A multiple-frame approach to crop yield estimation from satellite- remotely sensed data

Many studies have recently explored information from satellite-remotely sensed data (SRSD) for estimating crop production statistics. The value of this information depends on the aerial and spatial resolutions of SRSD. SRSD with fine spatial resolution is costly and aerial coverage is less. Use of multiple frames of SRSD in the estimation process of crop production can increase the precision. We propose an estimator for the average yield of wheat for the state of Haryana, India. This estimator uses information from Wide Field Sensor (WiFS) and Linear Imaging Self Scanner (LISS-III) data from the Indian Remote Sensing (IRS-1D) satellite and crop-cutting experiment data collected by probability sampling design from a list frame of villages. We find that relative efficiencies of multiple-frame estimators are high in comparison to single-frame estimators.     

Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation

The ongoing mountain pine beetle (Dendroctonus ponderosae Hopkins) outbreak in British Columbia, Canada, has reached epidemic proportions, with the beetle expanding into geographic areas outside its known biological range. In this study, estimates of red attack damage were derived from a logistic regression model using multi-date Landsat imagery, and ancillary information including terrain attributes and solar radiation. The model estimates were found to be approximately 70% accurate using an independent set of beetle survey data as validation. This probability surface of red attack damage, along with forest inventory and terrain attributes, were used as inputs to decision tree analyses, in order to identify which forest attributes were associated with stands that had a greater likelihood of mountain pine beetle red attack damage. Three distinct decision tree models were developed, with each having a different set of input variables. The results of the analyses indicated that site index (an indicator of the quality of a forest site) and slope were the principal discriminators of the current mountain pine beetle attack, followed by basal area of pine dominated stands, and to a lesser extent, crown closure and stem density. The results suggest that indicators of site quality, particularly site index, could be a complementary addition to existing stand susceptibility rating models.     

Use of remotely sensed and ancillary data for estimating forest gross primary productivity in Italy

The current paper describes the development and testing of a procedure which can use widely available remotely sensed and ancillary data to assess large-scale patterns of forest productivity in Italy. To reach this objective a straightforward model (C-Fix) was applied which is based on the relationship between photosynthetically active radiation absorbed by plant canopies and relevant gross primary productivity (GPP). The original C-Fix methodology was improved by using more abundant ancillary information and more efficient techniques for NDVI data processing. In particular, two extraction methods were applied to NDVI data, derived from two sensors (NOAA-AVHRR and SPOT-VGT) to feed C-Fix. The accuracy of the model outputs was assessed through comparison with annual and monthly values of forest GPP derived from eight eddy covariance flux towers. The results obtained indicated the superiority of SPOT-VGT over NOAA-AVHRR data and a higher efficiency of the more advanced NDVI extraction method. Globally, the procedure was proved to be of easy and objective implementation and allowed the evaluation of mean productivity levels of existing forests on the national scale.     

An approach to linking remotely sensed data and areal census data

With the increasing availability of remotely sensed data and census data, discussing their relationship is one of the important issues in GIS data integration. This paper proposed an approach to linking three levels (macro, medium and micro) of land classifications with areal census data on hierarchical census boundaries. Specifically, a method of building the correlations between areal census dwelling data and residential densities classified by a remote sensing approach was demonstrated. First, a texture statistic (homogeneity) along with six Thematic Mapper (TM) bands (bands 1-5 and 7) was put together to classify residential density levels. The homogeneity slightly enhanced classification accuracy. Then, to test the correlations between census dwelling data and residential densities, a multiple linear regression was conducted. It was found that areal census dwelling data had higher correlations with areas of different residential densities than with the aggregated area of a whole residential area at an individual census zone level. Finally, the paper discussed that dis-aggregation of areal census data based on dwelling densities within the framework of remote sensing and GIS would be very useful for multidisciplinary studies, such as natural hazards risk assessment.     

Using site-specific data to estimate energy crop yield

The estimation of energy crop yields is important, to help the firms responsible for collecting them to estimate biomass production in a given area, for example. A Bayesian modelling framework for site-specific yield estimation is presented in this paper. The proposed approach is based on a hierarchical model describing between-site and within-site yield variability. Probability distributions are used to describe the uncertainty of model estimations. The model can be fitted to site-specific yield data, to obtain both average and site-specific yield estimates. Site-specific yield data may be obtained from measurements for crop species other than those for which estimations are required, or from past measurements on perennial crop species grown over a period of several years at a given site. These two options were illustrated in two case studies, in which our model was used to estimate the yields of several energy crops. In most situations, site-specific yield estimations were more accurate than average estimations.     

Adaptive spatial reclassification kernels for urban mapping from remotely sensed data: the A-SPARK approach

The existing spatial reclassification kernel (SPARK) approach provides a simple and practical procedure for discrimination of complex land use classes from the primary land cover components. Previous works have shown that the spatial information extracted from a single kernel size often does not lead to a satisfactory result. Due to the complexity and diversity of most objects of interest, this limitation is more significant in urban dominated landscapes. To overcome this limitation, an adaptive approach for implementation of SPARK based on the automatic evaluation and selection of kernels has been developed in this research. Efficiency of the proposed approach for discrimination of spectrally confused and complex classes such as high-density and low-density residential areas has been evaluated by using SPOT data acquired from part of Tehran's metropolitan areas, Iran. Results of the practical examination have shown that considerable improvements in the classification accuracy of different classes such as high-density residential, low-density residential, industrial, orchards and bare lands can be achieved. The overall accuracy of classification has increased from 82.39% to 92% in the best fixed kernel size of 9 × 9; this is an indicator of the more effective information use in the proposed approach.     

Image processing for boundary extraction of remotely sensed data

Image processing algorithms for analysing remotely sensed data are developed. The algorithms proposed in the paper provide means for autoregressive texture modelling and for boundary detection of uniform subimage areas. The boundary detection methods make use of a semicircular entropy operator and of the binary hypothesis testing of the Poisson counting process. The proposed algorithms are applied to the pattern analysis of the isothermal distribution in the oceanic environment.     

Geostatistical interpolation of missing data and downscaling of spatial resolution for remotely sensed atmospheric methane column concentrations

Since the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) instrument on the Environmental Satellite (ENVISAT) was launched in 2002, CH₄ measurements from the satellite at regional or global scales became available. However, many gaps of missing data exist on the maps of the retrieved atmospheric CH₄ column concentrations from SCIAMACHY/ENVISAT. Moreover, the gridded CH₄ map with 50?×?50 km is a bit coarse for local interpretation. In this study, two geostatistical methods of ordinary kriging (OK) and ordinary cokriging (OCK) associated with 5 km normalized difference vegetation index (NDVI) images were examined to fill in missing data and to downscale the spatial resolution of CH₄ images. The 50 km CH₄ images interpolated by the two methods presented similar spatial patterns to the original 50 km CH₄ image and provided good results for the missing data. Taking into account the statistical results, the OCK method achieved better performance than OK in filling gaps of missing data. In further downscaling the CH₄ image from 50 to 5 km, the OCK method achieved a significant amount of spatial detail, and the statistical results also showed that OCK performed better than OK.     

Use of groundtruth data to correct land cover area estimates from remotely sensed data

Estimates of area by land cover category from photo interpretation are subject to bias arising from interpretation error. Quantification of this error by groundtruth survey allows corrections to be made to the estimates. The precision of estimates of interpretation error for a fixed cost depends on the survey design; simple survey designs arc inefficient for this purpose. The penalty for using more complex designs is that a sophisticated correction method is required. We note the assumptions of existing correction methods, develop a more general method, and show how the precision of corrected estimates of area may be quantified. We demonstrate the effect of‘correcting’ area estimates under different methods, and provide an example in which use of an inappropriate method leads to substantial bias (up to 40 per cent).     

The application of the coalescence clustering algorithm to remotely sensed multispectral data

The coalescence clustering concept of Watanabe has been implemented for the purpose of unsupervised classification of remotely sensed multispectral data. Modifications on the original algorithm were made to enable clustering of limited range discrete data. Application to simulated overlapping Gaussian distributions show that optimal separation of boundaries is achieved at almost every point. Clustering of real data from LANDSAT satellites also yields very meaningful results. Significance of the range parameter and computer requirements are also discussed.     

Improvement of remotely sensed low spatial resolution images by back-propagated neural networks using data fusion techniques

Abstract

An effect has been observed in the channel 4 output of the NOAA-12 satellite which has not been observed from previous satellites in the NOAA series. Essentially, when sunglint is being observed by channels 1–3, and especially when channel 3 is saturated, the output of channel 4 becomes paralysed, It is believed that this problem has existed throughout the lifetime of this satellite, since the effect was also observed shortly after launch during July 1991.     

Estimation of winter wheat acreage via a combination of remotely sensed data and an optimized spatial sampling scheme

Timely and accurate estimates of crop areas are critical to enhancing agriculture management and ensuring national food security. This study aims to combine remote-sensing data and an optimized spatial sampling scheme to improve the accuracy of crop area estimates and decrease the cost of crop surveys at a regional scale. This study focuses on winter wheat in Mengcheng County in Anhui Province, China. Advanced Land Observing Satellite (ALOS) Advanced Visible light and Near Infrared Radiometer (AVNIR)-2, and Landsat5 Thematic Mapper (TM) images from 2009 and 2010, respectively, are used to extract the winter wheat area and distribution. Additionally, a spatial sampling scheme was optimized by combining remotely sensed data, geographical information system (GIS), Geostatistics, and traditional sampling methods. The experimental results demonstrate that the variability in the proportion of winter wheat acreage in one sampling unit (PWS) increases with increasing sampling unit size. The PWS coefficient of variation (CV) varies from 32.75 to 43.46% among the eight sampling unit sizes. The spatial correlation thresholds of PWS increase with increasing sampling unit size. For small sampling unit sizes (500 m × 500 m–2000 m × 2000 m), the relative error and CV of the population extrapolation for the optimized sample layout are obviously lower than those of the simple random sampling method. For larger sampling unit sizes (2500 m × 2500 m–4000 m × 4000 m), the sample size is obviously lower for the optimized sample layout compared with that of the simple random sampling method, but there are no differences in the relative errors or CVs. By combining remote-sensing data and the optimized spatial sampling scheme, this research can improve the accuracy of crop area estimation at a regional scale.     

Estimation of total above-ground phytomass production using remotely sensed data

Remote sensing potentially offers a quick and nondestructive method for monitoring plant canopy condition and development. In this study, multispectral reflectance and thermal emittance data were used in conjunction with micrometeorological data in a simple model to estimate above-ground total dry phytomass production of several spring wheat canopies. The fraction of absorbed photosynthetic radiation (PAR) by plants was estimated from measurements of visible and near-infrared canopy reflectance. Canopy radiation temperature was used as a crop stress indicator in the model. Estimated above-ground phytomass values based on this model were strongly correlated with the measured phytomass values for a wide range of climate and plant-canopy conditions.     

Estimation of regional evapotranspiration and soil moisture conditions using remotely sensed crop surface temperatures

Crop surface temperature gives information on the evapotranspiration rate and on soil moisture conditions. The use of crop surface temperatures remotely sensed by infrared line scanning (IRLS) can provide this information over large hydrologically nonuniform areas. A composite method has been developed to calculate actual evapotranspiration rates from remotely sensed crop surface temperatures, with the aid of the energy budget equation and aerodynamic heat and water vapor transport equations, while incorporating soil-plant-water relations. This method has been tested for grassland in an area near Losser, in the eastern part of the Netherlands. In this area, drought damage occurs due to groundwater extraction for drinking-water supply. A way to analyze accuracy was established. This analysis did show small standard deviations for measurements of evapotranspiration near the potential level, and a doubling of standard deviations when evapotranspiration approaches zero.     

Supervised classification of remotely sensed data with ongoing learning capability

A methodology to implement an automatic system for classifying remotely sensed data with an ongoing learning capability is introduced. The Nearest Neighbour (NN) rule is employed as the central classifier and several techniques are added to cope with the increase in computational load and with the risk of incorporating noisy data into the training sample. Experimental results confirm the enhancement in classification accuracy.     

The use of the empirical line method to calibrate remotely sensed data to reflectance

To have lasting quantitative value, remotely sensed data must be calibrated to physical units of reflectance. The empirical line method offers a logistically simple means of generating acceptable estimates of surface reflectance. A review and case-study identify the ease with which this method can be applied, but also some of the pitfalls that can be encountered if it is not planned and implemented properly. A number of theoretical assumptions and practical considerations should be taken into account before applying this approach. It is suggested that the empirical line method allows the calibration of remotely sensed data to reflectance with errors of only a few percent.     

Statistical evaluation of remotely sensed data for water quality monitoring

The primary objective of this study was to determine relationships between water quality parameters (WQPs) and digital data from the Landsat satellite to estimate and map the WQP in the Porsuk Dam reservoir. Suspended sediments (SS), chlorophyll a (chl-a), NO₃-N and transmitted light intensity depth (TLID) were the parameters for water quality determination used in this study. Collection of these data, obtained from the General Directorate of State Hydraulic Works (GDSHW) was synchronized with the Landsat satellite overpass of the September 1987. The relationships between the brightness values (BV) of the TM data and WQP were determined. Using the TM data, we developed multiple regression equations to estimate the WQPs, and the validation of these equations was checked by using ANOVA. The effects of SS, NO₃-N and chl-a on TLID were tested not only for ground data, but also for TM datasets. Regression equations were developed for two different datasets and the homogeneity of those equations was tested. Finally, these regression equations evaluated from digital TM data and ground data were applied to map TLID values.     

Symbolic agglomerative clustering for quantitative analysis of remotely sensed data

An efficient nonparametric, hierarchical, symbolic agglomerative clustering procedure based on the mutual nearest neighbourhood concept is proposed for classifying remotely sensed multispectral data. The procedure utilized a data reduction technique and an innovative symbolic concept to minimize the memory and computational time requirements. A new non-metric similarity measure and a novel method of formulation of composite symbolic objects are proposed to enrich the performance of the algorithm. A Mean Difference Index (MDI) concept for identifying the optimal number of classes was used. Experiments were conducted on IRS (Indian Remote Sensing) satellite data to authenticate the efficacy of the procedure.     

Temporal variability of air temperature and remotely sensed surface temperature for bare soil

Surface and air temperatures were measured daily at 13.30 P.S.T. for three seasons on a bare soil in California. Analysis of the temporal variability on the original data did not exhibit second-order stationarity indicating a large seasonal drift. Calculated residuals for each temperature from a 29 day moving average exhibited a range of 7 days in air temperature after which the measured values became independent, while surface temperature in the different years did not exhibit a consistent range. Cross-variograms calculated for air and surface temperatures exhibited a range of 7 days with all three years exhibiting the same pattern. This suggests that surface temperature could be estimated from air temperature for up to 7 days for inclusion into energy balance models and could be cokriged in the intervening period from air temperature. The accuracy in the prediction of surface temperature decreases as the internal increases and these relationships will have to be assessed for a number of environments.