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

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.

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Evaluating the difference between the normalized difference vegetation index and net primary productivity as the indicators of vegetation vigor assessment at landscape scale

Environmental Monitoring and Assessment ◽

10.1007/s10661-011-2039-1 ◽

2011 ◽

Vol 184 (3) ◽

pp. 1275-1286 ◽

Cited By ~ 32

Author(s):

Chi Xu ◽

Yutong Li ◽

Jian Hu ◽

Xuejiao Yang ◽

Sheng Sheng ◽

...

Keyword(s):

Primary Productivity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Productivity ◽

Landscape Scale ◽

The Difference

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

Remote Sensing ◽

10.3390/rs13132522 ◽

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.

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

Remote Sensing ◽

10.3390/rs12193170 ◽

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.

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

Remote Sensing ◽

10.3390/rs11151823 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1823 ◽

Cited By ~ 9

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.

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Is the Global Vegetation Index a Good Predictor of Net Primary Productivity and Actual Evapotransportation?

[Proceedings] IGARSS '92 International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.1992.578313 ◽

2005 ◽

Author(s):

D. Lo Seen ◽

E. Mougin ◽

J.P. Gastellu-Etchegorry

Keyword(s):

Primary Productivity ◽

Good Predictor ◽

Vegetation Index ◽

Net Primary Productivity ◽

Global Vegetation

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Rapid primary productivity changes in one of the last coastal rainforests: the case of Kahua, Solomon Islands

Environmental Conservation ◽

10.1017/s0376892909990208 ◽

2009 ◽

Vol 36 (3) ◽

pp. 253-260 ◽

Cited By ~ 20

Author(s):

IRENE GARONNA ◽

IOAN FAZEY ◽

MOLLY E. BROWN ◽

NATHALIE PETTORELLI

Keyword(s):

Remote Sensing ◽

Primary Productivity ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Solomon Islands ◽

Remote Sensing Data ◽

Human Populations ◽

Sensing Data ◽

Relative Contribution ◽

Indirect Impacts

SUMMARYThe growth of human populations has many direct and indirect impacts on tropical forest ecosystems both locally and globally. This is particularly true in the Solomon Islands, where coastal rainforest cover still remains, but where climate change and a growing human population is putting increasing pressure on ecosystems. This study assessed recent primary productivity changes in the Kahua region (Makira, Solomon Islands) using remote sensing data (normalized difference vegetation index, NDVI). In this area, there has been no commercial logging and there is no existing information about the state of the forests. Results indicate that primary productivity has been decreasing in recent years, and that the recent changes are more marked near villages. Multiple factors may explain the reported pattern in primary productivity. The study highlights the need to (1) assess how accurately remote sensing data-based results match field data on the ground; (2) identify the relative contribution of the climatic, socioeconomic and political drivers of such changes; and (3) evaluate how primary productivity changes affect biodiversity level, ecosystem functioning and human livelihoods.

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Comparing global models of terrestrial net primary productivity (NPP): analysis of the seasonal atmospheric CO 2 signal

Global Change Biology ◽

10.1046/j.1365-2486.1999.00008.x ◽

1999 ◽

Vol 5 (S1) ◽

pp. 65-76 ◽

Cited By ~ 25

Author(s):

B. Nemry ◽

L. Fran&ccedilOis ◽

J. ‐C. Gérard ◽

A. Bondeau ◽

M. Heimann ◽

...

Keyword(s):

Primary Productivity ◽

Net Primary Productivity ◽

Global Models

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SPATIO-TEMPORAL SIMULATION AND ANALYSIS OF REGIONAL ECOLOGICAL SECURITY BASED ON LSTM

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w2-153-2017 ◽

2017 ◽

Vol IV-4/W2 ◽

pp. 153-160 ◽

Cited By ~ 1

Author(s):

C. Gong ◽

L. Qi ◽

L. Heming ◽

H. Karimian ◽

M. Yuqin

Keyword(s):

Deep Learning ◽

High Speed ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Net Primary Productivity ◽

Learning Ability ◽

Scientific Management ◽

Positive Trend ◽

Ecological Deficit ◽

Spatio Temporal

Region is a complicated system, where human, nature and society interact and influence. Quantitative modeling and simulation of ecology in the region are the key to realize the strategy of regional sustainable development. Traditional machine learning methods have made some achievements in the modeling of regional ecosystems, but it is difficult to determine the learning characteristics and to realize spatio-temporal simulation. Deep learning does not need prior identification of training characteristics, have excellent feature learning ability, can improve the accuracy of model prediction, so the use of deep learning model has a significant advantage. Therefore, we use net primary productivity (NPP), atmospheric optical depth (AOD), moderate-resolution imaging spectrometer (MODIS), Normalized Difference Vegetation Index (NDVI), landcover and population data, and use LSTM to do spatio-temporal simulation. We conduct spatial analysis and driving force analysis. The conclusions are as follows: the ecological deficit of northwestern Henan and urban communities such as Zhengzhou is higher. The reason of former lies in the weak land productivity of the Loess Plateau, the irrational crop cultivation mode. The latter lies in the high consumption of resources in the large urban agglomeration; The positive trend of Henan ecological development from 2013 is mainly due to the effective environmental protection policy in the 12th five-year plan; The main driver of the sustained ecological deficit growth of Henan in 2004-2013 is high-speed urbanization, increasing population and goods consumption. This article provides relevant basic scientific support and reference for the regional ecological scientific management and construction.

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