The effects of haze on the accuracy of satellite land cover classification

Remote sensing data have long been the primary source for land cover map derivation. Nevertheless, for countries within haze-affected regions such as Malaysia, the existence of haze in the atmosphere tends to degrade the data quality. Such scenario is due to attenuation of recorded reflectances in which consequently affects the land cover classification task prior to the map derivation. This study aims to determine the effects of haze on the accuracy of land cover classification. Landsat-5 TM (Thematic Mapper) satellite data over the district of Klang, located in the state of Selangor, Malaysia were used. To account for haze effects, the study made use the Landsat datasets that have been integrated with haze layers. Maximum Likelihood (ML) classification was performed on the hazy datasets using training pixels extracted from the respective datasets. The accuracy of the classification was computed using confusion matrices where individual class and overall accuracy were determined. The results show that individual class accuracy is influenced not only by haze concentration but also class spectral properties. Overall classification accuracy declines with faster rate as visibility gets poorer

Haze Modelling and Simulation in Remote Sensing Satellite Data

In atmospheric haze studies, it is almost impossible to obtain remote sensing data which have the required haze concentration levels. This problem can be overcome if we can generate haze layer based on the properties of real haze to be integrated with remote sensing data. This work aims to generate remote sensing datasets that have been degraded with haze by taking into account the spectral and spatial properties of real haze. Initially, we modelled solar radiances observed from satellite by taking into consideration direct and indirect radiances reflected from the Earth surface during hazy condition. These radiances are then simulated using the 6SV1 radiative transfer model so that the radiances due to haze, or the so called ‘haze layer’, can be computed. The spatial distribution of the haze layer is simulated based on multivariate Gaussian distribution. The haze layer is finally added to a real dataset to produce a hazy dataset. The generated hazy datasets are to be used in investigating the effects of haze on land cover classification in the future

The Effects of Haze on the Accuracy of Maximum Likelihood Classification

This study aims to investigate the effects of haze on the accuracy of Maximum Likelihood classification. Data containing eleven land covers recorded from Landsat 5 TM satellite were used. Two ways of selecting training pixels were considered which are choosing from the haze-affected and haze-free data. The accuracy of Maximum Likelihood classification was computed based on confusion matrices where the accuracy of the individual classes and the overall accuracy were determined. The result of the study shows that classification accuracies declines with faster rate as visibility gets poorer when using training pixels from clear compared to hazy data

The Effects of Haze on the Spectral and Statistical Properties of Land Cover Classification

Haze occurs almost every year in Malaysia and is caused by smoke which originates from forest fire in Indonesia. It causes visibility to drop, therefore affecting the data acquired for this area using optical sensor such as that on board Landsat satellite. The effects of haze on the data can be observed from the spectral and statistical properties of land cover classification. The work presented in this thesis is meant to analyse the statistical properties of land cover classification of hazy dataset. Maximum Likelihood (ML) was found to be a preferable classification scheme in which the effects of haze can be investigated. The study made use of hazy dataset that were simulated based on real haze spectral and statistical properties. By investigating these dataset, the spectral and statistical properties of the land classes can be systematically analysed, in which showing that haze modifies the class spectral signatures and statistical properties, consequently causing the data quality to decline

Comparative Analysis of Supervised and Unsupervised Classification on Multispectral Data

The aim of this study is to compare two methods of image classification, i.e. ML (Maximum Likelihood), a supervised method, and ISODATA (Iterative Self- Organizing Data Analysis Technique), an unsupervised method. The former is knowledge-driven, while the latter is data-driven. The former needs a priori knowledge about the study area but the latter does not. In practice, the former can classify land covers with a higher accuracy and therefore is more widely used but there have been very few attempts to investigate this. Here we use both methods in our study area, Selangor, Malaysia and compare the outcomes by means of qualitative and quantitative analyses to have a better understanding of the underlying reasons that drive the performance of both methods

Analysis Of Signal To Noise Ratio On Restored Multispectral Data

An analysis of signal to noise ratio (SNR) of restored multispectral data is reported. The data comes from multispectral satellite sensor and has undergone a restoration process due to the degradation by atmospheric haze. The restoration involves subtracting haze mean due to haze scattering and filtering haze randomness due to haze spatial variability. The results shows that the SNR of restored data after Gaussian filtering is higher than average and median filtering. The improvement of SNR at short and moderate visibilities is more significant than good visibilities

Parameter estimation and error calibration for multi-channel beam-steering SAR systems

Multi-channel beam-steering synthetic aperture radar (multi-channel BS-SAR) can achieve high resolution and wide-swath observations by combining beam-steering technology and azimuth multi-channel technology. Various imaging algorithms have been proposed for multi-channel BS-SAR but the associated parameter estimation and error calibration have received little attention. This paper focuses on errors in the main parameters in multi-channel BS-SAR (the derotation rate and constant Doppler centroid) and phase inconsistency errors. These errors can significantly reduce image quality by causing coarser resolution, radiometric degradation, and appearance of ghost targets. Accurate derotation rate estimation is important to remove the spectrum aliasing caused by beam steering, and spectrum reconstruction for multi-channel sampling requires an accurate estimate of the constant Doppler centroid and phase inconsistency errors. The time shift and scaling effect of the derotation error on the azimuth spectrum are analyzed in this paper. A method to estimate the derotation rate is presented, based on time shifting, and integrated with estimation of the constant Doppler centroid. Since the Doppler histories of azimuth targets are space-variant in multi-channel BS-SAR, the conventional estimation methods of phase inconsistency errors do not work, and we present a novel method based on minimum entropy to estimate and correct these errors. Simulations validate the proposed error estimation methods

Haze Effects On Satellite Remote Sensing Imagery And Their Corrections

Imagery recorded using satellite sensors operating at visible wavelengths can be contaminated by atmospheric haze that originates from large scale biomass burning. Such issue can reduce the reliability of the imagery and therefore having an effective method for removing such contamination is crucial. The principal aim of this study is to investigate the effects of haze on remote sensing imagery and develop a method for removing them. In order to get a better understanding on the behaviour of haze, the effects of haze on satellite imagery were initially studied. A methodology of removing haze based on haze subtraction and filtering was then developed. The developed haze removal method was then evaluated by means of signal-to-noise ratio (SNR) and classification accuracy. The results show that the haze removal method is able to improve the haze-affected imagery qualitatively and quantitatively

Multitemporal Cloud Detection and Masking Using MODIS Data

Abstract The aims of this study are to investigate the spectral properties of cloud and to carry out cloud detection and masking using MODIS (Moderate-resolution Imaging Spectroradiometer) data. To do this we make use of the spectrally rich satellite data provided by MODIS sensor, which is equipped with 36 bands ranging from visible to thermal wavelengths. Cloud detection and masking were first carried out individually using single date of MODIS data. Multitemporal cloud analysis was later carried out using MODIS data from 24 different dates from 2004 to 2005. The eastern parts of Malaysia were found to have more cloudy days than the western parts, in which consistence with the meteorological observations made by the Malaysian Meteorological Services

Carbon Stocks and Fluxes in Kenyan Forests and Wooded Grasslands Derived from Earth Observation and Model-Data Fusion

The characterization of carbon stocks and dynamics at the national level is critical for countries engaging in climate change mitigation and adaptation strategies. However, several tropical countries, including Kenya, lack the essential information typically provided by a complete national forest inventory. Here we present the most detailed and rigorous national-scale assessment of aboveground woody biomass carbon stocks and dynamics for Kenya to date. A non-parametric random forest algorithm was trained to retrieve aboveground woody biomass carbon (AGBC) for the year 2014 ± 1 and forest disturbances for the 2014–2017 period using in situ forest inventory plot data and satellite Earth Observation (EO) data. The ecosystem carbon cycling of Kenya’s forests and wooded grassland were assessed using a model-data fusion framework, CARDAMOM, constrained by the woody biomass datasets from this study as well as time series information on leaf area, fire events and soil organic carbon. Our EO-derived AGBC stocks were estimated as 140 Mt C for forests and 199 Mt C for wooded grasslands. The total AGBC loss during the study period was estimated as 1.89 Mt C with a dispersion below 1%. The CARDAMOM analysis estimated woody productivity to be three times larger in forests (mean = 1.9 t C ha−1 yr−1) than wooded grasslands (0.6 t C ha−1 yr−1), and the mean residence time of woody C in forests (16 years) to be greater than in wooded grasslands (10 years). This study stresses the importance of carbon sequestration by forests in the international climate mitigation efforts under the Paris Agreement, but emphasizes the need to include non-forest ecosystems such as wooded grasslands in international greenhouse gas accounting frameworks