Remote and field level quantification of vegetation covariates for malaria mapping in three rice agro-village complexes in Central Kenya

<p>Abstract</p> <p>Background</p> <p>We examined algorithms for malaria mapping using the impact of reflectance calibration uncertainties on the accuracies of three vegetation indices (VI)'s derived from QuickBird data in three rice agro-village complexes Mwea, Kenya. We also generated inferential statistics from field sampled vegetation covariates for identifying riceland <it>Anopheles arabiensis </it>during the crop season. All aquatic habitats in the study sites were stratified based on levels of rice stages; flooded, land preparation, post-transplanting, tillering, flowering/maturation and post-harvest/fallow. A set of uncertainty propagation equations were designed to model the propagation of calibration uncertainties using the red channel (band 3: 0.63 to 0.69 μm) and the near infra-red (NIR) channel (band 4: 0.76 to 0.90 μm) to generate the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). The Atmospheric Resistant Vegetation Index (ARVI) was also evaluated incorporating the QuickBird blue band (Band 1: 0.45 to 0.52 μm) to normalize atmospheric effects. In order to determine local clustering of riceland habitats <it>Gi*(d) </it>statistics were generated from the ground-based and remotely-sensed ecological databases. Additionally, all riceland habitats were visually examined using the spectral reflectance of vegetation land cover for identification of highly productive riceland <it>Anopheles </it>oviposition sites.</p> <p>Results</p> <p>The resultant VI uncertainties did not vary from surface reflectance or atmospheric conditions. Logistic regression analyses of all field sampled covariates revealed emergent vegetation was negatively associated with mosquito larvae at the three study sites. In addition, floating vegetation (-ve) was significantly associated with immature mosquitoes in Rurumi and Kiuria (-ve); while, turbidity was also important in Kiuria. All spatial models exhibit positive autocorrelation; similar numbers of log-counts tend to cluster in geographic space. The spectral reflectance from riceland habitats, examined using the remote and field stratification, revealed post-transplanting and tillering rice stages were most frequently associated with high larval abundance and distribution.</p> <p>Conclusion</p> <p>NDVI, SAVI and ARVI generated from QuickBird data and field sampled vegetation covariates modeled cannot identify highly productive riceland <it>An. arabiensis </it>aquatic habitats. However, combining spectral reflectance of riceland habitats from QuickBird and field sampled data can develop and implement an Integrated Vector Management (IVM) program based on larval productivity.</p

A high-resolution index for vegetation extraction in IKONOS images

ISBN: 978-0-8194-8341-6 - WOSInternational audienceIn monitoring vegetation change and urban planning, the measure and the mapping of the green vegetation over the Earth play an important role. The normalized difference vegetation index (NDVI) is the most popular approach to generate vegetation maps for remote sensing imagery. Unfortunately, the NDVI generates low resolution vegetation maps. Highresolution imagery, such as IKONOS imagery, can be used to overcome this weakness leading to better classification accuracy. Hence, it is important to derive a vegetation index providing the high-resolution data. Various scientific researchers have proposed methods based on high-resolution vegetation indices. These methods use image fusion to generate high-resolution vegetation maps. IKONOS produces high-resolution panchromatic (Pan) images and low-resolution multispectral (MS) images. Generally, for the image fusion purpose, the conventional linear interpolation bicubic scheme is used to resize the low-resolution images. This scheme fails around edges and consequently produces blurred edges and annoying artefacts in interpolated images. This study presents a new index that provides high-resolution vegetation maps for IKONOS imagery. This vegetation index (HRNDVI: High Resolution NDVI) is based on a new derived formula including the high-resolution information. We use an artefact free image interpolation method to upsample the MS images so that they have the same size as that of the Pan images. The HRNDVI is then computed by using the resampled MS and the Pan images. The proposed vegetation index takes the advantage of the high spatial resolution information of Pan images to generate artefact free vegetation maps. Visual analysis demonstrates that this index is promising and performs well in vegetation extraction and visualisation

A New Approach for the Analysis of Hyperspectral Data: Theory and Sensitivity Analysis of the Moment Distance Method

We present the Moment Distance (MD) method to advance spectral analysis in vegetation studies. It was developed to take advantage of the information latent in the shape of the reflectance curve that is not available from other spectral indices. Being mathematically simple but powerful, the approach does not require any curve transformation, such as smoothing or derivatives. Here, we show the formulation of the MD index (MDI) and demonstrate its potential for vegetation studies. We simulated leaf and canopy reflectance samples derived from the combination of the PROSPECT and SAIL models to understand the sensitivity of the new method to leaf and canopy parameters. We observed reasonable agreements between vegetation parameters and the MDI when using the 600 to 750 nm wavelength range, and we saw stronger agreements in the narrow red-edge region 720 to 730 nm. Results suggest that the MDI is more sensitive to the Chl content, especially at higher amounts (Chl \u3e 40 mg/cm2) compared to other indices such as NDVI, EVI, and WDRVI. Finally, we found an indirect relationship of MDI against the changes of the magnitude of the reflectance around the red trough with differing values of LAI

IDENTIFICATION OF AGE CLASS AND VARIETIES OF RICE PLANT USING SPECTRORADIOMETRY AND CHLOROPHYLL CONTENT INDEX: (Identifikasi Kelas Umur dan Varietas Tanaman Padi Menggunakan Spektroradiometri dan Indeks Kandungan Klorofil)

Rice is the staple food for Indonesian society because more than 90% population eat rice every day. Estimation of the rice production can be monitored from the plant growth phase by utilizing remote sensing data. Spectroradiometry can be used to validate the remote sensing spectral because it has a wide wavelength range. Research objectives are to identify transplanting age class and varieties of rice plant based on spectroradiometry and its vegetation index, to analyze the relationship between spectroradiometry and chlorophyll content index (CCI). The results show that the transplanting date of 14 days, 21-32 days, and 56-68 days in three varieties (Inpari32; Padjadjaran Agritan; Siliwangi Agritan) are difficult to be distinguished at visible wavelength but it easy at infrared wavelength. The plant age class for the Siliwangi Agritan can be distinguished well on NDVI, SAVI, EVI while the Pajajaran Agritan is only on NDVI and EVI. All vegetation indexes, where the plant age of 14 days and 21-32 days for the Inpari32 are difficult to be distinguished between them, but easy to be distinguished with 56-68 days. This is due to the high sensitivity of chlorophyll to infrared wavelengths and the characteristics of rice plants itself (many tillers and plant height). Meanwhile, rice plants of every veriety are difficult to be distinguished, either on visible wavelength, infrared wavelength or on all vegetation indexes. Spectroradiometry has a high correlation with chlorophyll content index (CCI) (R2=0,88). This shows that the higher chlorophyll content in rice plants, the higher spectroradiometry for infrared wavelength.&nbsp

Forecasting Vegetation Health in the MENA Region by Predicting Vegetation Indicators with Machine Learning Models

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA region. These models are built by a total of 4556 and 519 normalized samples for training and testing purposes, respectively and with 51820 samples used for model evaluation. Models such as multilinear regression, penalized regression models, support vector regression (SVR), neural network, instance-based learning K-nearest neighbor (KNN) and partial least squares were implemented to predict future values of EVI. The models show effective performance in predicting EVI values (R2\u3e 0.95) in the testing and (R2\u3e 0.93) in the evaluation process

Landsat ETM+ and MODIS EVI/NDVI Data Products for Climatic Variation and Agricultural Measurements in Cholistan Desert

The landsat ETM has shown great potential in agricultural mapping and monitoring due to its advantages over traditional receive procedures in terms of cost effectiveness and timeliness in availability of information over larger areas and ingredient the temporal dependence of multitemporal image data to identify the changing pattern of vegetation cover and consequently enhance the interpretation capabilities Integration of multi-sensor and multitemporal satellite data effectively improves the temporal attribute and accuracy of the results Since 2000 NASA s MODIS sensors onboard Terra satellite has provided composite data at 16- days interval to produce estimates of gross primary production GPP that compare well with direct measurements The MODIS Enhanced Vegetation Index EVI and Normalized Difference Vegetation Index NDVI which are independent of climatic drivers also appears as valuable surrogate for estimation of seasonal patterns in GP

Modeling grassland productivity through remote sensing products

Mixed grasslands in south Canada serve a variety of economic, environmental and ecological purposes. Numerical modeling has become a major method used to identify potential grassland ecosystem responses to environment changes and human activities. In recent years, the focus has been on process models because of their high accuracy and ability to describe the interactions among different environmental components and the ecological processes. At present, two commonly-used process models (CENTURY and BIOME-BGC) have significantly improved our understanding of the possible consequences and responses of terrestrial ecosystems under different environmental conditions. However, problems with these models include only using site-based parameters and adopting different assumptions on interactions between plant, environmental conditions and human activities in simulating such complex phenomenon. In light of this shortfall, the overall objective of this research is to integrate remote sensing products into ecosystem process model in order to simulate productivity for the mixed grassland ecosystem in the landscape level. Data used includes 4-years of field measurements and diverse satellite data (System Pour l’Observation de la Terre (SPOT) 4 and 5, Landsat TM and ETM, Advanced Very High Resolution Radiometer (AVHRR) imagery). Using wavelet analyses, the study first detects that the dominant spatial scale is controlled by topography and thus determines that 20-30 m is the optimum resolution to capture the vegetation spatial variation for the study area. Second, the performance of the RDVI (Renormalized Difference Vegetation Index), ATSAVI (Adjusted Transformed Soil-Adjusted Vegetation Index), and MCARI2 (Modified Chlorophyll Absorption Ratio Index 2) are slightly better than the other VIs in the groups of ratio-based, soil-line-related, and chlorophyll-corrected VIs, respectively. By incorporating CAI (Cellulose Absorption Index) as a litter factor in ATSAVI, a new VI is developed (L-ATSAVI) and it improves LAI estimation capability by about 10%. Third, vegetation maps are derived from a SPOT 4 image based on the significant relationship between LAI and ATSAVI to aid spatial modeling. Fourth, object-oriented classifier is determined as the best approach, providing ecosystem models with an accurate land cover map. Fifth, the phenology parameters are identified for the study area using 22-year AVHRR data, providing the input variables for spatial modeling. Finally, the performance of popular ecosystem models in simulating grassland vegetation productivity is evaluated using site-based field data, AVHRR NDVI data, and climate data. A new model frame, which integrates remote sensing data with site-based BIOME-BGC model, is developed for the mixed grassland prairie. The developed remote sensing-based process model is able to simulate ecosystem processes at the landscape level and can simulate productivity distribution with 71% accuracy for 2005

Convergence of dynamic vegetation net productivity responses to precipitation variability from 10 years of MODIS EVI

According to Global Climate Models (GCMs) the occurrence of extreme events of precipitation will be more frequent in the future. Therefore, important challenges arise regarding climate variability, which are mainly related to the understanding of ecosystem responses to changes in precipitation patterns. Previous studies have found that Above-ground Net Primary Productivity (ANPP) was positively related to increases in annual precipitation and this relation may converge across biomes during dry years. One challenge in studying this ecosystem response at the continental scale is the lack of ANPP field measurements over extended areas. In this study, the MODIS EVI was utilized as a surrogate for ANPP and combined with precipitation datasets from twelve different experimental sites across the United States over a 10-year period. Results from this analysis confirmed that integrated-EVI for different biomes converged toward common precipitation use efficiency during water-limited periods and may be a viable surrogate for ANPP measurements for further ecological research