Monitoring Vegetation Phenological Cycles in Two Different Semi-Arid Environmental Settings Using a Ground-Based NDVI System: A Potential Approach to Improve Satellite Data Interpretation

In semi-arid environmental settings with sparse canopy covers, obtaining remotely sensed information on soil and vegetative growth characteristics at finer spatial and temporal scales than most satellite platforms is crucial for validating and interpreting satellite data sets. In this study, we used a ground-based NDVI system to provide continuous time series analysis of individual shrub species and soil surface characteristics in two different semi-arid environmental settings located in the Great Basin (NV, USA). The NDVI system was a dual channel SKR-1800 radiometer that simultaneously measured incident solar radiation and upward reflectance in two broadband red and near-infrared channels comparable to Landsat-5 TM band 3 and band 4, respectively. The two study sites identified as Spring Valley 1 site (SV1) and Snake Valley 1 site (SNK1) were chosen for having different species composition, soil texture and percent canopy cover. NDVI time-series of greasewood (Sarcobatus vermiculatus) from the SV1 site allowed for clear distinction between the main phenological stages of the entire growing season during the period from January to November, 2007. NDVI time series values were significantly different between sagebrush (Artemisia tridentata) and rabbitbrush (Chrysothamnus viscidiflorus) at SV1 as well as between the two bare soil types at the two sites. Greasewood NDVI from the SNK1 site produced significant correlations with chlorophyll index (r = 0.97), leaf area index (r = 0.98) and leaf xylem water potential (r = 0.93). Whereas greasewood NDVI from the SV1 site produced lower correlations (r = 0.89, r = 0.73), or non significant correlations (r = 0.32) with the same parameters, respectively. Total percent cover was estimated at 17.5% for SV1 and at 63% for SNK1. Results from this study indicated the potential capabilities of using this ground-based NDVI system to extract spatial and temporal details of soil and vegetation optical properties not possible with satellite derived NDVI.

[1]  Pamela L. Nagler,et al.  Evapotranspiration on western U.S. rivers estimated using the Enhanced Vegetation Index from MODIS and data from eddy covariance and Bowen ratio flux towers , 2005 .

[2]  C. Field,et al.  A reanalysis using improved leaf models and a new canopy integration scheme , 1992 .

[3]  Philip N. Slater,et al.  Mapping surface energy balance components by combining landsat thematic mapper and ground-based meteorological data , 1989 .

[4]  Pamela L. Nagler,et al.  Integrating Remote Sensing and Ground Methods to Estimate Evapotranspiration , 2007 .

[5]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .

[6]  Rosa Lasaponara,et al.  Quantifying intra-annual persistent behaviour in SPOT-VEGETATION NDVI data for Mediterranean ecosystems of southern Italy , 2006 .

[7]  John A. Gamon,et al.  A mobile tram system for systematic sampling of ecosystem optical properties , 2006 .

[8]  R. Isaac,et al.  Determination of total nitrogen in plant tissue, using a block digestor , 1976 .

[9]  A. Huete,et al.  Leaf area index and normalized difference vegetation index as predictors of canopy characteristics and light interception by riparian species on the Lower Colorado River , 2004 .

[10]  P. M. Seevers,et al.  Evapotranspiration estimation using a normalized difference vegetation index transformation of satellite data , 1994 .

[11]  S. A. Samson,et al.  Two indices to characterize temporal patterns in the spectral response of vegetation , 1993 .

[12]  John F. Mustard,et al.  Extracting Phenological Signals From Multiyear AVHRR NDVI Time Series: Framework for Applying High-Order Annual Splines With Roughness Damping , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[14]  D. Watson Comparative Physiological Studies on the Growth of Field Crops: I. Variation in Net Assimilation Rate and Leaf Area between Species and Varieties, and within and between Years , 1947 .

[15]  Qinghan Xiao,et al.  Land cover classification with AVHRR multichannel composites in northern environments , 1996 .

[16]  John A. Gamon,et al.  Monitoring drought effects on vegetation water content and fluxes in chaparral with the 970 nm water band index , 2006 .

[17]  J. Monteith,et al.  Principles of Environmental Physics , 2014 .

[18]  T. Carlson,et al.  On the relation between NDVI, fractional vegetation cover, and leaf area index , 1997 .

[19]  N. Coops,et al.  Instrumentation and approach for unattended year round tower based measurements of spectral reflectance , 2007 .

[20]  Alfredo Huete,et al.  Assessing the seasonal dynamics of the Brazilian Cerrado vegetation through the use of spectral vegetation indices , 2004 .

[21]  J. Peñuelas,et al.  Estimation of plant water concentration by the reflectance Water Index WI (R900/R970) , 1997 .

[22]  Nicolas R. Dalezios,et al.  Basin-wide actual evapotranspiration estimation using NOAA/AVHRR satellite data , 2003 .

[23]  D. Riaño,et al.  Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating , 2004 .

[24]  P. Sellers Canopy reflectance, photosynthesis and transpiration , 1985 .

[25]  R. L. Morris,et al.  Assessing Canopy Spectral Reflectance of Hybrid Bermudagrass under Various Combinations of Nitrogen and Water Treatments , 2007 .

[26]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[27]  M. Schildhauer,et al.  Spectral Network (SpecNet)—What is it and why do we need it? , 2006 .

[28]  A. Karnieli,et al.  Remote sensing of the seasonal variability of vegetation in a semi-arid environment , 2000 .

[29]  Karl Fred Huemmrich,et al.  High temporal resolution NDVI phenology from micrometeorological radiation sensors , 1999 .

[30]  Alfredo Huete,et al.  A multi-scale analysis of dynamic optical signals in a Southern California chaparral ecosystem: A comparison of field, AVIRIS and MODIS data , 2004 .

[31]  William J. Massman,et al.  Handbook of micrometeorology : a guide for surface flux measurement and analysis , 2004 .

[32]  Jozsef Szilagyi,et al.  Vegetation Indices to Aid Areal Evapotranspiration Estimations , 2002 .

[33]  Clifford N. Dahm,et al.  Long-term vegetation monitoring with NDVI in a diverse semi-arid setting, central New Mexico, USA , 2004 .

[34]  Pamela L. Nagler,et al.  Predicting riparian evapotranspiration from MODIS vegetation indices and meteorological data , 2005 .

[35]  S. Goward,et al.  Visible-near infrared spectral reflectance of landscape components in western Oregon , 1994 .

[36]  Michael H. Young,et al.  Evapotranspiration of mixed shrub communities in phreatophytic zones of the Great Basin region of Nevada (USA) , 2011 .

[37]  Matthew F. McCabe,et al.  Scale influences on the remote estimation of evapotranspiration using multiple satellite sensors , 2006 .