MODIS and PROBA-V NDVI Products Differ when Compared with Observations from Phenological Towers at Four Tropical Dry Forests in the Americas

The Normalized Difference Vegetation Index (NDVI) is widely used to monitor vegetation phenology and productivity around the world. Over the last few decades, phenology monitoring at large scales has been possible due to the information and metrics derived from satellite sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) or the Project for On-Board Autonomy–Vegetation (PROBA-V). However, due to their temporal and spatial resolution, adequate ground comparison is lacking. In this paper, we analyze how NDVI products from MODIS (Aqua and Terra) and PROBA-V predict vegetation phenology when compared with near-surface observations. We conduct this comparison at four tropical dry forests (TDFs) in the Americas. We undertake this study by comparing the following: (i) Dissimilarities of the standardized NDVI (NDVIS) using dynamic time warping, (ii) the differences of daily NDVIS between seasons and ENSO months using generalized linear models, and (iii) phenometrics derived from NDVI time series. Overall, our results suggest that NDVIS from satellite observations present DTW distances (dissimilarities) between 2.98 and 46.57 (18.91 ± 12.31) when compared with near-surface observations. Furthermore, NDVIS comparisons reveal that overall differences between satellite and near-surface observations are close to zero, but this tends to differ between seasons or when El Nino Southern Oscillation (ENSO) is present. Phenometrics comparisons show that metrics derived from satellite observations such as green-up, maturity, and start and end of the wet season strongly correlate with those from near-surface observations. In contrast, phenometrics that describe the day of the highest or lowest NDVI tend to be inconsistent with those from near-surface observations. All findings were observed independently of the NDVI source. Our results suggest that satellite-based NDVI products tend to be inconsistent descriptors of vegetation events on tropical deciduous forests in comparison with near-surface observations. These results reinforce the idea that satellite-based NDVI products should be used and interpreted with great caution and only in ecosystems with well-established knowledge of their vegetation phenology.

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Assessing the Temporal Response of Tropical Dry Forests to Meteorological Drought

Remote Sensing ◽

10.3390/rs12142341 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2341

Author(s):

Lidong Zou ◽

Sen Cao ◽

Anzhou Zhao ◽

Arturo Sanchez-Azofeifa

Keyword(s):

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Standardized Precipitation Index ◽

Meteorological Drought ◽

Tropical Dry Forests ◽

Wet Season ◽

Dry Forests ◽

Human Disturbances ◽

Precipitation Regimes

Due to excessive human disturbances, as well as predicted changes in precipitation regimes, tropical dry forests (TDFs) are susceptible to meteorological droughts. Here, we explored the response of TDFs to meteorological drought by conducting temporal correlations between the MODIS-derived normalized difference vegetation index (NDVI) and land surface temperature (LST) to a standardized precipitation index (SPI) between March 2000 and March 2017 at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS), Guanacaste, Costa Rica. We conducted this study using monthly and seasonal scales. Our results indicate that the NDVI and LST are largely influenced by seasonality, as well as the magnitude, duration, and timing of precipitation. We find that greenness and evapotranspiration are highly sensitive to precipitation when TDFs suffer from long-term water deficiency, and they tend to be slightly resistant to meteorological drought in the wet season. Greenness is more resistant to short-term rainfall deficiency than evapotranspiration, but greenness is more sensitive to precipitation after a period of rainfall deficiency. Precipitation can still strongly influence evapotranspiration on the canopy surface, but greenness is not controlled by the rainfall, but rather phenological characteristics when leaves begin to senesce.

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A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales

10.5194/essd-2021-56 ◽

2021 ◽

Author(s):

Frans-Jan W. Parmentier ◽

Lennart Nilsen ◽

Hans Tømmervik ◽

Elisabeth J. Cooper

Keyword(s):

Plant Communities ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Lapse ◽

High Arctic ◽

The Arctic ◽

Vegetation Phenology ◽

Additional Time ◽

Svalbard Archipelago ◽

Near Surface

Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago, with the aim to monitor vegetation phenology. The network consists of ten racks equipped with sensors that measure NDVI (Normalized Difference Vegetation Index), soil temperature and moisture, as well as time-lapse RGB cameras. Three additional time-lapse cameras are placed on nearby mountain tops to provide an overview of the valley. The vegetation index GCC (Green Chromatic Channel) was derived from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust timeseries for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing, and an overview of the dataset which is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al. 2021). In addition, we provide some examples of how this data can be used to monitoring different vegetation communities in the landscape.

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A distributed time-lapse camera network to track vegetation phenology with high temporal detail and at varying scales

Earth System Science Data ◽

10.5194/essd-13-3593-2021 ◽

2021 ◽

Vol 13 (7) ◽

pp. 3593-3606

Author(s):

Frans-Jan W. Parmentier ◽

Lennart Nilsen ◽

Hans Tømmervik ◽

Elisabeth J. Cooper

Keyword(s):

Time Series ◽

Plant Communities ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Time Lapse ◽

The Arctic ◽

Vegetation Phenology ◽

Additional Time ◽

Svalbard Archipelago ◽

Near Surface

Abstract. Near-surface remote sensing techniques are essential monitoring tools to provide spatial and temporal resolutions beyond the capabilities of orbital methods. This high level of detail is especially helpful to monitor specific plant communities and to accurately time the phenological stages of vegetation – which satellites can miss by days or weeks in frequently clouded areas such as the Arctic. In this paper, we describe a measurement network that is distributed across varying plant communities in the high Arctic valley of Adventdalen on the Svalbard archipelago with the aim of monitoring vegetation phenology. The network consists of 10 racks equipped with sensors that measure NDVI (normalized difference vegetation index), soil temperature, and moisture as well as time-lapse RGB cameras (i.e. phenocams). Three additional time-lapse cameras are placed on nearby mountains to provide an overview of the valley. We derived the vegetation index GCC (green chromatic channel) from these RGB photos, which has similar applications as NDVI but at a fraction of the cost of NDVI imaging sensors. To create a robust time series for GCC, each set of photos was adjusted for unwanted movement of the camera with a stabilizing algorithm that enhances the spatial precision of these measurements. This code is available at https://doi.org/10.5281/zenodo.4554937 (Parmentier, 2021) and can be applied to time series obtained with other time-lapse cameras. This paper presents an overview of the data collection and processing and an overview of the dataset that is available at https://doi.org/10.21343/kbpq-xb91 (Nilsen et al., 2021). In addition, we provide some examples of how these data can be used to monitor different vegetation communities in the landscape.

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Changes in Vegetation Phenology and Productivity in Alaska Over the Past Two Decades

Remote Sensing ◽

10.3390/rs12101546 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1546 ◽

Cited By ~ 1

Author(s):

Christopher Potter ◽

Olivia Alexander

Keyword(s):

Time Series ◽

Coastal Plain ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Scale ◽

Data Sets ◽

Vegetation Phenology ◽

Vegetation Growth ◽

The Past ◽

Moderate Resolution Imaging Spectroradiometer

Understanding trends in vegetation phenology and growing season productivity at a regional scale is important for global change studies, particularly as linkages can be made between climate shifts and the vegetation’s potential to sequester or release carbon into the atmosphere. Trends and geographic patterns of change in vegetation growth and phenology from the MODerate resolution Imaging Spectroradiometer (MODIS) satellite data sets were analyzed for the state of Alaska over the period 2000 to 2018. Phenology metrics derived from the MODIS Normalized Difference Vegetation Index (NDVI) time-series at 250 m resolution tracked changes in the total integrated greenness cover (TIN), maximum annual NDVI (MAXN), and start of the season timing (SOST) date over the past two decades. SOST trends showed significantly earlier seasonal vegetation greening (at more than one day per year) across the northeastern Brooks Range Mountains, on the Yukon-Kuskokwim coastal plain, and in the southern coastal areas of Alaska. TIN and MAXN have increased significantly across the western Arctic Coastal Plain and within the perimeters of most large wildfires of the Interior boreal region that burned since the year 2000, whereas TIN and MAXN have decreased notably in watersheds of Bristol Bay and in the Cook Inlet lowlands of southwestern Alaska, in the same regions where earlier-trending SOST was also detected. Mapping results from this MODIS time-series analysis have identified a new database of localized study locations across Alaska where vegetation phenology has recently shifted notably, and where land cover types and ecosystem processes could be changing rapidly.

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Fire Recurrence and Normalized Difference Vegetation Index (NDVI) Dynamics in Brazilian Savanna

Fire ◽

10.3390/fire2010001 ◽

2018 ◽

Vol 2 (1) ◽

pp. 1 ◽

Cited By ~ 1

Author(s):

Níckolas Santana

Keyword(s):

Remote Sensing ◽

Time Series ◽

Trend Analysis ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Management Policy ◽

Brazilian Savanna ◽

Vegetation Phenology ◽

Modis Ndvi ◽

Fire Recurrence

Fire is one of the main modeling agents of savanna ecosystems, affecting their distribution, physiognomy and species diversity. Changes in the natural fire regime on savannas cause disturbances in the structural characteristics of vegetation. Theses disturbances can be effectively monitored by time series of remote sensing data in different terrestrial ecosystems such as savannas. This study used trend analysis in NDVI (Normalized Difference Vegetation Index)–MODIS (Moderate Resolution Imaging Spectroradiometer) time series to evaluate the influence of different fire recurrences on vegetation phenology of the Brazilian savanna in the period from 2001 to 2016. The trend analysis indicated several factors responsible for changes in vegetation: (a) The absence of fire in savanna phytophysiognomies causes a constant increase in MODIS–NDVI, ranging from 0.001 to 0.002 per year, the moderate presence of fire in these areas does not cause significant changes, while the high recurrence results in decreases of MODIS–NDVI, ranging from −0.002 to −0.008 per year; (b) Forest areas showed a high decrease in NDVI, reaching up to −0.009 MODIS–NDVI per year, but not related to fire recurrence, indicating the high degradation of these phytophysiognomies; (c) Changes in vegetation are highly connected to the protection status of the area, such as areas of integral protection or sustainable use, and consequently their conservation status. Areas with greater vegetation conservation had more than 70% of positive changes in pixels with significant tendencies. Absence or presence of fire are the main agents of vegetation change in areas with lower anthropic influence. These results reinforce the need for a suitable fire management policy for the different types of Cerrado phytophysiognomies, in addition to highlighting the efficiency of remote sensing time series for evaluation of vegetation phenology.

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Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

Sustainability ◽

10.3390/su11051410 ◽

2019 ◽

Vol 11 (5) ◽

pp. 1410 ◽

Cited By ~ 3

Author(s):

Suman Moparthy ◽

Dominique Carrer ◽

Xavier Ceamanos

Keyword(s):

Remote Sensing ◽

Surface Albedo ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Vegetation Indices ◽

Satellite Observations ◽

Atmospheric Effects ◽

Forest Regions ◽

Aerosol Emissions

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

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On the Hydroclimate-Vegetation Relationship in the Southwestern Amazon During the 2000–2019 Period

Frontiers in Water ◽

10.3389/frwa.2021.648499 ◽

2021 ◽

Vol 3 ◽

Author(s):

Omar Gutierrez-Cori ◽

Jhan Carlo Espinoza ◽

Laurent Z. X. Li ◽

Sly Wongchuig ◽

Paola A. Arias ◽

...

Keyword(s):

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Regional Climate ◽

Dry Season ◽

Shortwave Radiation ◽

Wet Season ◽

Evergreen Forests ◽

Terrestrial Water ◽

Southern Amazonia ◽

Hydroclimatic Variability

The southern Amazonia is undergoing a major biophysical transition, involving changes in land use and regional climate. This study provides new insights on the relationship between hydroclimatic variables and vegetation conditions in the upper Madeira Basin (~1 × 106 km2). Vegetative dynamics are characterised using the normalized difference vegetation index (NDVI) while hydroclimatic variability is analysed using satellite-based precipitation, observed river discharge, satellite measurements of terrestrial water storage (TWS) and downward shortwave radiation (DSR). We show that the vegetation in this region varies from energy-limited to water-limited throughout the year. During the peak of the wet season (January-February), rainfall, discharge and TWS are negatively correlated with NDVI in February-April (r = −0.48 to −0.65; p < 0.05). In addition, DSR is positively correlated with NDVI (r = 0.47–0.54; p < 0.05), suggesting that the vegetation is mainly energy-limited during this period. Outside this period, these correlations are positive for rainfall, discharge and TWS (r = 0.55–0.88; p < 0.05), and negative for DSR (r = −0.47 to −0.54; p < 0.05), suggesting that vegetation depends mainly on water availability, particularly during the vegetation dry season (VDS; late June to late October). Accordantly, the total rainfall during the dry season explains around 80% of the VDS NDVI interannual variance. Considering the predominant land cover types, differences in the hydroclimate-NDVI relationship are observed. Evergreen forests (531,350 km2) remain energy-limited during the beginning of the dry season, but they become water-limited at the end of the VDS. In savannas and flooded savannas (162,850 km2), water dependence occurs months before the onset of the VDS. These differences are more evident during extreme drought years (2007, 2010, and 2011), where regional impacts on NDVI were stronger in savannas and flooded savannas (55% of the entire surface of savannas) than in evergreen forests (40%). A spatial analysis reveals that two specific areas do not show significant hydroclimatic-NDVI correlations during the dry season: (i) the eastern flank of the Andes, characterised by very wet conditions, therefore the vegetation is not water-limited, and (ii) recent deforested areas (~42,500 km2) that break the natural response in the hydroclimate-vegetation system. These findings are particularly relevant given the increasing rates of deforestation in this region.

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DETECTION OF ACID SLUDGE CONTAMINATED AREA BASED ON NORMALIZED DIFFERENCE VEGETATION INDEX (NDVI) VALUE

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2014.v11.a2598 ◽

2017 ◽

Vol 11 (1) ◽

pp. 21

Author(s):

Nanik Suryo Haryani ◽

Sayidah Sulma ◽

Junita Monika Pasaribu

Keyword(s):

Satellite Images ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Remote Sensing Data ◽

Dry Season ◽

Wet Season ◽

Multi Temporal ◽

Acid Sludge ◽

Landsat Satellite ◽

Solid Form

The solid form of oil heavy metal waste is known as acid sludge. The aim of this research is to exercise the correlation between acid sludge concentration in soil and NDVI value, and further studying the Normalized Difference Vegetation Index (NDVI) anomaly by multi-temporal Landsat satellite images. The implemented method is NDVI. In this research, NDVI is analyzed using the remote sensing data on dry season and wet season. Between 1997 to 2012, NDVI value in dry season is around – 0.007 (July 2001) to 0.386 (May 1997), meanwhile in wet season NDVI value is around – 0.005 (November 2006) to 0.381 (December 1995). The high NDVI value shows the leaf health or thickness, where the low NDVI indicates the vegetation stress and rareness which can be concluded as the evidence of contamination. The rehabilitation has been executed in the acid sludge contaminated location, where the high value of NDVI indicates the successfull land rehabilitation effort.

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Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽

10.3390/s21248371 ◽

2021 ◽

Vol 21 (24) ◽

pp. 8371

Author(s):

Irina Ontel ◽

Anisoara Irimescu ◽

George Boldeanu ◽

Denis Mihailescu ◽

Claudiu-Valeriu Angearu ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Root Zone ◽

Standardized Precipitation Index ◽

In Situ Measurements ◽

Near Surface ◽

Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

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Prevalence and seasonal variation of Fasciola hepatica in slaughtered cattle: the role of climate and environmental factors in Mexico

Journal of Helminthology ◽

10.1017/s0022149x21000444 ◽

2021 ◽

Vol 95 ◽

Cited By ~ 1

Author(s):

K. Hernández-Guzmán ◽

P. Molina-Mendoza ◽

J. Olivares-Pérez ◽

Y. Alcalá-Canto ◽

A. Olmedo-Juárez ◽

...

Keyword(s):

Environmental Factors ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Fasciola Hepatica ◽

Temperate Climate ◽

Wet Season ◽

Dry Land

Abstract The objective of this study is to determine the prevalence of Fasciola hepatica infection in cattle slaughterhouses, as well as its association with climatic/environmental factors (derived from satellite data), seasonality and climate regions in two states in Mexico. Condemned livers from slaughtered animals were obtained from three abattoirs in the states of Puebla and Veracruz. The overall prevalence of the parasite in cattle between January and December of 2017 was 20.6% (1407 out of 6834); the highest rate of condemnation was observed in Veracruz (26.3%; tropical climate), and the lowest rate was found in Puebla (15.5%; temperate climate). The seasonal prevalence of fluke infection was 18.6%, 14.8% and 28.4% during the wet season, and 17.1%, 12.4% and 22.8% during the dry season in the three abattoir sites, located in the districts of Zacatlán, Teziutlán and Ciudad Alemán, respectively. Liver condemnations due to bovine fasciolosis were prevalent in the Zacatlán, Teziutlán and Ciudad Alemán districts during summer, autumn and summer, respectively. Using generalized estimating equations analysis, we determined six variables – rainfall (wet/dry), land surface temperature day, land surface temperature night, normalized difference vegetation index, seasonality and climate regions (temperate/tropical) – to be significantly associated with the prevalence of condemned livers. Climate region was the variable most strongly associated with F. hepatica infection (odds ratio (OR) 266.59; 95% confidence interval (CI): 241.90–353.34), followed by wet and dry seasons (OR 25.56; 95% CI: 20.56–55.67).

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