Abstract: Rapid urbanization changes the land use/land cover pattern of a developing region. Due to these land surface changes, stream flow of the rivers also changes. It is important to investigate the factors affecting hydrological characteristics of the river basin for better river basin management planning. This study is aimed to understand the effect of Land Use/Land Cover (LU/LC) changes on stream flow of Upper Bhima River basin which is highly stressed in terms of water resources. In this study, Upper Bhima River basin is divided into two adjacent sub-watersheds: Mula-Mutha (urbanized) sub-watershed and Bhima (non-urbanized) sub-watershed. First of all, LU/LC changes were estimated over 1980, 2002, and 2009 for both Mula-Mutha and Bhima sub-watersheds. Further, stream flow simulations were done using Soil and Water Assessment Tool (SWAT) for the streams draining both watersheds. Results revealed that stream flow was relatively higher for urbanized sub-watershed. Through Sensitivity Analysis it was observed that out of all the parameters used, base flow was the most sensitive parameter towards LU/LC changes.
Abstract: Hyperspectral images and remote sensing are important for many applications. A problem in the use of these images is the high volume of data to be processed, stored and transferred. Dimensionality reduction techniques can be used to reduce the volume of data. In this paper, an approach to band selection based on clustering algorithms is presented. This approach allows to reduce the volume of data. The proposed structure is based on Fuzzy C-Means (or K-Means) and NWHFC algorithms. New attributes in relation to other studies in the literature, such as kurtosis and low correlation, are also considered. A comparison of the results of the approach using the Fuzzy C-Means and K-Means with different attributes is performed. The use of both algorithms show similar good results but, particularly when used attributes variance and kurtosis in the clustering process, however applicable in hyperspectral images.
Abstract: In recent decades, rapid and incorrect changes in land-use have been associated with consequences such as natural resources degradation and environmental pollution. Detecting changes in land-use is one of the tools for natural resource management and assessment of changes in ecosystems. The target of this research is studying the land-use changes in Haraz basin with an area of 677000 hectares in a 15 years period (1996 to 2011) using LANDSAT data. Therefore, the quality of the images was first evaluated. Various enhancement methods for creating synthetic bonds were used in the analysis. Separate training sites were selected for each image. Then the images of each period were classified in 9 classes using supervised classification method and the maximum likelihood algorithm. Finally, the changes were extracted in GIS environment. The results showed that these changes are an alarm for the HARAZ basin status in future. The reason is that 27% of the area has been changed, which is related to changing the range lands to bare land and dry farming and also changing the dense forest to sparse forest, horticulture, farming land and residential area.
Abstract: Performances analysis of remote sensing sensor is required to pursue a range of scientific research and application objectives. Laboratory analysis of any remote sensing instrument is essential, but not sufficient to establish a valid inflight one. In this study, with the aid of the in situ measurements and corresponding image of three-gray scale permanent artificial target, the in-flight radiometric performances analyses (in-flight radiometric calibration, dynamic range and response linearity, signal-noise-ratio (SNR), radiometric resolution) of self-developed short-wave infrared (SWIR) camera are performed. To acquire the inflight calibration coefficients of the SWIR camera, the at-sensor radiances (Li) for the artificial targets are firstly simulated with in situ measurements (atmosphere parameter and spectral reflectance of the target) and viewing geometries using MODTRAN model. With these radiances and the corresponding digital numbers (DN) in the image, a straight line with a formulation of L = G × DN + B is fitted by a minimization regression method, and the fitted coefficients, G and B, are inflight calibration coefficients. And then the high point (LH) and the low point (LL) of dynamic range can be described as LH= (G × DNH + B) and LL= B, respectively, where DNH is equal to 2n − 1 (n is the quantization number of the payload). Meanwhile, the sensor’s response linearity (δ) is described as the correlation coefficient of the regressed line. The results show that the calibration coefficients (G and B) are 0.0083 W·sr−1m−2µm−1 and −3.5 W·sr−1m−2µm−1; the low point of dynamic range is −3.5 W·sr−1m−2µm−1 and the high point is 30.5 W·sr−1m−2µm−1; the response linearity is approximately 99%. Furthermore, a SNR normalization method is used to assess the sensor’s SNR, and the normalized SNR is about 59.6 when the mean value of radiance is equal to 11.0 W·sr−1m−2µm−1; subsequently, the radiometric resolution is calculated about 0.1845 W•sr-1m-2μm-1. Moreover, in order to validate the result, a comparison of the measured radiance with a radiative-transfer-code-predicted over four portable artificial targets with reflectance of 20%, 30%, 40%, 50% respectively, is performed. It is noted that relative error for the calibration is within 6.6%.
Abstract: The increasing availability of information about earth
surface elevation (Digital Elevation Models DEM) generated from
different sources (remote sensing, Aerial Images, Lidar) poses the
question about how to integrate and make available to the most than
possible audience this huge amount of data. In order to exploit the potential of 3D elevation representation the
quality of data management plays a fundamental role. Due to the high
acquisition costs and the huge amount of generated data, highresolution
terrain surveys tend to be small or medium sized and
available on limited portion of earth. Here comes the need to merge
large-scale height maps that typically are made available for free at
worldwide level, with very specific high resolute datasets. One the
other hand, the third dimension increases the user experience and the
data representation quality, unlocking new possibilities in data
analysis for civil protection, real estate, urban planning, environment
monitoring, etc. The open-source 3D virtual globes, which are
trending topics in Geovisual Analytics, aim at improving the
visualization of geographical data provided by standard web services
or with proprietary formats. Typically, 3D Virtual globes like do not
offer an open-source tool that allows the generation of a terrain
elevation data structure starting from heterogeneous-resolution terrain
datasets. This paper describes a technological solution aimed to set
up a so-called “Terrain Builder”. This tool is able to merge
heterogeneous-resolution datasets, and to provide a multi-resolution
worldwide terrain services fully compatible with CesiumJS and
therefore accessible via web using traditional browser without any
additional plug-in.
Abstract: Advances in spatial and spectral resolution of satellite
images have led to tremendous growth in large image databases. The
data we acquire through satellites, radars, and sensors consists of
important geographical information that can be used for remote
sensing applications such as region planning, disaster management.
Spatial data classification and object recognition are important tasks
for many applications. However, classifying objects and identifying
them manually from images is a difficult task. Object recognition is
often considered as a classification problem, this task can be
performed using machine-learning techniques. Despite of many
machine-learning algorithms, the classification is done using
supervised classifiers such as Support Vector Machines (SVM) as the
area of interest is known. We proposed a classification method,
which considers neighboring pixels in a region for feature extraction
and it evaluates classifications precisely according to neighboring
classes for semantic interpretation of region of interest (ROI). A
dataset has been created for training and testing purpose; we
generated the attributes by considering pixel intensity values and
mean values of reflectance. We demonstrated the benefits of using
knowledge discovery and data-mining techniques, which can be on
image data for accurate information extraction and classification from
high spatial resolution remote sensing imagery.
Abstract: The underutilization of biomass resources in the
Philippines, combined with its growing population and the rise in
fossil fuel prices confirms demand for alternative energy sources. The
goal of this paper is to provide a comparison of MODIS-based and
Landsat-based agricultural land cover maps when used in the
estimation of rice hull’s available energy potential. Biomass resource
assessment was done using mathematical models and remote sensing
techniques employed in a GIS platform.
Abstract: This paper focuses on the orbit avoidance strategy of
the optical remote sensing satellite. The optical remote sensing
satellite, moving along the Sun-synchronous orbit, is equipped with
laser warning equipment to alert CCD camera from laser attacks. This
paper explores the strategy of satellite avoidance to protect the CCD
camera and also the satellite. The satellite could evasive to several
target points in the orbital coordinates of virtual satellite. The so-called
virtual satellite is a passive vehicle which superposes the satellite at the
initial stage of avoidance. The target points share the consistent cycle
time and the same semi-major axis with the virtual satellite, which
ensures the properties of the satellite’s Sun-synchronous orbit remain
unchanged. Moreover, to further strengthen the avoidance capability
of satellite, it can perform multi-target-points avoid maneuvers. On
occasions of fulfilling the satellite orbit tasks, the orbit can be restored
back to virtual satellite through orbit maneuvers. There into, the avoid
maneuvers adopts pulse guidance. In addition, the fuel consumption is
optimized. The avoidance strategy discussed in this article is
applicable to optical remote sensing satellite when it is encountered
with hostile attack of space-based laser anti-satellite.
Abstract: Detecting changes in multiple images of the same
scene has recently seen increased interest due to the many
contemporary applications including smart security systems, smart
homes, remote sensing, surveillance, medical diagnosis, weather
forecasting, speed and distance measurement, post-disaster forensics
and much more. These applications differ in the scale, nature, and
speed of change. This paper presents an application of image
processing techniques to implement a real-time change detection
system. Change is identified by comparing the RGB representation of
two consecutive frames captured in real-time. The detection threshold
can be controlled to account for various luminance levels. The
comparison result is passed through a filter before decision making to
reduce false positives, especially at lower luminance conditions. The
system is implemented with a MATLAB Graphical User interface
with several controls to manage its operation and performance.
Abstract: The critical concern of satellite operations is to ensure
the health and safety of satellites. The worst case in this perspective
is probably the loss of a mission, but the more common interruption
of satellite functionality can result in compromised mission
objectives. All the data acquiring from the spacecraft are known as
Telemetry (TM), which contains the wealth information related to the
health of all its subsystems. Each single item of information is
contained in a telemetry parameter, which represents a time-variant
property (i.e. a status or a measurement) to be checked. As a
consequence, there is a continuous improvement of TM monitoring
systems to reduce the time required to respond to changes in a
satellite's state of health. A fast conception of the current state of the
satellite is thus very important to respond to occurring failures.
Statistical multivariate latent techniques are one of the vital learning
tools that are used to tackle the problem above coherently.
Information extraction from such rich data sources using advanced
statistical methodologies is a challenging task due to the massive
volume of data. To solve this problem, in this paper, we present a
proposed unsupervised learning algorithm based on Principle
Component Analysis (PCA) technique. The algorithm is particularly
applied on an actual remote sensing spacecraft. Data from the
Attitude Determination and Control System (ADCS) was acquired
under two operation conditions: normal and faulty states. The models
were built and tested under these conditions, and the results show that
the algorithm could successfully differentiate between these
operations conditions. Furthermore, the algorithm provides
competent information in prediction as well as adding more insight
and physical interpretation to the ADCS operation.
Abstract: Aurèsregion is one of the arid and semi-arid areas that
have suffered climate crises and overexploitation of natural resources
they have led to significant land degradation. The use of remote sensing data allowed us to analyze the land and
its spatiotemporal changes in the Aurès between 1987 and 2013, for
this work, we adopted a method of analysis based on the exploitation
of the images satellite Landsat TM 1987 and Landsat OLI 2013, from
the supervised classification likelihood coupled with field surveys of
the mission of May and September of 2013. Using ENVI EX software by the superposition of the ground cover
maps from 1987 and 2013, one can extract a spatial map change of
different land cover units. The results show that between 1987 and
2013 vegetation has suffered negative changes are the significant
degradation of forests and steppe rangelands, and sandy soils and
bare land recorded a considerable increase. The spatial change map land cover units between 1987 and 2013
allows us to understand the extensive or regressive orientation of
vegetation and soil, this map shows that dense forests give his place
to clear forests and steppe vegetation develops from a degraded forest
vegetation and bare, sandy soils earn big steppe surfaces that explain
its remarkable extension.
The analysis of remote sensing data highlights the profound
changes in our environment over time and quantitative monitoring of
the risk of desertification.
Abstract: Land Use Land Cover (LULC) changes due to human
activities and natural causes have become a major environmental
concern. Assessment of temporal remote sensing data provides
information about LULC impacts on environment. Land Surface
Temperature (LST) is one of the important components for modeling
environmental changes in climatological, hydrological, and
agricultural studies. In this study, LULC changes (September 7, 1984
and July 8, 2014) especially in agricultural lands together with
population changes (1985-2014) and LST status were investigated
using remotely sensed and census data in South Marmara Watershed,
Turkey. LULC changes were determined using Landsat TM and
Landsat OLI data acquired in 1984 and 2014 summers. Six-band TM
and OLI images were classified using supervised classification
method to prepare LULC map including five classes including Forest
(F), Grazing Land (G), Agricultural Land (A), Water Surface (W),
Residential Area-Bare Soil (R-B) classes. The LST image was also
derived from thermal bands of the same dates.
LULC classification results showed that forest areas, agricultural
lands, water surfaces and residential area-bare soils were increased as
65751 ha, 20163 ha, 1924 ha and 20462 ha respectively. In
comparison, a dramatic decrement occurred in grazing land (107985
ha) within three decades. The population increased 29% between
years 1984-2014 in whole study area. Along with the natural causes,
migration also caused this increase since the study area has an
important employment potential. LULC was transformed among the
classes due to the expansion in residential, commercial and industrial
areas as well as political decisions. In the study, results showed that
agricultural lands around the settlement areas transformed to
residential areas in 30 years.
The LST images showed that mean temperatures were ranged
between 26-32°C in 1984 and 27-33°C in 2014. Minimum
temperature of agricultural lands was increased 3°C and reached to
23°C. In contrast, maximum temperature of A class decreased to
41°C from 44°C. Considering temperatures of the 2014 R-B class and
1984 status of same areas, it was seen that mean, min and max
temperatures increased by 2°C.
As a result, the dynamism of population, LULC and LST resulted
in increasing mean and maximum surface temperatures, living
spaces/industrial areas and agricultural lands.
Abstract: For this study, a town based soil database created in
Gümüsçay District of Biga Town, Çanakkale, Turkey. Crop and
livestock production are major activities in the district. Nutrient
management is mainly based on commercial fertilizer application
ignoring the livestock manure. Within the boundaries of district, 122
soil sampling points determined over the satellite image. Soil samples
collected from the determined points with the help of handheld
Global Positioning System. Labeled samples were sent to a
commercial laboratory to determine 11 soil parameters including
salinity, pH, lime, organic matter, nitrogen, phosphorus, potassium,
iron, manganese, copper and zinc. Based on the test results soil maps
for mentioned parameters were developed using remote sensing, GIS,
and geostatistical analysis. In this study we developed a GIS database
that will be used for soil nutrient management. Methods were
explained and soil maps and their interpretations were summarized in
the study.
Abstract: Dengue outbreaks are affected by biological,
ecological, socio-economic and demographic factors that vary over
time and space. These factors have been examined separately and still
require systematic clarification. The present study aimed to investigate
the spatial-temporal clustering relationships between these factors and
dengue outbreaks in the northern region of Sri Lanka. Remote sensing
(RS) data gathered from a plurality of satellites were used to develop
an index comprising rainfall, humidity and temperature data. RS data
gathered by ALOS/AVNIR-2 were used to detect urbanization, and a
digital land cover map was used to extract land cover information.
Other data on relevant factors and dengue outbreaks were collected
through institutions and extant databases. The analyzed RS data and
databases were integrated into geographic information systems,
enabling temporal analysis, spatial statistical analysis and space-time
clustering analysis. Our present results showed that increases in the
number of the combination of ecological factor and socio-economic
and demographic factors with above the average or the presence
contribute to significantly high rates of space-time dengue clusters.
Abstract: Bir El Djir is an important coastal township in Oran
department, located at 450 Km far away from Algiers on northwest of
Algeria. In this coastal area, the urban sprawl is one of the main
problems that reduce the limited highly fertile land. So, using the
remote sensing and GIS technologies have shown their great
capabilities to solve many earth resources issues.
The aim of this study is to produce land use and cover map for the
studied area at varied periods to monitor possible changes that may
occurred, particularly in the urban areas and subsequently predict
likely changes. For this, two spatial images SPOT and Landsat
satellites from 1987 and 2014 respectively were used to assess the
changes of urban expansion and encroachment during this period
with photo-interpretation and GIS approach.
The results revealed that the town of Bir El Djir has shown a
highest growth rate in the period 1987-2014 which is 1201.5 hectares
in terms of area. These expansions largely concern the new real estate
constructions falling within the social and promotional housing
programs launched by the government.
The most urban expansion is characterized by the new
construction in the form of spontaneous or peripheral precarious
habitat, but also unstructured slums settled especially in the
southeastern part of town.
Abstract: It has become an increasing evident that large
development influences the climate. There are concerns that rising
temperature over developed areas could have negative impact and
increase living discomfort within city boundaries. Temperature trends
in Ibadan city have received little attention, yet the area has
experienced heavy urban expansion between 1972 and 2014. This
research aims at examining the impact of landuse change on surface
temperature knowing that the built-up environment absorb and store
solar energy, resulting into the Urban Heat Island (UHI) effect. The
Landsat imagery was used to examine the landuse change for a
period of 42 years (1972-2014). Land Surface Temperature (LST)
was obtained by converting the thermal band to a surface temperature
map and zonal statistic analyses was used to examine the relationship
between landuse and temperature emission. The results showed that
the settlement area increased to a large extent while the area covered
by vegetation reduced during the study period. The spatial and
temporal trends of surface temperature are related to the gradual
change in urban landuse/landcover and the settlement area has the
highest emission. This research provides useful insight into the
temporal behavior of the Ibadan city.
Abstract: Much attention is paid to the development of space
branch in Kazakhstan at present. Two Earth remote sensing satellites
of Kazakhstan have been launched successfully. Many projects
related to the development of components for satellite are carried in
Kazakhstan, in particular the project related to the development of
star tracker experimental model. It is planned to use the results of this
project for development of star tracker prototype in the future. This
article describes the main stages of development of star tracker
experimental model.
Abstract: Remote sensing plays a vital role in mapping of
resources and monitoring of environments of the earth. In the present
research study, mapping and monitoring of clay siltations occurred in
the Alkhod Dam of Muscat, Sultanate of Oman are carried out using
low-cost multispectral Landsat and ASTER data. The dam is
constructed across the Wadi Samail catchment for ground water
recharge. The occurrence and spatial distribution of siltations in the
dam are studied with five years of interval from the year 1987 of
construction to 2014. The deposits are mainly due to the clay, sand
and silt occurrences derived from the weathering rocks of ophiolite
sequences occurred in the Wadi Samail catchment. The occurrences
of clays are confirmed by minerals identification using ASTER
VNIR-SWIR spectral bands and Spectral Angle Mapper supervised
image processing method. The presence of clays and their spatial
distribution are verified in the field. The study recommends the
technique and the low-cost satellite data to similar region of the
world.
Abstract: One of the most important tasks in urban remote
sensing is the detection of impervious surfaces (IS), such as roofs and
roads. However, detection of IS in heterogeneous areas still remains
one of the most challenging tasks. In this study, detection of concrete
roof using an object-based approach was proposed. A new rule-based
classification was developed to detect concrete roof tile. This
proposed rule-based classification was applied to WorldView-2
image and results showed that the proposed rule has good potential to
predict concrete roof material from WorldView-2 images, with 85%
accuracy.
Abstract: In remote sensing, shadow causes problems in many
applications such as change detection and classification. It is caused
by objects which are elevated, thus can directly affect the accuracy of
information. For these reasons, it is very important to detect shadows
particularly in urban high spatial resolution imagery which created a
significant problem. This paper focuses on automatic shadow
detection based on a new spectral index for multispectral imagery
known as Shadow Detection Index (SDI). The new spectral index
was tested on different areas of WorldView-2 images and the results
demonstrated that the new spectral index has a massive potential to
extract shadows with accuracy of 94% effectively and automatically.
Furthermore, the new shadow detection index improved road
extraction from 82% to 93%.