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

Willem J.D Van Leeuwen,Jennifer E Davison,Stuart E Marsh,Grant M Casady

doi:10.3390/rs2020388

Willem J.D Van Leeuwen, Jennifer E Davison + Show 2 more

Open Access

https://doi.org/10.3390/rs2020388

Copy DOI

Journal: Remote Sensing	Publication Date: Jan 27, 2010
Citations: 46	License type: CC BY 4.0

Affiliation: University of Arizona

Abstract

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.

Highlights

Climate patterns from intra-seasonal to decadal and century scales directly influence the timing, magnitude, and spatial patterns of vegetation growth cycles, or phenology [1,2].Vegetation phenology can be detected by satellites and other remote sensing systems due to the unique seasonal and spectral reflectance and transmittance characteristics of canopy, plants and leaves [1,3].Compared to other land surface components, red and NIR reflectance values for green vegetation are low and high respectively
Metrics created from spectral vegetation indices, such as start of season, peak index or greenness value, and seasonal integrated greenness are used in many models and studies to describe phenology and to represent vegetation interactions with climate-based [5,10,16] and anthropogenic [17,18] factors
The spatial patterns observed in the difference from yearly average (2000–2006) Normalized Difference Vegetation Index (NDVI)

Summary

Introduction

Climate patterns from intra-seasonal to decadal and century scales directly influence the timing, magnitude (productivity), and spatial patterns of vegetation growth cycles, or phenology [1,2].Vegetation phenology can be detected by satellites and other remote sensing systems due to the unique seasonal and spectral reflectance and transmittance characteristics of canopy, plants and leaves [1,3].Compared to other land surface components (e.g., soils), red and NIR reflectance values for green vegetation are low and high respectively. Sensed vegetation phenological data have been used in global climate change studies, showing trends and responses such as earlier start of the growing season, later end of season, and higher seasonal productivity [9,10]. Remotely sensed vegetation phenology information is used to determine land cover classes [11,12] and vegetation dynamics in response to climate [13] and disturbances and stressors like fire [14] and drought [15]. Metrics created from spectral vegetation indices, such as start of season, peak index or greenness value, and seasonal integrated greenness are used in many models and studies to describe phenology and to represent vegetation interactions with climate-based [5,10,16] and anthropogenic [17,18] factors

Objectives

Results

Conclusion