Retrieval of real-time live fuel moisture content using MODIS measurements

Live fuel moisture content (LFMC) is one of the most important fuel properties and a critical parameter for wildland fire danger rating estimation and fire behavior analysis. Direct ground measurement of live fuel moisture content has disadvantages of high cost and limited spatial distribution extent. This paper presents an algorithm to retrieve live fuel moisture content from multiple bands of MODIS measurements. We analyzed the physical relationship between surface reflectance and live fuel moisture content using simulated MODIS measurements of diverse leaf samples, derived approximate inversion models, and proposed a semi-physical approach for live fuel moisture retrieval employing multiple MODIS bands. Using simulated MODIS measurements, the correlation coefficients between the true LFMC and estimated LFMC with our inversion models are 0.7738, 0.8397, 0.9560 and 0.9576 respectively. For validation, we tested our inversion method with woody live fuel moisture measurements at fire weather stations in Georgia. The correlation coefficients between measured LFMC and estimated LFMC with our inversion models are 0.5727, 0.6522, 0.7551, and 0.7737 respectively. Both model simulation and station measurements demonstrated advantages of our approach in accuracy. Our study suggests the potential for near real-time applications of live fuel moisture.     

Combining AVHRR and meteorological data for estimating live fuel moisture content

Spatial assessment of fire risk is very important for reducing the impacts of wildland fires. Several variables related to fire ignition, propagation and its effects are included in fire risk analysis. Life Fuel Moisture Content (LFMC) is one such variable, which is highly related to fire ignition, and propagation. A wide variety of methods have been applied to estimate LFMC, including field sampling and meteorological indices. Given the limitations of these methods, satellite images are a sound alternative for estimating LFMC because of their capability to spatially and temporally monitor the vegetation status.This paper aims to improve previous empirical models to estimate LFMC from satellite images, by considering meteorological information. The original models proposed by Chuvieco et al. [Chuvieco, E., Cocero, D., Riaño, D., Martin, M.P., Martinez-Vega, J., et al., (2004). Combining NDVI and surface temperature for the estimation of live fuel moisture content in forest fire danger rating. Remote Sensing of Environment, 92, 322–331] for grasslands and shrublands were used as starting point for this research. High over-estimation of LFMC values from those models was found when applied to dry years. Consequently, the new models proposed in this paper use a simple drought index to discriminate between dry and wet years at the beginning of the spring season. A different harmonic function was fitted to each group of hydrological years, to take into account the inter-annual variations in LFMC seasonal trends. Subsequently, two empirical models, one for grasslands and one for shrubs (Cistus ladanifer), were derived based on multivariate linear regression analysis of the data collected at Cabañeros National Park (Central Spain). Determination coefficients greater than 0.8 for grasslands and 0.7 for shrubs were found. The models showed good performance too when applied to other plots of grasslands (R² = 0.76) and shrubland (R² = 0.71) with similar environmental characteristics to the calibration site.     

Mapping surface temperature in large lakes with MODIS data

Satellite sensor MODIS on two platforms can produce Sea Surface Temperature over certain regions about three to four times per day. Our objective was to test if the MODIS SST product can be applied for lakes whose surface areas are large enough to be observed at the MODIS spatial resolution and to compare the satellite-derived lake surface temperatures with in situ measurements. Surface temperatures for Lakes Vänern and Vättern in Sweden, two of the largest European lakes, are extracted from the MODIS/Terra images for period 2001-2003. The results are analyzed on different quality levels, as all MODIS L2 products are equipped with an additional quality flag. We present temperature development over 2001-2003, and show the capability of the MODIS SST product to couple the known thermodynamical features in the lakes under study, where temperature varies greatly with space and time. These results can complement lake monitoring programs anywhere.     

Estimating live fuel moisture content from remotely sensed reflectance

Fuel moisture content (FMC) is used in forest fire danger models to characterise the moisture status of the foliage. FMC expresses the amount of water in a leaf relative to the amount of dry matter and differs from measures of leaf water content which express the amount of water in a leaf relative to its area. FMC is related to both leaf water content and leaf dry matter content, and the relationships between FMC and remotely sensed reflectance will therefore be affected by variation in both leaf biophysical properties. This paper uses spectral reflectance data from the Leaf Optical Properties EXperiment (LOPEX) and modelled data from the Prospect leaf reflectance model to examine the relationships between FMC, leaf equivalent water thickness (EWT) and a range of spectral vegetation indices (VI) designed to estimate leaf and canopy water content. Significant correlations were found between FMC and all of the selected vegetation indices for both modelled and measured data, but statistically stronger relationships were found with leaf EWT; overall, the water index (WI) was found to be most strongly correlated with FMC. The accuracy of FMC estimation was very low when the global range of FMC was examined, but for a restricted range of 0-100%, FMC was estimated with a root-mean-square error (RMSE) of 15% in the model simulations and 51% with the measured data. The paper shows that the estimation of live FMC from remotely sensed vegetation indices is likely to be problematic when there is variability in both leaf water content and leaf dry matter content in the target leaves. Estimating FMC from remotely sensed data at the canopy level is likely to be further complicated by spatial and temporal variations in leaf area index (LAI). Further research is required to assess the potential of canopy reflectance model inversion to estimate live fuel moisture content where a priori information on vegetation properties may be used to constrain the inversion process.     

Mapping insect defoliation in Scots pine with MODIS time-series data

Insect damage is a general problem that disturbs the growth of forests, causing economic losses and affecting carbon sequestration. Coarse-resolution data from satellites are potentially useful for national and regional mapping of forest damage, but the accuracy of these methods has not been fully examined. In this study, a method was tested for the mapping of defoliation in Scots pine [Pinus silvestris] forests in southeast Norway caused by the pine sawfly [Neodiprion sertifer], with the use of multi-temporal MODIS 16-day composite vegetation index data and the TIMESAT processing method. The damage mapping method used differences in summer mean values and angles of the seasonal profiles, indicating decreasing foliage density, to identify pixels that represent areas containing forest damage. In addition to 16-day NDVI the Wide Dynamic Range Vegetation Index (WDRVI) was tested. Damage areas were identified by classifying data into pixels representing damaged versus undamaged forest areas using a boolean combination of thresholded parameters. Classification results were evaluated against the change in LAI estimated from airplane LIDAR measurements, as an indicator of defoliation. The damage classifications detected 71% to 82% of the pixels with damage, and had kappa coefficients varying between 0.48 and 0.63, indicating some overestimation. This was due e.g. to failure to include clear-cut areas in the evaluation data. Damage classification with WDRVI only resulted in slight improvement compared to the NDVI. Only weak relationships were found between the LIDAR-estimated defoliation and the change parameters obtained from MODIS. Consequently, mapping of the degree of defoliation from MODIS was abandoned. In conclusion, the damage detection method based on MODIS data was found to be useful for locating insect damage, but not for estimating its intensity. Control of the detected damage areas using high-resolution remote sensing data, aerial survey, or fieldwork is recommended for accurate delineation in operational applications.     

A semiparametric Bayesian approach for joint-quantile regression with clustered data

Based on a semiparametric Bayesian framework, a joint-quantile regression method is developed for analyzing clustered data, where random effects are included to accommodate the intra-cluster dependence. Instead of posing any parametric distributional assumptions on the random errors, the proposed method approximates the central density by linearly interpolating the conditional quantile functions of the response at multiple quantiles and estimates the tail densities by adopting extreme value theory. Through joint-quantile modeling, the proposed algorithm can yield the joint posterior distribution of quantile coefficients at multiple quantiles and meanwhile avoid the quantile crossing issue. The finite sample performance of the proposed method is assessed through a simulation study and the analysis of an apnea duration data.     

Use of Normalized Difference Water Index for monitoring live fuel moisture

Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) were compared for monitoring live fuel moisture in a shrubland ecosystem. Both indices were calculated from 500?m spatial resolution Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data covering a 33‐month period from 2000 to 2002. Both NDVI and NDWI were positively correlated with live fuel moisture measured by the Los Angeles County Fire Department (LACFD). NDVI had R ² values ranging between 0.25 to 0.60, while NDWI had significantly higher R ² values, varying between 0.39 and 0.80. Water absorption measures, such as NDWI, may prove more appropriate for monitoring live fuel moisture than measures of chlorophyll absorption such as NDVI.     

Mapping cotton fields using data mining and MODIS time-series

ABSTRACT

Cotton is the most important fibre culture in the world. In Brazil, cotton cultivation is concentrated in the Cerrado biome, the Brazilian savanna, and is one of the most important commodities in the country. As an annual crop, the updating frequency of the spatial distribution data of cotton fields is extremely important for crop monitoring systems. In order to provide fast and accurate information for crop monitoring, time series of remote- sensing data has been used in the development of several applications in agriculture, since the high temporal resolution of some orbital sensor allows monitoring targets with high spectral-temporal variations in the land surface. However, there are still some challenges to systematize the processing of such a large amount of data available by long time series of remote-sensing imagery. Thus, this study contributes to the construction of models to identify and separate specific crop types with similar spectral behaviour to other crops practised in the same period. The objective of this study was to develop a systematic methodology based on data mining of time series of vegetation indices (VI) to map cotton fields at the regional scale. Field reference data and time series of NDVI and EVI images, obtained from MODIS sensor products during four cropping seasons (from 2012–2013 to 2015–2016), were used to construct mapping models based on decision tree algorithms. Phenological metrics were calculated from the VI time series and used to build classification rules for mapping cotton fields. Our results demonstrate that the proposed method to map cotton fields achieve high accuracy when field data and visual interpretation of NDVI temporal profiles were used for validation (accuracy higher than 95% and 93%, respectively). Comparisons with the official statistics indicated an optimal fit, with linear correlation (r) and coefficient of determination (R²) above 0.93. Therefore, the proposed method was efficient to distinguish cotton fields from other crop types with similar spectral behaviour. In addition, this method can also be applied to other cotton-producing regions and other production seasons, by reusing the models generated through machine learning approaches.     

A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data

The crop developmental stage represents essential information for irrigation scheduling/fertilizer management, understanding seasonal ecosystem carbon dioxide (CO₂) exchange, and evaluating crop productivity. In this study, we devised an approach called the Two-Step Filtering (TSF) for detecting the phenological stages of maize and soybean from time-series Wide Dynamic Range Vegetation Index (WDRVI) data derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m observations. The TSF method consists of a Two-Step Filtering scheme that includes: (i) smoothing the temporal WDRVI data with a wavelet-based filter and (ii) deriving the optimum scaling parameters from shape-model fitting procedure. The date of key crop development stages are then estimated by using the optimum scaling parameters and an initial value of the specific phenological date on the shape model, which are preliminary defined in reference to ground-based crop growth stage observations. The shape model is a crop-specific WDRVI curve with typical seasonal features, which were defined by averaging smoothed, multi-year WDRVI profiles from MODIS 250-m data collected over irrigated maize and soybean study sites.In this study, the TSF method was applied to MODIS-derived WDRVI data over a 6-year period (2003 to 2008) for two irrigated sites and one rainfed site planted to either maize or soybean as part of the Carbon Sequestration Program (CSP) at the University of Nebraska-Lincoln. A comparison of satellite-based retrievals with ground-based crop growth stage observations collected by the CSP over the six growing seasons for these three sites showed that the TSF method can accurately estimate the date of four key phenological stages of maize (V2.5: early vegetative stage, R1: silking stage, R5: dent stage and R6: maturity) and soybean (V1: early vegetative stage, R5: beginning seed, R6: full seed and R7: beginning maturity). The root mean square error (RMSE) of phenological-stage estimation for maize ranged from 2.9 [R1] to 7.0 [R5] days and from 3.2 [R6] to 6.9 [R7] days for soybean, respectively. In addition, the TSF method was also applied for two years (2001 and 2002) over eastern Nebraska to test its ability to characterize the spatio-temporal patterns of these key phenological stages over a larger geographic area. The MODIS-derived crop phenological stage dates agreed well with the statistical crop progress data reported by the United State Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) for eastern Nebraska's three crop agricultural statistic districts (ASDs). At the ASD-level, the RMSE of phenological-stage estimation ranged from 1.6 [R1] to 5.6 [R5] days for maize and from 2.5 [R7] to 5.3 [R5] days for soybean.     

Mapping incident photosynthetically active radiation from MODIS data over China

Photosynthetically active radiation (PAR) is a key input parameter for almost all terrestrial ecosystem models, but the spatial resolution of current PAR products is too coarse to satisfy regional application requirements. In this paper, we present an operational system for PAR retrieval from MODIS data that is based on an idea proposed by [Liang, S., Zheng, T., Liu, R., Fang, H., Tsay, S. -C., & Running, S. (2006). Estimation of incident photosynthetically active radiation from Moderate Resolution Imaging Spectrometer data. Journal of Geophysical Research, 111, D15208. doi:10.1029/2005JD006730]. However, the operational system for PAR retrieval described here contains several improvements. The algorithm utilizes MODIS 1B data combining MODIS land surface products and BRDF model parameters products to directly estimate diffuse PAR, direct PAR and total PAR. Times-series data interpolation removes the noise and cloud contamination of land surface reflectance. PAR is retrieved by searching look-up tables calculated using a radiative transfer model. The system can automatically process MODIS 1B data to generate instantaneous and daily PAR. The instantaneous PAR products are compared with observational data from seven ChinaFLUX stations, and daily total PAR estimates are compared with those estimates of global radiation from 98 meteorological stations over China. The results indicate that this approach can produce reasonable PAR estimates, although this method overestimates PAR for low values of PAR.     

Rank regression for analysis of clustered data: A natural induced smoothing approach

We consider rank regression for clustered data analysis and investigate the induced smoothing method for obtaining the asymptotic covariance matrices of the parameter estimators. We prove that the induced estimating functions are asymptotically unbiased and the resulting estimators are strongly consistent and asymptotically normal. The induced smoothing approach provides an effective way for obtaining asymptotic covariance matrices for between- and within-cluster estimators and for a combined estimator to take account of within-cluster correlations. We also carry out extensive simulation studies to assess the performance of different estimators. The proposed methodology is substantially much faster in computation and more stable in numerical results than the existing methods. We apply the proposed methodology to a dataset from a randomized clinical trial.     

Mapping plant functional types from MODIS data using multisource evidential reasoning

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster's Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach.     

Support vector regression with noisy data: a second order cone programming approach

A robust convex optimization approach is proposed for support vector regression (SVR) with noisy input data. The data points are assumed to be uncertain, but bounded within given hyper-spheres of radius η. The proposed Robust SVR model is equivalent to a Second Order Cone Programming (SOCP) problem. SOCP formulation with Gaussian noise models assumption is discussed. Computational results are presented both on real world and synthetic data sets. The robust SOCP approach is compared with several other regression algorithms such as SVR, least-square SVR, and artificial neural networks by injecting Gaussian noise to each of the data points. The proposed approach out performs the other regression algorithms for some data sets. Moreover, the generalization behavior of the SOCP method is better than the traditional SVR with increasing the uncertainty level η until a threshold value.     

A piece-wise approach to removing the nonlinear and irregular stripes in MODIS data

As a result of imaging acquisition conditions, Moderate Resolution Imaging Spectroradiometer (MODIS) imagery suffers from nonlinear and irregular striping. The nonlinear stripes are those whose degradation parameters change with the ground objects, and the irregular stripes are those in which only some of the pixels are contaminated. These kinds of stripes result in great difficulties for conventional statistical destriping methods. To deal with these problems effectively, we propose a piece-wise destriping method. This approach divides the recognized defective rows into different portions by the local statistical and mean curve information. The destriping is then performed in each portion, based on the different correction coefficients, with a neighbouring normal row as a reference. Experimental results demonstrate that the proposed algorithm can effectively destripe MODIS data.     

Development of an ATI-NDVI method for estimation of soil moisture from MODIS data

Estimation of soil moisture is essential for research of climatology, hydrology, and ecology. The commonly used remotely sensed approach is LST-NDVI (land-surface temperature-normalized difference vegetation index). In this study, the apparent thermal inertia (ATI) is used instead of surface temperature to develop an ATI-NDVI space for estimation of soil moisture. Comparison with ground-based measurements shows a root mean square error (RMSE) of 0.0378 m³ m^?3 between retrieved and measured soil moistures. Validation with time series in situ data indicates the RMSE as 0.0162, 0.0285, 0.0368, and 0.0093 m³ m^?3 for forest, shrub, cropland, and grassland, respectively, which is comparable to or even better than the results of previous studies. The proposed method in this study is a remote-sensing approach without elaborate ancillary data except for the percentage of sand in the soil, and it is practical and convenient to be applied to regions with surfaces from bare soil to full vegetation and the entire range of surface moisture contents from wet to dry.     

A multitime-steps-ahead prediction approach for scheduling live migration in cloud data centers

One of the major challenges facing cloud computing is to accurately predict future resource usage to provision data centers for future demands. Cloud resources are constantly in a state of flux, making it difficult for forecasting algorithms to produce accurate predictions for short times scales (ie, 5 minutes to 1 hour). This motivates the research presented in this paper, which compares nonlinear and linear forecasting methods with a sequence prediction algorithm known as a recurrent neural network to predict CPU utilization and network bandwidth usage for live migration. Experimental results demonstrate that a multitime-ahead prediction algorithm reduces bandwidth consumption during critical times and improves overall efficiency of a data center.     

A non-linear regression analysis program for describing electrophysiological data with multiple functions using Microsoft Excel

The objective of this present study was to demonstrate a method for fitting complex electrophysiological data with multiple functions using the SOLVER add-in of the ubiquitous spreadsheet Microsoft Excel. SOLVER minimizes the difference between the sum of the squares of the data to be fit and the function(s) describing the data using an iterative generalized reduced gradient method. While it is a straightforward procedure to fit data with linear functions, and we have previously demonstrated a method of non-linear regression analysis of experimental data based upon a single function, it is more complex to fit data with multiple functions, usually requiring specialized expensive computer software. In this paper we describe an easily understood program for fitting experimentally acquired data, in this case the stimulus-evoked compound action potential from the mouse optic nerve, with multiple Gaussian functions. The program is flexible and can be applied to describe data with a wide variety of user-input functions.     

Evaluating remotely sensed live fuel moisture estimations for fire behavior predictions in Georgia, USA

Research has shown that remote sensing techniques can be used for assessing live fuel moisture content (LFMC) from space. The need for dynamic monitoring of the fire risk environment favors the use of fast, site-specific, empirical models for assessing local vegetation moisture status, albeit with some uncertainties. These uncertainties may affect the accuracy of decisions made by fire managers using remote sensing derived LFMC. Consequently, the analysis of these LFMC retrieval uncertainties and their impact on applications, such as fire spread prediction, is needed to ensure the informed use of remote sensing derived LFMC measurements by fire managers. The Okefenokee National Wildlife Refuge, one of the most fire-prone regions in the southeastern United States was chosen as our study area. Our study estimates the uncertainties associated with empirical site specific retrievals using NDWI (Normalized Difference Water Index; (R_0.86 − R_1.24) / (R_0.86 + R_1.24)) and NDII (Normalized Difference Infrared Index; (R_0.86 − R_1.64) / (R_0.86 + R_1.64)) that are simulated by coupled leaf and canopy radiative transfer models. In order to support the findings from those simulations, a second approach estimates uncertainties using actual MODIS derived indices over Georgia Forestry Commission stations that provide NFDRS model estimates of LFMC. Finally, we used the FARSITE surface fire behavior model to examine the sensitivity of fire spread rates to live fuel moisture content for the NFDRS high pocosin and southern rough fuel models found in Okefenokee. This allowed us to evaluate the effectiveness of satellite based LFMC estimations for use in fire behavior predictions. Sensitivity to LFMC (measured as percentage of moisture weight per unit dry weight of fuel) was analyzed in terms of no-wind no-slope spread rates as well as normalized spread rates. Normalized spread rates, defined as the ratio of spread rate at a particular LFMC to the spread rate at LFMC of 125 under similar conditions, were used in order to make the results adaptable to any wind-slope conditions. Our results show that NDWI has a stronger linear relationship to LFMC than NDII, and can consequently estimate LFMC with lesser uncertainty. Uncertainty analysis shows that 66% of NDWI based LFMC retrievals over non-sparsely vegetated regions are expected to have errors less than 32, while 90% of retrievals should be within an error margin of 56. In pocosin fuel models, under low LFMC conditions (< 100), retrieval errors could lead to normalized spread rate errors of 6.5 which may be equivalent to an error of 47 m/h in no-wind no-slope conditions. For southern rough fuel models, when LFMC < 175, LFMC retrieval errors could amount to normalized spread rate errors of 0.6 or an equivalent error of 9.3 m/h in no-wind no-slope conditions. These spread rate error estimates represent approximately the upper bound of errors resulting from uncertainties in empirical retrievals of LFMC over forested regions.     

Soil moisture retrieval from MODIS data in Northern China Plain using thermal inertia model

Soil moisture plays an important role in surface energy balances, regional runoff, potential drought and crop yield. Early detection of potential drought or flood is important for the local government and people to take actions to protect their crop. Traditionally measurement of soil moisture is a time‐consuming job and only limited samples could be collected. Many problems would be results from extending those point measurements to 2D space, especially for a regional area with heterogeneous soil characteristics. The emergency of remote‐sensing technology makes it possible to rapidly monitor soil moisture on a regional scale. Thermal inertia represents the ability of a material to conduct and store heat, and in the context of planetary science, it is a measure of the subsurface's ability to store heat during the day and reradiate it during the night. One major application of thermal inertia is to monitor soil moisture. In this paper, a thermal inertia model was developed to be suitable in situations whether or not the satellite overpass time coincides with the local maximum and minimum temperature time. Besides, the sensibilities of thermal inertia with surface albedo and the surface temperature difference were discussed. It shows that the surface temperature difference has more effects on the thermal inertia than the surface albedo. When the temperature difference is less than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will lead to a big fluctuation of thermal inertia. When the temperature difference is more than 10 Kelvin degrees, 1 Kelvin degree error of temperature difference will cause a small change of thermal inertia. The temperature difference should be larger than 10 Kelvin degrees when the thermal inertia model is selected to derive soil moisture or other applications. Based on this thermal inertia model, the soil moisture map was obtained for North China Plain. It shows that the averaged difference between the soil moisture values derived from MODIS data and in situ measured soil moisture data is 4.32%. This model is promising for monitoring soil moisture on a large regional scale.     

Subset selection in multiple linear regression: a new mathematical programming approach

A new mathematical programming model is proposed to address the subset selection problem in multiple linear regression where the objective is to select a minimal subset of predictor variables without sacrificing any explanatory power. A parametric solution of this model yields a number of efficient subsets. To obtain this solution, an optimal or one of two heuristic algorithms is repeatedly used. The subsets generated are compared to ones generated by several standard procedures. The results suggest that the new approach finds subsets that compare favorably against the standard procedures in terms of the generally accepted measure: adjusted R².