Resolution Normalized Difference Vegetation Index Research Articles

Spatial Upscaling of Tree-Ring-Based Forest Response to Drought with Satellite Data

We have integrated the observational capability of satellite remote sensing with plot-scale tree-ring data to upscale the evaluation of forest responses to drought. Satellite data, such as the normalized difference vegetation index (NDVI), can provide a spatially continuous measure with limited temporal coverage, while tree-ring width index (RWI) provides an accurate assessment with a much longer time series at local scales. Here, we explored the relationship between RWI and NDVI of three dominant species in the Southwestern United States (SWUS) and predicted RWI spatial distribution from 2001 to 2017 based on Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km resolution NDVI data with stringent quality control. We detected the optimum time windows (around June–August) during which the RWI and NDVI were most closely correlated for each species, when the canopy growth had the greatest effect on growth of tree trunks. Then, using our upscaling algorithm of NDVI-based RWI, we were able to detect the significant impact of droughts in 2002 and in 2011–2014, which supported the validity of this algorithm in quantifying forest response to drought on a large scale.

English

Dekads in the prediction of yields under associated millet and cowpea crops were studied in three localities located in the Sahelian zone. Multiple linear regressions was applied to create predictive models based on variables extracted from the decadal Normalized Difference Vegetation Index (NDVI) image series from the SPOT VEGETATION satellite, the GeoWRSI tool, and the recorded ground rainfall. The earliest forecast of millet occurs as early as the third dekad of July with an estimation error of 53 Kg ha-1. Combined variables derived from NDVI, Water Requirements Satisfaction Index (WRSI) and rainfall explain better the variation of millet yield in the first and second dekads of September with estimation errors ranging from 50 to 75 Kg ha-1 according to the localities. The earliest forecast of cowpea can occur in the third dekad of August with an estimation error of 68 Kg ha-1. Cowpea forecasting also occurs later in the season in the second dekad of September by combining NDVI with rainfall or WRSI with rainfall with estimation errors ranging from 52 to 70 Kg ha-1. However, when sorghum is close to the associated millet and cowpea crops in the cultivated area and when the soil moisture are satisfactory for crops, NDVI and WRSI have little or no explanation for variation in millet and cowpea yields. The forecast under associated cereal and legume crops encountered in Sahelian production systems can be envisaged based on the environment topography and on high spatial resolution NDVI images. Key words: Cowpea, millet, normalized difference vegetation index (NDVI), prediction, water requirements satisfaction index (WRSI), yield. &nbsp

Complementarity between Textural and Radiometric Indices From Airborne and Spaceborne Multi VHSR Data: Disentangling the Complexity of Heterogeneous Landscape Matrix

The quantitative characterization of landscape structure is critical to assess conservation, and monitor and manage biodiversity. The Mediterranean Basin is a biodiversity hotspot that illustrates the strong relationship between biodiversity and the complexity of the landscape mosaic. Our objective was to test the relevance of two textural indices and one radiometric index (the normalized difference vegetation index (NDVI)) to characterize vegetation structure. These indices could be used as indicators of vegetation composition and organization of four vertical strata when derived from airborne and Pléiades space-borne VHSR imagery. More specifically, we analyzed the influence of the spatial resolution and the radiometric information on the characterization of the landscape structure. Our results indicated that NDVI information at 0.5 m spatial resolution was necessary to be able to incorporate the heterogeneity of vegetation structure. Indices derived from lower resolution NDVI images or different radiometric information than airborne images also proved to be sensitive to vegetation fragmentation and composition. NDVI images brought out details on ligneous/herbs patterns while panchromatic image brought out more details on herbs/bare soil patterns. Combined textural and NDVI indices show strong potential for vegetation structure understanding, allowing detailed mapping. NDVI information shows good potential for applications related to landscape closure dynamics; related habitat degradation indicators caused by shrub encroachment. Panchromatic derived information, on the other hand, provides information relevant in applications focusing grazing management.

Comparison of Spatiotemporal Fusion Models for Producing High Spatiotemporal Resolution Normalized Difference Vegetation Index Time Series Data Sets

It has a great significance to combine multi-source with different spatial resolution and temporal resolution to produce high spatiotemporal resolution Normalized Difference Vegetation Index (NDVI) time series data sets. In this study, four spatiotemporal fusion models were analyzed and compared with each other. The models included the spatial and temporal adaptive reflectance model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the flexible spatiotemporal data fusion model (FSDAF), and a spatiotemporal vegetation index image fusion model (STVIFM). The objective of is to: 1) compare four fusion models using Landsat-MODIS NDVI image from the Banan district, Chongqing Province; 2) analyze the prediction accuracy quantitatively and visually. Results indicate that STVIFM would be more suitable to produce NDVI time series data sets.

Disentangling Climate and Disturbance Effects on Regional Vegetation Greening Trends

Productivity of northern latitude forests is an important driver of the terrestrial carbon cycle and is already responding to climate change. Studies of the satellite-derived Normalized Difference Vegetation Index (NDVI) for northern latitudes indicate recent changes in plant productivity. These detected greening and browning trends are often attributed to a lengthening of the growing season from warming temperatures. Yet, disturbance-recovery dynamics are strong drivers of productivity and can mask direct effects of climate change. Here, we analyze 1-km resolution NDVI data from 1989 to 2014 for the northern latitude forests of the Greater Yellowstone Ecosystem for changes in plant productivity to address the following questions: (1) To what degree has greening taken place in the GYE over the past three decades? and (2) What is the relative importance of disturbance and climate in explaining NDVI trends? We found that the spatial extents of statistically significant productivity trends were limited to local greening and browning areas. Disturbance history, predominately fire disturbance, was a major driver of these detected NDVI trends. After accounting for fire-, insect-, and human-caused disturbances, increasing productivity trends remained. Productivity of northern latitude forests is generally considered temperature-limited; yet, we found that precipitation was a key driver of greening in the GYE.

Prior Season Crop Type Masks for Winter Wheat Yield Forecasting: A US Case Study

Monitoring and forecasting crop yields is a critical component of understanding and better addressing global food security challenges. Detailed spatial information on crop-type distribution is fundamental for in-season crop condition monitoring and yields forecasting over large agricultural areas, as it enables the extraction of crop-specific signals. Yet, the availability of such data within the growing season is often limited. Within this context, this study seeks to develop a practical approach to extract a crop-specific signal for yield forecasting in cases where crop rotations are prevalent, and detailed in-season information on crop type distribution is not available. We investigated the possibility of accurately forecasting winter wheat yields by using a counter-intuitive approach, which coarsens the spatial resolution of out-of-date detailed winter wheat masks and uses them in combination with easily accessibly coarse spatial resolution remotely sensed time series data. The main idea is to explore an optimal spatial resolution at which crop type changes will be negligible due to crop rotation (so a previous seasons’ mask, which is more readily available can be used) and an informative signal can be extracted, so it can be correlated to crop yields. The study was carried out in the United States of America (USA) and utilized multiple years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) data, US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) detailed wheat masks, and a regression-based winter wheat yield model. The results indicate that, in places where crop rotations were prevalent, coarsening the spatial scale of a crop type mask from the previous season resulted in a constant per-pixel wheat proportion over multiple seasons. This enables the consistent extraction of a crop-specific vegetation index time series that can be used for in-season monitoring and yield estimation over multiple years using a single mask. In the case of the USA, using a moderate resolution crop type mask from a previous season aggregated to 5 km resolution, resulted in a 0.7% tradeoff in accuracy relative to the control case where annually-updated detailed crop-type masks were available. These findings suggest that when detailed in-season data is not available, winter wheat yield can be accurately forecasted (within 10%) prior to harvest using a single, prior season crop mask and coarse resolution Normalized Difference Vegetation Index (NDVI) time series data.

Analysis of slight precipitation in China during the past decades and its relationship with advanced very high radiometric resolution normalized difference vegetation index

Precipitation is one of the most important factors determining the occurrence of extreme hydro‐meteorological events and water resource availability. Precipitation in different grades has diverse ecological effects, and slight precipitation (SP, defined as 0.1–1.0 mm/day) is the minimal level among them. In this study, we investigated SP trends from 1961 to 2013, as well as the relationship between SP and advanced very high radiometric resolution (AVHRR) normalized difference vegetation index (NDVI) in China during growing season from 1981 to 2006. The distributions and trends of SP were analysed by calculating the daily precipitation data. The average annual slight precipitation amount (SPA) and the number of slight precipitation days (SPD), derived from 839 monitoring stations in China, show a decreasing trend over the last five decades, which is in agreement with total precipitation (TP) but in different rates. When the trend was analysed seasonally, SP in most stations decreases significantly in September–October–November (SON) and June–July–August (JJA), and the largest decrease is found in SON. About 49.5 and 68.7% of monitoring stations show a decreasing trend in SON, in both SPA and SPD, whereas the trend is less popular in March–April–May (MAM, SPA: 19.7%, SPD: 41.4%) and December–January–February (JJF, SPA: 25.6%, SPD: 43.1%). Moreover, our analysis indicates that the decrease of SP is mainly due to the decrease of SPD as the median amount of daily SP was unchanged over the past five decades (close to 0.3 mm/day). Based on 26‐year (1981–2006) semi‐monthly AVHRR NDVI data and the records of SP data, the relationship between AVHRR NDVI and SP was also investigated. In regions with lower (<600 mm) TP, the correlation coefficients between NDVI and SP tend to be higher. These results highlight that SP has different effects than TP on vegetation growth. We also analysed time lag effects and concluded that the sensitivity of NDVI to SP for grass vegetation (the correlation coefficient is 0.327) is more noticeable than for trees (0.211) or shrubs (−0.058). The relationship between SP and NDVI also provides us new insights on the dependence of vegetation growth on meteorological factors.

Spatiotemporal fusion through the best linear unbiased estimator to generate fine spatial resolution NDVI time series

ABSTRACTSpatiotemporal fusion (STF) technologies are commonly used to acquire high spatiotemporal resolution remote sensing observations. However, most STF technologies fail to consider the nonlinear variation in vegetation in the time domain. Based on the Best Linear Unbiased Estimator (BLUE), this paper proposed a novel STF algorithm (referred to BLUE) which accounts for the phenological characteristics of vegetation. First, annual time series of normalized difference vegetation index (NDVI) data with high spatial resolution but low temporal resolution is fitted using a double logistic function and used as the background field. Then, NDVI data with low spatial resolution but high temporal resolution is used as the observation field. The information in the background and observation fields is fused using the BLUE to obtain high spatiotemporal resolution NDVI data. The proposed algorithm was used to produce dense time series of 30 m resolution NDVI data for a 10 km × 10 km experimental area in 2014. The experimental results demonstrate that the accuracy of fusion results from the proposed BLUE method are higher than those from the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and Linear Mixing Growth Model (LMGM), especially when the temporal component of surface heterogeneity is dominant. The proposed algorithm has broad prospects in vegetation monitoring at high spatiotemporal resolution.

Exploring improvement of impervious surface estimation at national scale through integration of nighttime light and Proba-V data

Directly mapping impervious surface area (ISA) at national and global scales using nighttime light data is a challenge due to the complexity of land surface components and the impacts of unbalanced economic conditions. Previous research mainly used the coarse spatial resolution Defense Meteorological Satellite Program’s Operational Linescan System (DMSP OLS) and Moderate Resolution Imaging Spectroradiometer (MODIS), normalized difference vegetation index (NDVI) data for ISA mapping; the improved spatial resolution and data quality in the Suomi National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite’s Day/Night Band (VIIRS DNB) and in Proba-V data provide a new opportunity to accurately map ISA distribution at the national scale, which has not been explored yet. This research aimed to develop a new index – modified impervious surface index (MISI) – based on VIIRS DNB and Proba-V data to improve ISA estimation and to compare the results with those from the combination of VIIRS DNB and MODIS NDVI data. Landsat data were used to develop ISA data for the typical sites for use as reference data. Regression analysis was used to establish the ISA estimation model in which the dependent variable was from the Landsat data and the independent variable was from the MISI, as well as the previously used Large-scale Impervious Surface Index (LISI). The results indicate that the major error is from the very small or very large proportion of ISA in a unit; improvement of spatial resolution through use of higher spatial resolution nighttime light data (e.g., VIIRS DNB) or NDVI (e.g., Proba-V NDVI) data is an effective approach to improve ISA estimation. Although different indices for the combination of nighttime light and NDVI data have been used, the MISI is especially valuable for reducing the estimation errors for the regions with a small or large ISA proportion.

Rice crop phenology mapping at high spatial and temporal resolution using downscaled MODIS time-series

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

A Spatio-Temporal Data Fusion Model for Generating NDVI Time Series in Heterogeneous Regions

Time series vegetation indices with high spatial resolution and high temporal frequency are important for crop growth monitoring and management. However, due to technical constraints and cloud contamination, it is difficult to obtain such datasets. In this study, a spatio-temporal vegetation index image fusion model (STVIFM) was developed to generate high spatial resolution Normalized Difference Vegetation Index (NDVI) time-series images with higher accuracy, since most of the existing methods have some limitations in accurately predicting NDVI in heterogeneous regions, or rely on very computationally intensive steps and land cover maps for heterogeneous regions. The STVIFM aims to predict the fine-resolution NDVI through understanding the contribution of each fine-resolution pixel to the total NDVI change, which was calculated from the coarse-resolution images acquired on two dates. On the one hand, it considers the difference in relationships between the fine- and coarse-resolution images on different dates and the difference in NDVI change rates at different growing stages. On the other hand, it neither needs to search similar pixels nor needs to use land cover maps. The Landsat-8 and MODIS data acquired over three test sites with different landscapes were used to test the spatial and temporal performance of the proposed model. Compared with the spatial and temporal adaptive reflectance fusion model (STARFM), enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data fusion (FSDAF) method, the proposed STVIFM outperforms the STARFM and ESTARFM at three study sites and different stages when the land cover or NDVI changes were captured by the two pairs of fine- and coarse-resolution images, and it is more robust and less computationally intensive than the FSDAF.

A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States

Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest.

Residential greenness and adiposity: Findings from the UK Biobank

BackgroundWith the rapid urbanization and prevailing obesity pandemic, the role of residential green exposures in obesity prevention has gained renewed focus. The study investigated the effects of residential green exposures on adiposity using a large and diverse population sample drawn from the UK Biobank. Materials and methodsThis was a population based cross-sectional study of 333,183 participants aged 38–73years with individual-level data on residential greenness and built environment exposures. Residential greenness was assessed through 0.50-metre resolution normalized difference vegetation index (NDVI) derived from spectral reflectance measurements in remotely sensed colour infrared data and measured around geocoded participants' dwelling. A series of continuous and binary outcome models examined the associations between residential greenness and markers of adiposity, expressed as body-mass index (BMI) in kg/m2, waist circumference (WC) in cm, whole body fat (WBF) in kg and obesity (BMI≥30kg/m2) after adjusting for other activity-influencing built environment and individual-level confounders. Sensitivity analyses involved studying effect modification by gender, age, urbanicity and SES as well as examining relationships between residential greenness and active travel behaviour. ResultsResidential greenness was independently and consistently associated with lower adiposity, the association being robust to adjustments. An interquartile increment in NDVI greenness was associated with lower BMI (βBMI=−0.123kg/m2, 95% CI: −0.14, −0.10kg/m2), WC (βWC=−0.551cm, 95% CI: −0.61, −0.50cm), and WBF (βWBF=−0.138kg, 95% CI: −0.18, −0.10kg) as well as a reduced relative risk of obesity (RR=0.968, 95% CI: 0.96, 0.98). Residential greenness was beneficially related with active travel, being associated with higher odds of using active mode for non-work travel (OR=1.093, 95% CI: 1.08, 1.11) as well as doing >30min walking (OR=1.039, 95% CI: 1.03, 1.05). ConclusionResiding in greener areas was associated with healthy weight outcomes possibly through a physical activity-related mechanism. Green allocation and design may act as upstream-level public health interventions ameliorating the negative health externalities of obesogenic urban environments. Further prospective studies are needed to identify potential causal pathways and thereby effectively guide such interventions.

Urbanization Alters the Influence of Weather and an Index of Forest Productivity on Avian Community Richness and Guild Abundance in the Seattle Metropolitan Area

Fluctuations in weather and forest productivity influence the abundance and richness of bird populations, however in a rapidly urbanizing landscape the relative importance of each factor may vary. We assessed this possibility in the Seattle, WA, USA region by correlating ten years of bird richness and relative abundance of nine guilds indicative of their tolerance of human development, migration, diet and use of human food subsidies with an annual index of forest productivity (vegetation greenness derived from a 250m resolution Normalized Difference Vegetation Index (NDVI) product from the MODIS satellite) and weather (variation in the Oceanic Nino Index, which estimates the strength of the El Nino-Southern Oscillation, a major driver of local temperature and precipitation). We found that variation in NDVI exerted a strong influence on the richness of the avian community and the abundance of guilds in landscapes undergoing active development, but was less influential in areas of established housing development or forested reserves. Relative to NDVI, weather was much less influential on the abundance of guilds at actively changing sites, and slightly more influential in forest reserves and established developments. Following the warm winter and during the dry summer associated with a strong El Nino, migrants and herbivores declined in changing landscapes, insectivores declined in established developments, and herbivores declined while synanthropic species increased in reserves. These changes may presage the effects of climate change in the Pacific Northwest, which are expected to be similar to El Nino conditions. To buffer these changes in native bird communities, planners, developers, regulators, and home owners should minimize the loss of vegetation during development and attempt to quickly achieve mature landscaping that preferably provides food and shelter for birds.

A comparison of geographic datasets and field measurements to model soil carbon using random forests and stepwise regressions (British Columbia, Canada)

We used geographic datasets and field measurements to examine the mechanisms that affect soil carbon (SC) storage for 65 grazed and non-grazed pastures in southern interior grasslands of British Columbia, Canada. Stepwise linear regression (SR) modeling was compared with random forest (RF) modeling. Models produced with SR performed better than those produced using RF models (r2 = 0.56–0.77 AIC = 0.16–0.30 for SR models; r2 = 0.38–0.53 and AIC = 0.18–0.30 for RF models). The factors most significant when predicting SC were elevation, precipitation, and the normalized difference vegetation index (NDVI). NDVI was evaluated at two scales using: (1) the MOD 13Q1 (250 m/16-day resolution) NDVI data product from the moderate resolution imaging spectro-radiometer (MODIS) (NDVIMODIS), and (2) a handheld multispectral radiometer (MSR, 1 m resolution) (NDVIMSR) in order to understand the potential for increasing model accuracy by increasing the spatial resolution of the gridded geographic datasets. When NDVIMSR data were used to predict SC, the percentage of the variance explained by the model was greater than for models that relied on NDVIMODIS data (r2 = 0.68 for SC for non-grazed systems, modeled with SR based on NDVIMODIS data; r2 = 0.77 for SC for non-grazed systems, modeled with SR based on NDVIMSR data). The outcomes of this study provide the groundwork for effective monitoring of SC using geographic datasets to enable a carbon offset program for the ranching industry.

A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products

An exploratory assessment was undertaken to determine the correlation strength and optimal timing of several commonly used Moderate Resolution Imaging Spectroradiometer (MODIS) composited imagery products against crop yields for 10 globally significant agricultural commodities. The crops analyzed included barley, canola, corn, cotton, potatoes, rice, sorghum, soybeans, sugarbeets, and wheat. The MODIS data investigated included the Normalized Difference Vegetation Index (NDVI), Fraction of Photosynthetically Active Radiation (FPAR), Leaf Area Index (LAI), and Gross Primary Production (GPP), in addition to daytime Land Surface Temperature (DLST) and nighttime LST (NLST). The imagery utilized all had 8-day time intervals, but NDVI had a 250m spatial resolution while the other products were 1000m. These MODIS datasets were also assessed from both the Terra and Aqua satellites, with their differing overpass times, to document any differences. A follow-on analysis, using the Terra 250m NDVI data as a benchmark, looked at the yield prediction utility of NDVI at two spatial scales (250m vs. 1000m), two time precisions (8-day vs. 16-day), and also assessed the Enhanced Vegetation Index (EVI, at 250m, 16-day). The analyses spanned the major farming areas of the United States (US) from the summers of 2008–2013 and used annual county-level average crop yield data from the US Department of Agriculture as a basis. All crops, except rice, showed at least some positive correlations to each of the vegetation related indices in the middle of the growing season, with NDVI performing slightly better than FPAR. LAI was somewhat less strongly correlated and GPP weak overall. Conversely, some of the crops, particularly canola, corn, and soybeans, also showed negative correlations to DLST mid-summer. NLST, however, was never correlated to crop yield, regardless of the crop or seasonal timing. Differences between the Terra and Aqua results were found to be minimal. The 1000m resolution NDVI showed somewhat poorer performance than the 250m and suggests spatial resolution is helpful but not a necessity. The 8-day versus 16-day NDVI relationships to yields were very similar other than for the temporal precision. Finally, the EVI often showed the very best performance of all the variables, all things considered.

Bayesian Method for Building Frequent Landsat-Like NDVI Datasets by Integrating MODIS and Landsat NDVI

Studies related to vegetation dynamics in heterogeneous landscapes often require Normalized Difference Vegetation Index (NDVI) datasets with both high spatial resolution and frequent coverage, which cannot be satisfied by a single sensor due to technical limitations. In this study, we propose a new method called NDVI-Bayesian Spatiotemporal Fusion Model (NDVI-BSFM) for accurately and effectively building frequent high spatial resolution Landsat-like NDVI datasets by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat NDVI. Experimental comparisons with the results obtained using other popular methods (i.e., the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM), and the Flexible Spatiotemporal DAta Fusion (FSDAF) method) showed that our proposed method has the following advantages: (1) it can obtain more accurate estimates; (2) it can retain more spatial detail; (3) its prediction accuracy is less dependent on the quality of the MODIS NDVI on the specific prediction date; and (4) it produces smoother NDVI time series profiles. All of these advantages demonstrate the strengths and the robustness of the proposed NDVI-BSFM in providing reliable high spatial and temporal resolution NDVI datasets to support other land surface process studies.

Mapping of rice-cropping pattern and cultural type using remote-sensing and ancillary data: a case study for South and Southeast Asian countries

A geospatial database on the spatial distribution of rice areas and rice cultural types of major rice-producing countries of South and Southeast Asia has been developed in this study using remote-sensing and ancillary data sets. Multitemporal SPOT VGT normalized difference vegetation index (NDVI) data for the period 2009–2010 were used for the analysis. The classification was performed adopting ISODATA clustering to build a non-agricultural area mask followed by rice area mapping. The derived rice area was stratified by logical modelling of ancillary data sets into five rice cultural types: irrigated wet, upland, flood-prone, drought-prone, and deep-water. The uniqueness of this study is a synergistic approach based solely on single-source, high-temporal remote-sensing data coupled with ancillary data, which demonstrate the application of SPOT VGT NDVI data in building a geospatial database for rice crops over a wide spatial extent. This approach was adopted for cost effectivity as the study extent was vast and thus lacking ground truth information. Comparison of the derived rice area against the reported literature values for validation yielded a good correlation (linear coefficient of determination, R2 = 0.95–0.99). The high-temporal resolution NDVI data enabled effective characterization of vegetation phenology. The derived spatial outputs can be used in various studies associated with the assessment of greenhouse gas emissions from paddy fields, change detection, and inputs to crop simulation models, which are significantly related to different rice cultural types.

Does EO NDVI seasonal metrics capture variations in species composition and biomass due to grazing in semi-arid grassland savannas?

Abstract. Most regional scale studies of vegetation in the Sahel have been based on Earth observation (EO) imagery due to the limited number of sites providing continuous and long term in situ meteorological and vegetation measurements. From a long time series of coarse resolution normalized difference vegetation index (NDVI) data a greening of the Sahel since the 1980s has been identified. However, it is poorly understood how commonly applied remote sensing techniques reflect the influence of extensive grazing (and changes in grazing pressure) on natural rangeland vegetation. This paper analyses the time series of Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI metrics by comparing it with data from the Widou Thiengoly test site in northern Senegal. Field data include grazing intensity, end of season standing biomass (ESSB) and species composition from sizeable areas suitable for comparison with moderate – coarse resolution satellite imagery. It is shown that sampling plots excluded from grazing have a different species composition characterized by a longer growth cycle as compared to plots under controlled grazing or communal grazing. Also substantially higher ESSB is observed for grazing exclosures as compared to grazed areas, substantially exceeding the amount of biomass expected to be ingested by livestock for this area. The seasonal integrated NDVI (NDVI small integral; capturing only the signal inherent to the growing season recurrent vegetation), derived using absolute thresholds to estimate start and end of growing seasons, is identified as the metric most strongly related to ESSB for all grazing regimes. However plot-pixel comparisons demonstrate how the NDVI/ESSB relationship changes due to grazing-induced variation in annual plant species composition and the NDVI values for grazed plots are only slightly lower than the values observed for the ungrazed plots. Hence, average ESSB in ungrazed plots since 2000 was 0.93 t ha−1, compared to 0.51 t ha−1 for plots subjected to controlled grazing and 0.49 t ha−1 for communally grazed plots, but the average integrated NDVI values for the same period were 1.56, 1.49, and 1.45 for ungrazed, controlled and communal, respectively, i.e. a much smaller difference. This indicates that a grazing-induced development towards less ESSB and shorter-cycled annual plants with reduced ability to turn additional water in wet years into biomass is not adequately captured by seasonal NDVI metrics.

Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images

Normalized Difference Vegetation Index (NDVI), a key parameter in understanding the vegetation dynamics, has high spatial and temporal variability. However, continuous monitoring of NDVI is not feasible at fine spatial resolution (<60m) owing to the long revisit time needed by the satellites to acquire the fine spatial resolution data. Further, the study attains significance in the case of humid tropical regions of the earth, where the prevailing atmospheric conditions restrict availability of fine resolution cloud free images at a high temporal frequency. As an alternative to the lack of high resolution images, the current study demonstrates a novel disaggregation method (DisNDVI) which integrates the spatial information from a single fine resolution image and temporal information in terms of crop phenology from time series of coarse resolution images to generate estimates of NDVI at fine spatial and temporal resolution. The phenological variation of the pixels captured at the coarser scale provides the basis for relating the temporal variability of the pixel with the NDVI available at fine resolution. The proposed methodology was tested over a 30km×25km spatially heterogeneous study area located in the south of Tamil Nadu, India. The robustness of the algorithm was assessed by an independent comparison of the disaggregated NDVI and observed NDVI obtained from concurrent Landsat ETM+ imagery. The results showed good spatial agreement across the study area dominated with agriculture and forest pixels, with a root mean square error of 0.05. The validation done at the coarser scale showed that disaggregated NDVI spatially averaged to 240m compared well with concurrent MODIS NDVI at 240m (R2>0.8). The validation results demonstrate the effectiveness of DisNDVI in improving the spatial and temporal resolution of NDVI images for utility in fine scale hydrological applications such as crop growth monitoring and estimation of evapotranspiration.