Willem J. D. van Leeuwen

Monitoring post-wildfire vegetation response with remotely sensed time-series data in Spain, USA and Israel

Due to the challenges faced by resource managers in maintaining post-fire ecosystem health, there is a need for methods to assess the ecological consequences of disturbances. This research examines an approach for assessing changes in post-fire vegetation dynamics for sites in Spain, Israel and the USA that burned in 1998, 1999 and 2002 respectively. Moderate Resolution Imaging Spectroradiometer satellite Normalized Difference Vegetation Index (NDVI) time-series data (2000-07) are used for all sites to characterise and track the seasonal and spatial changes in vegetation response. Post-fire trends and metrics for burned areas are evaluated and compared with unburned reference sites to account for the influence of local environmental conditions. Time-series data interpretation provides insights into climatic influences on the post-fire vegetation. Although only two sites show increases in post-fire vegetation, all sites show declines in heterogeneity across the site. The evaluation of land surface phenological metrics, including the start and end of the season, the base and peak NDVI, and the integrated seasonal NDVI, show promising results, indicating trends in some measures of post-fire phenology. Results indicate that this monitoring approach, based on readily available satellite-based time-series vegetation data, provides a valuable tool for assessing post-fire vegetation response.

Land surface albedo from the synergistic use of polar (EPS) and geo-stationary (MSG) observing systems: An assessment of physical uncertainties

Abstract This investigation aims at quantifying the various sources of uncertainties in the derivation of albedo products from geo-stationary and polar orbiting optical systems with emphasis on the sensor and surface type (soil, snow, vegetation) spectral characteristics, atmospheric condition, and angular sampling issues. This research specifically takes into account the uncertainties in albedo that we can expect due to the synergistic use of the European Polar System (EPS)/Advanced Very High Resolution Radiometer (AVHRR) and Meteosat Second Generation (MSG)/Scanning Enhanced Visible and Infrared Imager (SEVIRI) instruments data. Satellite orbital models and a Scattering by Arbitrarily Inclined Leaves (SAIL)-hotspot canopy radiative transfer model were used to simulate synthetic bidirectional reflectance data sets for a broad range of vegetation canopies. The surface bidirectional reflectance distribution function (BRDF) database derived from the Polarization and Directionality of Earth Reflectance (POLDER) data was used in support of the simulations. Spectral atmospheric effects were generated using the 6S atmospheric radiative transfer code. Linear BRDF models, which are candidates for operational use, were inverted with the synthetic reflectance data sets of MSG, AVHRR and MSG–AVHRR combined. The BRDF parameters were then used to derive albedo by hemispherical angular integration. The retrieved model coefficients are discussed with regard to angular sampling and the implications these yield on spectral and broadband albedo determinations. It appears that the sampling problem is better conditioned thanks to the synergistic nature of MSG and AVHRR data. This synergy contributed to enhanced albedo retrievals, but can introduce larger uncertainties due to issues related to the differences in spectral bands and atmospheric state. Hence, the quality of the operational albedo products derived from one or more sources of satellite data will depend to a large extent on sensor characteristics (spectral, radiometric, and geometric), cloud detection, atmospheric correction, the angular distribution of the observations, and finally, the narrow-to-broadband albedo conversion.

Fusion of High Resolution Aerial Multispectral and LiDAR Data: Land Cover in the Context of Urban Mosquito Habitat

Remotely sensed multi-spectral and -spatial data facilitates the study of mosquito-borne disease vectors and their response to land use and cover composition in the urban environment. In this study we assess the feasibility of integrating remotely sensed multispectral reflectance data and LiDAR (Light Detection and Ranging)-derived height information to improve land use and land cover classification. Classification and Regression Tree (CART) analyses were used to compare and contrast the enhancements and accuracy of the multi-sensor urban land cover classifications. Eight urban land-cover classes were developed for the city of Tucson, Arizona, USA. These land cover classes focus on pervious and impervious surfaces and microclimate landscape attributes that impact mosquito habitat such as water ponds, residential structures, irrigated lawns, shrubs and trees, shade, and humidity. Results show that synergistic use of LiDAR, multispectral and the Normalized Difference Vegetation Index data produced the most accurate urban land cover classification with a Kappa value of 0.88. Fusion of multi-sensor data leads to a better land cover product that is suitable for a variety of urban applications such as exploring the relationship between neighborhood composition and adult mosquito abundance data to inform public health issues.

Environmental Drivers of NDVI-Based Vegetation Phenology in Central Asia

Through the application and use of geospatial data, this study aimed to detect and characterize some of the key environmental drivers contributing to landscape-scale vegetation response patterns in Central Asia. The objectives of the study were to identify the variables driving the year-to-year vegetation dynamics in three regional landscapes (desert, steppe, and mountainous); and to determine if the identified environmental drivers can be used to explain the spatial-temporal variability of these spatio-temporal dynamics over time. It was posed that patterns of change in terrestrial phenology, derived from the 8 km bi-weekly time series of Normalized Difference Vegetation Index (NDVI) data acquired by the Advanced Very High Resolution Radiometer (AVHRR) satellites (1981–2008), can be explained through a multi-scale analysis of a suite of environmental drivers. Multiple linear stepwise regression analyses were used to test the hypotheses and address the objectives of the study. The annually computed phenological response variables or pheno-metricstime (season start, season length, and an NDVI-based productivity metric) were modeled as a function of ten environmental factors relating to soil, topography, and climate. Each of the three studied regional landscapes was shown to be governed by a distinctive suite of environmental drivers. The phenological responses of the steppe landscapes were affected by the year-to-year variation in temperature regimes. The phenology of the mountainous landscapes was influenced primarily by the elevation gradient. The phenological responses of desert landscapes were demonstrated to have the greatest variability over time and seemed to be affected by soil carbon content and year-to-year variation of both temperature regimes and winter precipitation patterns. Amounts and scales of observed phenological variability over time (measured through coefficient of variation for each pheno-metrictime) in each of the regional landscapes were interpreted in terms of their resistance and resilience capacities under existing and projected environmental settings.

Phenological Characterization of Desert Sky Island Vegetation Communities with Remotely Sensed and Climate Time Series Data

Climate change and variability are expected to impact the synchronicity and interactions between the Sonoran Desert and the forested sky islands which represent steep biological and environmental gradients. The main objectives were to examine how well satellite greenness time series data and derived phenological metrics (e.g., season start, peak greenness) can characterize specific vegetation communities across an elevation gradient, and to examine the interactions between climate and phenological metrics for each vegetation community. We found that representative vegetation types (11), varying between desert scrub, mesquite, grassland, mixed oak, juniper and pine, often had unique seasonal and interannual phenological trajectories and spatial patterns. Satellite derived land surface phenometrics (11) for each of the vegetation communities along the cline showed numerous distinct significant relationships in response to temperature (4) and precipitation (7) metrics. Satellite-derived sky island vegetation phenology can help assess and monitor vegetation dynamics and provide unique indicators of climate variability and patterns of change.

Phenological Classification of the United States: A Geographic Framework for Extending Multi-Sensor Time-Series Data

This study introduces a new geographic framework, phenological classification, for the conterminous United States based on Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) time-series data and a digital elevation model. The resulting pheno-class map is comprised of 40 pheno-classes, each having unique phenological and topographic characteristics. Cross-comparison of the pheno-classes with the 2001 National Land Cover Database indicates that the new map contains additional phenological and climate information. The pheno-class framework may be a suitable basis for the development of an Advanced Very High Resolution Radiometer (AVHRR)-MODIS NDVI translation algorithm and for various biogeographic studies.

Impacts of climate gradients on the vegetation phenology of major land use types in Central Asia (1981-2008)

Time-series of land surface phenology (LSP) data offer insights about vegetation growth patterns. They can be generated by exploiting the temporal and spectral reflectance properties of land surface components. Interannual and seasonal LSP data are important for understanding and predicting an ecosystem’s response to variations caused by natural and anthropogenic drivers. This research examines spatio-temporal change patterns and interactions between terrestrial phenology and 28 years of climate dynamics in Central Asia. Long-term (1981–2008) LSP records such as timing of the start, peak and length of the growing season and vegetation productivity were derived from remotely sensed vegetation greenness data. The patterns were analyzed to identify and characterize the impact of climate drivers at regional scales. We explored the relationships between phenological and precipitation and temperature variables for three generalized land use types that were exposed to decadelong regional drought events and intensified land and water resource use: rainfed agriculture, irrigated agriculture, and non-agriculture. To determine whether and how LSP dynamics are associated with climate patterns, a series of simple linear regression analyses between these two variables was executed. The three land use classes showed unique phenological responses to climate variation across Central Asia. Most of the phenological response variables were shown to be positively correlated to precipitation and negatively correlated to temperature. The most substantial climate variable affecting phenological responses of all three land use classes was a spring temperature regime. These results indicate that future higher temperatures would cause earlier and longer growing seasons.

Feasibility of invasive grass detection in a desertscrub community using hyperspectral field measurements and Landsat TM imagery.

Invasive species’ phenologies often contrast with those of native species, representing opportunities for detection of invasive species with multi-temporal remote sensing. Detection is especially critical for ecosystem-transforming species that facilitate changes in disturbance regimes. The African C4 grass, Pennisetum ciliare, is transforming ecosystems on three continents and a number of neotropical islands by introducing a grass-fire cycle. However, previous attempts at discriminating P. ciliare in North America using multi-spectral imagery have been unsuccessful. In this paper, we integrate field measurements of hyperspectral plant species signatures and canopy cover with multi-temporal spectral analysis to identify opportunities for detection using moderate-resolution multi-spectral imagery. By applying these results to Landsat TM imagery, we show that multi-spectral discrimination of P. ciliare in heterogeneous mixed desert scrub is feasible, but only at high abundance levels that may have limited value to land managers seeking to control invasion. Much higher discriminability is possible with hyperspectral shortwave infrared imagery because of differences in non-photosynthetic vegetation in uninvaded and invaded landscapes during dormant seasons but these spectra are unavailable in multispectral sensors. Therefore, we recommend hyperspectral imagery for distinguishing invasive grass-dominated landscapes from uninvaded desert scrub.

Fine Scale Spatial Urban Land Cover Factors Associated with Adult Mosquito Abundance and Risk in Tucson, Arizona

ABSTRACT: It is currently unclear what role microhabitat land cover plays in determining the seasonal spatial distribution of Aedes aegypti and Culex quinquefasciatus, disease vectors of dengue and West Nile Virus, respectively, in Tucson, AZ. We compared mosquito abundance to sixteen land cover variables derived from 2010 NAIP multispectral data and 2008 LiDAR height data. Mosquitoes were trapped with 30–49 traps from May to October of 2010 and 2011. Variables were extracted for five buffer zones (10–50 m radii at 10 m intervals) around trapping sites. Stepwise regression was performed to determine the best scale for observation and the influential land cover variables. The 30 m radius buffer was determined to be the best for observing the land cover-mosquito abundance relationship. Ae. aegypti presence was positively associated with structure and medium height trees and negatively associated with bare earth; Cx. quinquefasciatus presence was positively associated with pavement and medium height trees and negatively associated with shrubs. These findings emphasize vegetation, impervious surfaces, and soil influences on mosquito presence in an urban setting. Lastly, the land cover-mosquito abundance relationships were used to produce risk maps of seasonal presence that highlight high risk areas in Tucson, which may be useful for focusing mosquito control program actions.

Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response. We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.