Variability of green-up duration of deciduous broadleaf forests in Central Europe during 2000-2019 based on MODIS NDVI

2020 ◽  
Author(s):  
Anikó Kern ◽  
Hrvoje Marjanović ◽  
Zoltán Barcza
Keyword(s):  
Land Cover ◽  
Central Europe ◽  
Vegetation Index ◽  
Meteorological Conditions ◽  
Extreme Weather Events ◽  
Canopy Conductance ◽  
Linear Regression Models ◽  
Leaf Unfolding ◽  
Modis Ndvi ◽  
Vegetation Activity

<p>Spring leaf unfolding is a spectacular recurring event at the mid- and high latitudes that is associated with deciduous vegetation. Several lines of evidence indicate that the timing of spring green-up (i.e. the start of the season, SOS) changed in the past decades resulting in an earlier leaf unfolding - a phenomenon which is considered to be a major indicator of the effects of global warming. Contrary to the timing of the SOS, considerably less attention was paid to studying the dynamics of vegetation green-up, characterized by the leaf unfolding speed or the duration of spring green-up. The importance of studying the spring green-up dynamics lies in the fact that the duration of leaf development and timing of the onset of growth jointly determine the annual cycle of vegetation activity including carbon and energy balance, canopy conductance and evapotranspiration.</p><p>The aim of our research was to characterize the dynamics of leaf unfolding of deciduous broadleaf forests in the wider Carpathian Basin, located in Central Europe, using satellite remote sensing. The study was based on the Normalized Difference Vegetation Index (NDVI) time-series derived from the MOD09A1 official MODIS products during 2000–2019, the IGBP land cover classification dataset of the MCD12Q1 products, the CORINE 2012 (CLC2012) land cover dataset, the SRTM elevation dataset, and the FORESEE meteorological database. Our results clearly show that there is considerable interannual variability in the green-up duration of the deciduous broadleaf forest during 2000–2019. The last three years had, on average, the shortest (2018) and the two longest (2017 and 2019) recorded green-up durations in the region. Observed variability was partially attributed to the meteorological conditions, namely the extreme weather events occurring during the spring. We demonstrate that the meteorological conditions during the green-up period have a strong effect on the duration. The relationship between the SOS and the green-up duration reveals that the SOS also played an important role as a driver. Our results also reveal considerable elevation dependency both in the green-up duration and also in its correlation with SOS. Multiple linear regression models based on the SOS and the meteorological variables were also created to explain and predict the green-up duration.</p>

scholarly journals Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

2010 ◽  
Vol 14 (10) ◽  
pp. 2073-2084 ◽  
Author(s):  
F. Zabel ◽  
T. B. Hank ◽  
W. Mauser
Keyword(s):  
Land Cover ◽  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Arable Land ◽  
Area Index ◽  
Temporal Behaviour ◽  
Danube Catchment ◽  
Crop Types ◽  
The Impact

Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist but generally, they do not resolve arable land into different crop types. However, arable land comprises a huge variety of different crops with characteristic phenological behaviour, demonstrated in this paper with Leaf Area Index (LAI) measurements exemplarily for maize and winter wheat. This affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for central Europe based on CORINE Land Cover (CLC) 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for central Europe using multiseasonal MERIS Normalized Difference Vegetation Index (NDVI) data. The satellite data were used for the separation of spring and summer crops due to their different phenological behaviour. Subsequently, the generated phenological classes were subdivided following statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types. The impact of the improved land use/cover map on evapotranspiration was modelled exemplarily for the Upper Danube catchment with the hydrological model PROMET. Simulations based on the newly developed land cover approach showed a more detailed evapotranspiration pattern compared to model results using the traditional CLC map, which is ignorant of most arable subdivisions. Due to the improved temporal behaviour and spatial allocation of evapotranspiration processes in the new land cover approach, the simulated water balance more closely matches the measured gauge.

scholarly journals Vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019

2021 ◽  
Vol 12 (4) ◽  
pp. 1015-1035
Author(s):  
Ana Bastos ◽  
René Orth ◽  
Markus Reichstein ◽  
Philippe Ciais ◽  
Nicolas Viovy ◽  
...  
Keyword(s):  
Central Europe ◽  
Vegetation Index ◽  
Legacy Effects ◽  
Compound Event ◽  
Vegetation Activity ◽  
Predicted Values ◽  
The Stability ◽  
Drought And Heat Stress ◽  
Process Based Models

Abstract. In 2018 and 2019, central Europe was affected by two consecutive extreme dry and hot summers (DH18 and DH19). The DH18 event had severe impacts on ecosystems and likely affected vegetation activity in the subsequent year, for example through depletion of carbon reserves or damage from drought. Such legacies from drought and heat stress can further increase vegetation susceptibility to additional hazards. Temporally compound extremes such as DH18 and DH19 can, therefore, result in an amplification of impacts due to preconditioning effects of past disturbance legacies. Here, we evaluate how these two consecutive extreme summers impacted ecosystems in central Europe and how the vegetation responses to the first compound event (DH18) modulated the impacts of the second (DH19). To quantify changes in vegetation vulnerability to each compound event, we first train a set of statistical models for the period 2001–2017, which are then used to predict the impacts of DH18 and DH19 on enhanced vegetation index (EVI) anomalies from MODIS. These estimates correspond to expected EVI anomalies in DH18 and DH19 based on past sensitivity to climate. Large departures from the predicted values can indicate changes in vulnerability to dry and hot conditions and be used to identify modulating effects by vegetation activity and composition or other environmental factors on observed impacts. We find two regions in which the impacts of the two compound dry and hot (DH) events were significantly stronger than those expected based on previous climate–vegetation relationships. One region, largely dominated by grasslands and crops, showed much stronger impacts than expected in both DH events due to an amplification of their sensitivity to heat and drought, possibly linked to changing background CO2 and temperature conditions. A second region, dominated by forests and grasslands, showed browning from DH18 to DH19, even though dry and hot conditions were partly alleviated in 2019. This browning trajectory was mainly explained by the preconditioning role of DH18 on the impacts of DH19 due to interannual legacy effects and possibly by increased susceptibility to biotic disturbances, which are also promoted by warm conditions. Dry and hot summers are expected to become more frequent in the coming decades, posing a major threat to the stability of European forests. We show that state-of-the-art process-based models could not represent the decline in response to DH19 because they missed the interannual legacy effects from DH18 impacts. These gaps may result in an overestimation of the resilience and stability of temperate ecosystems in future model projections.

scholarly journals Integrating Surface-Based Temperature and Vegetation Abundance Estimates into Land Cover Classifications for Conservation Efforts in Savanna Landscapes

Sensors ◽  
2019 ◽  
Vol 19 (16) ◽  
pp. 3456 ◽  
Author(s):  
Herrero ◽  
Southworth ◽  
Bunting ◽  
Kohlhaas ◽  
Child
Keyword(s):  
Land Cover ◽  
Surface Temperature ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Support Vector ◽  
Wet Season ◽  
Rule Based ◽  
Modis Ndvi ◽  
In Situ Data ◽  
Over Time

Southern African savannas are an important dryland ecosystem, as they account for up to 54% of the landscape, support a rich variety of biodiversity, and are areas of key landscape change. This paper aims to address the challenges of studying this highly gradient landscape with a grass–shrub–tree continuum. This study takes place in South Luangwa National Park (SLNP) in eastern Zambia. Discretely classifying land cover in savannas is notoriously difficult because vegetation species and structural groups may be very similar, giving off nearly indistinguishable spectral signatures. A support vector machine classification was tested and it produced an accuracy of only 34.48%. Therefore, we took a novel continuous approach in evaluating this change by coupling in situ data with Landsat-level normalized difference vegetation index data (NDVI, as a proxy for vegetation abundance) and blackbody surface temperature (BBST) data into a rule-based classification for November 2015 (wet season) that was 79.31% accurate. The resultant rule-based classification was used to extract mean Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI values by season over time from 2000 to 2016. This showed a distinct separation between each of the classes consistently over time, with woodland having the highest NDVI, followed by shrubland and then grassland, but an overall decrease in NDVI over time in all three classes. These changes may be due to a combination of precipitation, herbivory, fire, and humans. This study highlights the usefulness of a continuous time-series-based approach, which specifically integrates surface temperature and vegetation abundance-based NDVI data into a study of land cover and vegetation health for savanna landscapes, which will be useful for park managers and conservationists globally.

scholarly journals Improving arable land heterogeneity information in available land cover products for land surface modelling using MERIS NDVI data

2010 ◽  
Vol 7 (4) ◽  
pp. 4145-4175
Author(s):  
F. Zabel ◽  
T. B. Hank ◽  
W. Mauser
Keyword(s):  
Land Cover ◽  
Central Europe ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Arable Land ◽  
Danube Catchment ◽  
Land Cover Map ◽  
Crop Types ◽  
Cover Class

Abstract. Regionalization of physical land surface models requires the supply of detailed land cover information. Numerous global and regional land cover maps already exist, but generally they do not resolve arable land into different crop types. However, the characteristic phenological behaviour of different crops affects the mass and energy fluxes on the land surface and thus its hydrology. The objective of this study is the generation of a land cover map for Central Europe based on CORINE Land Cover 2000, merged with CORINE Switzerland, but distinguishing different crop types. Accordingly, an approach was developed, subdividing the land cover class arable land into the regionally most relevant subclasses for Central Europe using statistical data from EUROSTAT. This database was analysed concerning the acreage of different crop types, taking a multiseasonal series of MERIS Normalized Difference Vegetation Index (NDVI) into account. The satellite data were used for the separation of spring and summer crops. The hydrological impact of the improved land cover map was modelled exemplarily for the Upper Danube catchment.

scholarly journals Increased vulnerability of European ecosystems to two compound dry and hot summers in 2018 and 2019

2021 ◽  
Author(s):  
Ana Bastos ◽  
René Orth ◽  
Markus Reichstein ◽  
Philippe Ciais ◽  
Nicolas Viovy ◽  
...  
Keyword(s):  
Central Europe ◽  
Vegetation Index ◽  
Legacy Effects ◽  
Vegetation Responses ◽  
Compound Event ◽  
Vegetation Activity ◽  
The Stability ◽  
Drought And Heat Stress ◽  
Process Based Models

Abstract. In 2018 and 2019, central Europe was stricken by two consecutive extreme dry and hot summers (DH2018 and DH2019). The DH2018 had severe impacts on ecosystems and likely affected vegetation activity in the subsequent year, for example though depletion of carbon reserves or damage from drought. Such legacies from drought and heat stress can further increase vegetation susceptibility to additional hazards. Temporally compound extremes such as DH2018 and DH2019 can, therefore, result in an amplification of impacts by preconditioning effects of past disturbance legacies.Here, we evaluate how these two consecutive extreme summers impacted ecosystems in central Europe and how the vegetation responses to the first compound event (DH2018) modulated the impacts of the second (DH2019). To quantify the modulating role of vegetation responses to the impacts of each compound event, we first train a set of statistical models for the period 2001–2017 to predict the impacts of DH2018 and DH2019 on Enhanced Vegetation Index (EVI) anomalies from MODIS. These estimates can be seen as the expected EVI anomalies, had the impacts of DH2018 and DH2019 been consistent with past sensitivity to climate. These can then be used to identify modulating effects by vegetation activity and composition or other environmental factors such as elevated CO2 or warming trends.We find two regions in which the impacts of the two DH events were significantly stronger than those expected based on previous climate–vegetation relationships. One region, largely dominated by grasslands and crops, showed much stronger impacts than expected in both DH events due to an amplification of their sensitivity to heat and drought, possibly linked to changing background CO2 and temperature conditions. A second region, dominated by forests, showed browning from DH2018 to DH2019, even though dry and hot conditions were partly alleviated in 2019. This browning trajectory was mainly explained by the preconditioning role of DH2018 to the observed response to DH2019 through legacy effects, and possibly by increased susceptibility to biotic disturbances, which are also promoted by warm conditions.Dry and hot summers are expected to become more frequent in the coming decades posing a major threat to the stability of European forests. We show that state-of-the-art process based models miss these legacy effects. These gaps may result in an overestimation of the resilience and stability of temperate ecosystems in future model projections.

Analysis of Land Use Land Cover in Annapurna Conservation Area in Gandaki Province, Nepal using Vegetation Index and Land Use Land Cover Datasets

2019 ◽  
Vol 2 (2) ◽  
pp. 87-99
Author(s):  
Shiva Pokhrel ◽  
Chungla Sherpa
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Index ◽  
National Level ◽  
Climate Mitigation ◽  
Land Use Land Cover ◽  
Conservation Area ◽  
Barren Land ◽  
Wide Range ◽  
Annapurna Conservation Area

Conservation areas are originally well-known for protecting landscape features and wildlife. They are playing key role in conserving and providing a wide range of ecosystem services, social, economic and cultural benefits as well as vital places for climate mitigation and adaptation. We have analyzed decadal changes in land cover and status of vegetation cover in the conservation area using both national level available data on land use land cover (LULC) changes (1990-2010) and normalized difference vegetation index (NDVI) (2010-2018) in Annapurna conservation area. LULC showed the barren land as the most dominant land cover types in all three different time series 1990, 2000 and 2010 with followed by snow cover, grassland, forest, agriculture and water body. The highest NDVI values were observed at Southern, Southwestern and Southeastern part of conservation area consisting of forest area, shrub land and grassland while toward low to negative in the upper middle to the Northern part of the conservation area.

scholarly journals Start of the Green Season and Normalized Difference Vegetation Index in Alaska’s Arctic National Parks

2021 ◽  
Vol 13 (13) ◽  
pp. 2554
Author(s):  
David K. Swanson
Keyword(s):  
National Parks ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Free Surfaces ◽  
Modis Ndvi ◽  
Curve Fit ◽  
Winter Snow ◽  
Systematic Sample ◽  
Low Levels ◽  
Northern Alaska

Daily Normalized Difference Vegetation Index (NDVI) values from the MODIS Aqua and Terra satellites were compared with on-the-ground camera observations at five locations in northern Alaska. Over half of the spring rise in NDVI was due to the transition from the snow-covered landscape to the snow-free surface prior to the deciduous leaf-out. In the fall after the green season, NDVI fluctuated between an intermediate level representing senesced vegetation and lower values representing clouds and intermittent snow, and then dropped to constant low levels after establishment of the permanent winter snow cover. The NDVI value of snow-free surfaces after fall leaf senescence was estimated from multi-year data using a 90th percentile smoothing spline curve fit to a plot of daily NDVI values vs. ordinal date. This curve typically showed a flat region of intermediate NDVI values in the fall that represent cloud- and snow-free days with senesced vegetation. This “fall plateau” was readily identified in a large systematic sample of MODIS NDVI values across the study area, in typical tundra, shrub, and boreal forest environments. The NDVI level of the fall plateau can be extrapolated to the spring rising leg of the annual NDVI curve to approximate the true start of green season.

scholarly journals Geospatial analysis of wetlands based on land use/land cover dynamics using remote sensing and GIS in Sindh, Pakistan

2021 ◽  
Vol 104 (2) ◽  
pp. 003685042110261
Author(s):  
Hamza Islam ◽  
Habibuulah Abbasi ◽  
Ahmed Karam ◽  
Ali Hassan Chughtai ◽  
Mansoor Ahmed Jiskani
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Vegetation Cover ◽  
Vegetation Index ◽  
Kappa Statistics ◽  
Natural Ecosystem ◽  
Land Use Land Cover ◽  
Dry Land ◽  
Lulc Changes ◽  
Manchar Lake

In this study, the Land Use/Land Cover (LULC) change has been observed in wetlands comprises of Manchar Lake, Keenjhar Lake, and Chotiari Reservoir in Pakistan over the last four decades from 1972 to 2020. Each wetland has been categorized into four LULC classes; water, natural vegetation, agriculture land, and dry land. Multitemporal Landsat satellite data including; Multi-Spectral Scanner (MSS), Thematic Mapper (TM), and Operational Land Imager (OLI) images were used for LULC changes evaluation. The Supervised Maximum-likelihood classifier method is used to acquire satellite imagery for detecting the LULC changes during the whole study period. Soil adjusted vegetation index technique (SAVI) was also used to reduce the effects of soil brightness values for estimating the actual vegetation cover of each study site. Results have shown the significant impact of human activities on freshwater resources by changing the natural ecosystem of wetlands. Change detection analysis showed that the impacts on the land cover affect the landscape of the study area by about 40% from 1972 to 2020. The vegetation cover of Manchar Lake and Keenjhar Lake has been decreased by 6,337.17 and 558.18 ha, respectively. SAVI analysis showed that soil profile is continuously degrading which vigorously affects vegetation cover within the study area. The overall classification accuracy and Kappa statistics showed an accuracy of >90% for all LULC mapping studies. This work demonstrates the LULC changes as a critical monitoring basis for ongoing analyses of changes in land management to enable decision-makers to establish strategies for effectively using land resources.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

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