The NDVI3g time series is an improved 8-km normalized difference vegetation index (NDVI) data set produced from Advanced Very High Resolution Radiometer (AVHRR) instruments that extends from 1981 to the present. The AVHRR instruments have flown or are flying on fourteen polar-orbiting meteorological satellites operated by the National Oceanic and Atmospheric Administration (NOAA) and are currently flying on two European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) polar-orbiting meteorological satellites, MetOp-A and MetOp-B. This long AVHRR record is comprised of data from two different sensors: the AVHRR/2 instrument that spans July 1981 to November 2000 and the AVHRR/3 instrument that continues these measurements from November 2000 to the present. The main difficulty in processing AVHRR NDVI data is to properly deal with limitations of the AVHRR instruments. Complicating among-instrument AVHRR inter-calibration of channels one and two is the dual gain introduced in late 2000 on the AVHRR/3 instruments for both these channels. We have processed NDVI data derived from the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS) from 1997 to 2010 to overcome among-instrument AVHRR calibration difficulties. We use Bayesian methods with high quality well-calibrated SeaWiFS NDVI data for deriving AVHRR NDVI calibration parameters. Evaluation of the uncertainties of our resulting NDVI values gives an error of ± 0.005 NDVI units for our 1981 to present data set that is independent of time within our AVHRR NDVI continuum and has resulted in a non-stationary climate data set.

A Non-Stationary 1981–2015 AVHRR NDVI 3g Time Series

Sustainable forest management (SFM) practices are required to conserve forest ecosystem services. International agreements and national management plans have been developed to avoid deforestation and increase forest sustainability. Due to the high rate of deforestation and its increasing trend in the past, the most important conservation policy (the Forest Rest Plan) in Hyrcanian forests (HFs) has been in place since 2016 to conserve the HFs, which are a UNESCO World Heritage site. The aim of the Forest Rest Plan (FRP) is to increase the crown cover, resilience, and production of HFs. Here, we apply a robust methodology based on Landsat-8-derived spectral-temporal features, spatial analysis, stepwise multilevel logistic regression, and scientific forest management knowledge to determine how the FRP, climate change, anthropogenic activities, and the environmental conditions influence deforestation and forest recovery. We identified and evaluated 18 explanatory variables, including socioeconomic, climatic, and physiographic factors, to determine the key drivers of forest cover change. Our findings revealed complex interactions between distinct land features that occur at multiple spatial and temporal scales in HFs as connected human–natural systems. Further, we found a decreasing trend in deforestation and a growing trend in forest recovery resulting from implementation of the FRP. Additionally, our stepwise multilevel logistic regression identified impacts of climate change, human pressure, and some physiographic factors, such as forest density, on deforestation and forest recovery. We conclude that while some positive impacts can already be identified, additional adaptive, preventative, and restorative strategies must be developed to reach all FRP objectives. 

How do conservation policies, climate and socioeconomic changes impact Hyrcanian forests of northern Iran?

The Caspian Sea and the Hyrcanian forest rely on each other to maintain balanced hydrological and climatological conditions and preserve the local wildlife and natural resources. Climate variation impacts this balance and the ecological status of the forest. In this study, thermal and phenological seasonality in three Hyrcanian protected areas is evaluated over a period of 33 years (1987–2019). The results suggest extensions of 3–18 days per decade in growing seasons. This could be mitigated by applying policies such as the expansion of protected areas, stringent endangered species conservation, updated silviculture practices, social awareness and cross-sectoral management. 

Satellite-based phenology analysis of the Hyrcanian forest in the Alborz mountains and Caspian Sea shores, Iran

Shifts in the timing of spring phenology are a central feature of global change research. Long-term observations of plant phenology have been used to track vegetation responses to climate variability but are often limited to particular species and locations and may not represent synoptic patterns. Satellite remote sensing is instead used for continental to global monitoring. Although numerous methods exist to extract phenological timing, in particular start-of-spring (SOS), from time series of reflectance data, a comprehensive intercomparison and interpretation of SOS methods has not been conducted. Here, we assess 10 SOS methods for North America between 1982 and 2006. The techniques include consistent inputs from the 8km Global Inventory Modeling and Mapping Studies Advanced Very High Resolution Radiometer NDVIg dataset, independent data for snow cover, soil thaw, lake ice dynamics, spring streamflow timing, over 16000 individual measurements of ground-based phenology, and two temperature-driven models of spring phenology. Compared with an ensemble of the 10 SOS methods, we found that individual methods differed in average day-of-year estimates by ! 60 days and in standard deviation by ! 20 days. The ability of the satellite methods to retrieve SOS estimates was highest in northern latitudes and lowest in arid, tropical, and Mediterranean ecoregions. The ordinal rank of SOS methods varied geographically, as did the relationships between SOS estimates and the cryospheric/hydrologic metrics. Compared with ground observations, SOS estimates were more related to the first leaf and first flowers expanding phenological stages. We found no evidence for time trends in spring arrival from ground- or model-based data; using an ensemble estimate from two methods that were more closely related to ground observations than other methods, SOS

Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006 M I C H A E L A. W H I T E*, K I R S T E N M. DE BEURS w , K A M E L D I D A Nz, D AV I D W. I N O U Y E § ,

The phenological state of vegetation significantly affects exchanges of heat, mass, and momentum between the Earth's surface and the atmosphere. Although current patterns can be estimated from satellites, we lack the ability to predict future trends in response to climate change. We searched the literature for a common set of variables that might be combined into an index to quantify the greenness of vegetation throughout the year. We selected as variables: daylength (photoperiod), evaporative demand (vapor pressure deficit), and suboptimal (minimum) temperatures. For each variable we set threshold limits, within which the relative phenological performance of the vegetation was assumed to vary from inactive (0) to unconstrained (1). A combined Growing Season Index (GSI) was derived as the product of the three indices. Ten-day mean GSI values for nine widely dispersed ecosystems showed good agreement (r>0.8) with the satellite-derived Normalized Difference Vegetation Index (NDVI). We also tested the model at a temperate deciduous forest by comparing model estimates with average field observations of leaf flush and leaf coloration. The mean absolute error of predictions at this site was 3 days for average leaf flush dates and 2 days for leaf coloration dates. Finally, we used this model to produce a global map that distinguishes major differences in regional phenological controls. The model appears sufficiently robust to reconstruct historical variation as well as to forecast future phenological responses to changing climatic conditions.

A generalized, bioclimatic index to predict foliar phenology in response to climate

 Climate change is one of the most important environmental challenges in the world and forest as a dynamic phenomenon is influenced by environmental changes The Hyrcanian forests is a unique natural heritage of global importance and we need monitoring this region The objective of this study was to detect start and end of season trends in Hyrcanian forests of Iran based on biweekly GIMMS (Global Inventory Modeling and Mapping Studies) NDVI3g in the period 1981-2012 In order to find response of vegetation activity to local temperature variations, we used air temperature provided from IR Iran Meteorological Organization (IRIMO) At the first step in order to remove the existing gap from the original time series, the iterative Interpolation for Data Reconstruction (IDR) model was applied to GIMMS and temperature dataset Then we applied significant Mann Kendall test to determine significant trend for each pixel of GIMMS and temperature datasets over the Hyrcanian forests The results demonstrated that start and end of season (SOS & EOS respectively) derived from GIMMS3g NDVI time series increased by -016 and +041 days per year respectively The trends derived from temperature time series indicated increasing trend in the whole of this region Results of this study showed that global warming and its effect on growth and photosynthetic activity can increased the vegetation activity in our study area Otherwise extension of the growing season, including an earlier start of the growing season, later autumn and higher rate of production increased NDVI value during the study period

/pdf/shifts-of-start-and-end-of-season-in-response-to-air-5bsd0v4lwl.pdf

Shifts of start and end of season in response to air temperature variation based on gimms dataset in hyrcanian forests

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Shifts of start and end of season in response to air temperature variation based on gimms dataset in hyrcanian forests