scholarly journals Directional Climate Trend, Intensified Intraannual Variability, and Changes in Land Cover Drive the Dynamics of Vegetation Greenness in Peri‐Urban China During 2001–2015

2020 ◽  
Vol 125 (2) ◽  
Author(s):  
Q. Gao ◽  
M. Yu ◽  
H. Xu
Keyword(s):  
Land Cover ◽  
Urban China ◽  
Climate Trend ◽  
Vegetation Greenness ◽  
Dynamics Of Vegetation

scholarly journals Identifying environmental controls on vegetation greenness phenology through model-data integration

2014 ◽  
Vol 11 (7) ◽  
pp. 10917-11025
Author(s):  
M. Forkel ◽  
N. Carvalhais ◽  
S. Schaphoff ◽  
W. v. Bloh ◽  
M. Migliavacca ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Water Availability ◽  
Boreal Forests ◽  
Growing Season ◽  
The Arctic ◽  
Data Sets ◽  
Vegetation Greenness ◽  
Environmental Controls ◽  
Dynamics Of Vegetation

Abstract. Existing dynamic global vegetation models (DGVMs) have a~limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus to enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a~new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal to decadal dynamics of vegetation greenness.

scholarly journals Attribution of the spatial heterogeneity of Arctic surface albedo feedback to the dynamics of vegetation, snow and soil properties and their interactions

2021 ◽  
Author(s):  
Linfei Yu ◽  
Guoyong Leng ◽  
Andre Python
Keyword(s):  
Land Cover ◽  
Spatial Heterogeneity ◽  
Spatial Pattern ◽  
Snow Cover ◽  
Surface Albedo ◽  
The Arctic ◽  
Krasnoyarsk Krai ◽  
Baffin Island ◽  
Albedo Feedback ◽  
Dynamics Of Vegetation

Abstract The Arctic warming rate is triple the global average, which is partially caused by surface albedo feedback (SAF). Understanding the varying pattern of SAF and the mechanisms is therefore critical for predicting future Arctic climate under anthropogenic warming. To date, however, how the spatial pattern of seasonal SAF is influenced by various land surface factors remains unclear. Here, we aim to quantify the strengths of seasonal SAF across the Arctic and to attribute its spatial heterogeneity to the dynamics of vegetation, snow and soil as well as their interactions. The results show a large positive SAF above -5%·K-1 across Baffin Island in January and eastern Yakutia in June, while a large negative SAF beyond 5%·K-1 is observed in Canada, Chukotka and low latitudes of Greenland in January and Nunavut, Baffin Island and Krasnoyarsk Krai in July. Overall, a great spatial heterogeneity of Arctic land warming induced by positive SAF is found with a coefficient of variation (CV) larger than 61.5%, and the largest spatial difference is detected in wintertime with a CV > 643.9%. Based on the optimal parameter-based geographic detector model, the impacts of snow cover fraction (SCF), land cover type (LC), normalized difference vegetation index (NDVI), soil water content (SW), soil substrate chemistry (SC) and soil type (ST) on the spatial pattern of positive SAF are quantified. The rank of determinant power is SCF > LC > NDVI > SW > SC > ST, which indicates that the spatial patterns of snow cover, land cover and vegetation coverage dominate the spatial heterogeneity of positive SAF in the Arctic. The interactions between SCF, LC and SW exert further influences on the spatial pattern of positive SAF in March, June and July. This work could provide a deeper understanding of how various land factors contribute to the spatial heterogeneity of Arctic land warming at the annual cycle.

scholarly journals Understanding global spatio-temporal trends and the relationship between vegetation greenness and climate factors by land cover during 1982–2014

2020 ◽  
Vol 24 ◽  
pp. e01299
Author(s):  
Munkhnasan Lamchin ◽  
Sonam Wangyel Wang ◽  
Chul-Hee Lim ◽  
Altansukh Ochir ◽  
Ukrainskiy Pavel ◽  
...  
Keyword(s):  
Land Cover ◽  
Temporal Trends ◽  
Climate Factors ◽  
Vegetation Greenness ◽  
Spatio Temporal ◽  
The Relationship

scholarly journals Identifying environmental controls on vegetation greenness phenology through model–data integration

2014 ◽  
Vol 11 (23) ◽  
pp. 7025-7050 ◽  
Author(s):  
M. Forkel ◽  
N. Carvalhais ◽  
S. Schaphoff ◽  
W. v. Bloh ◽  
M. Migliavacca ◽  
...  
Keyword(s):  
Time Series ◽  
Observational Data ◽  
Water Availability ◽  
Boreal Forests ◽  
Growing Season ◽  
The Arctic ◽  
Data Sets ◽  
Vegetation Greenness ◽  
Environmental Controls ◽  
Dynamics Of Vegetation

Abstract. Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and description of the environmental controls on phenology, which strongly influence the ability to simulate longer-term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main environmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the Arctic tundra. Whereas water availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and Arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.

scholarly journals Increasing Summer Rainfall and Asymmetrical Diurnal and Seasonal Warming Enhanced Vegetation Greenness in Temperate Deciduous Forests and Grasslands of Northern China

2020 ◽  
Vol 12 (16) ◽  
pp. 2569
Author(s):  
Mei Yu ◽  
Qiong Gao
Keyword(s):  
Land Cover ◽  
Partial Correlation ◽  
Growing Season ◽  
Northern China ◽  
Deciduous Forests ◽  
Ecosystem Functions ◽  
The South ◽  
Vegetation Greenness ◽  
Drought Tolerant ◽  
Growing Season Precipitation

Temperate forests and grasslands carry key ecosystem functions and provide essential services. Remote-sensing derived greenness has been widely used to assess the response of ecosystem function to climate and land-cover changes. Although reforestation and grassland restoration have been proposed to enhance the regional greenness in Northern China, the independent contribution of climate without the interference of land-cover change at meso and large scales has rarely been explored. To separate the impacts of climate change on vegetation greenness from those of land-cover/use change, we identified large patches of forests and grasslands in Northern China without land-cover/use changes in 2001–2015 and derived their greenness using MODIS enhanced vegetation index (EVI). We found that most deciduous-broadleaved forest patches showed greening, and the significant slope of the annual mean and maximum EVI are 3.97 ± 0.062 × 10−3 and 4.8 ± 0.116 × 10−3 yr−1, respectively. On the contrary, grassland patches showed great spatial heterogeneity and only those in the east showed greening. The partial correlation analysis between EVI and climate showed that the greening of grassland patches is primarily supported by the increased growing-season precipitation with mean significant coefficient of 0.72 ± 0.01. While wet-year (0.57 ± 0.01) and nongrowing-season precipitation (0.68 ± 0.01) significantly benefit greening of deciduous-broadleaved forests, the altered temperature seasonality modulates their greening spatial-heterogeneously. The increased growing-season minimum temperature might lengthen the growing season and contribute to the greening for the temperature-limited north as shown by positive partial correlation coefficient of 0.66 ± 0.01, but might elevate respiration and reduce greening of the forests in the south as shown by negative coefficient of −0.70 ± 0.01. Daytime warming in growing season is found to favor the drought-tolerant oak dominated forest in the south due to enhanced photosynthesis, but may not favor the forests dominated by less-drought-tolerant birch in the north due to potential water stress. Therefore, grassland greening was essentially promoted by the growing-season precipitation, however, in addition to being driven by precipitation, greening of deciduous forests was regulated spatial-heterogeneously by asymmetrical diurnal and seasonal warming which could be attributed to species composition.

scholarly journals Impacts of land cover change on climate trend in Padang Indonesia

2014 ◽  
Vol 46 (2) ◽  
pp. 138 ◽  
Author(s):  
Dedi Hermon
Keyword(s):  
Climate Change ◽  
Land Cover ◽  
Land Cover Change ◽  
Extreme Temperature ◽  
Extreme Rainfall ◽  
Land Cover Changes ◽  
Forest Land ◽  
Climate Trend ◽  
Extreme Climatic Events ◽  
Expert Team

ἀe purpose of this study was to analyze the trend of climate change through changes in the elements of Green House Gases (GHGs),   includes the trend of CO2, N2O, and CH4. ἀe change of the  extreme rainfall and temperature  indices due to land cover change into developed area in Padang. IdentiḀcation and analysis trends of climate change and extreme climatic events were analyzed by using RclimDex the Expert Team for Climate Change Detection and Indices (ETCCDMI) technique. Where as the analysis and interpretation of  land cover changes  into developed area used Landsat TM 5 and Landsat 1985 7 ETM +  of 2011 by ERDAS 9.2 GIS with the supervised classiḀcation method and GIS Matrix. ἀe results of the study provide informations of land cover changes into developed area at forest land  (11,758.9 ha), shrub (3,337.3 ha), rice Ḁelds (5,977.1 ha), and garden (5,872.4 ha). It has an implication on increasing of  the ele-ments of GHGs concentration such as CO2 (14,1 ppm), N2O (5,4 ppb) and CH4 (24,8 ppb). ἀis condition lead to an extreme temperature and presipitation indexs trends in Padang.

scholarly journals Monitoring of Changes in Land Use/Land Cover in Syria from 2010 to 2018 Using Multitemporal Landsat Imagery and GIS

Land ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 226 ◽  
Author(s):  
Mohamed Mohamed ◽  
Julian Anders ◽  
Christoph Schneider
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Urban Areas ◽  
Large Scale ◽  
Management Practices ◽  
Landsat Imagery ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Climate Trend ◽  
Lulc Change

Understanding the effects of socio-ecological shocks on land use/land cover (LULC) change is essential for developing land management strategies and for reducing adverse environmental pressures. Our study examines the impacts of the armed conflict in Syria, which began in mid-2011, and the related social and economic crisis on LULC between 2010 and 2018. We used remote sensing for change detection by applying a supervised maximum likelihood classification to Landsat images of the three target years 2010, 2014, and 2018. Based on the computed extent of our LULC classes and accuracy assessment, we calculated area-adjusted estimates and 95% confidence intervals. Our classification achieved an overall accuracy of 86.4%. Compared to 2010, we found an increase in spatial extent for bare areas (40,011 km2), forests (2576 km2), and urban and peri-urban areas (3560 km2), whereas rangelands (37,005 km2) and cultivated areas (9425 km2) decreased by 2018. It is not possible to determine whether the changes in LULC in Syria will be permanent or temporary. Natural conditions such as climate fluctuations had an impact on the uses of the natural environment and cultivated areas during the study period, especially in regions suffering from water stress. Although seasonal precipitation patterns and temperature affect LULC change, however, we could not identify a prevailing climate trend towards more drought-prone conditions. Our analysis focuses on (potential) direct and indirect implications of the Syrian conflict on LULC change, which most notably occurred between 2014 and 2018. Conflict-related main drivers were human activities and demographic changes, which are mainly attributable to large-scale population displacement, military operations, concomitant socio-economic status, and control of local resources. As the study provides quantitative and qualitative information on the dynamics of LULC changes in Syria, it may serve as a framework for further relevant conflict-related research and support planning, management practices, and sustainable development.

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