Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications

Novillo, Carlos J.; Arrogante-Funes, Patricia; Calcerrada, Raúl Romero

doi:10.3390/ijgi8010043

Cited by 31 publications

(24 citation statements)

References 104 publications

(80 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Vegetation cover is an important part of the ecosystem, which plays an important role in the soil and water conservation, restraining the desertification process, biodiversity protection and other ecological service and functions etc. [1,2]. From a functional point of view, vegetation can be divided into two parts: the photosynthetic vegetation (PV) and the non-photosynthetic vegetation (NPV) [3].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

View full text Add to dashboard Cite

Estimating the fractional coverage of the photosynthetic vegetation (fPV) and non-photosynthetic vegetation (fNPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the fPV, fNPV and the fractional coverage of the bare soil (fBS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the fPV, fNPV and fBS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and fPV, and NPVIs and fNPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of fPV and fNPV in the study area in 2020. The results showed that the GEMI and fPV revealed the best correlation coefficient (R2) of 0.59, while DFI and fNPV had the best correlation of R2 = 0.45. The accuracy of fPV, fNPV and fBS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The fPV and fNPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the fPV and fNPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

et al. 2021

View full text Add to dashboard Cite

show abstract

“…MODIS time series data are commonly used to track dynamic changes in the landscape over time. An example is the monitoring of forest by means of time series analysis with vegetation indices (VIs) [4]- [6]. These VIs, including NDVI, EVI, NBR, and other self-defined indices, represent the absorptive and…”

Section: Introductionmentioning

confidence: 99%

Improving Time Series Reconstruction by Fixing Invalid Values and its Fidelity Evaluation

Shen

Chen

2020

IEEE Access

View full text Add to dashboard Cite

MODIS time series data have been widely used in the research of regional and global ecosystems and climate change. For vegetation monitoring, vegetation indices such as NDVI (normalized difference vegetation index), EVI (enhanced vegetation index) and NBR (normalized burn ratio), are usually derived from MODIS reflectance data. However, noise usually makes it difficult to generate reliable time series of vegetation indices. Although some methods have been developed for reconstructing NDVI time series data, they still suffer from some limitations. First, there is no reliable approach for detecting and dealing with low-quality data, resulting in poor outcomes. Second, no effective evaluation of the fidelity of the corrected data to the original data has been discussed. For these reasons, we developed a new time series reconstruction approach, named Fixing Invalid Value (FIV) method. The proposed method assumes that the noise in surface reflectance data stems from invalid data, such as clouds, ice, and missing values. The FIV method first uses the spatially and temporally neighboring pixels to estimate the invalid values and then applies morphology operations to remove the residual noise. Finally, the Savitzky-Golay (S-G) filter is employed to generate the final results. The FIV method is tested on 8-day composite MODIS surface reflectance time series data from 2001 to 2012 in Jiangxi and Fujian provinces, China. The results show that the FIV method outperforms the conventional S-G filter and the HANTS method both in terms of visual inspection and quantitative evaluation. Furthermore, the fidelity evaluation reveals that the proposed FIV method produces high-quality time series data under all weather conditions. INDEX TERMS Time series reconstruction, removal of noise, Savitzky-Golay filtering, NDVI, MODIS.

show abstract

“…Based on a statistical analysis, it has been determined that water table depths of more than 13.3 m increase the probability of finding an ill-conditioned Tamarugo tree [15]. Satellite indices, such as the green canopy fraction (GCF) [16] and the normalized difference vegetation index (NDVI) [17], have been used for analyzing the health of the Tamarugo trees. The GCF is the ratio between the volumes of green canopy and total canopy and corresponds to an indirect measurement of the photosynthetic activity of the trees [16], whereas the NDVI represents the fraction of photosynthetically active radiation absorbed by the vegetation [17].…”

Section: Introductionmentioning

confidence: 99%

“…Satellite indices, such as the green canopy fraction (GCF) [16] and the normalized difference vegetation index (NDVI) [17], have been used for analyzing the health of the Tamarugo trees. The GCF is the ratio between the volumes of green canopy and total canopy and corresponds to an indirect measurement of the photosynthetic activity of the trees [16], whereas the NDVI represents the fraction of photosynthetically active radiation absorbed by the vegetation [17]. Chávez et al [18] proposed a relationship between water table depth and the vital state of the Tamarugo tree in which the GCF is integrated along with the NDVI, the 18 O isotope enrichment in foliar tissue and the twig growth to determine a range of tree damage as a function of water table depth.…”

Section: Introductionmentioning

confidence: 99%

An Unsaturated/Saturated Coupled Hydrogeological Model for the Llamara Salt Flat, Chile, to Investigate Prosopis tamarugo Survival

et al. 2019

View full text Add to dashboard Cite

The Propopis tamarugo Phil, also known as Tamarugo, is an endemic and protected tree that survives in the Atacama Desert—a hyper arid and highly saline environment. The Tamarugo is threatened because of groundwater overexploitation, and its preservation depends on the soil moisture in the vadose zone, as many of the tree roots do not reach the current water table levels. To improve the estimation of soil moisture available for the Tamarugo trees, we applied a hydrogeological model that couples the unsaturated and saturated zones. The model was used to represent different groundwater exploitation and recharge scenarios between February 2006 and September 2030 to predict simultaneously groundwater levels and soil moisture. The model results show that even at locations where water table depletion is relatively small (~1–1.5 m), soil moisture can drastically decrease (0.25–0.30 m3/m3). Therefore, Tamarugo survival can be better addressed, as the applied model provides a management tool to estimate response of Tamarugo trees to changing soil moisture. To further improve the model and its use to assess Tamarugo survival, more field data, such as soil hydrodynamic properties and soil moisture, should be collected. Additionally, relationships between the state of the Tamarugo trees and soil moisture should be further constructed. In this way, the developed model will be able to predict future conditions associated to the Tamarugo’s health state.

show abstract

Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications

Cited by 31 publications

References 104 publications

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data

Improving Time Series Reconstruction by Fixing Invalid Values and its Fidelity Evaluation

An Unsaturated/Saturated Coupled Hydrogeological Model for the Llamara Salt Flat, Chile, to Investigate Prosopis tamarugo Survival

Contact Info

Product

Resources

About