scholarly journals Spatial and temporal variations in Normalized Difference Vegetation Index in Cuba

Ecosistemas ◽  
2020 ◽  
Vol 29 (1) ◽  
Author(s):  
Daryl D. Cruz Flores ◽  
Emerio Alejandro Curbelo Benítez ◽  
Yarelis Ferrer Sánchez ◽  
Dennis Denis Ávila
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Spatial And Temporal Variations

scholarly journals A high-resolution spatial assessment of the impacts of drought variability on vegetation activity in Spain from 1981 to 2015

2019 ◽  
Vol 19 (6) ◽  
pp. 1189-1213 ◽  
Author(s):  
Sergio M. Vicente-Serrano ◽  
Cesar Azorin-Molina ◽  
Marina Peña-Gallardo ◽  
Miquel Tomas-Burguera ◽  
Fernando Domínguez-Castro ◽  
...  
Keyword(s):  
High Resolution ◽  
Forest Fires ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Forest Growth ◽  
Dominant Factor ◽  
Drought Severity ◽  
Vegetation Types ◽  
Vegetation Activity

Abstract. Drought is a major driver of vegetation activity in Spain, with significant impacts on crop yield, forest growth, and the occurrence of forest fires. Nonetheless, the sensitivity of vegetation to drought conditions differs largely amongst vegetation types and climates. We used a high-resolution (1.1 km) spatial dataset of the normalized difference vegetation index (NDVI) for the whole of Spain spanning the period from 1981 to 2015, combined with a dataset of the standardized precipitation evapotranspiration index (SPEI) to assess the sensitivity of vegetation types to drought across Spain. Specifically, this study explores the drought timescales at which vegetation activity shows its highest response to drought severity at different moments of the year. Results demonstrate that – over large areas of Spain – vegetation activity is controlled largely by the interannual variability of drought. More than 90 % of the land areas exhibited statistically significant positive correlations between the NDVI and the SPEI during dry summers (JJA). Nevertheless, there are some considerable spatio-temporal variations, which can be linked to differences in land cover and aridity conditions. In comparison to other climatic regions across Spain, results indicate that vegetation types located in arid regions showed the strongest response to drought. Importantly, this study stresses that the timescale at which drought is assessed is a dominant factor in understanding the different responses of vegetation activity to drought.

scholarly journals Study on spatial-temporal variations of Meteorological-Agricultural droughts in Turkey

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 483-490 ◽  
Author(s):  
A. Ü. Şorman ◽  
A. D. Mehr ◽  
S. J. Hadi
Keyword(s):  
Land Cover ◽  
Black Sea ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Meteorological Drought ◽  
Temporal Variations ◽  
Central Anatolia ◽  
Eastern Anatolia ◽  
Global Database ◽  
Noaa Avhrr

<p><strong>Abstract.</strong> In this study, the meteorological drought represented by Standardized Precipitation Evapotranspiration Index (SPEI) and agriculture drought represented by Vegetation Condition Index (VCI) are analysed in seven regions over Turkey. VCI calculated using the Normalized Difference Vegetation Index (NDVI) data obtained from NOAA AVHRR, SPEI obtained from the SPEI global database with the version (SPEI base v2.5), and Land use/cover obtained from CORINE datasets. The study covers the period from January 1982 to December 2015 due to the availability of NDVI data. The correlation between monthly and seasonal VCI and SPEI (lag months 1, 3, 6, 9, and 12) was investigated in a regional and provincial scale. Monthly correlation found to be the highest in the Central Anatolia, Aegean, Marmara and Mediterranean regions respectively, while other regions have lower and non-homogenous values. One lag time of the VCI with respect to SPEI 12 improves the correlation. The regional correlation showed that, the highest correlation between two parameters is obtained for all the regions with SPEI 12 during summer, then followed by Autumn, and Spring months, the maximum values are recorded for the Central Anatolia (0.656) and Mediterranean (0.625) in Summer, and Aegean (0.643) in Autumn respectively; rather lower correlation values did occur in Marmara (0.515) in Autumn, Eastern Anatolia (0.501), SE Anatolia (0.375) and Black Sea (0.297) regions in Summer. The provincial investigation between seasonal VCI and SPEI indicated that the presence of a positive correlation in general in most of the provinces in all seasons with several exceptions in the Eastern Anatolia, South eastern Anatolia, Black sea, and Marmara. The land cover types with high correlation coefficients are noticed to be covered by forest, agricultural lands, non-irrigable lands and mostly covered by fruits (grape, olive etc.) using CORINE land cover map.</p>

scholarly journals A new global dataset of phase synchronization of temperature and precipitation: its climatology and contribution to global vegetation productivity

2018 ◽  
Author(s):  
Zhigang Sun ◽  
Zhu Ouyang ◽  
Xubo Zhang ◽  
Wei Ren
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Phase Synchronization ◽  
Temporal Variations ◽  
Coefficient Of Determination ◽  
Terrestrial Vegetation ◽  
Maximum Increase ◽  
Vegetation Productivity ◽  
Temperature And Precipitation ◽  
Maximum Value

Abstract. Besides cumulative temperature and precipitation, the phase synchronization of temperature and precipitation also helps to regulate vegetation distribution and productivity across global lands. However, the phase synchronization has been rarely considered in previous studies related to climate and biogeography due to a lack of a robust and quantitative approach. In this study, we proposed a synchronization index of temperature and precipitation (SI-TaP) and then investigated its global spatial distribution, interannual fluctuation, and long-term trend derived from a global 60-year dataset of meteorological forcings. Further investigation was conducted to understand the relationship between SI-TaP and the annually summed Normalized Difference Vegetation Index (NDVI), which could be a proxy of terrestrial vegetation productivity. Results show differences in both spatial patterns and temporal variations between SI-TaP and air temperature and precipitation, but SI-TaP may help to explain the distribution and productivity of terrestrial vegetation. About 60 % of regions where annually summed NDVI is greater than half of its maximum value overlap regions where SI-TaP is greater than half of its maximum value. By using SI-TaP to explain vegetation productivity along with temperature and precipitation, the maximum increase in the coefficient of determination is 0.66 across global lands. Results from this study suggest that the proposed SI-TaP index is helpful to better understand climate change and its relation to the biota. Dataset available at http://www.dx.doi.org/10.11922/sciencedb.642 or http://www.sciencedb.cn/dataSet/handle/642.

scholarly journals Impact of rural depopulation and climate change on vegetation, runoff and sediment load in the Gan River basin, China

2020 ◽  
Vol 51 (4) ◽  
pp. 768-780
Author(s):  
Lidong Huang ◽  
Aizhong Ye ◽  
Chongjun Tang ◽  
Qingyun Duan ◽  
Yahai Zhang
Keyword(s):  
Climate Change ◽  
River Basin ◽  
Rural Population ◽  
Urban Areas ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Farmland Abandonment ◽  
Causality Test ◽  
Rural Depopulation

Abstract Climate change and rural depopulation are changing the ecological and hydrological cycles in China. Data on the normalized difference vegetation index (NDVI), temperature, precipitation, streamflow, sediment and rural population are available for the Gan River basin from 1981 to 2017. We investigated the spatio-temporal variations in climate, human activity and vegetation mainly using the Mann–Kendall test and examined their relationship using the Granger causality test. The results showed that (1) the temperature markedly increased in all seasons; (2) the precipitation increased in summer and winter but decreased in spring and autumn; (3) overall, the NDVI increased markedly during 2005–2017, but showed seasonal differences, with decreases in summer and winter and increases in spring and autumn; (4) the annual sediment transport showed a significant decreasing trend and (5) a large number of the population shifted from rural to urban areas, resulting in a decrease in the rural population between 1998 and 2018. Rural depopulation has brought about farmland abandonment, conversion of farmland to forests, which was the factor driving the recovery of the vegetation and the decrease in sediment. The results of this study can provide support for climate change adaptation and sustainable development.

scholarly journals Temporal variations on NDVI predict temporal changes in vegetation cover across Patagonian drylands (Argentina)

Ecosistemas ◽  
2021 ◽  
Vol 30 (3) ◽  
pp. 2229
Author(s):  
Juan Gaitan ◽  
Nicolas Ciano ◽  
Gabriel Oliva ◽  
Donaldo Bran ◽  
Lucas Butti ◽  
...  
Keyword(s):  
Vegetation Cover ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Temporal Changes ◽  
Toma De Decisiones

La variación temporal del índice NDVI predice los cambios temporales de la cobertura vegetal en las tierras secas de la Patagonia argentina. En las tierras secas, la vegetación natural es una fuente importante de sustento para las comunidades que viven en ellas, dado que la utilizan como alimento, combustible y forraje para el ganado. Además de los bienes y servicios que brinda a las comunidades, la vegetación de las tierras secas también juega un papel importante en muchos procesos ecosistémicos, como por ejemplo el reciclaje de nutrientes o la protección del suelo frente a la erosión. Por lo tanto, el monitoreo a largo plazo de la cobertura vegetal es clave para la toma de decisiones en la gestión de estas regiones. En este estudio, analizamos la variación de la cobertura vegetal en 239 sitios de una red de monitoreo a largo plazo (red MARAS), en uno de los biomas de tierras secas más grandes del mundo: la estepa patagónica argentina. A continuación, la relacionamos con la variación de diferentes períodos del Índice de Vegetación de Diferencia Normalizada (Normalized Difference Vegetation Index, NDVI), obtenido del sensor MODIS, que sirve como variable predictora. El modelo empírico ajustado explicó hasta un 40% de la variación en la cobertura vegetal medida a campo. Con este sencillo modelo empírico hemos estimado y cartografiado los cambios temporales en la cobertura vegetal de un extenso bioma de tierras secas a bajo coste.

scholarly journals The Effect of Soil Moisture on the Reflectance Spectra Correlations in Beech and Sessile Oak Foliage/ Talajnedvesség hatása bükk és kocsánytalan tölgy lombozat reflexiós spektrumainak korrelációira.

2015 ◽  
Vol 11 (1) ◽  
pp. 9-26 ◽  
Author(s):  
Attila Eredics ◽  
Zsolt István Németh ◽  
Rita Rákosa ◽  
Ervin Rasztovits ◽  
Norbert Móricz ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Indices ◽  
Vapour Pressure Deficit ◽  
Temporal Variations ◽  
Reflectance Spectra ◽  
Changing Environment ◽  
State Dependent ◽  
Sensitivity Change

Abstract Reflectance intensities of foliage are mostly due to biomaterials synthesised by plants. Adaptation to the continuously changing environment requires the regulated alteration of metabolic processes, which also influences the UV-VIS (Ultraviolet-Visible) and IR (Infra Red) spectra of leaves. For the calculation of various Vegetation Indices (VIs), e.g. NDVI (Normalized Difference Vegetation Index), the common practice is to use the reflectance spectrum of the whole foliage and when individual leaves of the same plant are sampled, an average VI is derived. On the contrary, our method exploits the small differences between individual leaves of the same plant, making use of the similar distributions of measured reflectance values. Using particular wavelength pairs, linear regressions of reflectance intensities have been investigated. The parameters of these regressions (slope and intercept) have been compared to the temporal variations of the environmental factors, such as temperature, vapour pressure deficit and soil moisture. By assessing the sensitivity of the regression coefficient (slope) to the changing environment, wavelength pairs can be selected whose sensitivity change reflects the effect of soil moisture deficit on the plant. Based on the state-dependent correlations of the reflectance spectra of plant foliage, a new concept is presented that is capable of indicating the level of environmental stress, e.g. drought stress.

scholarly journals Remote Sensing Derived Indices for Tracking Urban Land Surface Change in Case of Earthquake Recovery

2020 ◽  
Vol 12 (5) ◽  
pp. 895 ◽  
Author(s):  
Sahar Derakhshan ◽  
Susan L. Cutter ◽  
Cuizhen Wang
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Disaster Recovery ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Temporal Variations ◽  
Growth Trend ◽  
Reconstruction Process ◽  
Water Index ◽  
Post Disaster

The study of post-disaster recovery requires an understanding of the reconstruction process and growth trend of the impacted regions. In case of earthquakes, while remote sensing has been applied for response and damage assessment, its application has not been investigated thoroughly for monitoring the recovery dynamics in spatially and temporally explicit dimensions. The need and necessity for tracking the change in the built-environment through time is essential for post-disaster recovery modeling, and remote sensing is particularly useful for obtaining this information when other sources of data are scarce or unavailable. Additionally, the longitudinal study of repeated observations over time in the built-up areas has its own complexities and limitations. Hence, a model is needed to overcome these barriers to extract the temporal variations from before to after the disaster event. In this study, a method is introduced by using three spectral indices of UI (urban index), NDVI (normalized difference vegetation index) and MNDWI (modified normalized difference water index) in a conditional algebra, to build a knowledge-based classifier for extracting the urban/built-up features. This method enables more precise distinction of features based on environmental and socioeconomic variability, by providing flexibility in defining the indices’ thresholds with the conditional algebra statements according to local characteristics. The proposed method is applied and implemented in three earthquake cases: New Zealand in 2010, Italy in 2009, and Iran in 2003. The overall accuracies of all built-up/non-urban classifications range between 92% to 96.29%; and the Kappa values vary from 0.79 to 0.91. The annual analysis of each case, spanning from 10 years pre-event, immediate post-event, and until present time (2019), demonstrates the inter-annual change in urban/built-up land surface of the three cases. Results in this study allow a deeper understanding of how the earthquake has impacted the region and how the urban growth is altered after the disaster.

scholarly journals A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks

2018 ◽  
Vol 15 (19) ◽  
pp. 5779-5800 ◽  
Author(s):  
Yao Zhang ◽  
Joanna Joiner ◽  
Seyed Hamed Alemohammad ◽  
Sha Zhou ◽  
Pierre Gentine
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Gross Primary Production ◽  
Terrestrial Ecosystems ◽  
Temporal Variations ◽  
Clear Sky ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Regression Slopes ◽  
The Individual ◽  
Sky Conditions

Abstract. Satellite-retrieved solar-induced chlorophyll fluorescence (SIF) has shown great potential to monitor the photosynthetic activity of terrestrial ecosystems. However, several issues, including low spatial and temporal resolution of the gridded datasets and high uncertainty of the individual retrievals, limit the applications of SIF. In addition, inconsistency in measurement footprints also hinders the direct comparison between gross primary production (GPP) from eddy covariance (EC) flux towers and satellite-retrieved SIF. In this study, by training a neural network (NN) with surface reflectance from the MODerate-resolution Imaging Spectroradiometer (MODIS) and SIF from Orbiting Carbon Observatory-2 (OCO-2), we generated two global spatially contiguous SIF (CSIF) datasets at moderate spatiotemporal (0.05∘ 4-day) resolutions during the MODIS era, one for clear-sky conditions (2000–2017) and the other one in all-sky conditions (2000–2016). The clear-sky instantaneous CSIF (CSIFclear-inst) shows high accuracy against the clear-sky OCO-2 SIF and little bias across biome types. The all-sky daily average CSIF (CSIFall-daily) dataset exhibits strong spatial, seasonal and interannual dynamics that are consistent with daily SIF from OCO-2 and the Global Ozone Monitoring Experiment-2 (GOME-2). An increasing trend (0.39 %) of annual average CSIFall-daily is also found, confirming the greening of Earth in most regions. Since the difference between satellite-observed SIF and CSIF is mostly caused by the environmental down-regulation on SIFyield, the ratio between OCO-2 SIF and CSIFclear-inst can be an effective indicator of drought stress that is more sensitive than the normalized difference vegetation index and enhanced vegetation index. By comparing CSIFall-daily with GPP estimates from 40 EC flux towers across the globe, we find a large cross-site variation (c.v. = 0.36) of the GPP–SIF relationship with the highest regression slopes for evergreen needleleaf forest. However, the cross-biome variation is relatively limited (c.v. = 0.15). These two contiguous SIF datasets and the derived GPP–SIF relationship enable a better understanding of the spatial and temporal variations of the GPP across biomes and climate.

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