning? Global Environmental Change, Part B: Environmental Hazards 1, 13–25. Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Running, S.W., Zhao, M., Costa, M.H., Kirschbaum, A.A., Ham, J.M., Saleska, S.R., Ahl, D.E., 2006. Evaluation of MODIS NPP and GPP products across multiple biomes. Remote Sensing of Environment 102, 282–292. Wang, F., Xu, Y.J., 2009. Hurricane Katrina-induced forest damage in relation to ecological factors at landscape scale. Environmental Monitoring and Assessment 156, 491–507. Washington-Allen, R.A., Ramsey, R.D., West, N.E., Norton, B.E., 2008. Quantification of the ecological resilience of drylands using digital remote sensing. Ecology and Society 13 (1), 33. Wu, C., Munger, J.W., Niu, Z., Kuang, D., 2010. Comparison of multiple models for estimating gross primary production using MODIS and eddy covariance data in Harvard Forest. Remote Sensing of Environment 114, 2925–2939. Zhao, M., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sensing of Environment 95, 164–176. Zhao, M.S., Running, W., Nemani, R.R., 2006. Sensitivity of Moderate Resolution Imaging Spectroradiometer (MODIS) terrestrial primary production to the accuracy of meteorological reanalyses. Journal of Geophysical Research 111, 1–13.