Saleem Ullah

An accurate retrieval of leaf water content from mid to thermal infrared spectra using continuous wavelet analysis

Leaf water content determines plant health, vitality, photosynthetic efficiency and is an important indicator of drought assessment. The retrieval of leaf water content from the visible to shortwave infrared spectra is well known. Here for the first time, we estimated leaf water content from the mid to thermal infrared (2.5-14.0 μm) spectra, based on continuous wavelet analysis. The dataset comprised 394 spectra from nine plant species, with different water contents achieved through progressive drying. To identify the spectral feature most sensitive to the variations in leaf water content, first the Directional Hemispherical Reflectance (DHR) spectra were transformed into a wavelet power scalogram, and then linear relations were established between the wavelet power scalogram and leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlations with leaf water content (R(2)=0.86 maximum, 0.83 minimum), as well as low RMSE (minimum 8.56%, maximum 9.27%). The combination of four wavelet features produced the most accurate model (R(2)=0.88, RMSE=8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from the mid to thermal infrared domain of the electromagnetic radiation.

A novel approach to estimate canopy height using ICESat/GLAS data: a case study in the New Forest National Park, UK

The Geoscience Laser Altimeter System (GLAS) aboard Ice, Cloud and land Elevation Satellite (ICESat) is a spaceborne LiDAR sensor. It is the first LiDAR instrument which can digitize the backscattered waveform and offer near global coverage. Among others, scientific objectives of the mission include precise measurement of vegetation canopy heights. Existing approaches of waveform processing for canopy height estimation suggest Gaussian decomposition of the waveform which has the limitation to properly characterize significant peaks and results in discrepant information. Moreover, in most cases, Digital Terrain Models (DTMs) are required for canopy height estimation. This paper presents a new automated method of GLAS waveform processing for extracting vegetation canopy height in the absence of a DTM. Canopy heights retrieved from GLAS waveforms were validated with field measured heights. The newly proposed method was able to explain 79% of variation in canopy heights with an RMSE of 3.18 m, in the study area. The unexplained variation in canopy heights retrieved from GLAS data can be due to errors introduced by footprint eccentricity, decay of energy between emitted and received signals, uncertainty in the field measurements and limited number of sampled footprints. Results achieved with the newly proposed method were encouraging and demonstrated its potential of processing full-waveform LiDAR data for estimating forest canopy height. The study also had implications on future full-waveform spaceborne missions and their utility in vegetation studies.

Using a genetic algorithm as an optimal band selector in the mid and thermal infrared (2.5-14 µm) to discriminate vegetation species

Genetic variation between various plant species determines differences in their physio-chemical makeup and ultimately in their hyperspectral emissivity signatures. The hyperspectral emissivity signatures, on the one hand, account for the subtle physio-chemical changes in the vegetation, but on the other hand, highlight the problem of high dimensionality. The aim of this paper is to investigate the performance of genetic algorithms coupled with the spectral angle mapper (SAM) to identify a meaningful subset of wavebands sensitive enough to discriminate thirteen broadleaved vegetation species from the laboratory measured hyperspectral emissivities. The performance was evaluated using an overall classification accuracy and Jeffries Matusita distance. For the multiple plant species, the targeted bands based on genetic algorithms resulted in a high overall classification accuracy (90%). Concentrating on the pairwise comparison results, the selected wavebands based on genetic algorithms resulted in higher Jeffries Matusita (J-M) distances than randomly selected wavebands did. This study concludes that targeted wavebands from leaf emissivity spectra are able to discriminate vegetation species.

Spatial assessment of water quality parameters in Jhelum city (Pakistan)

In this study, we assess the drinking water quality of Jhelum city. Two hundred and ninety-two drinking water samples were randomly collected in the study area. These samples were chemically analyzed for three key toxic (in excess) elements such as pH, total dissolved solids (TDS), and calcium. Geostatistical techniques such as variogram and kriging were used to investigate the spatial variations of these minerals across the city. The spatial structure for each element was found to be anisotropic, and thus, anisotropic variograms were used. The kriging predictions revealed significant concentrations of the above-stated elements at some locations in the study area. While comparing with the World Health Organization, United States Environmental Protection Agency, and Pakistan Environmental Protection Agency standards, the water samples were found to be unsatisfactory for drinking. We conclude that the drinking water in this region is of poor quality and needs proper treatment to make it palatable.

Estimation of leaf water content from far infrared (2.5–14 µm) spectra using continuous wavelet analysis

The objective of this study was to estimate leaf water content based on continuous wavelet analysis from the far infrared (2.5 - 14.0 μm) spectra. The entire dataset comprised of 394 far infrared spectra which were divided into calibration (262 spectra) and validation (132 spectra) subsets. The far infrared (2.5 - 14.0 μm) spectra were first transformed into a wavelet power scalogram, and then linearly plotted against leaf water content. The six individual wavelet features identified in the mid infrared yielded high correlation with leaf water content (R2= 0.86 maximum, 0.83 minimum), as well as low RMSE (maximum 8.56%, minimum 9.27%). The combination of four wavelet features produced the most accurate model (R2= 0.88, RMSE= 8.00%). The models were consistent in terms of accuracy estimation for both calibration and validation datasets, indicating that leaf water content can be accurately retrieved from mid to thermal infrared electromagnetic radiation.

Thermal emissivity of avian eggshells.

The hypothesis has been tested that evolution has resulted in lower thermal emissivity of eggs of birds breeding openly in cold climates than of eggs of birds that nest under protective covering or in warmer climates. Directional thermal emissivity has been estimated from directional-hemispherical reflectance spectra. Due to several methodological difficulties the absolute emissivity is not accurately determined, but differences between species are obvious. Most notably, small waders of the genus Calidris, breeding in cold climates on the tundra, and in most cases with uniparental nest attendance, have low directional emissivity of their eggshells, about 0.92 when integration is carried out for wavelengths up to 16μm. Species belonging to Galloanserinae have the highest directional emissivity, about 0.96, of their eggs. No differences due to climate or breeding conditions were found within this group. Eggs of most other birds tested possess intermediate emissivity, but the values for Pica pica and Corvus corone cornix are as low as for Calidris. Large species-dependent differences in spectral reflectance were found at specific wavelengths. For instance, at 4.259μm the directional-hemispherical reflectance for galliforms range from 0.05 to 0.09, while for Fratercula arctica and Fulmarus glacialis it is about 0.3. The reflection peaks at 6.5 and 11.3μm due to calcite are differentially attenuated in different species. In conclusion, the hypothesis that evolution has resulted in lower thermal emissivity of bird eggs being exposed in cold climates is not supported by our results. The emissivity is not clearly related to nesting habits or climate, and it is unlikely that the small differences observed are ecologically important. The spectral differences between eggs that nevertheless exist should be taken into account when using infrared thermometers for estimating the surface temperature of avian eggs.

An Automation System for Controlling Streetlights and Monitoring Objects Using Arduino

We present an Arduino-based automation system to control the streetlights based on solar rays and object’s detection. We aim to design various systems to achieve the desired operations, which no longer require time-consuming manual switching of the streetlights. The proposed work is accomplished by using an Arduino microcontroller, a light dependent resistor (LDR) and infrared-sensors while, two main contributions are presented in this work. Firstly, we show that the streetlights can be controlled based on the night and object’s detection. In which the streetlights automatically turn to DIM state at night-time and turn to HIGH state on object’s detection, while during day-time the streetlights will remain OFF. Secondly, the proposed automated system is further extended to skip the DIM condition at night time, and streetlights turn ON based on the objects’ detection only. In addition, an automatic door system is introduced to improve the safety measurements, and most importantly, a counter is set that will count the number of objects passed through the road. The proposed systems are designed at lab-scale prototype to experimentally validate the efficiency, reliability, and low-cost of the systems. We remark that the proposed systems can be easily tested and implemented under real conditions at large-scale in the near future, that will be useful in the future applications for automation systems and smart homes.

Estimation of leaf water content from mid- and thermal-infrared spectra by coupling genetic algorithm and partial least squares regression

Abstract. Leaf water content (LWC) is an essential constituent of plant leaves that determines vegetation health and its productivity. An accurate and on-time measurement of water content is crucial for planning irrigation, forecasting drought, and predicting woodland fire. The retrieval of LWC from visible to shortwave infrared (VSWIR: 0.4 to 2.5  μm) has been extensively investigated but little has been done in the mid- and thermal-infrared (MIR and TIR: 2.50 to 14.0  μm) windows of electromagnetic spectrum. This study is mainly focused on retrieval of LWC from MIR and TIR, using genetic algorithm (GA) integrated with partial least square regression (PLSR). GA fused with PLSR selects spectral wavebands with high predictive performance, i.e., yields high adjusted-R2 and low root-mean-square error (RMSE). In our case, GA-PLSR selected eight variables (bands) and yielded highly accurate models with adjusted-R2 of 0.93 and RMSE cross validation equal to 7.1%. This study also demonstrated that MIR is more sensitive to the variation in LWC as compared to TIR. However, the combined use of MIR and TIR spectra enhances the predictive performance in retrieval of LWC. The integration of GA and PLSR not only increases the estimation precision by selecting the most sensitive spectral bands but also helps in identifying the important spectral regions for quantifying water stresses in vegetation. The findings of this study will allow the future space missions (like HyspIRI) to position wavebands at sensitive regions for characterizing vegetation stresses.

Spatial modeling of rat bites and prediction of rat infestation in Peshawar valley using binomial kriging with logistic regression

In this study, we propose to develop a geostatistical computational framework to model the distribution of rat bite infestation of epidemic proportion in Peshawar valley, Pakistan. Two species Rattus norvegicus and Rattus rattus are suspected to spread the infestation. The framework combines strengths of maximum entropy algorithm and binomial kriging with logistic regression to spatially model the distribution of infestation and to determine the individual role of environmental predictors in modeling the distribution trends. Our results demonstrate the significance of a number of social and environmental factors in rat infestations such as (I) high human population density; (II) greater dispersal ability of rodents due to the availability of better connectivity routes such as roads, and (III) temperature and precipitation influencing rodent fecundity and life cycle.

Bayesian spatial analysis and prediction of groundwater contamination in Jhelum city (Pakistan)

Access to clean drinking water, which is essential for healthy human life, is becoming hard with every day, especially in densely populated cities and towns. Ground waters contain many minerals that need to be constantly monitored and in case of any discrepancy with the minerals’ levels immediate remedial measures become necessary to keep it safe to drink. We conduct model-based (aka likelihood based) maximum likelihood estimation and Bayesian kriging predictions for six water quality parameters, namely pH, turbidity, total dissolved solids, calcium, hardness and chloride in groundwater in Jhelum city. Our results show that the concentrations of all the six parameters are, in general, higher in study region, especially four of them have concentrations well above the upper bound of standard limits, of WHO and EPA criteria of USA and Pakistan, and need the immediate attention of concerned authorities for remedial measures.