305 research outputs found
A novel framework for parameter selection of the Autocorrelation Change detection method using 250m MODIS time-series data in the Gauteng province of South Africa
Human settlement expansion is one of the most prominent types of land cover change in South Africa. These changes typically occur in areas that are covered by natural vegetation. Methods that can rapidly indicate areas having a high probability of change are very valuable to analysts as this can be used to direct their attention to high probability change areas for further evaluation. MODIS time-series data (8-daily composite) at a resolution of 500 m has been proven to be an effective data source for detecting human settlements in South Africa and it was proposed in Kleynhans et al., 2012 that a Temporal Autocorrelation Change detection method (TACD) be used to detect the formation of new settlements in the Gauteng province of South Africa. In this paper, the TACD that was proposed by Kleynhans et al., 2012 is adapted to be usable with variable sampled temporal resolutions for 250m MODIS data by using a novel framework for parameter selection. The proposed method is applied to variably sampled 250m MODIS time-series data ranging from daily to semi-annually and a comparison of change detection accuracy vs. false alarm rate is done in each instance. Key results indicate that there is little difference in performance between daily sampled and 2-monthly sampled 250m MODIS time-series data for the use case evaluated in this paper
A novel framework for parameter selection of the autocorrelation change detection method using 250m MODIS time-series data in the Gauteng province of South Africa
Human settlement expansion is one of the most prominent types of land cover change in South Africa. These changes typically occur in areas that are covered by natural vegetation. Methods that can rapidly indicate areas having a high probability of change are very valuable to analysts as this can be used to direct their attention to high probability change areas for further evaluation. MODIS time-series data (8-daily composite) at a resolution of 500 m has been proven to be an effective data source for detecting human settlements in South Africa and it was proposed in Kleynhans et al., 2012 that a Temporal Autocorrelation Change detection method (TACD) be used to detect the formation of new settlements in the Gauteng province of South Africa. In this paper, the TACD that was proposed by Kleynhans et al., 2012 is adapted to be usable with variable sampled temporal resolutions for 250m MODIS data by using a novel framework for parameter selection. The proposed method is applied to variably sampled 250m MODIS time-series data ranging from daily to semi-annually and a comparison of change detection accuracy vs. false alarm rate is done in each instance. Key results indicate that there is little difference in performance between daily sampled and 2-monthly sampled 250m MODIS time-series data for the use case evaluated in this paper.http://www.sajg.org.za/index.php/sajgam2018Electrical, Electronic and Computer Engineerin
Rapid detection of new and expanding human settlements in the Limpopo province of South Africa using a spatio-temporal change detection method
Recent development has identified the benefits of using hyper-temporal satellite time series data for land
cover change detection and classification in South Africa. In particular, the monitoring of human settlement
expansion in the Limpopo province is of relevance as it is the one of the most pervasive forms of
land-cover change in this province which covers an area of roughly 125 000km2. In this paper, a spatiotemporal
autocorrelation change detection (STACD) method is developed to improve the performance
of a pixel based temporal Autocorrelation change detection (TACD) method previously proposed. The
objective is to apply the algorithm to large areas to detect the conversion of natural vegetation to settlement
which is then validated by an operator using additional data (such as high resolution imagery).
Importantly, as the objective of the method is to indicate areas of potential change to operators for further
analysis, a low false alarm rate is required while achieving an acceptable probability of detection. Results
indicate that detection accuracies of 70% of new settlement instances are achievable at a false alarm rate
of less than 1% with the STACD method, an improvement of up to 17% compared to the original TACD
formulation.http://www.elsevier.com/locate/jag2016-08-30hb201
L-band synthetic aperture radar imagery performs better than optical datasets at retrieving woody fractional cover in deciduous, dry savannahs
Woody canopy cover (CC) is the simplesttwo dimensional metric for assessing the presence ofthe woody
component in savannahs, but detailed validated maps are not currently available in southern African
savannahs. A number of international EO programs (including in savannah landscapes) advocate and
use optical LandSAT imagery for regional to country-wide mapping of woody canopy cover. However,
previous research has shown that L-band Synthetic Aperture Radar (SAR) provides good performance at
retrieving woody canopy cover in southern African savannahs. This study’s objective was to evaluate,
compare and use in combination L-band ALOS PALSAR and LandSAT-5 TM, in a Random Forest environment,
to assess the benefits of using LandSAT compared to ALOS PALSAR. Additional objectives saw
the testing of LandSAT-5 image seasonality, spectral vegetation indices and image textures for improved
CC modelling. Results showed that LandSAT-5 imagery acquired in the summer and autumn seasons
yielded the highest single season modelling accuracies (R2 between 0.47 and 0.65), depending on the
year but the combination of multi-seasonal images yielded higher accuracies (R2 between 0.57 and 0.72).
The derivation of spectral vegetation indices and image textures and their combinations with optical
reflectance bands provided minimal improvement with no optical-only result exceeding the winter SAR
L-band backscatter alone results (R2 of ∼0.8). The integration of seasonally appropriate LandSAT-5 image
reflectance and L-band HH and HV backscatter data does provide a significant improvement for CC modelling
at the higher end of the model performance (R2 between 0.83 and 0.88), but we conclude that
L-band only based CC modelling be recommended for South African regionshttp://www.elsevier.com/locate/jag2017-10-31hb2016Geography, Geoinformatics and Meteorolog
Hyper-temporal C-band SAR for baseline woody structural assessments in deciduous savannas
Savanna ecosystems and their woody vegetation provide valuable resources and ecosystem
services. Locally calibrated and cost effective estimates of these resources are required in order
to satisfy commitments to monitor and manage change within them. Baseline maps of woody
resources are important for analyzing change over time. Freely available, and highly repetitive,
C-band data has the potential to be a viable alternative to high-resolution commercial SAR imagery
(e.g., RADARSAT-2, ALOS2) in generating large-scale woody resources maps. Using airborne LiDAR
as calibration, we investigated the relationships between hyper-temporal C-band ASAR data and
woody structural parameters, namely total canopy cover (TCC) and total canopy volume (TCV), in a
deciduous savanna environment. Results showed that: the temporal filter reduced image variance;
the random forest model out-performed the linear model; while the TCV metric consistently showed
marginally higher accuracies than the TCC metric. Combinations of between 6 and 10 images could
produce results comparable to high resolution commercial (C- & L-band) SAR imagery. The approach
showed promise for producing a regional scale, locally calibrated, baseline maps for the management
of deciduous savanna resources, and lay a foundation for monitoring using time series of data from
newer C-band SAR sensors (e.g., Sentinel1).Greg Asner, through
the CAO campaign and acknowledged partners, provided funding for the LiDAR acquisition and LiDAR
processing, as well as interpretation and review of the results.http://www.mdpi.com/journal/remotesensingam2016Electrical, Electronic and Computer EngineeringGeography, Geoinformatics and Meteorolog
Savannah woody structure modelling and mapping using multi-frequency (X-, C- and L-band) synthetic aperture radar data
Structural parameters of the woody component in African savannahs provide estimates of carbon stocks
that are vital to the understanding of fuelwood reserves, which is the primary source of energy for 90% of
households in South Africa (80% in Sub-Saharan Africa) and are at risk of over utilisation. The woody component
can be characterised by various quantifiable woody structural parameters, such as tree cover, tree
height, above ground biomass (AGB) or canopy volume, each been useful for different purposes. In contrast
to the limited spatial coverage of ground-based approaches, remote sensing has the ability to sense
the high spatio-temporal variability of e.g. woody canopy height, cover and biomass, as well as species
diversity and phenological status – a defining but challenging set of characteristics typical of African
savannahs. Active remote sensing systems (e.g. Light Detection and Ranging – LiDAR; Synthetic
Aperture Radar – SAR), on the other hand, may be more effective in quantifying the savannah woody
component because of their ability to sense within-canopy properties of the vegetation and its insensitivity
to atmosphere and clouds and shadows. Additionally, the various components of a particular target’s
structure can be sensed differently with SAR depending on the frequency or wavelength of the
sensor being utilised. This study sought to test and compare the accuracy of modelling, in a Random
Forest machine learning environment, woody above ground biomass (AGB), canopy cover (CC) and total
canopy volume (TCV) in South African savannahs using a combination of X-band (TerraSAR-X), C-band
(RADARSAT-2) and L-band (ALOS PALSAR) radar datasets. Training and validation data were derived from
airborne LiDAR data to evaluate the SAR modelling accuracies. It was concluded that the L-band SAR frequency
was more effective in the modelling of the CC (coefficient of determination or R2 of 0.77), TCV (R2
of 0.79) and AGB (R2 of 0.78) metrics in Southern African savannahs than the shorter wavelengths (X- and
C-band) both as individual and combined (X + C-band) datasets. The addition of the shortest wavelengths
also did not assist in the overall reduction of prediction error across different vegetation conditions (e.g.
dense forested conditions, the dense shrubby layer and sparsely vegetated conditions). Although the integration
of all three frequencies (X + C + L-band) yielded the best overall results for all three metrics
(R2 = 0.83 for CC and AGB and R2 = 0.85 for TCV), the improvements were noticeable but marginal in comparison
to the L-band alone. The results, thus, do not warrant the acquisition of all three SAR frequency
datasets for tree structure monitoring in this environment.Council for Scientific and Industrial Research (CSIR) – South Africa, the
Department of Science and Technology, South Africa (Grant Agreement DST/CON 0119/2010, Earth Observation Application Development in Support of SAEOS) and the European Union’s Seventh Framework Programme (FP7/2007-2013, Grant
Agreement No. 282621, AGRICAB) for funding this study. The Xband StripMap TerraSAR-X scenes were acquired under a proposal submitted to the TerraSAR-X Science Service of the German Aerospace Center (DLR). The C-band Quad-Pol RADARSAT-2 scenes were provided by MacDonald Dettwiler and Associates Ltd. –
Geospatial Services Inc. (MDA GSI), the Canadian Space Agency (CSA), and the Natural Resources Canada’s Centre for Remote Sensing (CCRS) through the Science and Operational Applications Research (SOAR) programme. The L-band ALOS PALSAR FBD scenes were acquired under a K&C Phase 3 agreement with the Japanese Aerospace Exploration Agency (JAXA). The Carnegie Airborne Observatory is supported by the Avatar Alliance Foundation, John D. and Catherine T. MacArthur Foundation, Gordon and Betty Moore Foundation, W.M. Keck Foundation, the Margaret A. Cargill Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst III. The application of the CAO data in South Africa is made possible by the Andrew Mellon Foundation, Grantham Foundation for the Protection of the Environment, and
the endowment of the Carnegie Institution for Science.http://www.elsevier.com/locate/isprsjprs2016-07-31hb201
From microscopic to macroscopic descriptions of cell\ud migration on growing domains
Cell migration and growth are essential components of the development of multicellular organisms. The role of various cues in directing cell migration is widespread, in particular, the role of signals in the environment in the control of cell motility and directional guidance. In many cases, especially in developmental biology, growth of the domain also plays a large role in the distribution of cells and, in some cases, cell or signal distribution may actually drive domain growth. There is a ubiquitous use of partial differential equations (PDEs) for modelling the time evolution of cellular density and environmental cues. In the last twenty years, a lot of attention has been devoted to connecting macroscopic PDEs with more detailed microscopic models of cellular motility, including models of directional sensing and signal transduction pathways. However, domain growth is largely omitted in the literature. In this paper, individual-based models describing cell movement and domain growth are studied, and correspondence with a macroscopic-level PDE describing the evolution of cell density is demonstrated. The individual-based models are formulated in terms of random walkers on a lattice. Domain growth provides an extra mathematical challenge by making the lattice size variable over time. A reaction-diffusion master equation formalism is generalised to the case of growing lattices and used in the derivation of the macroscopic PDEs
The use of a multilayer perceptron for detecting new human settlements from a time series of MODIS images
This paper presents a novel land cover change detection method that employs a sliding window over hyper-temporal multi-spectral images acquired from the 7 bands of the MODerate-resolution Imaging Spectroradiometer (MODIS) land surface reflectance product. The method uses a Feedforward Multilayer Perceptron (MLP) for supervised change detection that operates on multi-spectral time series extracted with a sliding window from the dataset. The method was evaluated on both real and simulated land cover change examples. The simulated land cover change comprises of concatenated time series that are produced by blending actual time series of pixels from human settlements to those from adjacent areas covered by natural vegetation. The method employs an iteratively retrained MLP to capture all local patterns and to compensate for the time-varying climate in the geographical area. The iteratively retrained MLP was compared to a classical batch mode trained MLP. Depending on the length of the temporal sliding window used, an overall change detection accuracy between 83% and 90% was achieved. It is shown that a sliding window of 6 months using all 7 bands of MODIS data is sufficient to detect land cover change reliably. Window sizes of 18 months and longer provide minor improvements to classification accuracy and change detection performance at the cost of longer time delays.The CSIR Strategic Research Panelhttp://www.elsevier.com/locate/jagai201
Detecting land cover change using an extended Kalman filter on MODIS NDVI time-series data
A method for detecting land cover change using
NDVI time series data derived from 500m MODIS satellite
data is proposed. The algorithm acts as a per pixel change
alarm and takes as input the NDVI time series of a 3x3 grid
of MODIS pixels. The NDVI time series for each of these
pixels was modeled as a triply (mean, phase and amplitude)
modulated cosine function, and an extended Kalman Filter was
used to estimate the parameters of the modulated cosine function
through time. A spatial comparison between the center pixel
of the the 3x3 grid and each of its neighboring pixel’s mean
and amplitude parameter sequence was done to calculate a
change metric which yields a change or no-change decision after
thresholding. Although the development of new settlements is
the most prevalent form of land cover change in South Africa,
it is rarely mapped and known examples amounts to a limited
number of changed MODIS pixels. Therefore simulated change
data was generated and used for preliminary optimization of
the change detection method. After optimization the method was
evaluated on examples of known land cover change in the study
area and experimental results indicate a 89% change detection
accuracy, while a traditional annual NDVI differencing method
could only achieve a 63% change detection accuracy.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=885
Meta-optimization of the Extended Kalman filter's parameters through the use of the Bias-Variance Equilibrium Point criterion
The extraction of information on land cover classes
using unsupervised methods has always been of relevance to the
remote sensing community. In this paper, a novel criterion is proposed,
which extracts the inherent information in an unsupervised
fashion from a time series. The criterion is used to fit a parametric
model to a time series, derive the corresponding covariance matrices
of the parameters for the model, and estimate the additive noise
on the time series. The proposed criterion uses both spatial and
temporal information when estimating the covariance matrices
and can be extended to incorporate spectral information. The
algorithm used to estimate the parameters for the model is the
extended Kalman filter (EKF). An unsupervised search algorithm,
specifically designed for this criterion, is proposed in conjunction
with the criterion that is used to rapidly and efficiently estimate the
variables. The search algorithm attempts to satisfy the criterion by
employing density adaptation to the current candidate system. The
application in this paper is the use of an EKF to model Moderate
Resolution Imaging Spectroradiometer time series with a triply
modulated cosine function as the underlying model. The results
show that the criterion improved the fit of the triply modulated
cosine function by an order of magnitude on the time series over
all seven spectral bands when compared with the other methods.
The state space variables derived from the EKF are then used for
both land cover classification and land cover change detection.
The method was evaluated in the Gauteng province of South
Africa where it was found to significantly improve on land cover
classification and change detection accuracies when compared
with other methods.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=36hb201
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