An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A.

Yang, Wunian; Yang, Limin; Merchant, James W.

doi:10.1080/014311697217819

Cited by 136 publications

(86 citation statements)

References 23 publications

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“…A very weak negative correlation was found at the end (late fall and winter) and the beginning of the growing season (part of the spring months). Previous studies have concluded that temporal variations of NDVI are closely related to precipitation and a strong linear relationship between NDVI and precipitation exits on regional [47][48][49][50][51][52] and global scales [53]. Wang et al [47] observed that NDVI curves and precipitation curves are parallel, displaying an increasing trend during warm months and a decreasing pattern in cold months.…”

Section: Climatic Influences On Ndvimentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

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Vegetation mapping based on niche theory has proven useful in understanding the rules governing species assembly at various spatial scales. Remote-sensing derived distribution maps depicting occurrences of target species are frequently based on biophysical and biochemical properties of species. However, environmental conditions, such as climatic variables, also affect spectral signals simultaneously. Further, climatic variables are the major drivers of species distribution at macroscales. Therefore, the objective of this study is to determine if species distribution can be modeled using an indirect link to climate and remote sensing data (MODIS NDVI time series). We used plant occurrence data in the US states of North Carolina and South Carolina and 19 climatic variables to generate floristic and climatic gradients using principal component analysis, then we further modeled the correlations between floristic gradients and NDVI using Partial Least Square regression. We found strong statistical relationship between species distribution and NDVI time series in a region where clear floristic and climatic gradients exist. If this precondition is given, the use of niche-based proxies may be suitable for predictive modeling of species distributions at regional scales. This indirect estimation of vegetation patterns may be a viable alternative to mapping approaches using biochemistry-driven spectral signature of species.

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Section: Climatic Influences On Ndvimentioning

confidence: 99%

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Feilhauer

Rocchini

2012

Remote Sensing

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“…Dealing with the remotesensing-climate relationship, the influence of climatic conditions on vegetation growth, and its impacts on NDVI, are often indirect. Specific interpretation of observed variations in NDVI is not well documented in terms of interactions among vegetation, climate and soil (Yang et al, 1997). Last but not least, the mechanism of the phenology-remote-sensing relationship is relatively clear, because NDVI derived from satellite measurements can be conceptually described as a function of visible and near-infrared reflectance from the plant canopy, soil, and atmosphere, in which plants play an essential role.…”

Section: Introductionmentioning

confidence: 99%

“…In the latter case, several authors have developed procedures to determine satellite sensor-derived growing season using normalized difference vegetation index (NDVI) data for a single pixel (Reed et al, 1994;White et al, 1997) or for large spatial scales (Myneni et al, 1997;Zhou et al, 2001). Other authors have used the onset and end of greenness and time-integrated (TI) NDVI as surrogates for growing season and primary production, and established statistical relationships between NDVI or TI NDVI and climate variables for gauging and monitoring vegetation dynamics (Yang W. et al, 1997;Yang L. et al, 1998;Fu and Wen, 1999). However, because NDVI metrics and thresholds may not directly correspond to conventional, ground-based phenological events, but rather provide indicators of vegetation dynamics (Justice et al, 1986;Lloyd, 1990), a detailed comparison of these satellite measures with ground-based phenological events is needed (Reed et al, 1994;Schwartz and Reed, 1999).…”

Section: Introductionmentioning

confidence: 99%

Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China

Chen

Pan

2002

Intl Journal of Climatology

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Phenological, meteorological, and time-integrated normalized difference vegetation index (TI NDVI) data from 1982 to 1993, at three sample stations, were used to investigate the response of the growing season of local plant communities to climate change and the linkage of satellite sensor-derived greenness to the surface growing season. Results suggest that mean air temperature and growing degree days (GDDs) above 5°C during late winter and spring, and precipitation in autumn, are the most important controls on the beginning and end dates of the growing season (BGS and EGS). In contrast, annual mean air temperature, annual GDD totals, mean air temperature during late winter and spring, and growing season TI NDVI are the most important controls on length of the growing season (LGS).Using correlation and regression analysis, simple and multiple linear regression models were developed for individual and all stations. Since the standard error of the estimates (SE) of the BGS models for all stations are smaller than those of the EGS models, estimates of the beginning date of the growing season are probably more reliable than estimates of the end date. On average, if the mean air temperature in late winter and spring increases by 1°C, then the beginning date of the growing season will advance 5-6 days, and the end date will be 5 days later. Moreover, if autumn precipitation increases 100 mm, then the end date will advance 6-8 days. In terms of the LGS models, mean air temperature in late winter and spring, annual mean air temperature, and annual GDD totals have significant positive correlations with growing season duration, whereas growing season TI NDVI has a negative relationship. Comparing the SE of different LGS models, those developed with each of the three temperature variables fit the observed growing season duration data much better than those using growing season TI NDVI.

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“…It is important to note that the phenological events in this study are determined by satellite data only. the spring events were increasingly occurring earlier in the recent decades [2,4,8,13,16,19,23] . This study estimated that the onset date of the green up of the selected zone of Iran advanced on average by 0.63 days year −1 from 1982-1999.…”

Section: Discussionmentioning

confidence: 99%

Monitoring Growing Season Length of Deciduous Broad Leaf Forest Derived From Satellite Data in Iran

Kafaki¹,

Mataji²,

Hashemi³

2009

American J. of Environmental Sciences

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Problem statement: Leaf phenology describes the seasonal cycle of leaf functioning and is essential for understanding the interactions between the biosphere, the climate and biogeochemical cycles. This study aimed to quantify changes in plant phenology of deciduous broadleaf forests between the years 1982-1999 and investigate the relationships between the onset dates of phenology and climatic factors. Approach: We studied the climate changes effected on the growing season duration in vegetation of Iran, using the AVHRR/NDVI biweekly time-series data collected from 1982-1999 and concurrent mean temperature and precipitation data. The first estimated fastest changes of NDVI corresponded to the vegetation green-up and dormancy from the seasonal cycle of NDVI during 1982-1999. The onset dates of vegetation green-up and dormancy were determined based on the estimated rates and the NDVI seasonal cycles. Results: The results showed that over the study period, the growing season duration has lengthened by 0.94 days year

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An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A.

Cited by 136 publications

References 23 publications

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Modeling Species Distribution Using Niche-Based Proxies Derived from Composite Bioclimatic Variables and MODIS NDVI

Relationships among phenological growing season, time‐integrated normalized difference vegetation index and climate forcing in the temperate region of eastern China

Monitoring Growing Season Length of Deciduous Broad Leaf Forest Derived From Satellite Data in Iran

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