scholarly journals The Ongoing Greening in Southwest China despite Severe Droughts and Drying Trends

2021 ◽  
Vol 13 (17) ◽  
pp. 3374
Author(s):  
Xin Chen ◽  
Tiexi Chen ◽  
Qingyun Yan ◽  
Jiangtao Cai ◽  
Renjie Guo ◽  
...  
Keyword(s):  
Vegetation Change ◽  
Vegetation Index ◽  
Southwest China ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Global Scale ◽  
Area Index ◽  
Huge Impact ◽  
Increasing Trends

Vegetation greening, which refers to the interannual increasing trends of vegetation greenness, has been widely found on the regional to global scale. Meanwhile, climate extremes, especially several drought, significantly damage vegetation. The Southwest China (SWC) region experienced massive drought from 2009 to 2012, which severely damaged vegetation and had a huge impact on agricultural systems and life. However, whether these extremes have significantly influenced long-term (multiple decades) vegetation change is unclear. Using the latest remote sensing-based records, including leaf area index (LAI) and gross primary productivity (GPP) for 1982–2016 and enhanced vegetation index (EVI) for 2001–2019, drought events of 2009–2012 only leveled off the greening (increasing in vegetation indices and GPP) temporally and long-term greening was maintained. Meanwhile, drying trends were found to unexpectedly coexist with greening.

scholarly journals Vegetation Change and Its Response to Climate Extremes in the Arid Region of Northwest China

2021 ◽  
Vol 13 (7) ◽  
pp. 1230
Author(s):  
Simeng Wang ◽  
Qihang Liu ◽  
Chang Huang
Keyword(s):  
Arid Region ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Pearson Correlation ◽  
Time Lag ◽  
Climate Extremes ◽  
Northwest China ◽  
Precipitation Extremes ◽  
Temperature Extremes ◽  
Increasing Trend

Changes in climate extremes have a profound impact on vegetation growth. In this study, we employed the Moderate Resolution Imaging Spectroradiometer (MODIS) and a recently published climate extremes dataset (HadEX3) to study the temporal and spatial evolution of vegetation cover, and its responses to climate extremes in the arid region of northwest China (ARNC). Mann-Kendall test, Anomaly analysis, Pearson correlation analysis, Time lag cross-correlation method, and Least absolute shrinkage and selection operator logistic regression (Lasso) were conducted to quantitatively analyze the response characteristics between Normalized Difference Vegetation Index (NDVI) and climate extremes from 2000 to 2018. The results showed that: (1) The vegetation in the ARNC had a fluctuating upward trend, with vegetation significantly increasing in Xinjiang Tianshan, Altai Mountain, and Tarim Basin, and decreasing in the central inland desert. (2) Temperature extremes showed an increasing trend, with extremely high-temperature events increasing and extremely low-temperature events decreasing. Precipitation extremes events also exhibited a slightly increasing trend. (3) NDVI was overall positively correlated with the climate extremes indices (CEIs), although both positive and negative correlations spatially coexisted. (4) The responses of NDVI and climate extremes showed time lag effects and spatial differences in the growing period. (5) Precipitation extremes were closely related to NDVI than temperature extremes according to Lasso modeling results. This study provides a reference for understanding vegetation variations and their response to climate extremes in arid regions.

scholarly journals Vegetation Indices Do Not Capture Forest Cover Variation in Upland Siberian Larch Forests

2018 ◽  
Vol 10 (11) ◽  
pp. 1686 ◽  
Author(s):  
Michael Loranty ◽  
Sergey Davydov ◽  
Heather Kropp ◽  
Heather Alexander ◽  
Michelle Mack ◽  
...  
Keyword(s):  
Carbon Storage ◽  
Vegetation Change ◽  
Vegetation Index ◽  
Forest Cover ◽  
Global Climate ◽  
Vegetation Indices ◽  
Temporal Trends ◽  
Siberian Larch ◽  
Field Observations ◽  
Larch Forests

Boreal forests are changing in response to climate, with potentially important feedbacks to regional and global climate through altered carbon cycle and albedo dynamics. These feedback processes will be affected by vegetation changes, and feedback strengths will largely rely on the spatial extent and timing of vegetation change. Satellite remote sensing is widely used to monitor vegetation dynamics, and vegetation indices (VIs) are frequently used to characterize spatial and temporal trends in vegetation productivity. In this study we combine field observations of larch forest cover across a 25 km2 upland landscape in northeastern Siberia with high-resolution satellite observations to determine how the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) are related to forest cover. Across 46 forest stands ranging from 0% to 90% larch canopy cover, we find either no change, or declines in NDVI and EVI derived from PlanetScope CubeSat and Landsat data with increasing forest cover. In conjunction with field observations of NDVI, these results indicate that understory vegetation likely exerts a strong influence on vegetation indices in these ecosystems. This suggests that positive decadal trends in NDVI in Siberian larch forests may correspond primarily to increases in understory productivity, or even to declines in forest cover. Consequently, positive NDVI trends may be associated with declines in terrestrial carbon storage and increases in albedo, rather than increases in carbon storage and decreases in albedo that are commonly assumed. Moreover, it is also likely that important ecological changes such as large changes in forest density or variable forest regrowth after fire are not captured by long-term NDVI trends.

scholarly journals Evaluation of a Global Vegetation Model using time series of satellite vegetation indices

2011 ◽  
Vol 4 (4) ◽  
pp. 1103-1114 ◽  
Author(s):  
F. Maignan ◽  
F.-M. Bréon ◽  
F. Chevallier ◽  
N. Viovy ◽  
P. Ciais ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Global Scale ◽  
Vegetation Dynamic ◽  
Rate Of Increase ◽  
Vegetation Models ◽  
Global Vegetation ◽  
The Impact

Abstract. Atmospheric CO2 drives most of the greenhouse effect increase. One major uncertainty on the future rate of increase of CO2 in the atmosphere is the impact of the anticipated climate change on the vegetation. Dynamic Global Vegetation Models (DGVM) are used to address this question. ORCHIDEE is such a DGVM that has proven useful for climate change studies. However, there is no objective and methodological way to accurately assess each new available version on the global scale. In this paper, we submit a methodological evaluation of ORCHIDEE by correlating satellite-derived Vegetation Index time series against those of the modeled Fraction of absorbed Photosynthetically Active Radiation (FPAR). A perfect correlation between the two is not expected, however an improvement of the model should lead to an increase of the overall performance. We detail two case studies in which model improvements are demonstrated, using our methodology. In the first one, a new phenology version in ORCHIDEE is shown to bring a significant impact on the simulated annual cycles, in particular for C3 Grasses and C3 Crops. In the second case study, we compare the simulations when using two different weather fields to drive ORCHIDEE. The ERA-Interim forcing leads to a better description of the FPAR interannual anomalies than the simulation forced by a mixed CRU-NCEP dataset. This work shows that long time series of satellite observations, despite their uncertainties, can identify weaknesses in global vegetation models, a necessary first step to improving them.

scholarly journals The Greening and Wetting of the Sahel Have Leveled off since about 1999 in Relation to SST

2020 ◽  
Vol 12 (17) ◽  
pp. 2723
Author(s):  
Tiexi Chen ◽  
Shengjie Zhou ◽  
Chuanzhuang Liang ◽  
Daniel Fiifi Tawia Hagan ◽  
Ning Zeng ◽  
...  
Keyword(s):  
Piecewise Linear ◽  
Vegetation Indices ◽  
Positive Trend ◽  
Causality Analysis ◽  
Area Index ◽  
Piecewise Linear Regression ◽  
Significant Positive Trend ◽  
Tropical Oceans ◽  
Regression Approach

The Sahel, a semi-arid climatic zone with highly seasonal and erratic rainfall, experienced severe droughts in the 1970s and 1980s. Based on remote sensing vegetation indices since early 1980, a clear greening trend is found, which can be attributed to the recovery of contemporaneous precipitation. Here, we present an analysis using long-term leaf area index (LAI), precipitation, and sea surface temperature (SST) records to investigate their trends and relationships. LAI and precipitation show a significant positive trend between 1982 and 2016, at 1.72 × 10 −3 yr −1 (p < 0.01) and 4.63 mm yr−1 (p < 0.01), respectively. However, a piecewise linear regression approach indicates that the trends in both LAI and precipitation are not continuous throughout the 35 year period. In fact, both the greening and wetting of the Sahel have been leveled off (pause of rapid growth) since about 1999. The trends of LAI and precipitation between 1982 and 1999 and 1999–2016 are 4.25 × 10 − 3 yr −1 to − 0.27 × 10 −3 yr −1, and 9.72 mm yr −1 to 2.17 mm yr −1, respectively. These declines in trends are further investigated using an SST index, which is composed of the SSTs of the Mediterranean Sea, the subtropical North Atlantic, and the global tropical oceans. Causality analysis based on information flow theory affirms this precipitation stabilization between 2003 and 2014. Our results highlight that both the greening and the wetting of the Sahel have been leveled off, a feature that was previously hidden in the apparent long-lasting greening and wetting records since the extreme low values in the 1980s.

scholarly journals Estimation of Peanut Leaf Area Index from Unmanned Aerial Vehicle Multispectral Images

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6732
Author(s):  
Haixia Qi ◽  
Bingyu Zhu ◽  
Zeyu Wu ◽  
Yu Liang ◽  
Jianwen Li ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Field Experiments ◽  
Predictive Accuracy ◽  
Vegetation Indices ◽  
Planting Density ◽  
Coefficient Of Determination ◽  
Multispectral Images ◽  
Area Index

Leaf area index (LAI) is used to predict crop yield, and unmanned aerial vehicles (UAVs) provide new ways to monitor LAI. In this study, we used a fixed-wing UAV with multispectral cameras for remote sensing monitoring. We conducted field experiments with two peanut varieties at different planting densities to estimate LAI from multispectral images and establish a high-precision LAI prediction model. We used eight vegetation indices (VIs) and developed simple regression and artificial neural network (BPN) models for LAI and spectral VIs. The empirical model was calibrated to estimate peanut LAI, and the best model was selected from the coefficient of determination and root mean square error. The red (660 nm) and near-infrared (790 nm) bands effectively predicted peanut LAI, and LAI increased with planting density. The predictive accuracy of the multiple regression model was higher than that of the single linear regression models, and the correlations between Modified Red-Edge Simple Ratio Index (MSR), Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), and LAI were higher than the other indices. The combined VI BPN model was more accurate than the single VI BPN model, and the BPN model accuracy was higher. Planting density affects peanut LAI, and reflectance-based vegetation indices can help predict LAI.

scholarly journals Fluxo de CO2 e os Índices de Vegetação do Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brasil

2020 ◽  
Vol 13 (07) ◽  
pp. 3585
Author(s):  
Luana De Castro Pereira ◽  
Arnon Batista Nunes ◽  
Israel Lobato Rocha ◽  
Janeil Lustosa De Oliveira ◽  
Maria Letícia Stefany Monteiro Brandão ◽  
...  
Keyword(s):  
Carbon Balance ◽  
National Park ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Carbon Flow ◽  
Natural Environments ◽  
Area Index ◽  
Vegetation Indexes ◽  
Co2 Flow ◽  
Good Coverage

As emissões dos gases de efeito estufa na atmosfera trazem consequências para o meio ambiente e saúde pública. Logo, ambientes naturais, como as Florestas Nativas do Cerrado são essenciais no processo de equilíbrio de carbono, pela fixação do mesmo. Com o objetivo estimar o fluxo de CO2 com base em diferentes índices de vegetação do Parque Nacional das Nascentes do Rio Parnaíba (PNNRP), essa pesquisa, utilizou-se dos seguintes índices: Pré Processamento das Imagens (PPI), Índice de Vegetação por Diferença Normalizada – NDVI, Índice de Vegetação Fotossintético – PRI, Índice de Vegetação Ajustado ao Solo – SAVI, Índice de Área Foliar- IAF e CO2FLUX.  Referente ao Índice de Vegetação por Diferença Normalizada (NDVI), verificou-se que a maior parte da área PNNRP se encontra sob a vegetação considerada densa, sendo os  valores de SAVI encontrados próximos aos valores de NDVI, que pode estar relacionado a uma boa cobertura vegetal presente, indicando pouca influência das características do solo sob os índices de vegetação. A partir dos resultados encontrados através do IAF do PNNRP verificou que em áreas que os valores são maiores encontram-se as vegetações com o melhor desenvolvimento. Levando em conta os valores relacionados ao CO2Flux, IAF, NDVI e os demais índices, percebeu-se a capacidade do Parque no aproveitamento da luz solar e a realização da fotossíntese, além de abrigar uma vegetação saudável, podendo assim afirmar o grande potencial do PNNRP em armazenar carbono. Portanto, evidencia-se que o Parque Nacional das Nascentes do Rio Parnaíba possuí uma alto potencial de fluxo de carbono.   CO2 flow and vegetation indices of the Parque Nacional das Nascentes do Rio Parnaíba, Piauí, Brazil A B S T R A C TEmissions of greenhouse gases into the atmosphere have consequences for the environment and public health. Therefore, natural environments, such as the Cerrado's Native Forests are essential in the carbon balance process, due to its fixation. With the objective of estimating the CO2 flow based on different vegetation indexes of the Nascentes do Rio Parnaíba National Park (PNNRP), this research used the following indexes: Pre-Processing of Images (PPI), Vegetation Index by Difference Normalized - NDVI, Photosynthetic Vegetation Index - PRI, Soil Adjusted Vegetation Index - SAVI, Leaf Area Index - IAF and CO2FLUX. Regarding the Index of Vegetation by Normalized Difference (NDVI), it was found that most of the PNNRP area is under dense vegetation, with SAVI values found close to NDVI values, which may be related to good coverage present, indicating little influence of soil characteristics on vegetation indexes. From the results found through the IAF of the PNNRP verified that in areas with higher values are the vegetation with the best development. Taking into account the values related to CO2Flux, IAF, NDVI and other indexes, the Park's capacity to use sunlight and photosynthesis was observed, as well as to house healthy vegetation, thus confirming the great potential of PNNRP in storing carbon. Therefore, it is evident that the Parnaíba River National Park has a high carbon flow potential.Keywords: biomass, cerrado biome, carbon flow

scholarly journals NEW VEGETATION INDICES FOR FULL AND COMPACT POLARIMETRIC SAR DATA: IN PREPARATION FOR THE RADARSAT CONSTELLATION MISSION (RCM)

2020 ◽  
Vol IV-3/W2-2020 ◽  
pp. 41-46
Author(s):  
D. Ratha ◽  
D. Mandal ◽  
S. Dey ◽  
A. Bhattacharya ◽  
A. Frery ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Geodesic Distance ◽  
Vegetation Indices ◽  
Series Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Plant Area Index ◽  
Sar Data

Abstract. In this paper, we present two radar vegetation indices for full-pol and compact-pol SAR data, respectively. Both are derived using the notion of a geodesic distance between observation and well-known scattering models available in the literature. While the full-pol version depends on a generalized volume scattering model, the compact-pol version uses the ideal depolariser to model the randomness in the vegetation. We have utilized the RADARSAT Constellation Mission (RCM) time-series data from the SAMPVEX16-MB campaign in the Manitoba region of Canada for comparing and assessing the indices in terms of the change in the biophysical parameters as well. The compact-pol data for comparison is simulated from the full-pol RCM time series. Both the indices show better performance at correlating with biophysical parameters such as Plant Area Index (PAI) and Volumetric Water Content (VWC) for wheat and soybean crops, in comparison to the state-of-art Radar Vegetation Index (RVI) of Kim and van Zyl. These indices are timely for the upcoming release of the data from the RCM, which will provide data in both full and compact-pol modes, aimed at better crop monitoring from space.

scholarly journals Estimativa de emissão de metano na formação do reservatório da hidrelétrica Serra do Facão / Estimate of methane emission from the filling of hydroelectric dam of Serra do Facão in the State of Goiás - Brazil

2017 ◽  
Vol 27 (48) ◽  
pp. 1-26
Author(s):  
Simone Pereira Ferreira ◽  
Rita de Cássia Marques Alves ◽  
Flavio Varone Gonçalves
Keyword(s):  
Vegetation Index ◽  
Hydroelectric Plant ◽  
Vegetation Indices ◽  
Hydroelectric Power Plant ◽  
Methane Emissions ◽  
Landsat Tm ◽  
Hydroelectric Power ◽  
Area Index ◽  
Cerrado Vegetation ◽  
Hydroelectric Dam

hidrelétrica Serra do Facão utilizando imagens Landsat TM. A metodologia desenvolvida neste trabalho compreende as etapas das correções geométricas e radiométricas, recorte e processamento das imagens. Para realizar a estimativa foi necessário calcular índices de vegetação e identificar características como biomassa, índice de área foliar, atividade fotossintética, produtividade. Os resultados obtidos neste trabalho são do metano estimado durante o enchimento do lago da represa da usina hidrelétrica Serra do Facão. Os valores estimados de metano variaram entre 2,38 e 64,08 kg/km2/dia e estão de acordo com os dados publicados no Relatório de Referência do Ministério de Ciência e Tecnologia (MCT) e com outros trabalhos desenvolvidos em reservatórios tropicais. A metodologia aqui descrita pode servir para mitigar os efeitos resultantes do enchimento de grandes reservatórios. Com a técnica pode-se identificar regiões prioritárias para a supressão da vegetação que ficará submersa.Palavras–chave: Sensoriamento remoto, Landsat, Cerrado, Índices de vegetaçãoAbstract The aim of this study is to estimate methane emissions during the filling of the reservoir of the Serra do Facão hydroelectric plant using Landsat TM images. The methodology developed in this work comprises the steps of radiometric and geometric corrections, cropping and image processing. To estimate methane emissions, we had to calculate vegetation indices, identify features like biomass, leaf area index, photosynthetic activity, productivity. The results obtained in this work are the estimated methane during the filling of the Lake of the dam of Serra do Facão hydroelectric power plant. The estimated values of methane varied between 2.38 and 64.08 kg/km2/day and are according to the data published in the report of Ministry of science and technology (MCT) and with other projects developed in tropical reservoirs. The methodology described here can serve to mitigate the effects of filling of large reservoirs. With the technique can identify priority regions for the removal of vegetation that will be submerged. Keywords: Remote sensing, Landsat, Cerrado, Vegetation index. 

scholarly journals Spectral Index-Based Monitoring (2000–2017) in Lowland Forests to Evaluate the Effects of Climate Change

Geosciences ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 411 ◽  
Author(s):  
Ferenc Kovács ◽  
András Gulácsi
Keyword(s):  
Climate Change ◽  
Biomass Production ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Climate Extremes ◽  
Vegetation Period ◽  
Great Plain ◽  
Data Series ◽  
Water Shortages

In the next decades, climate change will put forests in the Hungarian Great Plain in the Carpathian Basin to the test, e.g., changing seasonal patterns, more intense storms, longer dry periods, and pests are expected to occur. To aid in the decision-making process for the conservation of ecosystems depending on forestry, how woods could adapt to changing meso- and microclimatic conditions in the near future needs to be defined. In addition to trendlike warming processes, calculations show an increase in climate extremes, which need to be monitored in accordance with spatial planning, at least for medium-scale mappings. We can use the MODIS sensor dataset if up-to-date terrestrial conditions and multi-decadal geographical processes are of interest. For geographic evaluations of changes, we used vegetation spectral indices; Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI), based on the summer half year, 16-day MODIS data composites between 2000 and 2017 in an intensively forested study area in the Hungarian Great Plain. We delineated forest areas on the Danube–Tisza Interfluve using Corine Land Cover maps (2000, 2006, and 2012). Mid-year changes over the nearly two-decade-long period are currently in balance; however, based on their reactions, forests are highly sensitive to abrupt changes caused by extreme climatic events. The higher occurrence of years or periods with extreme water shortages marks an observable decrease in biomass production, even in shorter index time series, such as that between 2004 and 2012. In the drought-stricken July-August periods, the effect of a dry year, subsequent to years with more precipitation, immediately pushes back the green mass and the reduction in the biomass production could become persistent, according to climatology predictions. The changes of specific sub-periods in the vegetation period can be evaluated even in a relatively short, 18-year data series, including the change in the growing values of the vegetative growth in spring or the increase in the summertime biomass production. Standardized differences highlight spatial differences in the biomass production; in response to years with the highest (negative) biomass difference; typically, the northern and southwestern parts of the Danube–Tisza Interfluve in the study area have longer lasting losses in biomass production. A comparison of NDVI and EVI values with the PaDI drought index and the vegetation indices of LANDSAT Operational Land Imager sensor respectively confirms our results.

scholarly journals The Impact of Non-Photosynthetic Vegetation on LAI Estimation by NDVI in Mixed Grassland

2020 ◽  
Vol 12 (12) ◽  
pp. 1979
Author(s):  
Dandan Xu ◽  
Deshuai An ◽  
Xulin Guo
Keyword(s):  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Time Series Data ◽  
Vegetation Indices ◽  
Series Data ◽  
Area Index ◽  
Plant Area Index ◽  
The Impact

Leaf area index (LAI) is widely used for algorithms and modelling in the field of ecology and land surface processes. At a global scale, normalized difference vegetation index (NDVI) products generated by different remote sensing satellites, have provided more than 40 years of time series data for LAI estimation. NDVI saturation issues are reported in agriculture and forest ecosystems at high LAI values, creating a challenge when using NDVI to estimate LAI. However, NDVI saturation is not reported on LAI estimation in grasslands. Previous research implies that non-photosynthetic vegetation (NPV) reduces the accuracy of LAI estimation from NDVI and other vegetation indices. A question arises: is the absence of NDVI saturation in grasslands a result of low LAI value, or is it caused by NPV? This study aims to explore whether there is an NDVI saturation issue in mixed grassland, and how NPV may influence LAI estimation by NDVI. In addition, in-situ measured plant area index (PAI) by sensors that detect light interception through the vegetation canopy (e.g., Li-cor LAI-2000), the most widely used field LAI collection method, might create bias in LAI estimation or validation using NDVI. Thus, this study also aims to quantify the contribution of green vegetation (GV) and NPV on in-situ measured PAI. The results indicate that NDVI saturation (using the portion of NDVI only contributed by GV) exists in grassland at high LAI (LAI threshold is much lower than that reported for other ecosystems in the literature), and that the presence of NPV can override the saturation effects of NDVI used to estimate green LAI. The results also show that GV and NPV in mixed grassland explain, respectively, the 60.33% and 39.67% variation of in-situ measured PAI by LAI-2000.

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