scholarly journals Monitoring Phenology in the Temperate Grasslands of China from 1982 to 2015 and Its Relation to Net Primary Productivity

2019 ◽  
Vol 12 (1) ◽  
pp. 12
Author(s):  
Chaobin Zhang ◽  
Ying Zhang ◽  
Zhaoqi Wang ◽  
Jianlong Li ◽  
Inakwu Odeh
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Temperate Grasslands ◽  
The North ◽  
Phenological Changes ◽  
Grass Growth ◽  
Global Inventory

Both vegetation phenology and net primary productivity (NPP) are crucial topics under the background of global change, but the relationships between them are far from clear. In this study, we quantified the spatial-temporal vegetation start (SOS), end (EOS), and length (LOS) of the growing season and NPP for the temperate grasslands of China based on a 34-year time-series (1982–2015) normalized difference vegetation index (NDVI) derived from global inventory modeling and mapping studies (GIMMS) and meteorological data. Then, we demonstrated the relationships between NPP and phenology dynamics. The results showed that more than half of the grasslands experienced significant changes in their phenology and NPP. The rates of their changes exhibited spatial heterogeneity, but their phenological changes could be roughly divided into three different clustered trend regions, while NPP presented a polarized pattern that increased in the south and decreased in the north. Different trend zones’ analyses revealed that phenology trends accelerated after 1997, which was a turning point. Prolonged LOS did not necessarily increase the current year’s NPP. SOS correlated with the NPP most closely during the same year compared to EOS and LOS. Delayed SOS contributed to increasing the summer NPP, and vice versa. Thus, SOS could be a predictor for current year grass growth. In view of this result, we suggest that future studies should further explore the mechanisms of SOS and plant growth.

scholarly journals Assessment of Drought Impact on Net Primary Productivity in the Terrestrial Ecosystems of Mongolia from 2003 to 2018

2021 ◽  
Vol 13 (13) ◽  
pp. 2522
Author(s):  
Lkhagvadorj Nanzad ◽  
Jiahua Zhang ◽  
Battsetseg Tuvdendorj ◽  
Shanshan Yang ◽  
Sonam Rinzin ◽  
...  
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystems ◽  
Severe Drought ◽  
Sparse Vegetation ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Boreal Ecosystem ◽  
The Impact

Drought has devastating impacts on agriculture and other ecosystems, and its occurrence is expected to increase in the future. However, its spatiotemporal impacts on net primary productivity (NPP) in Mongolia have remained uncertain. Hence, this paper focuses on the impact of drought on NPP in Mongolia. The drought events in Mongolia during 2003–2018 were identified using the Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI). The Boreal Ecosystem Productivity Simulator (BEPS)-derived NPP was computed to assess changes in NPP during the 16 years, and the impacts of drought on the NPP of Mongolian terrestrial ecosystems was quantitatively analyzed. The results showed a slightly increasing trend of the growing season NPP during 2003–2018. However, a decreasing trend of NPP was observed during the six major drought events. A total of 60.55–87.75% of land in the entire country experienced drought, leading to a 75% drop in NPP. More specifically, NPP decline was prominent in severe drought areas than in mild and moderate drought areas. Moreover, this study revealed that drought had mostly affected the sparse vegetation NPP. In contrast, forest and shrubland were the least affected vegetation types.

scholarly journals Assessment of net primary productivity over India using Indian geostationary satellite (INSAT-3A) data

2014 ◽  
Vol XL-8 ◽  
pp. 561-568 ◽  
Author(s):  
S. K. Goroshi ◽  
R. P. Singh ◽  
R. Pradhan ◽  
J. S. Parihar
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Carbon Fixation ◽  
Net Primary Productivity ◽  
Meteorological Data ◽  
Mixed Forest ◽  
Terrestrial Ecosystems ◽  
Spatial And Temporal Variability ◽  
Vegetation Types ◽  
Wide Range

Polar orbiting satellites (MODIS and SPOT) have been commonly used to measure terrestrial Net Primary Productivity (NPP) at regional/global scale. Charge Coupled Device (CCD) instrument on geostationary INSAT-3A platform provides a unique opportunity for continuous monitoring of ecosystem pattern and process study. An <i>improved</i> Carnegie-Ames-Stanford Approach (<i>i</i>CASA) model is one of the most expedient and precise ecosystem models to estimate terrestrial NPP. In this paper, an assessment of terrestrial NPP over India was carried out using the iCASA ecosystem model based on the INSAT CCD derived Normalized Difference Vegetation Index (NDVI) with multisource meteorological data for the year 2009. NPP estimated from the INSAT CCD followed the characteristic growth profile of most of the vegetation types in the country. NPP attained maximum during August and September, while minimum in April. Annual NPP for different vegetation types varied from 1104.55 gC m<sup>&minus;2</sup> year<sup>&minus;1</sup> (evergreen broadleaf forest) to 231.9 gC m<sup>&minus;2</sup> year<sup>&minus;1</sup> (grassland) with an average NPP of 590 gC m<sup>&minus;2</sup> year<sup>&minus;1</sup>. We estimated 1.9 PgC of net carbon fixation over Indian landmass in 2009. Biome level comparison between INSAT derived NPP and MODIS NPP indicated a good agreement with the Willmott’s index of agreement (d) ranging from 0.61 (Mixed forest) to 0.99 (Open Shrubland). Our findings are consistent with the earlier NPP studies in India and indicate that INSAT derived NPP has the capability to detect spatial and temporal variability of terrestrial NPP over a wide range of terrestrial ecosystems in India. Thus INSAT-3A data can be used as one of the potential satellite data source for accurate biome level carbon estimation in India.

Estimation of the average district winter wheat yield based on satellite and ground meteorological data

2020 ◽  
Vol 3 ◽  
pp. 103-121
Author(s):  
A.D. Kleschenko ◽  
◽  
O.V. Savitskaya ◽  
S.A. Kosyakin ◽  
◽  
...  
Keyword(s):  
Winter Wheat ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Wheat Yield ◽  
Condition Index ◽  
Area Index ◽  
The North ◽  
Independent Information ◽  
Winter Wheat Yield

The research results of the dependence of the average district winter wheat yield on satellite and ground meteorological information for the subjects of the North Caucasian and Volga UGMS are presented. The following satellite indices were used in the work: NDVI (Normalized Difference Vegetation Index), VCI (Vegetation Condition Index) and LAI (Leaf Area Index). The method of interpolation of inverse weighted squares of distances for obtain a set of meteorological parameters for districts there were no weather stations was used. Districts for taking into account agroclimatic conditions were combined into groups using Shashko's Agroclimatic Regionalization method. The selection of parameters that have the greatest impact on the yield was carried out using the correlation-regression analysis method. The corresponding regression models were obtained for the researched regions of the Russian Federation. Verification of the obtained models on dependent and independent information showed a fairly good result. Keywords: NDVI, LAI, interpolation, Shashko's Agroclimatic Regionalization, average district yield, meteorological information Tab. 5. Fig. 7. Ref. 20.

Comparing global models of terrestrial net primary productivity (NPP): comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI)

1999 ◽  
Vol 5 (S1) ◽  
pp. 25-34 ◽  
Author(s):  
A. L. Schloss ◽  
D. W. Kicklighter ◽  
J. Kaduk ◽  
U. Wittenberg ◽  
ThE. Participants OF. ThE. Potsdam Intercomparison
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Global Models

Air quality at open-cast coal mining regions at Hunter Valley, Australia and Ramagundam, India

2021 ◽  
Author(s):  
Harsh Kamath ◽  
Chanchal Chauhan ◽  
Sameer Mishra ◽  
Aariz Ahmed ◽  
Raman Srikanth
Keyword(s):  
Power Plants ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Data ◽  
Thermal Power ◽  
Coal Mines ◽  
Export Market ◽  
P Value ◽  
Open Cast ◽  
Monitoring Stations

&lt;p&gt;The upper Hunter Valley region in New South Wales (NSW), Australia has several open-cast coal mines, which supply coal to two large thermal power plants (TPPs) in the area, beside the export market. Long-term Particulate Matter (PM) pollutants and meteorological measurements are recorded by a network of 13 NSW government-owned continuous monitoring stations in the upper Hunter Valley region. The Ramagundam area in the state of Telangana, India has similar pollution source characteristics (coal mines and TPPs), but PM pollutant measurements are largely carried out with manual monitoring stations at 24-hour intervals, not more than twice a week. As the coal and overburden excavation from open-cast coal mines and stack emissions from TPPs lead to local PM pollution, we have used MODIS-MAIAC Aerosol Optical Depth (AOD) at 550 nm and Normalized Difference Vegetation Index (NDVI) along with the local meteorological data such as ambient temperature, relative humidity, wind speed and direction to model PM10 and PM2.5 at the upper Hunter Valley and Ramagundam regions. Our model can explain about 60% of variation in PM10 (p-value &lt; 0.0001), while a similar model is able to explain about 75% of the variation in the PM2.5 (p-value &lt; 0.0001). We will extend our model results from Hunter Valley to Ramagundam area and comment on the potential of using geospatial products such as AOD as a proxy to ground-based pollution measurements in developing countries such as India, where pollution data is scarce.&lt;/p&gt;

THE NORMALIZED DIFFERENCE VEGETATION INDEX AS AN INDICATOR OF DYNAMICS

2021 ◽  
Author(s):  
Haddad Amar ◽  
Beldjazia Amina ◽  
Kadi Zahia ◽  
Redjaimia Lilia ◽  
Rached-Kanouni Malika
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Climatic Factors ◽  
Mediterranean Ecosystems ◽  
Point Of View ◽  
Aleppo Pine ◽  
Landsat 8 ◽  
North East ◽  
The North ◽  
Pine Trees

Mediterranean ecosystems are considered particularly sensitive to climate change. Any change in climatic factors affects the structure and functioning of these ecosystems and has an influence on plant productivity. The main objective of this work is to characterize one of the Mediterranean ecosystems; the Chettaba forest massif (located in the North-East of Algeria) from a vegetation point of view and their link with monthly variations using Landsat 8 satellite images from five different dates (June 25, 2017, July 27, 2017, August 28, 2017, October 15, 2017). The comparison of NDVI values in Aleppo pine trees was performed using analysis of variance and the use of Friedman's non-parametric test. The Mann-Kendall statistical method was applied to the monthly distribution of NDVI values to detect any trends in the data over the study period. The statistical results of NDVI of Aleppo pine trees indicate that the maximum value is recorded in the month of June, while the lowest values are observed in the month of August where the species studied is exposed to periods of thermal stress.

scholarly journals Explicitly Identifying the Desertification Change in CMREC Area Based on Multisource Remote Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3170
Author(s):  
Zemeng Fan ◽  
Saibo Li ◽  
Haiyan Fang
Keyword(s):  
Climate Change ◽  
Human Activities ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Net Primary Productivity ◽  
Regression Tree ◽  
Google Earth ◽  
Classification And Regression Tree ◽  
Support Vector ◽  
Driving Mechanisms

Explicitly identifying the desertification changes and causes has been a hot issue of eco-environment sustainable development in the China–Mongolia–Russia Economic Corridor (CMREC) area. In this paper, the desertification change patterns between 2000 and 2015 were identified by operating the classification and regression tree (CART) method with multisource remote sensing datasets on Google Earth Engine (GEE), which has the higher overall accuracy (85%) than three other methods, namely support vector machine (SVM), random forest (RF) and Albedo-normalized difference vegetation index (NDVI) models. A contribution index of climate change and human activities on desertification was introduced to quantitatively explicate the driving mechanisms of desertification change based on the temporal datasets and net primary productivity (NPP). The results show that the area of slight desertification land had increased from 719,700 km2 to 948,000 km2 between 2000 and 2015. The area of severe desertification land decreased from 82,400 km2 to 71,200 km2. The area of desertification increased by 9.68%, in which 69.68% was mainly caused by human activities. Climate change and human activities accounted for 68.8% and 27.36%, respectively, in the area of desertification restoration. In general, the degree of desertification showed a decreasing trend, and climate change was the major driving factor in the CMREC area between 2000 and 2015.

scholarly journals Evaluating the Performance of Satellite-Derived Vegetation Indices for Estimating Gross Primary Productivity Using FLUXNET Observations across the Globe

2019 ◽  
Vol 11 (15) ◽  
pp. 1823 ◽  
Author(s):  
Xiaojuan Huang ◽  
Jingfeng Xiao ◽  
Mingguo Ma
Keyword(s):  
Primary Productivity ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Gross Primary Productivity ◽  
Surface Reflectance ◽  
Monthly Scale ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Annual Scale

Satellite-derived vegetation indices (VIs) have been widely used to approximate or estimate gross primary productivity (GPP). However, it remains unclear how the VI-GPP relationship varies with indices, biomes, timescales, and the bidirectional reflectance distribution function (BRDF) effect. We examined the relationship between VIs and GPP for 121 FLUXNET sites across the globe and assessed how the VI-GPP relationship varied among a variety of biomes at both monthly and annual timescales. We used three widely-used VIs: normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and 2-band EVI (EVI2) as well as a new VI - NIRV and used surface reflectance both with and without BRDF correction from the moderate resolution imaging spectroradiometer (MODIS) to calculate these indices. The resulting traditional (NDVI, EVI, EVI2, and NIRV) and BRDF-corrected (NDVIBRDF, EVIBRDF, EVI2BRDF, and NIRV, BRDF) VIs were used to examine the VI-GPP relationship. At the monthly scale, all VIs were moderate or strong predictors of GPP, and the BRDF correction improved their performance. EVI2BRDF and NIRV, BRDF had similar performance in capturing the variations in tower GPP as did the MODIS GPP product. The VIs explained lower variance in tower GPP at the annual scale than at the monthly scale. The BRDF-correction of surface reflectance did not improve the VI-GPP relationship at the annual scale. The VIs had similar capability in capturing the interannual variability in tower GPP as MODIS GPP. VIs were influenced by temperature and water stresses and were more sensitive to temperature stress than to water stress. VIs in combination with environmental factors could improve the prediction of GPP than VIs alone. Our findings can help us better understand how the VI-GPP relationship varies among indices, biomes, and timescales and how the BRDF effect influences the VI-GPP relationship.

scholarly journals Variations in Persistence and Regenerative Zones in Coastal Forests Triggered by Sea Level Rise and Storms

2019 ◽  
Vol 11 (17) ◽  
pp. 2019 ◽  
Author(s):  
Sergio Fagherazzi ◽  
Giovanna Nordio ◽  
Keila Munz ◽  
Daniele Catucci ◽  
William S. Kearney
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Forest Disturbance ◽  
Remote Sensing Data ◽  
Storm Events ◽  
Coastal Forests ◽  
The North ◽  
Significant Difference

Retreat of coastal forests in relation to sea level rise has been widely documented. Recent work indicates that coastal forests on the Delmarva Peninsula, United States, can be differentiated into persistence and regenerative zones as a function of sea-level rise and storm events. In the lower persistence zone trees cannot regenerate because of frequent flooding and high soil salinity. This study aims to verify the existence of these zones using spectral remote sensing data, and determine whether the effect of large storm events that cause damage to these forests can be detected from satellite images. Spectral analysis confirms a significant difference in average Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) values in the proposed persistence and regenerative zones. Both NDVI and NDWI indexes decrease after storms triggering a surge above 1.3 m with respect to the North American Vertical Datum of 1988 (NAVD88). NDWI values decrease more, suggesting that this index is better suited to detect the effect of hurricanes on coastal forests. In the regenerative zone, both NDVI and NDWI values recover three years after a storm, while in the persistence zone the NDVI and NDWI values keep decreasing, possibly due to sea level rise causing vegetation stress. As a result, the forest resilience to storms in the persistence zone is lower than in the regenerative zone. Our findings corroborate the ecological ratchet model of coastal forest disturbance.

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