Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands

Wylie, Bruce K.; Meyer, David; Tieszen, Larry L.; Mannel, Sylvio

doi:10.1016/s0034-4257(01)00278-4

Cited by 133 publications

(80 citation statements)

References 27 publications

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“…Our results do not support the applicability of a proxy-biomass relationship in a subsequent year as found by Wylie et al (2002), and question the use of a single relationship across multiple years in long-term studies (Brancaleoni et al, 2007;Filella et al, 2004;Lee et al, 2014;Wardle et al, 2016;Wu et al, 2012;Yahdjian and Sala, 2006).…”

Section: Discussioncontrasting

confidence: 70%

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Ónodi

Kertész

Kovács-Láng

et al. 2017

Ecological Indicators

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Direct measurements of aboveground plant biomass are often not feasible, thus various biomass proxies are in use. To obtain biomass estimates, these proxies are calibrated against actual biomass, and the resulting proxy-biomass relationship is often used across multiple years and experimental treatments within a study. We investigated how the proxy-biomass relationship varied across years and considered interannual precipitation variability as a contributing factor.We sampled a perennial grassland for ten consecutive years (2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012) in central Hungary and estimated vegetation cover and Normalized Difference Vegetation Index (NDVI); two frequently used biomass proxies representing two contrasting methods. Aboveground live herbaceous plant biomass was harvested from each plot after sampling, and regression models were used to assess the relationship between biomass proxies and actual aboveground biomass.We found that cover and NDVI were equally effective at estimating biomass. However, the relationship between either biomass proxy and actual biomass varied amongst years, and this was related to the amount of precipitation. In wetter years, proxy-biomass relationships were steeper than in drier years.These results indicate that using the same proxy-biomass relationship across different years or precipitation regimes may not be valid and may introduce systematic error into biomass estimations in long-term studies or precipitation manipulation experiments. Keywords aboveground plant biomass, field spectroscopy, interannual validity, long-term studies, NDVI, precipitation, proxy-biomass relationship, visual cover estimation Nomenclature The Plant List (2010) 4

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Section: Discussioncontrasting

confidence: 70%

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Ónodi

Kertész

Kovács-Láng

et al. 2017

Ecological Indicators

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“…In comparison with results for aboveground biomass shown in Table 3, Landsat-based vegetation indices have a much improved power of explanation for foliage biomass (Table 4). This is in good agreement with the fact that optical sensors mainly capture canopy information, thus the optical sensor data are more suitable for estimation of canopy parameters such as foliage biomass than aboveground biomass, as demon stared by www.intechopen.com previous studies (Franklin & Hiernaux, 1991;Hall et al, 1995;Chen & Cihlar 1996;Turner et al, 1999;Brown et al, 2000;Chen et al, 2002;Wylie et al, 2002;Phua & Saito, 2003;Laidler & Treitz, 2003;Lu, 2004). The best fit relationship between foliage biomass (B f , g m -2 ) and Landsat-based simple ratio (SR) over the Porcupine caribou habitat is given as follows…”

Section: Relationships Between Foliage Biomass and Remote Sensing Indsupporting

confidence: 80%

Mapping Aboveground and Foliage Biomass Over the Porcupine Caribou Habitat in Northern Yukon and Alaska Using Landsat and JERS-1/SAR Data

Chen¹,

Li²,

Fraser³

et al. 2012

Remote Sensing of Biomass - Principles and Applications

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“…Previous studies have identified strong relationships between vegetation indices and AGB for grassland ecosystems (Wylie et al, 2002). For instance, Dancy et al (1986) reported an R 2 of 0.72 when deriving ground cover in Botswana rangelands and Beeri et al (2007) reported a prediction error of 18% for total biomass in moderately grazed pastures due to overestimation by NDVI.…”

Section: Introductionmentioning

confidence: 97%

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

Mundava

Helmholz

Schut

et al. 2014

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The objective of this paper is to test the relationships between Above Ground Biomass (AGB) and remotely sensed vegetation indices for AGB assessments in the Kimberley area in Western Australia. For 19 different sites, vegetation indices were derived from eight Landsat ETM+ scenes over a period of two years (2011)(2012)(2013). The sites were divided into three groups (Open plains, Bunch grasses and Spinifex) based on similarities in dominant vegetation types. Dry and green biomass fractions were measured at these sites. Single and multiple regression relationships between vegetation indices and green and total AGB were calibrated and validated using a "leave site out" cross validation. Four tests were compared: 1) relationships between AGB and vegetation indices combining all sites; 2) separate relationships per site group; 3) multiple regressions including selected vegetation indices per site group; and 4) as in 3 but including rainfall and elevation data.

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Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands

Cited by 133 publications

References 27 publications

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Estimating aboveground herbaceous plant biomass via proxies: The confounding effects of sampling year and precipitation

Mapping Aboveground and Foliage Biomass Over the Porcupine Caribou Habitat in Northern Yukon and Alaska Using Landsat and JERS-1/SAR Data

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

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