Estimation of grassland biomass and nitrogen using MERIS data

Ullah, Saleem; Si, Youbin; Schlerf, Martin; Skidmore, Andrew K.; Shafique, Muhammad; Iqbal, Irfan Akhtar

doi:10.1016/j.jag.2012.05.008

Cited by 73 publications

(51 citation statements)

References 42 publications

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“…However, when this relationship was investigated at the species scale, significant results were found for Quercus ilex (EBF) plots. This is accordance with a previous study examining the (Ullah et al, 2012) and by a simple ratio index in heterogeneous rangelands (Mirik et al, 2005) at various spatial scales, 300 m and 1 m, respectively. In the literature, canopy N[%] is more often used to detect N state of foliage in forest while canopy N[area] is regularly employed in grasslands but also in crops (Clevers and Gitelson, 2013;Schlemmer et al, 2013).…”

Section: Canopy N Content Detectionsupporting

confidence: 93%

“…In comparison, previous studies conducted in grasslands reported higher prediction accuracy e.g. by using spaceborne MTCI at 300 m spatial resolution or a simple ratio-type vegetation index computed from airborne imagery at 1 m spatial resolution, canopy N[area] was detected with r 2 values equal to 0.29 and 0.66, respectively (Mirik et al, 2005;Ullah et al, 2012). 325…”

Section: Canopy N Content Detectionmentioning

confidence: 74%

“…Finally, the difference between sensing canopy N concentration (N[%], %N) and canopy N content (N[area], g m -2 ) has rarely been investigated. The relationship between 95 MTCI and both of these variables has been studied separately (Clevers and Gitelson, 2013;Wang et al, 2016), but very few analyses (Mirik et al, 2005;Ullah et al, 2012) have compared the ability to detect canopy N concentration and canopy N content simultaneously, especially in forest ecosystems. …”

mentioning

confidence: 99%

“…A few were directed towards sensing N concentration in natural environments using airborne data, e.g. in temperate forests (Wang 70 et al, 2016), or spaceborne data, for example in grasslands (Ramoelo et al, 2012;Ullah et al, 2012) or sub-tropical forests (Cho et al, 2013).…”

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confidence: 99%

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Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

Preprint

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) from a Mediterranean forests inventory in the region of Catalonia, NE of Spain. The relationships between the datasets were studied after resampling both datasets to lower spatial resolutions (20 km, 15 km, 10 km and 5 km) and at the initial higher spatial resolution of 1 km. The results at the higher spatial resolution yielded significant relationships between MTCI and both canopy N concentration and content, r 2 = 0.32 and r 2 = 0.17, respectively.We also investigated these relationships per plant functional type. While the relationship between MTCI and canopy N 20 concentration was strongest for deciduous broadleaf and mixed plots (r 2 = 0.25 and r 2 = 0.47, respectively), the relationship between MTCI and canopy N content was strongest for evergreen needleleaf trees (r 2 = 0.20). At the species level, canopy N concentration was strongly related to MTCI for European Beech plots (r 2 = 0.71). These results present a new perspective on the application of MTCI time series for canopy N detection, ultimately leading towards the generation of canopy N maps that can be used to constrain global vegetation models. capacity (Evans, 1989;Reich et al., 1995;Reich et al., 1997;Reich et al., 1999;Wright et al., 2004), specific leaf area, leaf life span (Reich et al., 1999;Wright et al., 2004) and light use efficiency (Kergoat et al., 2008). Leaf N concentration expressed on a leaf area basis, also called leaf N content (N g m -2 ) has also been linked with chlorophyll content, Rubisco content (Evans, 35 1989) and photosynthetic capacity (Evans, 1989;Reich et al., 1995). At stand scale, canopy nitrogen concentration, which represents the leaf N concentration averaged over the stand canopy, has also been found to correlate with above ground Net Primary Productivity (NPP) (Reich, 2012), while canopy N content has been linked with the canopy light use efficiency (Green et al., 2003).Given their links to many vegetation processes, leaf and canopy N variables could be used to constrain N cycle modules in 40 global vegetation models. At the global scale, ample data is available to constrain models for the C cycle; however, data to constrain the N cycle are limited. Currently, canopy N data is not widely available and canopy N sampling campaigns are timeconsuming and thus expensive tasks. Moreover, upscaling from local sampling campaign measurements represents an additional limitation. In this perspective, local, regional or even global remotely sensed canopy N estimates will be a valuable addition, enabling us to collect information in a less time intensive and expensive manner than traditional on-field sampling 45 campaigns. Such near global canopy N estimates will be beneficial as input in global vegetation models or to calibrate and validate these models.Currently, different remote sensing techniques have been applied to detect canopy N in terrestrial vegetation. Imaging spectrometry from either airborne or spaceborne sensors coupled with different analysis methods, including partial least squares regression (PLS), continuu...

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Section: Canopy N Content Detectionsupporting

confidence: 93%

Section: Canopy N Content Detectionmentioning

confidence: 74%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Loozen

Rebel

Karssenberg

et al. 2017

Preprint

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“…However, they underlined that the spectro-temporal response of the grassland does not only depend on the practice itself, but also on the phenological stage when the practice occurs, the weather conditions, and the topography [170]. Notably, the identification of mowing events or gradual grazing management can also be derived from the numerous approaches initially used for the biomass production amount and biomass quality [171,172]. One example is Dusseux et al [173] who monitored grassland mowing and grazing in a study case in Brittany, France, using the NDVI and two biophysical variables (Leaf Area Index and fraction of vegetation cover) derived from a series of three SPOT images and field data.…”

Section: Grazing Vs Mowingmentioning

confidence: 99%

Remote Sensing and Cropping Practices: A Review

et al. 2018

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For agronomic, environmental, and economic reasons, the need for spatialized information about agricultural practices is expected to rapidly increase. In this context, we reviewed the literature on remote sensing for mapping cropping practices. The reviewed studies were grouped into three categories of practices: crop succession (crop rotation and fallowing), cropping pattern (single tree crop planting pattern, sequential cropping, and intercropping/agroforestry), and cropping techniques (irrigation, soil tillage, harvest and post-harvest practices, crop varieties, and agro-ecological infrastructures). We observed that the majority of the studies were exploratory investigations, tested on a local scale with a high dependence on ground data, and used only one type of remote sensing sensor. Furthermore, to be correctly implemented, most of the methods relied heavily on local knowledge on the management practices, the environment, and the biological material. These limitations point to future research directions, such as the use of land stratification, multi-sensor data combination, and expert knowledge-driven methods. Finally, the new spatial technologies, and particularly the Sentinel constellation, are expected to improve the monitoring of cropping practices in the challenging context of food security and better management of agro-environmental issues.

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Reindeer control over shrubification in subarctic wetlands: spatial analysis based on unoccupied aerial vehicle imagery

Villoslada

Ylänne

Juutinen

et al. 2023

Remote Sens Ecol Conserv

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Herbivores can exert a controlling effect on the reproduction and growth of shrubs, thereby counter‐acting the climate‐driven encroachment of shrubs in the Arctic and the potential consequences. This control is particularly evident in the case of abundant herbivores, such as reindeer (Rangifer tarandus tarandus), whose grazing patterns are affected by management. Here, we tested how different reindeer grazing practices on the border between Finland and Norway impact the occurrence of willow (Salix spp.) dominated patches, their above‐ground biomass (AGB) and the ability of willows to form dense thickets. We used a combination of multispectral and RGB imagery obtained from unoccupied aerial vehicles field data and an ensemble of machine‐learning models, which allowed us to model the occurrence of plant community types (Overall accuracy = 0.80), AGB fractions (maximum R2 = 0.90) and topsoil moisture (maximum R2 = 0.89). With this combination of approaches, we show that willows are kept in a browsing‐trap under spring and early summer grazing by reindeer, growing mostly small and scattered in the landscape. In contrast, willows under the winter grazing regime formed dense stands, particularly within riparian areas. We confirm this pattern using a random forest willow habitat distribution model based on topographical parameters. The model shows that willow biomass correlated with parameters of optimal habitat quality only in the winter grazing regime and did not respond to the same parameters under spring and summer grazing of reindeer.

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Estimation of grassland biomass and nitrogen using MERIS data

Cited by 73 publications

References 42 publications

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Regional detection of canopy nitrogen in Mediterranean forests using the spaceborne MERIS Terrestrial Chlorophyll Index

Remote Sensing and Cropping Practices: A Review

Reindeer control over shrubification in subarctic wetlands: spatial analysis based on unoccupied aerial vehicle imagery

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