NDVI patterns as indicator of morphodynamic activity in the middle Paraná River floodplain

Marchetti, Zuleica Yael; Minotti, Priscilla Gail; Ramonell, Carlos; Schivo, Facundo; Kandus, Patricia

doi:10.1016/j.geomorph.2015.10.003

Cited by 37 publications

(35 citation statements)

References 34 publications

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“…Values close to zero indicate sparse vegetation on bare soil [24]. The NDVI is the remote sensing product most widely used worldwide to analyse and map differences in vegetation types and plant phenology [25] including to estimate the diversity of trees over large areas when the vegetation is at the maximum growing season [26][27][28][29]. The NDVI can be utilized for future urban planning, urban restoration and monitoring of urban tree health in Bangkok.…”

Section: Discussionmentioning

confidence: 99%

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

Sommeechai

Wachrinrat

Dell

et al. 2018

Forests

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Abstract:Rapid urbanization has changed the structure and function of natural ecosystems, especially floodplain ecosystems in SE Asia. The ecological structure of vegetation stands and the usefulness of satellite images was investigated to characterize a disturbed tropical urban forest located in the Chao Phraya River lower floodplain, Thailand. Nine sample plots were established on the Bang Kachao Peninsula (BKP) within 4 tropical forest types in an urban area: rehabilitation forest, home-garden agroforestry, mangrove and park. The tree habitats were beach forest, swamp forest, moist evergreen forest, dry evergreen forest, mangrove forest and abandoned orchard or home-garden. Normalized difference vegetation index (NDVI) values obtained from Landsat 7 satellite images were correlated with plant structure from field surveys. NDVI had the highest relationship with stand factors for number of families, number of species, Shannon-Weiner index and total basal area. Linear regression predicted well the correlation between NDVI and stand factors for families and basal area. NDVI trends reflected urban tropical forest typing and biodiversity, being high in rehabilitation and mangrove forests, moderate in home-gardens and low in parks. We suggest that the application of NDVI for assessments can be useful for future planning, monitoring and management of the BKP and hence may contribute for increasing biodiversity and complexity of these urban forests.

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Section: Discussionmentioning

confidence: 99%

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

Sommeechai

Wachrinrat

Dell

et al. 2018

Forests

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“…This NDVI signal allows us to estimate seasonal changes in light absorption of a surface [9] and may summarize the annual and interannual variability of plant penology [47]. The fused images produced by STARFM were used to calculate NDVI values (for example, see Figure 5) at an eight-day time series interval for each of the three methods with the following formula [46]: This produced a range of values from −1 to 1, where negative values were related to areas with open water and positive values were areas covered by green vegetation [47]. All locations with no data in either of the synthetic images were set to be well outside of this range.…”

Section: Ndvi Time Series Preparation and Seasonality Extractionmentioning

confidence: 99%

“…During seasonality extraction, the date range of synthetic images that specified when each SOS was expected to occur included three images (time steps) before the start of the year through the end of the year (i.e., image range 44-92 was used for the year 2000 which spanned images 47-92), for EOS the date range included the start of the year to three images after the end of the year (i.e., image range 47-95 for the year 2000), and the date range for LOS included three images before the start of the year through three images after the end of the year (i.e., image range 44-95 for the year 2000). This produced a range of values from −1 to 1, where negative values were related to areas with open water and positive values were areas covered by green vegetation [47]. All locations with no data in either of the synthetic images were set to be well outside of this range.…”

Section: Ndvi Time Series Preparation and Seasonality Extractionmentioning

confidence: 99%

Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

2017

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Abstract:Understanding the effects that the Urban Heat Island (UHI) has on plant phenology is important in predicting ecological impacts of expanding cities and the impacts of the projected global warming. However, the underlying methods to monitor phenological events often limit this understanding. Generally, one can either have a small sample of in situ measurements or use satellite data to observe large areas of land surface phenology (LSP). In the latter, a tradeoff exists among platforms with some allowing better temporal resolution to pick up discrete events and others possessing the spatial resolution appropriate for observing heterogeneous landscapes, such as urban areas. To overcome these limitations, we applied the Spatial and Temporal Adaptive Reflectance Model (STARFM) to fuse Landsat surface reflectance and MODIS nadir BRDF-adjusted reflectance (NBAR) data with three separate selection conditions for input data across two versions of the software. From the fused images, we derived a time-series of high temporal and high spatial resolution synthetic Normalized Difference Vegetation Index (NDVI) imagery to identify the dates of the start of the growing season (SOS), end of the season (EOS), and the length of the season (LOS). The results were compared between the urban and exurban developed areas within the vicinity of Ogden, UT and across all three data scenarios. The results generally show an earlier urban SOS, later urban EOS, and longer urban LOS, with variation across the results suggesting that phenological parameters are sensitive to input changes. Although there was strong evidence that STARFM has the potential to produce images capable of capturing the UHI effect on phenology, we recommend that future work refine the proposed methods and compare the results against ground events.

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“…At the same time, vegetation is one of the most important and sensitive components of global land cover, which can reflect the impacts of climate change and human activities in a short period (Zhang et al, 2008). As the best indicator of vegetation growth and coverage, Normalized difference vegetation index (NDVI) is the surface characteristic parameter of remote sensing data, which reflects the distribution characteristics and changes of vegetation, and is the basic data of vegetation research (Zhao, 2012;Marchetti et al, 2016;Wu et al, 2017;Birtwistle et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Analysis of the spatial-temporal change of the vegetation index in the upper reach of Han River Basin in 2000–2016

et al. 2018

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Abstract. Han River is the water source region of the middle route of South-to-North Water Diversion in China and the ecological projects were implemented since many years ago. In order to monitor the change of vegetation in Han River and evaluate the effect of ecological projects, it is needed to reveal the spatial-temporal change of the vegetation in the upper reach of Han River quantitatively. The study is based on MODIS/Terra NDVI remote sensing data, and analyzes the spatial-temporal changes of the NDVI in August from 2000 to 2016 at pixel scale in the upper reach of Han River Basin. The results show that, the area with increasing NDVI between 0 and 0.005 per year accounts for 62.07 % of the area of upper reach of Han River Basin, and the area with changing rate between −0.005 and 0 per year accounts for 26.65 % of the research area. The area with significant decreasing trend only accounts for 2.76 %, while area significant increasing trend accounts for 13.47 %, and the area with increasing NDVI is much larger than the area with reducing NDVI. The vegetation index of each county is evaluated and found that, the areal proportion with significant decreasing trend in Hantai is the biggest, reaching 35.57 %. The areal proportion with significant increasing trend in Zhenba County, Ziyang County, Xunyang County, Zhashui County, Shangzhou District, Shanyang County and Yun County is larger than the others, and the areal proportions are more than 20 %. The largest areal proportion with significant increasing trend is in Shangzhou District and it reaches 31.11 %. On the whole, the area ratio in all districts and counties with increasing NDVI is much larger than the area ratio with decreasing NDVI.

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NDVI patterns as indicator of morphodynamic activity in the middle Paraná River floodplain

Cited by 37 publications

References 34 publications

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

Ecological Structure of a Tropical Urban Forest in the Bang Kachao Peninsula, Bangkok

Modeling the Effects of the Urban Built-Up Environment on Plant Phenology Using Fused Satellite Data

Analysis of the spatial-temporal change of the vegetation index in the upper reach of Han River Basin in 2000–2016

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