Spatio-temporal variation indicators for landscape structure dynamics monitoring using dense normalized difference vegetation index time series

Meng, Yuanyuan; Liu, Xiangnan; Wu, Ling; Liu, Meiling; Zhang, Biyao; Zhao, Shuang

doi:10.1016/j.ecolind.2019.105607

Cited by 15 publications

(8 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The impacts of various solar zenith angles on the pretreatment of remote sensing time‐series data are rather limited and smooth, despite the existence of the differences all year round. In particular, the cross‐sensor radiometric calibrations from previous studies have revealed that the responsivities of the Landsat TM, ETM+ and OLI instruments are highly consistent (Meng et al, 2019). Hence, the Landsat datasets from GEE platform contribute to the assessment of long‐term SI dynamics owing to the consistent spectral properties from a practical standpoint.…”

Section: Methodsmentioning

confidence: 97%

“…Remotely sensed spectral indices (SIs) have been widely used to quantify vegetation structure and functioning (Asner et al, 2016; Kharuk et al, 2013). Some commonly used SIs of vegetation greenness, such as the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) can well indicate variations in aboveground vegetation productivity (Cohen et al, 2016; Meng et al, 2019; Ogaya et al, 2015). The temporal loss of vegetation productivity used to be extracted as singularity points in long‐term SI dynamics, which could be further adopted as early warning signals (EWS) of forest mortality (Liu et al, 2019; Rogers et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Old‐growth forests show low canopy resilience to droughts at the southern edge of the taiga

Liu

et al. 2021

Global Change Biology

View full text Add to dashboard Cite

Forest mortality and resilience driven by drought disturbances have attracted tons of attention. However, the acquisition of continuous spatial–temporal data is generally enslaved to the conventional field investigations. In this study, the resilience of semiarid forest was characterized with canopy dynamics from remote sensing observations, combining the variations in canopy greenness and water content. We integrated dense normalized difference vegetation index (NDVI) and normalized difference infrared index (NDII) time series from Landsat datasets, intending to assess the canopy resilience in 24 conifer patches along a climatic aridity gradient at the southern edge of the taiga in northern Mongolia and southern Siberia of Russia. The results exhibited four patterns of coordinated NDVI–NDII variation trends, indicating that the canopy water content of coniferous forests may decrease at first during a drought period, and sustained water loss may, in turn, induce an accompanying reduction in canopy greenness. Meanwhile, the patches with canopy recovery growth after initial declines were considered to have resilience to climate change. We further observed the combined effects of aridity degree and tree age on canopy resilience, and all seven patches with no resilience corresponded to the old‐tree group (the oldest trees reached or exceeded the age of 90). The observations indicated that the old‐growth forests in semiarid regions were less likely to show canopy resilience, which corresponded to a higher risk of sustained decline.

show abstract

Section: Methodsmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Old‐growth forests show low canopy resilience to droughts at the southern edge of the taiga

Liu

et al. 2021

Global Change Biology

View full text Add to dashboard Cite

show abstract

“…Vegetation growth, production and distribution are highly reliant on-and sensitive to-natural and anthropogenic factors, such as precipitation, temperature, water resources, farming, and urban expansion. The condition of the global vegetation is constantly changing at various spatial and temporal scales, driven by natural and anthropogenic factors [29][30][31]. The response of vegetation dynamics to differences in environmental factors varies significantly across regions, according to the regional climate conditions, water availability, and land cover [32,33].…”

Section: Introductionmentioning

confidence: 99%

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

et al. 2021

View full text Add to dashboard Cite

Information about the growth, productivity, and distribution of vegetation, which are highly relied on and sensitive to natural and anthropogenic factors, is essential for agricultural production management and eco-environmental sustainability in the Amur River Basin (ARB). In this paper, the spatial–temporal trends of vegetation dynamics were analyzed at the pixel scale in the ARB for the period of 1982–2013 using remotely sensed data of long-term leaf area index (LAI), fractional vegetation cover (FVC), and terrestrial gross primary productivity (GPP). The spatial autocorrelation characteristics of the vegetation indexes were further explored with global and local Moran’s I techniques. The spatial–temporal relationships between vegetation and climatic factors, land use/cover types and hydrological variables in the ARB were determined using a geographical and temporal weighted regression (GTWR) model based on the observed meteorological data, remotely sensed vegetation information, while the simulated hydrological variables were determined with the soil and water assessment tool (SWAT) model. The results suggest that the variation in area-average annual FVC was significant with an increase rate of 0.0004/year, and LAI, FVC, and GPP all exhibited strong spatial heterogeneity trends in the ARB. For LAI and FVC, the most significant changes in local spatial autocorrelation were recognized over the Sanjiang Plain, and the low–low agglomeration in the Sanjiang Plain decreased continuously. The GTWR model results indicate that natural and anthropogenic factors jointly took effect and interacted with each other to affect the vegetated regime of the region. The decrease in the impact of precipitation to vegetation growth over the Songnen Plain was determined as having started around 1991, which was most likely attributed to dramatic changes in water use styles induced by local land use changes, and corresponded to the negative correlation between pasture areas and vegetation indexes during the same period. The analysis results presented in this paper can provide vital information to decision-makers for use in managing vegetation resources.

show abstract

“…This algorithm can explain most ecological processes and natural phenomena driven by mathematical and physical models, which cannot be described by Euclidean geometry (Burn, 1984). It emphasizes the multidimensional relationship between the whole and the parts of GI (Meng et al, 2019), which can truly characterize the two-dimensional geometric morphology of non-regular space.…”

Section: Landscape Complexity Indicators Of Spatial Configurationmentioning

confidence: 99%

The Cooling Intensity Dependent on Landscape Complexity of Green Infrastructure in the Metropolitan Area

Wang

Huang

Chen

et al. 2021

Journal of Environmental Engineering and Landscape Management

View full text Add to dashboard Cite

The cooling effect of green infrastructure (GI) is becoming a hot topic on mitigating the urban heat island (UHI) effect. Alterations to the green space are a viable solution for reducing land surface temperature (LST), yet few studies provide specific guidance for landscape planning adapted to the different regions. This paper proposed and defined the landscape complexity and the threshold value of cooling effect (TVoE). Results find that: (1) GI provides a better cooling effect in the densely built-up area than the green belt; (2) GI with a simple form, aggregated configuration, and low patch density had a better cooling intensity; (3) In the densely built-up area, TVoE of the forest area is 4.5 ha, while in the green belt, TVoE of the forest and grassland area is 9 ha and 2.25 ha. These conclusions will help the planners to reduce LST effectively, and employ environmentally sustainable planning.

show abstract

Spatio-temporal variation indicators for landscape structure dynamics monitoring using dense normalized difference vegetation index time series

Cited by 15 publications

References 68 publications

Old‐growth forests show low canopy resilience to droughts at the southern edge of the taiga

Old‐growth forests show low canopy resilience to droughts at the southern edge of the taiga

Spatial–Temporal Vegetation Dynamics and Their Relationships with Climatic, Anthropogenic, and Hydrological Factors in the Amur River Basin

The Cooling Intensity Dependent on Landscape Complexity of Green Infrastructure in the Metropolitan Area

Contact Info

Product

Resources

About