Satellite observations of forest resilience to hurricanes along the northern Gulf of Mexico
Introduction
Hurricanes, one of the most powerful and destructive climate disasters, pose great threats to both human and natural systems, especially along the coastal regions of the United States (Wang et al., 2010, Wang and D’Sa, 2010). The 2005 Atlantic hurricane season is considered to be a record-breaker in terms of the intensity and frequency of hurricanes as well as the catastrophic total damage of US$74 billion (Weinkle et al., 2018, Beven et al., 2008). Terrestrial ecosystems, especially forests, are deemed most vulnerable to frequent hurricane damage, particularly along the Gulf of Mexico (McNulty, 2002, Negrón-Juárez et al., 2014, Wang and D’Sa, 2010). The substantial impacts of hurricanes on forests include defoliation, the bending or breaking of branches, and the blowdown or even uprooting of entire trees (Boose et al., 1994, Boose et al., 2004). The intensity of these impacts usually depends on biotic and abiotic factors, such as wind strength, topography, forest type, forest density, and the distance of the forest from the hurricane track (Dahal et al., 2015; McNulty, 2002). Alterations of the composition and structure of coastal ecosystems resulting from hurricanes definitely affect the carbon and nitrogen cycles at the landscape and regional scales, owing to powerful winds, heavy rainfall, and subsequent flooding (McNulty, 2002, Chambers et al., 2007). From 1850 to 2000, hurricanes caused carbon release from the continental U.S. forests at the rate of 29 Tg/yr (Zeng et al., 2009). In 2005, Hurricane Katrina alone produced 105 Tg of carbon emissions, the magnitude of which is comparable to the total U.S. forest carbon sink (Chambers et al., 2007). In addition, the hurricane-driven flooding events cause an intrusion of salty water into coastal wetlands and inland watersheds, which can destroy marsh vegetation and increase release of greenhouse gases (Vidon et al., 2017, Wang and D’Sa, 2010).
The timely and accurate identification of post-hurricane damage is essential for the ability of land managers and government officials to take immediate protection and to guide the post-hurricane restoration (Stanturf et al., 2007, Wang et al., 2010, Wang and D’Sa, 2010). The existing estimation of post-hurricane impacts generally includes ground observation and remote sensing-based methods. Ground or aerial surveys can provide detailed information on forest damage along the track of hurricanes (Boutet and Weishampel, 2003, Chambers et al., 2007, Imbert, 2018). However, field observation-based studies are often constrained to small spatial scales due to the limited forest inventory plots and the resources consumed. The development of remote sensing technology has facilitated the quantification of post-hurricane damage at extended spatial and temporal scales (Dahal et al., 2015, Hu and Smith, 2018, Wang and D’Sa, 2010, Zeng et al., 2009). The Moderate Resolution Imaging Spectroradiometer (MODIS) products have frequently been used to assess of hurricane damage (Dahal et al., 2015, de Beurs et al., 2019, Negrón-Juárez et al., 2014, Potter, 2014, Wang et al., 2010, Wang and D’Sa, 2010). Vegetation indices, such as the normalized difference vegetation index (NDVI) (Ayala-Silva and Twumasi, 2004, Ramsey et al., 1998, Ramsey et al., 2001), enhanced vegetation index (EVI) (Rogan et al., 2011, Wang and D’Sa, 2010), tasseled cap water index (TCWI) (Mostafiz and Chang 2018), normalized difference infrared index (NDII) (Aosier et al., 2007, Dahal et al., 2015, Wang et al., 2010), are widely used to detect the forest canopy change by comparing the difference between pre- and post-hurricane status. In addition, Potter (2014) performed a global analysis concerning the damage to coastal ecosystem vegetation resulting from tropical storms between 2006 and 2012 based on a MODIS quarterly indicator of cover change. Most of these extensive studies, however, were narrowly focused on a single event or were based on a single indicator. In order to accurately quantify forest resilience to hurricane disturbances, as well as satellite-observational uncertainties, it is important to use multiple indicators and detect their performances on hurricanes of different intensities.
Compared with the large amount of research focusing on the assessment of post-hurricane forest damage, the trajectory of recovery has received less attention. Ground surveys of post-hurricane recovery are conducted by comparing the composition and physical parameters of trees pre- and post- hurricane (Burslem et al., 2000, Imbert, 2018). The lack of long-term post-disturbance data has made it difficult in monitoring the recruitment process of forests at a larger spatial scale (Bellingham et al., 1995, Burslem et al., 2000). Imbert (2018) found that mangroves in inner, tall-canopy stands need 23 years to recover from a hurricane disturbance, while the fringe and scrub stands require even more time. Satellite monitoring facilitates capturing the damage extent and the long-term pattern of forest recovery at large spatial scales. Wang and D’Sa (2010) assessed the utility of MODIS EVI in detecting the forest damage and monitoring the forest recovery after hurricanes Katrina, Rita, Ike, and Lili. de Beurs et al. (2019) developed a MODIS-based disturbance index to detect the damage from hurricane and drought on four major Caribbean islands since 2001. These efforts have improved our understanding concerning the post-hurricane recovery of forests, but it is still unclear how long a forest needs to recover from the damage induced by hurricanes, or whether different forest types have varied recovery speeds.
The frequent hurricanes in the Gulf of Mexico have repeatedly exposed the forests to strong winds, floods, heavy rain, landslides, and storm surges (Chapman et al., 2008, Wang and D’Sa, 2010). More than 250 hurricanes have struck the northern Gulf of Mexico since 1851, and nearly one-third of them were categorized as major hurricanes (Blake et al., 2005). The coastal areas of the southern U.S. will be exposed to a greater risk of hurricanes over the next 40 years (Stanturf et al., 2007). Therefore, in order to better understand the impacts of hurricanes on forests, we quantified the forest resilience to hurricanes that made landfall along the northern Gulf of Mexico from 2000 to 2015 through the utilization of four remotely sensed vegetation indices (VIs), including the NDII, EVI, LAI, and solar-induced chlorophyll fluorescence (SIF). Specifically, the objectives of this study were to: (1) quantify the resistance (how much a VI changes during a hurricane) and net change (how much the VI changed compared to the state before hurricane) of forest after each hurricane over the study period; (2) estimate the seasonal and interannual variations of VIs during the post-hurricane period; and (3) assess how long the damaged forests take to recover from a hurricane event and determine the potential influencing factors. By comparing the results among the four VIs, we also attempted to identify an optimal indicator for characterizing the post-hurricane damage and recovery trajectory of forests.
Section snippets
Hurricane data
The best tracks for hurricanes that made landfall along the northern Gulf of Mexico during 2001–2015 were extracted from the National Hurricane Center (NHC) data archive (https://www.nhc.noaa.gov/data/). NHC, which is a component of the National Centers for Environmental Prediction (NCEP), archives hurricane track database including location, central pressure, and maximum wind speed of tropical cyclones since 1851 (Dahal et al., 2015, Landsea and Franklin, 2013). During the period 2001–2005, 13
Resistance and net change of forest VIs after hurricanes
Post-hurricane forest damage was detected by evaluating the resistance and net change based on the four VIs. The three Category 3 hurricanes, namely Ivan, Katrina, and Rita, caused NDII decreases (NDIIres) of 17.39%, 15.15%, and 16.07%, respectively, which led to net NDII decreases (NDIInc) of 11.97%, 12.38%, and 12.39% compared to the NDII in the previous years without hurricane disturbances, respectively (Fig. 2A). In contrast, the NDII increased slightly (0.57%) after the landfall of
Factors that affect forest resilience to hurricanes
Hurricane impacts are largely determined by wind speed, forest structure, environmental conditions and the topography of a given area (Stanturf et al., 2007, Chapman et al., 2008). Wind speed is the most influential factor affecting the severity of forest damage. Category 1 and 2 hurricanes, with sustained wind speeds of 74–110 mph, will cause large tree branches to snap and trees with shallow roots to topple, while major hurricanes with sustained wind speed ≥111 mph will result in the snapping
Conclusions
In this study, we used four remotely-sensed VI indicators, namely NDII, EVI, LAI, and SIF, to evaluate the forest resilience to hurricanes along the northern Gulf of Mexico from 2001 to 2015. Wind speed, i.e. hurricane intensity, is the leading factor affecting forest resilience. Generally, the impacted forest canopy began to recover approximately one month after the hurricane landfall. The impacts of hurricanes were found to be stronger in regions dominated by evergreen forests than in regions
CRediT authorship contribution statement
Chengcheng Gang: Conceptualization, Methodology, Writing - original draft, Funding acquisition. Shufen Pan: Conceptualization, Resources, Writing - review & editing. Hanqin Tian: Conceptualization, Resources, Supervision, Funding acquisition. Zhuonan Wang: Data curation, Investigation. Rongting Xu: Validation, Writing - review & editing. Zihao Bian: Data curation, Visualization, Investigation. Naiqing Pan: Data curation, Visualization. Yuanzhi Yao: Data curation, Writing - review & editing. Hao
Acknowledgment
This work was supported by the National Natural Science Foundation of China, China (31602004); the National Key Research and Development Program of China, China (2016YFC0501707); the CAS “Light of West China” program, China (XAB2016B05); the Fundamental Research Funds for the Central Universities, China (2452017184); NSF-NSFC Joint INFEWS Project (1903722), China and United States; NOAA Center for Sponsored Coastal Ocean Research (NA16NOS4780204), United States, and OUC-AU Joint Center Research
References (63)
- et al.
Hurricane Katrina impacts on forest trees of Louisiana's Pearl River basin
Forest Ecol. Manag.
(2008) - et al.
Effect of thinning on LAI variance in heterogeneous forests
Forest Ecol. Manag.
(2008) - et al.
Hurricane damage detection on four major Caribbean islands
Remote Sens. Environ.
(2019) - et al.
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
Remote Sens. Environ.
(2002) - et al.
Ouragans et diversité biologique dans les forêts tropicales. L'exemple de la Guadeloupe
Acta Oecologica
(1998) - et al.
Tasseled cap transformation for assessing hurricane landfall impact on a coastal watershed
Int. J. Appl. Earth Obs
(2018) - et al.
A Global, 0.05-Degree product of solar-induced chlorophyll fluorescence derived from OCO-2, MODIS, and Reanalysis Data
Remote Sens.-Basel
(2019) Hurricane impacts on US forest carbon sequestration
Environ. Pollut.
(2002)- et al.
Multi-scale sensitivity of Landsat and MODIS to forest disturbance associated with tropical cyclones
Remote Sens. Environ.
(2014) - et al.
Forest impact estimated with NOAA AVHRR and Landsat TM data related to an empirical hurricane wind-field distribution
Remote Sens. Environ.
(2001)
Disturbance and coastal forests: A strategic approach to forest management in hurricane impact zones
Forest Ecol. Manag.
Monitoring and assessing the 2012 drought in the great plains: analyzing satellite-retrieved solar-induced chlorophyll fluorescence, drought indices, and gross primary production
Remote Sens.-Basel
Post-hurricane forest damage assessment using satellite remote sensing
Agr. Forest Meteorol.
Capturing rapid land surface dynamics with Collection V006 MODIS BRDF/NBAR/Albedo (MCD43) products
Remote Sens. Environ.
Remote sensing of the terrestrial carbon cycle: a review of advances over 50 years
Remote Sens. Environ.
Modeling the climate-induced changes of lake ecosystem structure under the cascade impacts of hurricanes and droughts
Ecol. Model.
Continental-scale quantification of post-fire vegetation greenness recovery in temperate and boreal North America
Remote Sens. Environ.
The 2010 Russian drought impact on satellite measurements of solar-induced chlorophyll fluorescence: insights from modeling and comparisons with parameters derived from satellite reflectances
Remote Sens. Environ.
Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling
Remote Sens. Environ.
Imaging chlorophyll fluorescence with an airborne narrow-band multispectral camera for vegetation stress detection
Remote Sens. Environ.
Hurricane Georges and vegetation change in Puerto Rico using AVHRR satellite data
Int. J. Remote Sens.
Regeneration in fringe mangrove forests damaged by Hurricane Andrew
Plant Ecol.
Damage and responsiveness of Jamaican montane tree species after disturbance by a hurricane
Ecology
Atlantic hurricane season of 2005
Mon. Weather Rev.
Hurricane impacts to tropical and temperate forest landscapes
Ecol. Monogr.
Landscape and regional impacts of hurricanes in Puerto Rico
Ecol. Monogr.
Spatial pattern analysis of pre-and post-hurricane forest canopy structure in North Carolina, USA
Landscape Ecol.
Vegetation activity monitoring as an indicator of eco-hydrological impacts of extreme events in the southeastern USA
Int. J. Remote Sens.
Short-term effects of cyclone impact and long-term recovery of tropical rain forest on Kolombangara, Solomon Islands
J. Ecol.
Cited by (20)
Unraveling the spatial-temporal patterns of typhoon impacts on maize during the milk stage in Northeast China in 2020
2024, European Journal of AgronomyIntraseasonal interactive effects of successive typhoons characterize canopy damage of forests in Taiwan: A remote sensing-based assessment
2022, Forest Ecology and ManagementCitation Excerpt :Clouds prevented the observation of additional consecutive disturbances in our analysis even though MODIS sensors provide images with a temporal resolution finer than one day. For landscape to regional studies, geostationary satellites with very fine temporal resolution, such as Himawari 8 (Hashimoto et al., 2021; Khan et al., 2021), and the use of solar-induced fluorescence as well as drone- and LiDAR-based methods such as GEDI (Dubayah et al., 2020) may help to explore the effects of future cyclones on forest canopies (Duan et al., 2017; Gang et al., 2020; Leitold et al., 2021; Miura and Nagai, 2020). Typhoon-mediated disturbances of Taiwanese forests between 2001 and 2017 shows that canopy damage (as seen through ΔNDIIs) is, in part, explained by the pre-disturbance vegetation state (NDIIpre-typhoon), but also by the canopy change associated with the preceding typhoon (ΔNDIIprevious typhoon).
Effects of heavy grazing on the microclimate of a humid grassland mountain ecosystem: Insights from a biomass removal experiment
2022, Science of the Total EnvironmentCitation Excerpt :In our study, albedo effectively informed about the resilience of Andean grasslands, in agreement with findings from other ecosystems worldwide (e.g., Gong Li et al., 2000; Lukeš et al., 2016). Nevertheless, variables such as vegetation coverage, plant diversity, vegetation indexes, and soil properties also measure resilience (e.g., Chou et al., 2020; Gang et al., 2020; Guillaume et al., 2016). We suggest testing these variables to measure resilience in páramo ecosystems.
Spectral index-based time series analysis of canopy resistance and resilience in a watershed under intermittent weather changes
2022, Ecological InformaticsCitation Excerpt :Thus, the symmetry of functional capacity and the tipping point of agricultural ecosystems can be altered prior to cyclic wet and dry events, indicating a potential cause for reduced resilience over intermittent weather events (Fig. 5; Antón et al., 2021; Emadodin et al., 2021). Significant fluctuations in climatic and meteorological factors affect the biophysical and biochemical dynamics of vegetation canopies in terrestrial environments (Gang et al., 2020). The outcomes of the correlation analysis of biophysical and biochemical features highlighted the higher level of linkages among GPP, EVI, LAI, and LST in the two types of forested land.
Quantifying the spatiotemporal characteristics of multi-dimensional karst ecosystem stability with Landsat time series in southwest China
2021, International Journal of Applied Earth Observation and Geoinformation