Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

Goswami, Santonu; Gamon, John A.; Vargas, S. A.; Tweedie, C. E.

doi:10.7287/peerj.preprints.913v1

Cited by 54 publications

(57 citation statements)

References 29 publications

(55 reference statements)

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“…The simulation could proceed based on growing degree days, or a sub-function simulation may be required, which restricts the applications of these models [4,5,11,45] to some extent. In this study, the RNDVI models based on RAGDD were constructed for both Japonica and Indica high-yield rice types.…”

Section: Discussionmentioning

confidence: 99%

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Liu

Ferguson

Zheng

et al. 2017

Sensors

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The successful development of an optimal canopy vegetation index dynamic model for obtaining higher yield can offer a technical approach for real-time and nondestructive diagnosis of rice (Oryza sativa L) growth and nitrogen (N) nutrition status. In this study, multiple rice cultivars and N treatments of experimental plots were carried out to obtain: normalized difference vegetation index (NDVI), leaf area index (LAI), above-ground dry matter (DM), and grain yield (GY) data. The quantitative relationships between NDVI and these growth indices (e.g., LAI, DM and GY) were analyzed, showing positive correlations. Using the normalized modeling method, an appropriate NDVI simulation model of rice was established based on the normalized NDVI (RNDVI) and relative accumulative growing degree days (RAGDD). The NDVI dynamic model for high-yield production in rice can be expressed by a double logistic model: RNDVI=(1+e−15.2829×(RAGDDi−0.1944))−1−(1+e−11.6517×(RAGDDi−1.0267))−1 (R2 = 0.8577**), which can be used to accurately predict canopy NDVI dynamic changes during the entire growth period. Considering variation among rice cultivars, we constructed two relative NDVI (RNDVI) dynamic models for Japonica and Indica rice types, with R2 reaching 0.8764** and 0.8874**, respectively. Furthermore, independent experimental data were used to validate the RNDVI dynamic models. The results showed that during the entire growth period, the accuracy (k), precision (R2), and standard deviation of RNDVI dynamic models for the Japonica and Indica cultivars were 0.9991, 1.0170; 0.9084**, 0.8030**; and 0.0232, 0.0170, respectively. These results indicated that RNDVI dynamic models could accurately reflect crop growth and predict dynamic changes in high-yield crop populations, providing a rapid approach for monitoring rice growth status.

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Section: Discussionmentioning

confidence: 99%

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Liu

Ferguson

Zheng

et al. 2017

Sensors

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“…Several studies have shown that NDVI could be a good predictor of forest carbon at low forest carbon values 15,16 . Contrary to this conclusion, Table 1 shows the result of regression from the study area in the form of coefficient of determination (R 2 ).…”

Section: Resultsmentioning

confidence: 99%

Remote Sensing-Derived Spectral Vegetation Indices and Forest Carbon:Testing the Validity of Models in Mountainous Terrain Covered with High Biodiversity

Kumar

Ghose

2017

Current Science

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“…When relating NDVI to vegetation parameters, including Leaf Area Index, biomass and production, saturation in the relationship can occur [36,37]. Saturation of the NDVI-ANPP relationship was observed in the summer images and is shown by the lack of relationship between NDVI and ln(ANPP) (Figure 3 July images) and the lack of spread of NDVI values in the summer ( Figure 5).…”

Section: Limitations and Future Workmentioning

confidence: 99%

Regional-Scale High Spatial Resolution Mapping of Aboveground Net Primary Productivity (ANPP) from Field Survey and Landsat Data: A Case Study for the Country of Wales

et al. 2017

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This paper presents an alternative approach for high spatial resolution vegetation productivity mapping at a regional scale, using a combination of Normalised Difference Vegetation Index (NDVI) imagery and widely distributed ground-based Above-ground Net Primary Production (ANPP) estimates. Our method searches through all available single-date NDVI imagery to identify the images which give the best NDVI-ANPP relationship. The derived relationships are then used to predict ANPP values outside of field survey plots. This approach enables the use of the high spatial resolution (30 m) Landsat 8 sensor, despite its low revisit frequency that is further reduced by cloud cover. This is one of few studies to investigate the NDVI-ANPP relationship across a wide range of temperate habitats and strong relationships were observed (R 2 = 0.706), which increased when only grasslands were considered (R 2 = 0.833). The strongest NDVI-ANPP relationships occurred during the spring "green-up" period. A reserved subset of 20% of ground-based ANPP estimates was used for validation and results showed that our method was able to estimate ANPP with a RMSE of 15-21%. This work is important because we demonstrate a general methodological framework for mapping of ANPP from local to regional scales, with the potential to be applied to any temperate ecosystems with a pronounced green up period. Our approach allows spatial extrapolation outside of field survey plots to produce a continuous surface product, useful for capturing spatial patterns and representing small-scale heterogeneity, and well-suited for modelling applications. The data requirements for implementing this approach are also discussed.

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Relationships of NDVI, Biomass, and Leaf Area Index (LAI) for six key plant species in Barrow, Alaska

Cited by 54 publications

References 29 publications

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Using an Active-Optical Sensor to Develop an Optimal NDVI Dynamic Model for High-Yield Rice Production (Yangtze, China)

Remote Sensing-Derived Spectral Vegetation Indices and Forest Carbon:Testing the Validity of Models in Mountainous Terrain Covered with High Biodiversity

Regional-Scale High Spatial Resolution Mapping of Aboveground Net Primary Productivity (ANPP) from Field Survey and Landsat Data: A Case Study for the Country of Wales

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