scholarly journals Relative impacts of vegetation coverage and leaf area index on climate change in a greener north

2007 ◽  
Vol 34 (15) ◽  
Author(s):  
Jing Zhang ◽  
John E. Walsh
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Coverage ◽  
Area Index

Integrating remote sensing and climate data with process-based models to map forest productivity within west-central Alberta's boreal forest: Ecoleap-West

2006 ◽  
Vol 82 (2) ◽  
pp. 159-176 ◽  
Author(s):  
R J Hall ◽  
F. Raulier ◽  
D T Price ◽  
E. Arsenault ◽  
P Y Bernier ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Management Practices ◽  
Forest Productivity ◽  
Process Models ◽  
Growth And Yield ◽  
Area Index ◽  
Study Region

Forest yield forecasting typically employs statistically derived growth and yield (G&Y) functions that will yield biased growth estimates if changes in climate seriously influence future site conditions. Significant climate warming anticipated for the Prairie Provinces may result in increased moisture deficits, reductions in average site productivity and changes to natural species composition. Process-based stand growth models that respond realistically to simulated changes in climate can be used to assess the potential impacts of climate change on forest productivity, and hence can provide information for adapting forest management practices. We present an application of such a model, StandLEAP, to estimate stand-level net primary productivity (NPP) within a 2700 km2 study region in western Alberta. StandLEAP requires satellite remote-sensing derived estimates of canopy light absorption or leaf area index, in addition to spatial data on climate, topography and soil physical characteristics. The model was applied to some 80 000 stand-level inventory polygons across the study region. The resulting estimates of NPP correlate well with timber productivity values based on stand-level site index (height in metres at 50 years). This agreement demonstrates the potential to make site-based G&Y estimates using process models and to further investigate possible effects of climate change on future timber supply. Key words: forest productivity, NPP, climate change, process-based model, StandLEAP, leaf area index, above-ground biomass

scholarly journals The effect of light availability on leaf area index, biomass production and plant species composition of park grasslands in Warsaw  

2013 ◽  
Vol 59 (No. 12) ◽  
pp. 543-548 ◽  
Author(s):  
P. Dąbrowski ◽  
B. Pawluśkiewicz ◽  
Kalaji HM ◽  
Baczewska AH
Keyword(s):  
Solar Radiation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Light Availability ◽  
Vegetation Coverage ◽  
Light Conditions ◽  
Area Index ◽  
Sward Height ◽  
Poa Trivialis ◽  
Leaf Density

How light conditions affect development of park grasslands is a question that has not been satisfactory addressed. The aim of this study was therefore determination of the level to which unfavorable light conditions influence grassy parks area and relationships between parameters which determine state of turf grasses. Researches were conducted in two parks in Warsaw, in various light conditions and included measurement of: leaf density, sward height, leaf area index (LAI), and botanical composition of the communities. The leaf density of shaded areas did not exceed 70%. LAI value varied from 0.5 to 0.9-fold lower than in the areas in half-shade and in sun. The participation of basic lawn species at Skaryszewski Park was higher under shade, while at Łazienki Królewskie was higher in full-sunlight areas. The state of tested grassy areas in limited solar radiation does not satisfy the requirements of recreational and representational functions. The development processes of vegetation coverage were inhibited at the sites of lower solar radiation. LAI was influenced by both leaf coverage and sward height. Agrostis stolonifera and Poa trivialis may be recommended to create grass areas under limited solar radiation.

scholarly journals Assessment of Climate Change Impact on Crop yield and Evaluation of Coping Strategy using Crop Growth Simulation Model

2020 ◽  
Vol 15 (1) ◽  
pp. 106-122
Author(s):  
J. Alam ◽  
R. K. Panda
Keyword(s):  
Climate Change ◽  
Grain Yield ◽  
Simulation Model ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Concentration ◽  
Maize Yield ◽  
Crop Growth ◽  
Climate Change Scenarios ◽  
Area Index

 Any change in climate will have implications for climate-sensitive systems such as agriculture, forestry and some other natural resources. Changes in solar radiation, temperature and precipitation will produce changes in crop yields and hence economics of agriculture. It is possible to understand the phenomenon of climate change on crop production and to develop adaptation strategies for sustainability in food production, using a suitable crop simulation model. CERES-Maize model of DSSAT v4.0 was used to simulate the maize yield of the region under climate change scenarios using the historical weather data at Kharagpur (1977-2007), Damdam (1974-2003) and Purulia (1986-2000), West Bengal, India. The model was calibrated using the crop experimental data, climate data and soil data for two years (1996-1997) and was validated by using the data of the year 1998 at Kharagpur. The change in values of weather parameters due to climate change and its effects on the maize crop growth and yield was studied. It was observed that increase in mean temperature and leaf area index have negative impacts on maize yield. When the maximum leaf area index increased, the grain yield was found to be decreased. Increase in CO2 concentration with each degree incremental temperature decreased the grain yield but increase in CO2 concentration with fixed temperature increased the maize yield. Adjustments were made in the date of sowing to investigate suitable option for adaptation under the future climate change scenarios. Highest yield was obtained when the sowing date was advanced by a week at Kharagpur and Damdam whereas for Purulia, the experimental date of sowing was found to be beneficial.

scholarly journals Seasonal Net Carbon Exchange in Rotation Crops in the Temperate Climate of Central Lithuania

2019 ◽  
Vol 11 (7) ◽  
pp. 1966 ◽  
Author(s):  
Ligita Baležentienė ◽  
Ovidijus Mikša ◽  
Tomas Baležentis ◽  
Dalia Streimikiene
Keyword(s):  
Climate Change ◽  
Exchange Rate ◽  
Soil Respiration ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Co2 Emissions ◽  
Vegetation Period ◽  
Temperate Climate ◽  
Carbon Exchange ◽  
Area Index

Intelligent agricultural solutions require data on the environmental impacts of agriculture. In order for operationalize decision-making for sustainable agriculture, one needs to establish the corresponding datasets and protocols. Increasing anthropogenic CO2 emissions into the atmosphere force the choice of growing crops aimed at mitigating climate change. For this reason, investigations of seasonal carbon exchange were carried out in 2013–2016 at the Training Farm of the Vytautas Magnus University (former Aleksandras Stulginskis University), Lithuania. This paper compares the carbon exchange rate for different crops, viz., maize, ley, winter wheat, spring rapeseed and barley under conventional farming. This study focuses on the carbon exchange rate. We measure the emitted and absorbed CO2 fluxes by applying the closed chamber method. The biomass measurement and leaf area index (LAI) calculations at different plant growth stages are used to evaluate carbon exchange in different agroecosystems. The differences in photosynthetically assimilated CO2 rates were significantly impacted by the leaf area index (p = 0.04) during the plant vegetation period. The significantly (p = 0.02–0.05) strong correlation (r = 0.6–0.7) exists between soil respiration and LAI. Soil respiration composed only 21% of the agroecosystem carbon exchange. Plant respiration ranged between 0.034 and 3.613 µmol m−2 s−1 during the vegetation period composed of a negligible ratio (mean 16%) of carbon exchange. Generally, respiration emissions were obviously recovered by the gross primary production (GPP) of crops. Therefore, the ecosystems were acting as an atmospheric CO2 sink. Barley accumulated the lowest mean GPP 12.77 µmol m−2 s−1. The highest mean GPP was determined for ley (14.28 µmol m−2 s−1) and maize (15.68 µmol m−2 s−1) due to the biggest LAI and particular bio-characteristics. Due to the highest NEP, the ley (12.66 µmol m−2 s−1) and maize (12.76 µmol m−2 s−1) agroecosystems sank the highest C from the atmosphere and, thus, they might be considered the most sustainable items between crops. Consequently, the appropriate choice of crops and their area in crop rotations may reduce CO2 emissions and their impact on the environment and climate change.

Spatiotemporal evolution of drought events and its contribution on vegetation growth in the river source region

2020 ◽  
Author(s):  
Shuang Zhu
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Standardized Precipitation Index ◽  
Precipitation Index ◽  
Spatiotemporal Evolution ◽  
Area Index ◽  
Vegetation Growth ◽  
Evolution Characteristics ◽  
The Standardized Precipitation Index

<p><span lang="EN-US"><span>Climate change has been proved to exacerbate drought events and further cause huge economic and ecological losses worldwide. Therefore, it is of great significance to study the long-term evolution characteristics of drought events and quantify the impact of drought events on typical ecological indexes. Based on the measured historical precipitation data, the standardized precipitation index of different time scales was extracted to measure water deficit. The leaf area index with wide range and high precision was generated based on the Modis remote sensing image and denoising processing to represent vegetation growth. Trend analysis and change point analysis were carried out to study the spatiotemporal evolution characteristics of the concerned drought indexes. Then, with hypothesis test, appropriate copula multivariate analysis method was innovatively introduced to construct joint distribution of the standardized precipitation index and leaf area index. The contribution of drought on vegetation growth was expected to be quantified by deriving the conditional copula and preset marginal distributions. The upper Yangtze River where biomass is extremely sensitive to climate change was taken as a study area. The results show that drought events in this region have significant spatial heterogeneity. The leaf area index is highly influenced by the meteorological drought index. From no drought to severe drought, the vegetation index is distributed more and more toward the low value. Copula is very potential to find the inner relationship of the standardized precipitation index and leaf area index. The study is useful to deepen the understanding of the internal mechanism of drought events and discuss reasonable disaster prevention and mitigation countermeasures.</span></span></p> <p> </p>

scholarly journals Investigating the leaf area index changes in response to climate change (case study: Kasilian catchment, Iran)

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Mohammad Reza Ramezani ◽  
Ali Reza Massah Bavani ◽  
Mostafa Jafari ◽  
Ali Binesh ◽  
Stefan Peters
Keyword(s):  
Climate Change ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Area Index ◽  
Index Changes

scholarly journals The Application of Modified Normalized Difference Water Index (MNDWI) by Leaf Area Index in the Retrieval of Regional Drought Monitoring

2015 ◽  
Vol XL-7/W3 ◽  
pp. 141-147
Author(s):  
H.-w. Zhang ◽  
H.-l. Chen
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Coverage ◽  
Area Index ◽  
Water Index ◽  
Retrieval Accuracy ◽  
Normalized Difference Water Index ◽  
Green Stage

The vegetation coverage is one of the important factors that restrict the accuracy of remote sensing retrieval of soil moisture. In order to effectively improve the accuracy of the remote sensing retrieval of soil moisture and to reduce the impact of vegetation coverage variation on the retrieval accuracy, the Leaf Area Index (LAI) is introduced to the Normalized Difference Water Index (NDWI) to greatly improve the accuracy of the soil moisture retrieval. In its application on the regional drought monitoring, the paper uses the relative LAI from two places which locate in the north and south of Henan Province respectively (Xin Xiang and Zhu Ma Dian) as indicators. It uses the days after turned-green stage to conduct difference value correction on the Relative Leaf Area Index (RLAL) of the entire province, so as to acquire the distribution of RLAI of the province’s wheat producing area. After this, the local remote sensing NDWI will be Modified （MNDWI = NDWI ×RLAI ） to acquire the soil moisture distribution status of the entire province’s wheat producing area. The result shows that, the Modified Normalized Difference Water Index of LAI which based on the days after turned-green stage can improve the real time retrieval accuracy of soil moisture under different vegetation coverage.

The Extraction and Dynamic Monitoring of Leaf Area Index in Northeast of China Based on SPOT VEGETATION Data

2012 ◽  
Vol 500 ◽  
pp. 529-533
Author(s):  
Zheng Hong Miao ◽  
Zong Ming Wang ◽  
Jing Ming Chen ◽  
Kai Shan Song ◽  
Chun Ying Ren
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Liaoning Province ◽  
Dynamic Monitoring ◽  
Vegetation Coverage ◽  
Heilongjiang Province ◽  
Area Index ◽  
Increasing Trend ◽  
The Rich ◽  
Northeast Of China

Using SPOT VEGETATION data, the extraction method of experience model of LAI (leaf area index), we estimate and dynamic analysis the LAI of the northeast in 2000, 2004 and 2008. The results showed that: the LAI values of the Northeast region from 2000 to 2008 appears gradually increasing trend, the lowest average of 3.10 throughout the region in 2000, the highest in 2008, reaching 4.97. Judging from the different regions, changes in Liaoning province is the largest one which increases 136.40 points between 8 years, while Heilongjiang Province is the lowest, increased at only 21.53%. LAI values of Heilongjiang province are all the highest in 2000, 2004 and 2008 mainly because of the rich forest resources, vegetation coverage are relatively good; while in Liaoning province and Inner Mongolia, vegetation coverage is relatively poor. Therefore, the LAI is relatively low.

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