scholarly journals How Does Scale Effect Influence Spring Vegetation Phenology Estimated from Satellite-Derived Vegetation Indexes?

2019 ◽  
Vol 11 (18) ◽  
pp. 2137 ◽  
Author(s):  
Liu ◽  
Cao ◽  
Shen ◽  
Chen ◽  
Wang ◽  
...  
Keyword(s):  
Scale Effect ◽  
Land Surface ◽  
Spatial Scales ◽  
Growth Speed ◽  
Vegetation Phenology ◽  
Spring Phenology ◽  
Vegetation Growth ◽  
Land Surface Parameter ◽  
Mathematical Analyses ◽  
Phenological Metrics

As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.

scholarly journals Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

2020 ◽  
Vol 12 (20) ◽  
pp. 3282
Author(s):  
Chi Hong Lim ◽  
Song Hie Jung ◽  
A Reum Kim ◽  
Nam Shin Kim ◽  
Chang Seok Lee
Keyword(s):  
South Korea ◽  
Air Temperature ◽  
Rural Areas ◽  
Land Surface ◽  
Urban Areas ◽  
Spatial Scales ◽  
Vegetation Phenology ◽  
Spring Phenology ◽  
Mongolian Oak ◽  
Start Dates

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change.

A new approach for improving representation of boreal forest phenology in land surface models

2021 ◽  
Author(s):  
Titta Majasalmi ◽  
Miina Rautiainen
Keyword(s):  
Land Surface ◽  
Spatial Scales ◽  
Growing Season ◽  
Vegetation Phenology ◽  
Threshold Values ◽  
Conifer Forest ◽  
Season Length ◽  
New Approach ◽  
Growing Season Length ◽  
Satellite Sensors

<p>In a boreal region, terrestrial vegetation carbon balance is controlled by vegetation phenology, which steers photosynthesis, respiration, and biomass turnover processes. In the absence of a full mechanistic understanding of environmental processes controlling vegetation phenology (i.e. senescence, dormancy, chilling), empirically-based models are often applied. These models are typically based on greenness proxies (obtained from satellite data) with fixed amplitude thresholds (e.g. of 15%, 50%, 90%) to determine timings of different phenological events. Yet, it is not known how well those percentiles correspond with the timings of events such as the green-up and the senescence. Especially in the boreal region, estimating timings of different phenological events across large spatial scales remains challenging due to the lack of sufficient ground validation data representative of both forest tree canopy and forest understory species compositions, which are both observed by a satellite sensor. From the land surface modeling perspective, there is a need to develop methods to improve the mapping of phenological events for prudent prediction of the land vegetation-atmosphere interactions under different future climates. In this study, we developed a new approach for calibrating boreal forest greenness amplitude thresholds which indicate timings of different phenological transitions in satellite data. The new approach to calibrate satellite-based greenness thresholds was demonstrated using boreal Finland as a case study area (60-70 N°). Using the approach, we computed satellite-based phenological events and compared them to ground reference data on temperature from a network of meteorological stations across Finland. We also investigated the effects of using different phenological events or ground reference temperature data on estimated growing season length. Results showed that while the standard greenness amplitude threshold values corresponded fairly well with the growing season start, the autumn phenology was not well captured by the standard greenness amplitude threshold values, which has direct impact on growing season length. Based on our data, boreal conifer forest senescence (default is 90%) corresponds with the timing of greenness amplitude of ~45%, while boreal conifer forest dormancy (default is 15%) corresponds with the timing of reaching greenness amplitude of ~0%. The approach allows flexible application across spatial scales (i.e. point or grid) and different satellite sensors, and may be combined with any land cover product, and it provides a meaningful linking between surface temperature data and seasonal reflectance measured by satellite sensors.</p>

scholarly journals Vegetation Phenology Influenced by Rapid Urbanization of The Yangtze Delta Region

2020 ◽  
Vol 12 (11) ◽  
pp. 1783 ◽  
Author(s):  
Haiyong Ding ◽  
Luming Xu ◽  
Andrew J. Elmore ◽  
Yuli Shi
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Land Surface ◽  
Urban Areas ◽  
Vegetation Phenology ◽  
Rapid Urbanization ◽  
Spring Phenology ◽  
Surface Temperatures ◽  
Land Surface Temperatures ◽  
Over Time

Impacts of urbanization and climate change on ecosystems are widely studied, but these drivers of change are often difficult to isolate from each other and interactions are complicated. Ecosystem responses to each of these drivers are perhaps most clearly seen in phenology changes due to global climate change (warming climate) and urbanization (heat island effect). The phenology of vegetation can influence many important ecological processes, including primary production, evapotranspiration, and plant fitness. Therefore, evaluating the interacting effects of urbanization and climate change on vegetation phenology has the potential to provide information about the long-term impact of global change. Using remotely sensed time series of vegetation on the Yangtze River Delta in China, this study evaluated the impacts of rapid urbanization and climate change on vegetation phenology along an urban to rural gradient over time. Phenology markers were extracted annually from an 18-year time series by fitting the asymmetric Gaussian function model. Thermal remote sensing acquired at daytime and nighttime was used to explore the relationship between land surface temperature and vegetation phenology. On average, the spring phenology marker was 9.6 days earlier and the autumn marker was 6.63 days later in urban areas compared with rural areas. The spring phenology of urban areas advanced and the autumn phenology delayed over time. Across space and time, warmer spring daytime and nighttime land surface temperatures were related to earlier spring, while autumn daytime and nighttime land surface temperatures were related to later autumn phenology. These results suggest that urbanization, through surface warming, compounds the effect of climate change on vegetation phenology.

scholarly journals A near-global, high resolution land surface parameter dataset for the variable infiltration capacity model

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Jacob R. Schaperow ◽  
Dongyue Li ◽  
Steven A. Margulis ◽  
Dennis P. Lettenmaier
Keyword(s):  
Land Surface ◽  
Global Scale ◽  
Radiation Budget ◽  
Soil Parameters ◽  
Variable Infiltration Capacity ◽  
Infiltration Capacity ◽  
Vic Model ◽  
Spatial Coverage ◽  
Land Surface Parameter ◽  
Vegetation Parameters

AbstractHydrologic models predict the spatial and temporal distribution of water and energy at the land surface. Currently, parameter availability limits global-scale hydrologic modelling to very coarse resolution, hindering researchers from resolving fine-scale variability. With the aim of addressing this problem, we present a set of globally consistent soil and vegetation parameters for the Variable Infiltration Capacity (VIC) model at 1/16° resolution (approximately 6 km at the equator), with spatial coverage from 60°S to 85°N. Soil parameters derived from interpolated soil profiles and vegetation parameters estimated from space-based MODIS measurements have been compiled into input files for both the Classic and Image drivers of the VIC model, version 5. Geographical subsetting codes are provided, as well. Our dataset provides all necessary land surface parameters to run the VIC model at regional to global scale. We evaluate VICGlobal’s ability to simulate the water balance in the Upper Colorado River basin and 12 smaller basins in the CONUS, and their ability to simulate the radiation budget at six SURFRAD stations in the CONUS.

scholarly journals A Review of Reconstructing Remotely Sensed Land Surface Temperature under Cloudy Conditions

2021 ◽  
Vol 13 (14) ◽  
pp. 2838
Author(s):  
Yaping Mo ◽  
Yongming Xu ◽  
Huijuan Chen ◽  
Shanyou Zhu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Scales ◽  
Remotely Sensed ◽  
Urban Heat Islands ◽  
Reconstruction Algorithms ◽  
Gap Filling ◽  
Practical Applications ◽  
Data Gaps

Land surface temperature (LST) is an important environmental parameter in climate change, urban heat islands, drought, public health, and other fields. Thermal infrared (TIR) remote sensing is the main method used to obtain LST information over large spatial scales. However, cloud cover results in many data gaps in remotely sensed LST datasets, greatly limiting their practical applications. Many studies have sought to fill these data gaps and reconstruct cloud-free LST datasets over the last few decades. This paper reviews the progress of LST reconstruction research. A bibliometric analysis is conducted to provide a brief overview of the papers published in this field. The existing reconstruction algorithms can be grouped into five categories: spatial gap-filling methods, temporal gap-filling methods, spatiotemporal gap-filling methods, multi-source fusion-based gap-filling methods, and surface energy balance-based gap-filling methods. The principles, advantages, and limitations of these methods are described and discussed. The applications of these methods are also outlined. In addition, the validation of filled LST values’ cloudy pixels is an important concern in LST reconstruction. The different validation methods applied for reconstructed LST datasets are also reviewed herein. Finally, prospects for future developments in LST reconstruction are provided.

scholarly journals Comparison of Satellite and Drone-Based Images at Two Spatial Scales to Evaluate Vegetation Regeneration after Post-Fire Treatments in a Mediterranean Forest

2021 ◽  
Vol 11 (12) ◽  
pp. 5423
Author(s):  
Jose Luis Martinez ◽  
Manuel Esteban Lucas-Borja ◽  
Pedro Antonio Plaza-Alvarez ◽  
Pietro Denisi ◽  
Miguel Angel Moreno ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Spatial Scales ◽  
Soil Surface ◽  
Plant Biodiversity ◽  
Mediterranean Forests ◽  
Experimental Conditions ◽  
Vegetation Growth ◽  
Surface Conditions ◽  
Vegetation Regeneration ◽  
Forest Managers

The evaluation of vegetation cover after post-fire treatments of burned lands is important for forest managers to restore soil quality and plant biodiversity in burned ecosystems. Unfortunately, this evaluation may be time consuming and expensive, requiring much fieldwork for surveys. The use of remote sensing, which makes these evaluation activities quicker and easier, have rarely been carried out in the Mediterranean forests, subjected to wildfire and post-fire stabilization techniques. To fill this gap, this study evaluates the feasibility of satellite (using LANDSAT8 images) and drone surveys to evaluate changes in vegetation cover and composition after wildfire and two hillslope stabilization treatments (log erosion barriers, LEBs, and contour-felled log debris, CFDs) in a forest of Central Eastern Spain. Surveys by drone were able to detect the variability of vegetation cover among burned and unburned areas through the Visible Atmospherically Resistant Index (VARI), but gave unrealistic results when the effectiveness of a post-fire treatment must be evaluated. LANDSAT8 images may be instead misleading to evaluate the changes in land cover after wildfire and post-fire treatments, due to the lack of correlation between VARI and vegetation cover. The spatial analysis has shown that: (i) the post-fire restoration strategy of landscape managers that have prioritized steeper slopes for treatments was successful; (ii) vegetation growth, at least in the experimental conditions, played a limited influence on soil surface conditions, since no significant increases in terrain roughness were detected in treated areas.

Legacy effects of spring phenology on vegetation growth under preseason meteorological drought in the Northern Hemisphere

2021 ◽  
Vol 310 ◽  
pp. 108630
Author(s):  
Zhaoqi Zeng ◽  
Wenxiang Wu ◽  
Quansheng Ge ◽  
Zhaolei Li ◽  
Xiaoyue Wang ◽  
...  
Keyword(s):  
Northern Hemisphere ◽  
Meteorological Drought ◽  
Legacy Effects ◽  
Spring Phenology ◽  
Vegetation Growth

scholarly journals Interactive coupling of regional atmosphere with biosphere in the new generation regional climate system model REMO-iMOVE

2014 ◽  
Vol 7 (3) ◽  
pp. 1093-1114 ◽  
Author(s):  
C. Wilhelm ◽  
D. Rechid ◽  
D. Jacob
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Regional Model ◽  
Horizontal Resolution ◽  
Soil Conditions ◽  
Data Sets ◽  
Vegetation Phenology ◽  
Land Surface Scheme ◽  
New Model ◽  
Surface Scheme

Abstract. The main objective of this study is the coupling of the regional climate model REMO with a new land surface scheme including dynamic vegetation phenology, and the evaluation of the new model version called REMO with interactive MOsaic-based VEgetation: REMO-iMOVE. First, we focus on the documentation of the technical aspects of the new model constituents and the coupling mechanism. The representation of vegetation in iMOVE is based on plant functional types (PFTs). Their geographical distribution is prescribed to the model which can be derived from different land surface data sets. Here, the PFT distribution is derived from the GLOBCOVER 2000 data set which is available on 1 km × 1 km horizontal resolution. Plant physiological processes like photosynthesis, respiration and transpiration are incorporated into the model. The vegetation modules are fully coupled to atmosphere and soil. In this way, plant physiological activity is directly driven by atmospheric and soil conditions at the model time step (two minutes to some seconds). In turn, the vegetation processes and properties influence the exchange of substances, energy and momentum between land and atmosphere. With the new coupled regional model system, dynamic feedbacks between vegetation, soil and atmosphere are represented at regional to local scale. In the evaluation part, we compare simulation results of REMO-iMOVE and of the reference version REMO2009 to multiple observation data sets of temperature, precipitation, latent heat flux, leaf area index and net primary production, in order to investigate the sensitivity of the regional model to the new land surface scheme and to evaluate the performance of both model versions. Simulations for the regional model domain Europe on a horizontal resolution of 0.44° had been carried out for the time period 1995–2005, forced with ECMWF ERA-Interim reanalyses data as lateral boundary conditions. REMO-iMOVE is able to simulate the European climate with the same quality as the parent model REMO2009. Differences in near-surface climate parameters can be restricted to some regions and are mainly related to the new representation of vegetation phenology. The seasonal and interannual variations in growth and senescence of vegetation are captured by the model. The net primary productivity lies in the range of observed values for most European regions. This study reveals the need for implementing vertical soil water dynamics in order to differentiate the access of plants to water due to different rooting depths. This gets especially important if the model will be used in dynamic vegetation studies.

scholarly journals The effect of climate type on timescales of drought propagation in an ensemble of global hydrological models

2018 ◽  
Vol 22 (9) ◽  
pp. 4649-4665 ◽  
Author(s):  
Anouk I. Gevaert ◽  
Ted I. E. Veldkamp ◽  
Philip J. Ward
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Spatial Scales ◽  
Standardized Precipitation Index ◽  
Meteorological Drought ◽  
Hydrological Drought ◽  
Propagation Time ◽  
Hydrological Models ◽  
Land Surface Models ◽  
Surface Models

Abstract. Drought is a natural hazard that occurs at many temporal and spatial scales and has severe environmental and socioeconomic impacts across the globe. The impacts of drought change as drought evolves from precipitation deficits to deficits in soil moisture or streamflow. Here, we quantified the time taken for drought to propagate from meteorological drought to soil moisture drought and from meteorological drought to hydrological drought. We did this by cross-correlating the Standardized Precipitation Index (SPI) against standardized indices (SIs) of soil moisture, runoff, and streamflow from an ensemble of global hydrological models (GHMs) forced by a consistent meteorological dataset. Drought propagation is strongly related to climate types, occurring at sub-seasonal timescales in tropical climates and at up to multi-annual timescales in continental and arid climates. Winter droughts are usually related to longer SPI accumulation periods than summer droughts, especially in continental and tropical savanna climates. The difference between the seasons is likely due to winter snow cover in the former and distinct wet and dry seasons in the latter. Model structure appears to play an important role in model variability, as drought propagation to soil moisture drought is slower in land surface models (LSMs) than in global hydrological models, but propagation to hydrological drought is faster in land surface models than in global hydrological models. The propagation time from SPI to hydrological drought in the models was evaluated against observed data at 127 in situ streamflow stations. On average, errors between observed and modeled drought propagation timescales are small and the model ensemble mean is preferred over the use of a single model. Nevertheless, there is ample opportunity for improvement as substantial differences in drought propagation are found at 10 % of the study sites. A better understanding and representation of drought propagation in models may help improve seasonal drought forecasting as well as constrain drought variability under future climate scenarios.

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