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•Building a new assessment BPSR framework to evaluate alpine pastoral ecosystem health.•Entropy method was applied to determine the weights for indicators.•Spatiotemporal dynamics of ...ecosystem health in Gannan pastoral were analyzed comprehensively.•The spatial distribution of ecosystem health in Gannan pastoral decreased from southwest to northeast.
Ecosystem health is the goal of eco-environmental management, and its assessment is necessary for improving regional ecological environments and promoting regional sustainable development. However, previous studies on regional ecosystem health assessment have mainly concentrated on rapidly developing urbanized areas, with very few having been conducted on alpine pastoral regions. Taking the Gannan alpine pastoral region of China as the study area, and based on remote-sensing and GIS technologies, we used entropy methods to calculate the relative weights of several indicators to quantify the uncertainty in the data processing so that the accuracy of the results of the ecosystem health evaluation could be improved. In this study, a new basic-pressure-state-response assessment framework for alpine pastoral ecosystem health assessment is proposed, based on the pressure-state-response. It was found that the levels of ecosystem health of the Gannan pastoral had a spatial distribution pattern that decreased from southwest to northeast, from 2000 to 2015. Notably, the areas of the well and weak health showed a decreasing trend, with more regions tending toward ordinary levels. Among all the assessing indicators, the average value of the pressure indicator was the greatest, with the basic indicator value being the lowest. Our assessment results could be used as a guide for eco-environment managers tasked with taking effective measures to improve the status of the ecosystem health in alpine pastoral regions.
The power graph
Γ
G
of a finite group
G
is the graph with the vertex set
G
, where two distinct elements are adjacent if and only if one is a power of the other. An
L
(2, 1)-labeling of a graph
Γ
is ...an assignment of labels from nonnegative integers to all vertices of
Γ
such that vertices at distance two get different labels and adjacent vertices get labels that are at least 2 apart. The lambda number of
Γ
, denoted by
λ
(
Γ
)
, is the minimum span or range over all
L
(2, 1)-labelings of
Γ
. In this paper, we obtain bounds for
λ
(
Γ
G
)
and give necessary and sufficient conditions when the bounds are attained. As applications, we compute the exact value of
λ
(
Γ
G
)
if
G
is a dihedral group, a generalized quaternion group, a
P
-group or a cyclic group of order
p
q
n
, where
p
and
q
are distinct primes and
n
is a positive integer.
The power graph
P
G
of a finite group
G
is the graph whose vertex set is
G
, two distinct vertices are adjacent if one is a power of the other. The order supergraph
S
G
of
P
G
is the graph with ...vertex set
G
, and two distinct vertices
x
,
y
are adjacent if
o
(
x
)|
o
(
y
) or
o
(
y
)|
o
(
x
). In this paper, we study the independence number of
S
G
and answer a question was posed by Hamzeh and Ashrafi.
Let
Γ
be a graph with vertex set
V
. If a subset
C
of
V
is independent in
Γ
and every vertex in
V
\
C
is adjacent to exactly one vertex in
C
, then
C
is called a perfect code of
Γ
. Let
G
be a finite ...group and let
S
be a square-free normal subset of
G
. The Cayley sum graph of
G
with respect to
S
is a simple graph with vertex set
G
and two vertices
x
and
y
are adjacent if
x
y
∈
S
. A subset
C
of
G
is called a perfect code of
G
if there exists a Cayley sum graph of
G
which admits
C
as a perfect code. In particular, if a subgroup of
G
is a perfect code of
G
, then the subgroup is called a subgroup perfect code of
G
. In this paper, we give a necessary and sufficient condition for a non-trivial subgroup of an abelian group with non-trivial Sylow 2-subgroup to be a subgroup perfect code of the group. This reduces the problem of determining when a given subgroup of an abelian group is a perfect code to the case of abelian 2-groups. As an application, we classify the abelian groups whose every non-trivial subgroup is a subgroup perfect code. Moreover, we determine all subgroup perfect codes of a cyclic group, a dihedral group and a generalized quaternion group.
Given a connected graph
G
=
(
V
(
G
)
,
E
(
G
)
)
, the length of a shortest path from a vertex
u
to a vertex
v
is denoted by
d
(
u
,
v
). For a proper subset
W
of
V
(
G
), let
m
(
W
) be the ...maximum value of
d
(
u
,
v
) as
u
ranging over
W
and
v
ranging over
V
(
G
)
\
W
. The proper subset
W
=
{
w
1
,
…
,
w
|
W
|
}
is a
completeness-resolving set
of
G
if
Ψ
W
:
V
(
G
)
\
W
⟶
m
(
W
)
|
W
|
,
u
⟼
(
d
(
w
1
,
u
)
,
…
,
d
(
w
|
W
|
,
u
)
)
is a bijection, where
m
(
W
)
|
W
|
=
{
(
a
(
1
)
,
…
,
a
(
|
W
|
)
)
∣
1
≤
a
(
i
)
≤
m
(
W
)
for each
i
=
1
,
…
,
|
W
|
}
.
A graph is
completeness-resolvable
if it admits a completeness-resolving set. In this paper, we first construct the set of all completeness-resolvable graphs by using the edge coverings of some vertices in given bipartite graphs, and then establish posets on some subsets of this set by the spanning subgraph relationship. Based on each poset, we find the maximum graph and give the lower and upper bounds for the number of edges in a minimal graph. Furthermore, minimal graphs satisfying the lower or upper bound are characterized.
Remote sensing of phenology usually works at the regional and global scales, which imposes considerable variations in the solar zenith angle (SZA) across space and time. Variations in SZA alters the ...shape and profile of the surface reflectance and vegetation index (VI) time series, but this effect on remote-sensing-derived vegetation phenology has not been adequately evaluated. The objective of this study is to understand the behaviour of VIs response to SZA, and to further improve the interpretation of satellite observed vegetation dynamics, across space and time. In this study, the sensitivity of two widely used VIs—the normalised difference vegetation index (NDVI) and the enhanced vegetation index (EVI)—to SZA was investigated at four northern Australian savanna sites, over a latitudinal distance of 9.8° (~1100 km). Complete time series of surface reflectances, as acquired with different SZA configurations, were simulated using Bidirectional Reflectance Distribution Function (BRDF) parameters provided by MODerate Resolution Imaging Spectroradiometer (MODIS). The sun-angle dependency of the four phenological transition dates were assessed. Results showed that while NDVI was very sensitive to SZA, such sensitivity was nearly absent for EVI. A negative correlation was also observed between NDVI sensitivity to SZA and vegetation cover, with sensitivity declining to the same level as EVI when vegetation cover was high. Different sun-angle configurations resulted in considerable variations in the shape and magnitude of the phenological profiles. The sensitivity of VIs to SZA was generally greater during the dry season (with only active trees present) than in the wet season (with both active trees and grasses), thus, the sun-angle effect on VIs was phenophase-dependent. The sun-angle effect on NDVI time series resulted in considerable differences in the phenological metrics across different sun-angle configurations. Across four sites, the sun-angle effect caused 15.5 days, 21.6 days, and 20.5 days differences in the start, peak, and the end of the growing season derived from NDVI time series, with seasonally varying SZA at local solar noon, as compared to those metrics derived from NDVI time series with fixed SZA. In comparison, those differences in the start, peak, and end of the growing season for EVI were significantly smaller, with only 4.8 days, 4.9 days, and 3 days, respectively. Our results suggest the potential importance of considering the seasonal SZA effect on VI time series prior to the retrieval of phenological metrics.
•Ecosystem health dynamics was explicitly assessed in Gannan Plateau since 2000.•PSR and AHP methods were combined to explore the driving forces of ecosystem health.•Ecosystem health showed clearly ...differences under different influencing factors.•Ecological project showed a positive impact on ecological health in alpine area.•The ecosystem health status has been improved by 15.04% in Gannan plateau since 2000.
Ecosystem health plays a vital role in the development of regional ecological environment. Gannan Plateau, an important water conservation area in the upper reaches of the Yellow River, is located at the northeastern margin of the Qinghai-Tibet Plateau. Due to the importance of the region, understanding the spatiotemporal patterns of the ecosystem health within this region is particularly critical. Previous studies on regional ecosystem health assessment mainly focused on single ecosystem type such as wetland, forest, and grassland, while there are relatively few studies that comprehensively assessed ecosystem health at high-spatial resolution in Gannan alpine areas. In this study, 11 counties and cities in Gannan Tibetan and Linxia Hui Autonomous Prefecture of the Gansu Province, China were used as the study area. We evaluated the ecosystem health of 154 township units from 2000 to 2020 with methods of Pressure-State-Response model (PSR) and Analytic Hierarchy Process (AHP), using a combination of satellite remote sensing data, social statistics data, meteorological data, and other geospatial data sources. The results showed that overall ecosystems in the southwestern part of the study area exhibited healthier condition than those located in the northeastern part, which is mainly due to the combination of the natural environment and the intensity of human activities in different regions. Over the past 20 years, due to the active implementation of relevant ecological protection and restoration policies by various regional governments and the improvement of the natural environment, the average ecosystem health status of Gannan Plateau has increased by 15.04 %, showing an improving trend in varying degrees or being stable, no declining trend was observed except for a small town. Our results implied that management should take different measures according to the differences of regional ecosystem health level, also pay more attention to areas where the ecosystem health status has not improved.
Australia experienced one of the worst droughts in history during the early 21st-century (termed the ‘big dry’), exerting negative impacts on food production and water supply, with increased forest ...die-back and bushfires across large areas. Following the ‘big dry’, one of the largest La Niña events in the past century, in conjunction with an extreme positive excursion of the Southern Annular Mode (SAM), resulted in dramatic increased precipitation from 2010 to 2011 (termed the ‘big wet’), causing widespread flooding and a recorded sea level drop. Despite these extreme hydroclimatic impacts, the spatial partitioning and temporal evolution of total water storage across Australia remains unknown. In this study we investigated the spatial-temporal impacts of the recent ‘big dry’ and ‘big wet’ events on Australia's water storage dynamics using the total water storage anomaly (TWSA) data derived from the Gravity Recovery and Climate Experiment (GRACE) satellites.
Results showed widespread, continental-scale decreases in TWS during the ‘big dry’, resulting in a net loss of 3.89±0.47cm (299km3) total water, while the ‘big wet’ induced a sharp increase in TWS, equivalent to 11.68±0.52cm (898km3) of water, or three times the total water loss during the ‘big dry’. We found highly variable continental patterns in water resources, involving differences in the direction, magnitude, and duration of TWS responses to drought and wet periods. These responses clustered into three distinct geographic zones that correlated well with the influences from multiple large-scale climate modes. Specifically, a persistent increasing trend in TWS was recorded over northern and northeastern Australia, where the climate is strongly influenced by El Niño-Southern Oscillation (ENSO). By contrast, western Australia, a region predominantly controlled by the Indian Ocean Dipole (IOD), exhibited a continuous decline in TWS during the ‘big dry’ and only a subtle increase during the ‘big wet’, indicating a weak recovery of water storage. Southeastern Australia, influenced by combined ENSO, IOD and SAM interactions, exhibited a pronounced TWS drying trend during the ‘big dry’ followed by rapid TWS increases during the ‘big wet’, with complete water storage recoveries. A spatial intensification of the water cycle was further identified, with a wetting trend over wetter regions (northern and northeastern Australia) and a drying trend over drier regions (western Australia). Our results highlight the value of GRACE derived TWSA as an important indicator of hydrological system performance for improved water impact assessments and management of water resources across space and time.
•Hydroclimatic impacts on Australia's water storage were examined using GRACE.•Australia gained 3 times more water during the ‘big wet’ than lost in the ‘big dry’.•Variations in TWS dry-wet patterns resulted in three distinct geographic zones.•TWS spatial-temporal patterns were correlated to three large-scale climate modes.•Spatial patterns of water cycle intensification were observed.
The phenology of a landscape is a key parameter in climate and biogeochemical cycle models and its correct representation is central to the accurate simulation of carbon, water and energy exchange ...between the land surface and the atmosphere. Whereas biogeographic phenological patterns and shifts have received much attention in temperate ecosystems, much less is known about the phenology of savannas, despite their sensitivity to climate change and their coverage of approximately one eighth of the global land surface. Savannas are complex assemblages of multiple tree, shrub, and grass vegetation strata, each with variable phenological responses to seasonal climate and environmental variables. The objectives of this study were to investigate biogeographical and inter-annual patterns in savanna phenology along a 1100km ecological rainfall gradient, known as North Australian Tropical Transect (NATT), encompassing humid coastal Eucalyptus forests and woodlands to xeric inland Acacia woodlands and shrublands. Key phenology transition dates (start, peak, end, and length of seasonal greening periods) were extracted from 13years (2000–2012) of Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) data using Singular Spectrum Analysis (SSA).
Two distinct biogeographical patterns in phenology were observed, controlled by different climate systems. The northern (mesic) portion of the transect, from 12°S, to around 17.7°S, was influenced by the Inter-Tropical Convergence Zone (ITCZ) seasonal monsoon climate system, resulting in strong latitudinal shifts in phenology patterns, primarily associated with the functional response of the C4 grass layer. Both the start and end of the greening (enhanced vegetation activity) season occurred earlier in the northern tropical savannas and were progressively delayed towards the southern limit of the Eucalyptus-dominated savannas resulting in relatively stable length of greening periods. In contrast, the southern xeric portion of the study area was largely decoupled from monsoonal influences and exhibited highly variable phenology that was largely rainfall pulse driven. The seasonal greening periods were generally shorter but fluctuated widely from no detectable greening during extended drought periods to length of greening seasons that exceeded those in the more mesic northern savannas in some wet years. This was in part due to more extreme rainfall variability, as well as a C3/C4 grass-forb understory that provided the potential for extended greening periods. Phenology of Acacia dominated savannas displayed a much greater overall responsiveness to hydroclimatic variability. The variance in annual precipitation alone could explain 80% of the variances in the length of greening season across the major vegetation groups. We also found that increased variation in the timing of phenology was coupled with a decreasing tree-grass ratio. We further compared the satellite-based phenology results with tower-derived measures of Gross Ecosystem Production (GEP) fluxes at three sites over two contrasting savanna classes. We found good convergence between MODIS EVI and tower GEP, thereby confirming the potential to link these two independent data sources to better understand savanna ecosystem functioning.
•We retrieved wet to dry savanna phenology over North Australia with MODIS.•Understorey dynamics controlled landscape phenology throughout the transect.•We found large inter-annual variations in rainfall & phenology in southern savannas.•MODIS EVI & tower GPP matched well, in both Eucalyptus and Acacia savannas.
In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological ...changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.
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