Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra

Boelman, Natalie T.; Stieglitz, Marc; Rueth, Heather M.; Sommerkorn, Martin; Griffin, Kevin L.; Shaver, Gaius R.; Gamon, John A.

doi:10.1007/s00442-003-1198-3

Cited by 211 publications

(194 citation statements)

References 55 publications

(75 reference statements)

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“…Previous attempts at relating an optical vegetation index (NDVI) to above-ground phytomass for this environment were mixed [9], with r 2 values for Cape Bounty of 0.55. Other studies have found stronger relationships [3,37,59], but were at much coarser spatial resolutions. To show that the ANN results (estimated SAVI values from SAR variables) can be used to predict actual ground-based measures of above-ground phytomass, regression analyses were used with field-sampled values of above-ground phytomass collected for previous research [9].…”

Section: Vegetation Modelingmentioning

confidence: 79%

“…Vegetation is very limited in arctic environments, especially in the High Arctic bioclimatic zones [1,2]. The limited vegetation that is present, however, can have significant effects on the terrestrial carbon balance [3,4] and methane fluxes [5]. Further, knowledge of vegetation density is important for monitoring forage quality and quantity for ungulates such as Peary Caribou (listed as Endangered in the Canadian Species At Risk Act) and muskoxen [6,7].…”

Section: Introductionmentioning

confidence: 99%

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Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

et al. 2014

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Vegetation in the Arctic is often sparse, spatially heterogeneous, and difficult to model. Synthetic Aperture Radar (SAR) has shown some promise in above-ground phytomass estimation at sub-arctic latitudes, but the utility of this type of data is not known in the context of the unique environments of the Canadian High Arctic. In this paper, Artificial Neural Networks (ANNs) were created to model the relationship between variables derived from high resolution multi-incidence angle RADARSAT-2 SAR data and optically-derived (GeoEye-1) Soil Adjusted Vegetation Index (SAVI) values. The modeled SAVI values (i.e., from SAR variables) were then used to create maps of above-ground phytomass across the study area. SAVI model results for individual ecological classes of polar semi-desert, mesic heath, wet sedge, and felsenmeer were reasonable, with r 2 values of 0.43, 0.43, 0.30, and 0.59, respectively. When the outputs of these models were combined to analyze the relationship between the model output and SAVI as a group, the r 2 value was 0.60, with an 8% normalized root mean square error (% of the total range of phytomass values), a positive indicator of a relationship. The above-ground phytomass model also resulted in a very strong relationship (r 2 = 0.87) between SAR-modeled and field-measured phytomass. A positive relationship was also found between optically derived SAVI values and field measured phytomass (r 2 = 0.79). These relationships demonstrate the utility of SAR data, compared to using optical data alone, for modeling OPEN ACCESSRemote Sens. 2014, 6 2135 above-ground phytomass in a high arctic environment possessing relatively low levels of vegetation.

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Section: Vegetation Modelingmentioning

confidence: 79%

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

et al. 2014

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“…Studies on the relationships of the spectral characteristics of the vegetation cover with CO 2 fluxes measured using the chamber technique have been conducted relatively rarely till now (Boelman et al, 2003;Dagg and Lafleur, 2010;Perez-Priego et al, 2015). However, considering that both chamber and spectral measurements can be performed at a small scale of crop fields where the EC technique cannot be applied, our aim was to demonstrate how these two methods can be successfully combined to develop simple empirical models which allow estimation of the GEP and NEP of different crops with remote sensing.…”

Section: Introductionmentioning

confidence: 99%

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Uździcka¹,

Stróżecki²,

Urbaniak³

et al. 2017

International Agrophysics

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'Assessment of a seasonal and spatial variation of plant biophysical and spectral indices in the context of CO 2 , CH 4 , H 2 O gas exchange between wetland, forest and arable ecosystems and the atmosphere', 2010-2013.A b s t r a c t. The aim of this paper is to demonstrate that spectral vegetation indices are good indicators of parameters describing the intensity of CO 2 exchange between crops and the atmosphere. Measurements were conducted over 2011-2013 on plots of an experimental arable station on winter wheat, winter rye, spring barley, and potatoes. CO 2 fluxes were measured using the dynamic closed chamber system, while spectral vegetation indices were determined using SKYE multispectral sensors. Based on spectral data collected in 2011 and 2013, various models to estimate net ecosystem productivity and gross ecosystem productivity were developed. These models were then verified based on data collected in 2012. The R 2 for the best model based on spectral data ranged from 0.71 to 0.83 and from 0.78 to 0.92, for net ecosystem productivity and gross ecosystem productivity, respectively. Such high R 2 values indicate the utility of spectral vegetation indices in estimating CO 2 fluxes of crops. The effects of the soil background turned out to be an important factor decreasing the accuracy of the tested models.

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“…The NDVI, which is proved to directly relate to the land cover condition, is applied to estimating primary productivity. The possibility of predicting average primary productivity by its regression relationship with NDVI has been observed in the Arctic tundra (Boelman et al, 2003) and in steppe ecosystems (Wylie et al, 2003). NDVI tends to be saturated when biomass increases to a high value (Myneni et al, 1995;Hao et al, 2008), this being especially obvious in evergreen forest area (Rahman et al, 2001).…”

Section: Carbon Flux Related Ecosystem Servicesmentioning

confidence: 99%

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

Feng

Yang

et al. 2010

Chin. Geogr. Sci.

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Ecosystem service is an emerging concept that grows to be a hot research area in ecology. Spatially explicit ecosystem service values are important for ecosystem service management. However, it is difficult to quantify ecosystem services. Remote sensing provides images covering Earth surface, which by nature are spatially explicit. Thus, remote sensing can be useful for quantitative assessment of ecosystem services. This paper reviews spatially explicit ecosystem service studies conducted in ecology and remote sensing in order to find out how remote sensing can be used for ecosystem service assessment. Several important areas considered include land cover, biodiversity, and carbon, water and soil related ecosystem services. We found that remote sensing can be used for ecosystem service assessment in three different ways: direct monitoring, indirect monitoring, and combined use with ecosystem models. Some plant and water related ecosystem services can be directly monitored by remote sensing. Most commonly, remote sensing can provide surrogate information on plant and soil characteristics in an ecosystem. For ecosystem process related ecosystem services, remote sensing can help measure spatially explicit parameters. We conclude that acquiring good in-situ measurements and selecting appropriate remote sensor data in terms of resolution are critical for accurate assessment of ecosystem services.

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Response of NDVI, biomass, and ecosystem gas exchange to long-term warming and fertilization in wet sedge tundra

Cited by 211 publications

References 55 publications

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Artificial Neural Network Modeling of High Arctic Phytomass Using Synthetic Aperture Radar and Multispectral Data

Dependence of spectral characteristics on parameters describing CO2 exchange between crop species and the atmosphere

Remote sensing of ecosystem services: An opportunity for spatially explicit assessment

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