Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data

2015 ◽  
Vol 156 ◽  
pp. 457-472 ◽  
Author(s):  
Xiaoyang Zhang
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Avhrr Data ◽  
Global Time

scholarly journals Phenological behavior of Atlas cedar (Cedrus atlantica) forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria

2019 ◽  
Vol 21 (1) ◽  
Author(s):  
Khaled Missaoui ◽  
Rachid Gharzouli ◽  
Yamna Djellouli ◽  
Frençois Messner
Keyword(s):  
Time Series ◽  
Satellite Images ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Descriptive Analysis ◽  
Precipitation Variability ◽  
Monthly Precipitation ◽  
Cedrus Atlantica ◽  
The Past

Abstract. Missaoui K, Gharzouli R, Djellouli Y, Messner F. 2020. Phenological behavior of Atlas cedar (Cedrus atlantica)  forest to snow and precipitation variability in Boutaleb and Babors Mountains, Algeria. Biodiversitas 21: 239-245. Understanding the changes in snow and precipitation variability and how forest vegetation response to such changes is very important to maintain the long-term sustainability of the forest. However, relatively few studies have investigated this phenomenon in Algeria. This study was aimed to find out the response of Atlas cedar (Cedrus atlantica (Endl.) G.Manetti ex Carrière) forest in two areas (i.e Boutaleb and Babors Mountains) and their response to the precipitation and snow variability. The normalized difference vegetation index (NDVI) generated from satellite images of MODIS time series was used to survey the changes of the Atlas cedar throughout the study area well as dataset of monthly precipitation and snow of the province of Setif (northeast of Algeria) from 2000 to 2018. Descriptive analysis using Standarized Precipitation Index (SPI) showed the wetter years were more frequent in the past than in the last two decades. The NDVI values changes in both areas with high values were detected in Babors Mountains with statistically significant differences. Our findings showed important difference in Atlas cedar phenology from Boutaleb mountains to Babors Mountains which likely related to snow factor.

scholarly journals Retrieval of aerosol optical depth over land surfaces from AVHRR data

2013 ◽  
Vol 6 (1) ◽  
pp. 2227-2251 ◽  
Author(s):  
L. Mei ◽  
Y. Xue ◽  
A. A. Kokhanovsky ◽  
W. von Hoyningen-Huene ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Surface Reflectance ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Land Surfaces ◽  
Very High

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) radiance data provide a global, long-term, consistent time series having high spectral and spatial resolution and thus being valuable for the retrieval of surface spectral reflectance, albedo and surface temperature. Long term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. These data have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land and land surface albedo are challenging because of the information content of the measurement is limited and the inversion of these data products being ill defined. Solving the radiative transfer equations requires additional information and knowledge to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, following the MODIS dark target approach, the surface reflectance at 0.64 μm was obtained. The comparison of the estimated surface reflectance at 0.64 μm with MODIS reflectance products (MOD09) shows a strong correlation (R = 0.7835). Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to Advanced Very High Resolution Radiometer (AVHRR) data. A simplified Look-Up Table (LUT) method, adopted from Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The Aerosol Optical Depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with a correlation coefficient R = 0.861 and Root Mean Square Error (RMSE) = 0.17. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to geostationary satellites.

scholarly journals A Data-Intensive Approach to Address Food Sustainability: Integrating Optic and Microwave Satellite Imagery for Developing Long-Term Global Cropping Intensity and Sowing Month from 2001 to 2015

2020 ◽  
Vol 12 (8) ◽  
pp. 3227 ◽  
Author(s):  
Anjar Dimara Sakti ◽  
Wataru Takeuchi
Keyword(s):  
Time Series ◽  
Land Surface ◽  
Vegetation Index ◽  
Time Series Data ◽  
Global Analysis ◽  
Local Level ◽  
Series Data ◽  
Pixel Resolution ◽  
Cropping Intensity

It is necessary to develop a sustainable food production system to ensure future food security around the globe. Cropping intensity and sowing month are two essential parameters for analyzing the food–water–climate tradeoff as food sustainability indicators. This study presents a global-scale analysis of cropping intensity and sowing month from 2000 to 2015, divided into three groups of years. The study methodology integrates the satellite-derived normalized vegetation index (NDVI) of 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) and daily land-surface-water coverage (LSWC) data obtained from The Advanced Microwave Scanning Radiometer (AMSR-E/2) in 1-km aggregate pixel resolution. A fast Fourier transform was applied to normalize the MODIS NDVI time-series data. By using advanced methods with intensive optic and microwave time-series data, this study set out to anticipate potential dynamic changes in global cropland activity over 15 years representing the Millennium Development Goal period. These products are the first global datasets that provide information on crop activities in 15-year data derived from optic and microwave satellite data. The results show that in 2000–2005, the total global double-crop intensity was 7.1 million km2, which increased to 8.3 million km2 in 2006–2010, and then to approximately 8.6 million km2 in 2011–2015. In the same periods, global triple-crop agriculture showed a rapid positive growth from 0.73 to 1.12 and then 1.28 million km2, respectively. The results show that Asia dominated double- and triple-crop growth, while showcasing the expansion of single-cropping area in Africa. The finer spatial resolution, combined with a long-term global analysis, means that this methodology has the potential to be applied in several sustainability studies, from global- to local-level perspectives.

scholarly journals Retrieval of aerosol optical depth over land surfaces from AVHRR data

2014 ◽  
Vol 7 (8) ◽  
pp. 2411-2420 ◽  
Author(s):  
L. L. Mei ◽  
Y. Xue ◽  
A. A. Kokhanovsky ◽  
W. von Hoyningen-Huene ◽  
G. de Leeuw ◽  
...  
Keyword(s):  
Time Series ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Limited Information ◽  
Additional Information ◽  
Aerosol Retrieval ◽  
Avhrr Data ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Land Surfaces

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) provides a global, long-term, consistent time series of radiance data in several wavebands which are used for the retrieval of surface spectral reflectance, albedo and surface temperature. Long-term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. AVHRR radiances have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land is challenging because of the limited information content in the data which renders the inversion problem ill defined. Solving the radiative transfer equations requires additional information to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following the MODIS dark target approach, this relationship is used to obtain the surface reflectance at 0.64 μm. The comparison of the estimated surface reflectances with MODIS reflectance products (MOD09) shows a strong correlation. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to AVHRR data. A simplified look-up table (LUT) method, adopted from the Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The aerosol optical depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with 71.8% of the points located within ±(0.1 + 0.15τ) (τ is the AOD) of the identity line. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to be used on geostationary satellites.

scholarly journals Analysis of Grassland Degradation in Zona da Mata, MG, Brazil, Based on NDVI Time Series Data with the Integration of Phenological Metrics

2019 ◽  
Vol 11 (24) ◽  
pp. 2956
Author(s):  
Marcos C. Hott ◽  
Luis M. T. Carvalho ◽  
Mauro A. H. Antunes ◽  
João C. Resende ◽  
Wadson S. D. Rocha
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Time Series Data ◽  
Multicriteria Analysis ◽  
Series Data ◽  
Full Time ◽  
Socioeconomic Indicators ◽  
Ndvi Time Series ◽  
Phenological Metrics

There is currently a lot of interest in determining the state of Brazilian grasslands. Governmental actions and programs have recently been implemented for grassland recovery in Brazilian states, with the aim of improving production systems and socioeconomic indicators. The aim of this study is to evaluate the vegetative growth, temporal vigor, and long-term scenarios for the grasslands in Zona da Mata, Minas Gerais State, Brazil, by integrating phenological metrics. We used metrics derived from the normalized difference vegetation index (NDVI) time series from moderate resolution imaging spectroradiometer (MODIS) data, which were analyzed in a geographic information system (GIS), using multicriteria analysis, the analytical hierarchy process, and a simplified expert system (ESS). These temporal metrics, i.e., the growth index (GI) for 16-day periods during the growing season; the slope; and the maximum, minimum, and mean for the time series, were integrated to investigate the grassland vegetation conditions and degradation level. The temporal vegetative vigor was successfully described using the rescaled range (R/S statistic) and the Hurst exponent, which, together with the metrics estimated for the full time series, imagery, and field observations, indicated areas undergoing degradation or areas that were inadequately managed (approximately 61.5%). Time series analysis revealed that most grasslands showed low or moderate vegetative vigor over time with long-term persistence due to farming practices associated with burning and overgrazing. A small part of the grasslands showed high and sustainable plant densities (approximately 8.5%). A map legend for grassland management guidelines was developed using the proposed method with remote sensing data, which were applied using GIS software and a field campaign.

scholarly journals Long-Term Monitoring of Cork and Holm Oak Stands Productivity in Portugal with Landsat Imagery

2019 ◽  
Vol 11 (5) ◽  
pp. 525 ◽  
Author(s):  
Valentine Aubard ◽  
Joana Amaral Paulo ◽  
João M.N. Silva
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Forest Type ◽  
Google Earth ◽  
Cork Oak ◽  
Holm Oak ◽  
Mountainous Regions ◽  
Cork Production ◽  
Shallow Soils

Oak stands are declining in many regions of southern Europe. The goal of this paper is to assess this process and develop an effective monitoring tool for research and management. Long-term trends of the Normalized Difference Vegetation Index (NDVI) were derived and mapped at 30-m spatial resolution for all areas with a stable land cover of cork oak (Quercus suber L.) and holm oak (Quercus ilex L.) forests and agroforestry systems in mainland Portugal. NDVI, a good proxy for forest health and productivity monitoring, was obtained for the 1984–2017 period using Landsat-5 TM and Landsat-7 ETM+ imagery. TM values were adjusted to those of ETM+, after a comparison of site-specific and literature linear equations. The spatiotemporal trend analysis was performed using only July and August NDVI values, in order to minimize the spectral contribution of understory vegetation and its phenological variability, and thus, focus on the tree layer. Signs and significance of trends were obtained for six representative oak stands and the whole country with the Mann Kendall and Contextual Mann-Kendall test, respectively, and their slope was assessed with the Theil-Sen estimator. Long-term forest inventories of six study sites and NDVI time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) allowed validating the methodology and results with independent data. NDVI has a good relationship with cork production at the forest stand level. Pettitt tests reveal significant change-points within the trends in the period 1996–2005, when changes in drought patterns occurred. Twelve percent of the area of oak stands in Portugal presents significant decreasing trends, most of them located in mountainous regions with shallow soils. Cork oak agroforestry is the most declining oak forest type, compared to cork oak and holm oak forests. The Google Earth Engine platform proved to be a powerful tool to deal with long-term time series and for the monitoring of forests health and productivity.

scholarly journals Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1894
Author(s):  
Thomas P. Higginbottom ◽  
Elias Symeonakis
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Vegetation Index ◽  
Change Point Analysis ◽  
Annual Rainfall ◽  
Small Scale ◽  
Growth Season ◽  
Continental Scale ◽  
Normalised Difference Vegetation Index

Time-series of vegetation greenness data, derived from Earth-observation imagery, have become a key source of information for studying large-scale environmental change. The ever increasing length of such series allows for a range of indicators to be derived and for increasingly complex analyses to be applied. This study presents an analysis of trends in vegetation productivity—measured using the Global Inventory Monitoring and Modelling System third generation (GIMMS3g) Normalised Difference Vegetation Index (NDVI) data—for African savannahs, over the 1982–2015 period. Two annual metrics were derived from the 34 year dataset: the monthly, smoothed NDVI (the aggregated growth season NDVI) and the associated Rain Use Efficiency (growth season NDVI divided by annual rainfall). These indicators were then used in a BFAST-based change-point analysis, allowing the direction of change over time to change and the detection of one major break in the time-series. We also analysed the role of land cover type and climate zone as associations of the observed changes. Both methods agree that vegetation greening was pervasive across African savannahs, although RUE displayed less significant changes than NDVI. Monotonically increasing trends were the most common trend type for both indicators. The continental scale of the greening may suggest global processes as key drivers, such as carbon fertilization. That NDVI trends were more dynamic than RUE suggests that a large component of vegetation trends is driven by precipitation variability. Areas of negative trends were conspicuous by their minimalism. However, some patterns were apparent. In the southern Sahel and West Africa, declining NDVI and RUE overlapped with intensive population and agricultural regions. Dynamic trend reversals, in RUE and NDVI, located in Angola, Zambia and Tanzania, coincide with areas where a long-term trend of forest degradation and agricultural expansion has recently given way to increases in woody biomass. Meanwhile in southern Africa, monotonic increases in RUE with varying NDVI trend types may be indicative of shrub encroachment. However, all these processes are small-scale relative to the GIMMS NDVI data, and reconciling these conflicting drivers is not a trivial task. Our study highlights the importance of considering multiple options when undertaking trend analyses, as different inputs and methods can reveal divergent patterns.

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