Measuring the dead component of mixed grassland with Landsat imagery

Xu, Dandan; Guo, Xulin; Li, Zhaoqin; Yang, Xiaohui; Yin, Han

doi:10.1016/j.rse.2013.11.017

Cited by 70 publications

(49 citation statements)

References 84 publications

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“…The spectra for the various components in the landscape also show some confusion between senescent vegetation, litter, and green lichens, which also confounds the ability to differentiate the different components. Ambiguity in the separation of NPV and soil, moss, and lichens using the SWIR2 region has been noted previously (Numata et al 2008;Xu et al 2014). Our results confirm that discrimination of NPV and background components is difficult using sensors such as Landsat due to the absence of a narrow ligno-cellulose band.…”

Section: Discussionsupporting

confidence: 85%

“…Although some authors report a good relationship between total and/or green biomass or LAI and the NDVI (r 2 = 0.68-0.92; Thomson et al 1985;Wylie et al 1996;Paruelo et al 2000;Gianelle and Vescovo 2007), others observed only weak relationships (r 2 = 0.01 to 0.42; Boschetti et al 2007;Numata et al 2008;Zhang et al 2008). The variation in the strength of these relationships has been attributed to a number of factors including the density of the vegetation (Price, Pyke, and Mendes 1992), the nature of the soil background (Huete et al 1992;Paruelo et al 1997), and the amount of NPV (Todd et al 1998;Brinkmann et al 2011;Yang et al 2013;Xu et al 2014). In an attempt to reduce the influence of B and NPV, soil-adjusted vegetation indices such as the Soil-Adjusted Total Vegetation Index (SATVI; Marsett et al 2006), Soil-Adjusted Vegetation Index (SAVI; Huete 1988) or the Modified Soil-Adjusted Vegetation Index (MSAVI; Qi et al 1994), and the Normalized Difference Senescent Vegetation Index (NDSVI; Qi et al 2002) were developed.…”

Section: Introductionmentioning

confidence: 99%

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Estimating Ground Cover in the Mixed Prairie Grassland of Southern Alberta Using Vegetation Indices Related to Physiological Function

Smith

Hill

Zhang

2015

Canadian Journal of Remote Sensing

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Native grasslands are an important forage resource for the cattle industry and play a vital role in hydrological, carbon, and nutrient cycles; energy flow; faunal and floral biodiversity; and recreational services. Despite their importance, information on the health of Canada's native grasslands is extremely limited. This study investigated the use of functional relationships, namely, differences in the Normalized Difference Vegetation Index (NDVI) and a shortwave infrared index (SWIR75 or CAI) related to cellulose and lignin content, along with spectral mixture analysis (SMA), to derive estimates of broad categories of grassland ground cover at 4 test sites established in the mixed prairie grassland in southern Alberta, Canada. Field campaigns were carried out in 2009 and 2010, at peak grass production, to collect fractional ground cover of photosynthetic vegetation (f PV), nonphotosynthetic vegetation (f NPV), and background (f B), as well as ground spectra of various grassland components. The ability to separate PV, NPV, and B using NDVI and the SWIR75 or CAI indices derived from field spectroradiometer data and Landsat-5 TM data was investigated. Although reasonable estimates of f PV were derived using the NDVI and SWIR75 indices with SMA (r = 0.82) from Landsat imagery, the similarity in the spectral characteristics of soil, lichens, litter, and standing senescent vegetation confounded the ability to estimate f NPV and f B. In the absence of a hyperspectral satellite system offering the ability to derive CAI, the ability to evaluate grassland health in the mixed grass prairie is limited. Résumé. Les pâturages naturels sont une ressource fourragère importante pour l'industrie du bétail et jouent un rôle vital dans les cycles hydrologique, du carbone et deséléments nutritifs, ainsi que dans les flux d'énergie, la biodiversité de la faune et de la flore, et la fourniture des services récréatifs. Malgré leur importance, les informations sur la santé des pâturages naturels du Canada sont extrêmement limitées. Cetteétude a examiné l'utilisation des relations fonctionnelles,à savoir les différences de l'indice de végétation par différence normalisée (NDVI) et un indice d'ondes courtes (SWIR75 ou CAI) liéà la cellulose et la teneur en lignine, avec l'analyse des spectres mixtes (SMA) pour obtenir des estimations de grandes catégories de couverture végétale. Quatre sites d'essai ontétéétablis dans la prairie mixte du sud de l'Alberta, Canada. Les campagnes de terrain ontété réalisées en 2009 et 2010, au pic de production de l'herbe, afin de recueillir la couverture fractionnaire du sol de la végétation photosynthétique (f PV), la végétation non photosynthétique (f NPV) et le fond (f B), ainsi que des spectres du sol des différentes composantes de prairies. La capacité de séparer PV, NPV, et B en utilisant les indices NDVI et SWIR75 (ou CAI) dérivés des données spectroradiométriques de terrain et des données Landsat-5 TM aétéétudiée. Bien que des estimations raisonnables de f PV ontété calculées en utilisant les indic...

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Section: Discussionsupporting

confidence: 85%

Section: Introductionmentioning

confidence: 99%

Estimating Ground Cover in the Mixed Prairie Grassland of Southern Alberta Using Vegetation Indices Related to Physiological Function

Smith

Hill

Zhang

2015

Canadian Journal of Remote Sensing

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“…The results of this study, that the thermal band is sensitive to litter cover difference, proves the ecological function in the theory that litter reduces the ground surface temperature (Xu et al . ). Biomass (Tucker ; Tucker et al .…”

Section: Discussionmentioning

confidence: 97%

Evaluating the impacts of nearly 30 years of conservation on grassland ecosystem using Landsat TM images

Guo

2015

Grassland Science

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When grassland degradation became a global issue because of overgrazing and other human activities, grassland managers implemented different management methods in an attempt to restore grassland ecosystems (e.g. conservation actions). Few studies have investigated the impacts of conservation actions (removing large grazers and conserving biodiversity) on grassland ecosystems, therefore, this study aims to evaluate the impacts of conservation actions and to measure the time lag of the significant influences of grassland conservations on mixed grasslands that were assessed by five different biophysical parameters (biodiversity, soil organic matter, fresh biomass, litter cover and green cover). Instead of measuring the biophysical parameters from field data and remotely sensed images, the methodology of this study focused on the difference in biophysical parameters between the ecological comparison sites (grazing sites and conserved sites), which enhance the impacts of grassland conservation on grasslands comparing to grazing management. The results show that: (i) biodiversity in conserved grasslands increased gradually in the first 3-5 years, decreased gradually over the next 4 or 5 years, and then stabilized after that; (ii) soil organic matter increased and reached its maximum value within 7-9 years of conservation and then remained at this level, while litter accumulated the maximum level one year later than soil organic matter; (iii) soil organic matter is the primary factor of biodiversity in the grasslands with low litter accumulation, while high-density litter layers are the primary cause of decreases in biodiversity; (iv) fresh biomass decreased over a period of 7-10 years under conservation and remained nearly unchanged after that; (v) green vegetation fraction was also increased by conservation action in about 6-7 years. The result of this study provides the fundamental information for implementing and adjusting grassland management policies.

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“…The performances of existing satellite models have shown contrasting results at different levels of curing (Newnham et al 2010), and hence at different stages of the fire season. For NDVI alone, the relationship between this index and the percentage cover of dead material (in this case curing) has been shown to vary at different levels of dead material cover (Xu et al 2014). Generally, models tend to overestimate early in the fire season when curing levels are low and underestimate later in the season when curing levels are high.…”

Section: Satellite Model Derivationmentioning

confidence: 99%

Integrating ground and satellite-based observations to determine the degree of grassland curing

Martin

Chen

Nichols

et al. 2015

Int. J. Wildland Fire

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In Australia, the Grassland Fire Danger Index is determined by several inputs including an essential component, the degree of grassland curing, defined as the proportion of senescent material. In the state of Victoria (south-eastern Australia), techniques used for curing assessment have included the use of ground-based observations and the use of satellite imagery. Both techniques alone have inherent limitations. An improved technique has been developed for estimating the degree of curing that entails the use of satellite observations adjusted by observations from the ground. First, a satellite model was developed, named MapVictoria, based on historical satellite and ground-based observations. Second, with use of the new (MapVictoria) satellite model, an integrated model was developed, named the Victorian Improved Satellite Curing Algorithm, combining near-real-time satellite data with weekly observations of curing from the ground. This integrated model was deployed in operations supporting accurate fire danger calculations for grasslands in Victoria in 2013.

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Measuring the dead component of mixed grassland with Landsat imagery

Cited by 70 publications

References 84 publications

Estimating Ground Cover in the Mixed Prairie Grassland of Southern Alberta Using Vegetation Indices Related to Physiological Function

Estimating Ground Cover in the Mixed Prairie Grassland of Southern Alberta Using Vegetation Indices Related to Physiological Function

Evaluating the impacts of nearly 30 years of conservation on grassland ecosystem using Landsat TM images

Integrating ground and satellite-based observations to determine the degree of grassland curing

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