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Delineating Native Pasture Phenoregion Based on Land Surface Phenology in the Fitzroy Basin, Queensland

Delineating Native Pasture Phenoregion Based on Land Surface Phenology in the Fitzroy Basin, Queensland 0lMDn
Phenology is the study of biological cyclic of vegetation and can explain the role of climatic variables and global change who can drive on ecosystem changes (Moore et al. , 2016; Zhao et al. , 2015). Timing of vegetation phenology events, such as timing of onset and length of growth, are highly sensitive to climate fluctuation into species level (Ren et al. , 2018). Land Surface Phenology (LSP) is an innovative technique for characterization of vegetation phase shift determine from remote sensing imagery (Broich, et al. 2015). Land surface phenology have been widely used for monitoring dynamics of climate change (Ren et al. , 2018), wetland ecosystem (Ghosh & Mishra, 2017), snow cover (Qiao & Wang, 2019) and cropping activities (Pringle et al. , 2012). The capabilities of remotely sensed data to capturing characteristic of vegetation phenology encourage recent study for utilize the LSP for delineating land-use land-cover (Leinenkugel et al. , 2013), Xue Zhaohui 2014, and type of crop vegetation (Konduri et al 2020). These possibilities are highly potential to implement in the delineation of phenoregion. Generalization and regionalization of spatial data can reduce complexity of information and provide meaningfully information of spatial arrangement (Gu, et al. 2010). For instance, the utilization of ecoregion information has an important element for policy decision-making, especially on natural resource management and conservation aspect (Thompson. 2005). Ecoregion maps produced by calculate self-similarity of several parameters such as, elevation, rainfall and soil characteristic (hangrove, 2005). An extended version of ecoregion is called phenoregion, which is proposed first time by White (2005). Phenoregions identified by the homogenous areas with characterization of a number of climatological data combined with vegetation characteristic such as greenness index (White et al, 2005). Phenoregion information can facilitate the improvement of natural resource monitoring especially in vegetation phenology (Zhang, 2017) as impact of climate change (Dannenberg, 2020). In recent studies, delineation of phenoregions have more focuses on the input of phenological-forcing (climate variables, topography, soil, ect) for classifying the homogenous areas (White, et al 2005, Zhang, et al. 2012). Another study accommodates vegetation characteristic by using combination of three different vegetation indices by optical to microwave sensors to delineate planetary scale phenoregions (Dannenberg, et al. 2020). Milla (2018) look at the potential of land surface phenological metric, spring onset was used for determine phenoregions at continental scales. Highly improvement of utilization of phenometrics have showed by Gu (2010) with capture nine seasonal parameters combined with elevation data to generate pheno-class in the United States.
Phenology is the
study
of biological cyclic of
vegetation
and can
explain
the role of climatic variables and global
change
who can drive on ecosystem
changes
(Moore et al.
,
2016; Zhao et al.
,
2015). Timing of
vegetation
phenology
events
, such as timing of onset and length of growth, are
highly
sensitive to
climate
fluctuation into species level (
Ren et
al.
,
2018). Land Surface Phenology (LSP) is an innovative technique for characterization of
vegetation
phase shift determine from remote sensing imagery (
Broich
, et al. 2015). Land surface phenology have been
widely
used
for monitoring dynamics of
climate
change
(
Ren et
al.
,
2018), wetland ecosystem (
Ghosh
& Mishra, 2017), snow cover (
Qiao
& Wang, 2019) and cropping activities (
Pringle
et al.
,
2012). The capabilities of
remotely
sensed
data
to capturing
characteristic
of
vegetation
phenology encourage recent
study
for utilize the LSP for delineating land-
use
land-cover (
Leinenkugel
et al.
,
2013),
Xue
Zhaohui
2014, and type of crop
vegetation
(
Konduri
et al
2020). These possibilities are
highly
potential to implement in the delineation of
phenoregion
.

Generalization and regionalization of spatial
data
can
reduce
complexity of
information
and provide
meaningfully
information
of spatial arrangement (
Gu
, et al. 2010).
For instance
, the utilization of
ecoregion
information
has an
important
element for policy decision-making,
especially
on natural resource management and conservation aspect (Thompson. 2005).
Ecoregion
maps produced by calculate self-similarity of several parameters such as, elevation, rainfall and soil
characteristic
(
hangrove
, 2005). An extended version of
ecoregion
is called
phenoregion
, which
is proposed
first
time by White (2005).
Phenoregions
identified by the homogenous areas with characterization of a number of
climatological
data
combined with
vegetation
characteristic
such as greenness index (White
et al
, 2005).
Phenoregion
information
can facilitate the improvement of natural resource monitoring
especially
in
vegetation
phenology (Zhang, 2017) as impact of
climate
change
(
Dannenberg
, 2020).

In recent
studies
, delineation of
phenoregions
have more focuses on the input of phenological-forcing
(climate
variables, topography, soil,
ect
) for classifying the homogenous areas (White,
et al
2005, Zhang, et al. 2012). Another
study
accommodates
vegetation
characteristic
by using combination of three
different
vegetation
indices by optical to microwave sensors to delineate planetary scale
phenoregions
(
Dannenberg
, et al. 2020).
Milla
(2018) look at the potential of land surface phenological metric, spring onset was
used
for determine
phenoregions
at continental scales.
Highly
improvement of utilization of
phenometrics
have
showed
by
Gu
(2010) with capture nine seasonal parameters combined with elevation
data
to generate pheno-
class
in the United States.
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IELTS academic Delineating Native Pasture Phenoregion Based on Land Surface Phenology in the Fitzroy Basin, Queensland

Academic
  American English
3 paragraphs
413 words
6.0
Overall Band Score
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