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 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 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 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.

scholarly journals Estimate the Impact of Payments for Environmental Services on Local Livelihoods and Environment: An Application of Propensity Scores

SAGE Open ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 215824402110407
Author(s):  
Thang Quyet Nguyen ◽  
Nguyen Tan Huynh ◽  
Wen-Kai K. Hsu
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Propensity Scores ◽  
Environmental Services ◽  
Payments For Environmental Services ◽  
Agricultural Income ◽  
Local Livelihoods ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Hired Labor ◽  
The Impact

Payments for environmental services (PES) are usually considered as a useful tool to both protect the environment and generate multiple income streams for mountainous households who receive the payments, and thus, it has been widely implementing in many developing countries so far; however, the impact of it on local livelihoods and environment has been questioned. Therefore, the article aimed to evaluate the Vietnamese PES scheme’s effect on both environment and local livelihoods by surveying 282 households living in Quang Nam, Vietnam, and utilized the propensity score matching (PSM) technique to investigate the intervention policy’s influence. Furthermore, to evaluate PES’s effect on the environment, the article used Normalized Difference Vegetation Index (NDVI) as a measure of the photosynthetic level of forest trees. The calculation of NDVI relied on satellite images downloaded from the Moderate Resolution Imaging Spectroradiometer. The results indicated that (a) the natural forest status has been improved during PES implementation compared with that of pre-PES period regarding total forest areas, percentage of forest coverage, and vegetation cover; and (b) PES-participants have got a significantly lower income than nonparticipants regarding total annual income, agricultural income, and hired labor income. The limitation is that the impact of interventions on livelihoods and the environment is determined by the mutual combination of implemented programs rather than only the PES regime. So, we highly recommend that the future study separate the PES scheme’s actual impact to precisely evaluate the PES project’s effect on financial and environmental outcomes.

scholarly journals GRASSLAND NPP MONITORING BASED ON MULTI-SOURCE REMOTE SENSING DATA FUSION

2018 ◽  
Vol XLII-3 ◽  
pp. 113-118
Author(s):  
Y. R. Cai ◽  
J. H. Zheng ◽  
M. J. Du ◽  
C. Mu ◽  
J. Peng
Keyword(s):  
Carbon Cycle ◽  
Primary Productivity ◽  
Vegetation Index ◽  
Net Primary Productivity ◽  
Terrestrial Ecosystem ◽  
Remote Sensing Data ◽  
Terrestrial Ecosystems ◽  
The United States ◽  
Estimation Accuracy ◽  
Modis Ndvi

Vegetation is an important part of the terrestrial ecosystem. It plays an important role in the energy and material exchange of the ground-atmosphere system and is a key part of the global carbon cycle process.Climate change has an important influence on the carbon cycle of terrestrial ecosystems. Net Primary Productivity (Net Primary Productivity)is an important parameter for evaluating global terrestrial ecosystems. For the Xinjiang region, the study of grassland NPP has gradually become a hot issue in the ecological environment.Increasing the estimation accuracy of NPP is of great significance to the development of the ecosystem in Xinjiang. Based on the third-generation GIMMS AVHRR NDVI global vegetation dataset and the MODIS NDVI (MOD13A3) collected each month by the United States Atmospheric and Oceanic Administration (NOAA),combining the advantages of different remotely sensed datasets, this paper obtained the maximum synthesis fusion for New normalized vegetation index (NDVI) time series in 2006&amp;ndash;2015.Analysis of Net Primary Productivity of Grassland Vegetation in Xinjiang Using Improved CASA Model The method described in this article proves the feasibility of applying data processing, and the accuracy of the NPP calculation using the fusion processed NDVI has been greatly improved. The results show that: (1) The NPP calculated from the new normalized vegetation index (NDVI) obtained from the fusion of GIMMS AVHRR NDVI and MODIS NDVI is significantly higher than the NPP calculated from these two raw data; (2) The grassland NPP in Xinjiang Interannual changes show an overall increase trend; interannual changes in NPP have a certain relationship with precipitation.

scholarly journals Response of net primary productivity to grassland phenological changes in Xinjiang, China

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10650
Author(s):  
Renping Zhang ◽  
Jing Guo ◽  
Gang Yin
Keyword(s):  
Primary Productivity ◽  
Net Primary Productivity ◽  
Growing Season ◽  
Casa Model ◽  
Phenological Changes ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Phenological Metrics ◽  
The Impact

Determining the relationship between net primary productivity (NPP) and grassland phenology is important for an in-depth understanding of the impact of climate change on ecosystems. In this study, the NPP of grassland in Xinjiang, China, was simulated using the Carnegie-Ames-Stanford approach (CASA) model with Moderate Resolution Imaging Spectroradiometer (MODIS) grassland phenological (MCD12Q2) data to study trends in phenological metrics, grassland NPP, and the relations between these factors from 2001–2014. The results revealed advancement of the start of the growing season (SOS) for grassland in most regions (55.2%) in Xinjiang. The percentage of grassland area in which the end of the growing season (EOS) was delayed (50.9%) was generally the same as that in which the EOS was advanced (49.1%). The percentage of grassland area with an increase in the length of the growing season (LOS) for the grassland area (54.6%) was greater than that with a decrease in the LOS (45.4%). The percentage of grassland area with an increase in NPP (61.6%) was greater than that with a decrease in NPP (38.4%). Warmer regions featured an earlier SOS and a later EOS and thus a longer LOS. Regions with higher precipitation exhibited a later SOS and an earlier EOS and thus a shorter LOS. In most regions, the SOS was earlier, and spring NPP was higher. A linear statistical analysis showed that at various humidity (K) levels, grassland NPP in all regions initially increased but then decreased with increasing LOS. At higher levels of K, when NPP gradually increased, the LOS gradually decreased.

scholarly journals The Midsummer Dry Spell’s Impact on Vegetation in Jamaica

2010 ◽  
Vol 49 (7) ◽  
pp. 1590-1595 ◽  
Author(s):  
Theodore L. Allen ◽  
Scott Curtis ◽  
Douglas W. Gamble
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Tropical Rainfall Measuring Mission ◽  
Early Summer ◽  
Annual Rainfall ◽  
Dry Spell ◽  
Moderate Resolution ◽  
Rainfall Reduction ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Abstract The annual rainfall pattern of the intra-Americas sea reveals a bimodal feature with a minimum during the midsummer known as the midsummer dry spell (MSD). A first attempt is made to examine the impact of the MSD on vegetation through a normalized difference vegetation index (NDVI) analysis in Jamaica. Tropical Rainfall Measuring Mission rainfall estimates and NDVI derived from the Terra Moderate Resolution Imaging Spectroradiometer highlight a consistent MSD feature in both rainfall and vegetative vigor. Spatial variation of this MSD NDVI response is evident throughout Jamaica, with the strongest relationship between the rainfall reduction and NDVI decline throughout the southern portions of Jamaica including the area of major domestic food production. In all years except 2005 there is a notable reduction from early-summer NDVI to midsummer NDVI in this agricultural region. However, the lagged vegetative response undergoes clear interannual variation and is affected by other forcings besides rainfall, such as brush fires and extreme wind.

scholarly journals A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

2018 ◽  
Vol 15 (19) ◽  
pp. 5779-5800 ◽  
Author(s):  
Yao Zhang ◽  
Joanna Joiner ◽  
Seyed Hamed Alemohammad ◽  
Sha Zhou ◽  
Pierre Gentine
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Clear Sky ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Regression Slopes ◽  
The Individual ◽  
Sky Conditions

Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

scholarly journals Is Drought Increasing in Maine and Hurting Wild Blueberry Production?

Climate ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 178
Author(s):  
Kallol Barai ◽  
Rafa Tasnim ◽  
Bruce Hall ◽  
Parinaz Rahimzadeh-Bajgiran ◽  
Yong-Jiang Zhang
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Economic Losses ◽  
Severe Drought ◽  
Water Conditions ◽  
Wild Blueberries ◽  
The Impact ◽  
Wild Blueberry

A few severe drought events occurred in the Northeast (NE) USA in recent decades and caused significant economic losses, but the temporal pattern of drought incidents and their impacts on agricultural systems have not been well assessed. Here, we analyzed historical changes and patterns of drought using a drought index (standardized precipitation-evapotranspiration index (SPEI)), and assessed drought impacts on remotely sensed vegetation indices (enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) and production (yield) of the wild blueberry fields in Maine, USA. We also analyzed the impact of short- and long-term water conditions of the growing season on the wild blueberry vegetation condition and production. No significant changes in the SPEI were found in the past 71 years, despite a significant warming pattern. There was also a significant relationship between the relatively long-term SPEI and the vegetation indices (EVI and NDVI), but not the short-term SPEI (one year). This suggests that the crop vigor of wild blueberries is probably determined by water conditions over a relatively long term. There were also significant relationships between 1-year water conditions (SPEI) and yield for a non-irrigated field, and between 4-year-average SPEI and the yield of all fields in Maine. The vegetation indices (EVI and NDVI) are not good predictors of wild blueberry yield, possibly because wild blueberry yield does not only depend on crop vigor, but also on other important variables such as pollination. We also compared an irrigated and a non-irrigated wild blueberry field at the same location (Deblois, Maine) where we found that irrigation decoupled the relationship between the SPEI and NDVI or EVI.

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