Abstract: A biosphere reserve is developed to create harmony
amongst economic development, community development, and
environmental protection, through partnership between human and
nature. Giam Siak Kecil Bukit Batu Biosphere Reserve (GSKBB BR)
in Riau Province, Indonesia, is unique in that it has peat soil
dominating the area, many springs essential for human livelihood,
high biodiversity. Furthermore, it is the only biosphere reserve
covering privately managed production forest areas. In this research, we aimed at analyzing the threat of deforestation
and forest fire, and the potential of CO2 emission at GSKBB BR. We
used Landsat image, arcView software, and ERDAS IMAGINE 8.5
Software to conduct spatial analysis of land cover and land use
changes, calculated CO2 emission based on emission potential from
each land cover and land use type, and exercised simple linear
regression to demonstrate the relation between CO2 emission
potential and deforestation. The result showed that, beside in the buffer zone and transition
area, deforestation also occurred in the core area. Spatial analysis of
land cover and land use changes from years 2010, 2012, and 2014
revealed that there were changes of land cover and land use from
natural forest and industrial plantation forest to other land use types,
such as garden, mixed garden, settlement, paddy fields, burnt areas,
and dry agricultural land. Deforestation in core area, particularly at
the Giam Siak Kecil Wildlife Reserve and Bukit Batu Wildlife
Reserve, occurred in the form of changes from natural forest in to
garden, mixed garden, shrubs, swamp shrubs, dry agricultural land,
open area, and burnt area. In the buffer zone and transition area,
changes also happened, what once swamp forest changed into garden,
mixed garden, open area, shrubs, swamp shrubs, and dry agricultural
land. Spatial analysis on land cover and land use changes indicated
that deforestation rate in the biosphere reserve from 2010 to 2014 had
reached 16 119 ha/year. Beside deforestation, threat toward the
biosphere reserve area also came from forest fire. The occurrence of forest fire in 2014 had burned 101 723 ha of the
area, in which 9 355 ha of core area, and 92 368 ha of buffer zone
and transition area. Deforestation and forest fire had increased CO2
emission as much as 24 903 855 ton/year.
Abstract: Indonesia has experienced annual forest fires that have
rapidly destroyed and degraded its forests. Fires in the peat swamp
forests of Riau Province, have set the stage for problems to worsen,
this being the ecosystem most prone to fires (which are also the most
difficult, to extinguish). Despite various efforts to curb deforestation,
and forest degradation processes, severe forest fires are still
occurring. To find an effective solution, the basic causes of the
problems must be identified. It is therefore critical to have an indepth
understanding of the underlying causal factors that have
contributed to deforestation and forest degradation as a whole, in
order to attain reductions in their rates. An assessment of the drivers of deforestation and forest
degradation was carried out, in order to design and implement
measures that could slow these destructive processes. Research was
conducted in Giam Siak Kecil–Bukit Batu Biosphere Reserve
(GSKBB BR), in the Riau Province of Sumatera, Indonesia. A
biosphere reserve was selected as the study site because such reserves
aim to reconcile conservation with sustainable development. A
biosphere reserve should promote a range of local human activities,
together with development values that are in line spatially and
economically with the area conservation values, through use of a
zoning system. Moreover, GSKBB BR is an area with vast peatlands,
and is experiencing forest fires annually. Various factors were
analysed to assess the drivers of deforestation and forest degradation
in GSKBB BR; data were collected from focus group discussions
with stakeholders, key informant interviews with key stakeholders,
field observation and a literature review. Landsat satellite imagery was used to map forest-cover changes
for various periods. Analysis of landsat images, taken during the
period 2010-2014, revealed that within the non-protected area of core
zone, there was a trend towards decreasing peat swamp forest areas,
increasing land clearance, and increasing areas of community oilpalm
and rubber plantations. Fire was used for land clearing and most
of the forest fires occurred in the most populous area (the transition
area). The study found a relationship between the deforested/
degraded areas, and certain distance variables, i.e. distance from
roads, villages and the borders between the core area and the buffer
zone. The further the distance from the core area of the reserve, the
higher was the degree of deforestation and forest degradation. Research findings suggested that agricultural expansion may be
the direct cause of deforestation and forest degradation in the reserve,
whereas socio-economic factors were the underlying driver of forest
cover changes; such factors consisting of a combination of sociocultural,
infrastructural, technological, institutional (policy and governance), demographic (population pressure) and economic
(market demand) considerations. These findings indicated that local
factors/problems were the critical causes of deforestation and
degradation in GSKBB BR. This research therefore concluded that
reductions in deforestation and forest degradation in GSKBB BR
could be achieved through ‘local actor’-tailored approaches such as
community empowerment.
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: In present study, it was aimed to determine potential
agricultural lands (PALs) in Gokceada (Imroz) Island of Canakkale
province, Turkey. Seven-band Landsat 8 OLI images acquired on
July 12 and August 13, 2013, and their 14-band combination image
were used to identify current Land Use Land Cover (LULC) status.
Principal Component Analysis (PCA) was applied to three Landsat
datasets in order to reduce the correlation between the bands. A total
of six Original and PCA images were classified using supervised
classification method to obtain the LULC maps including 6 main
classes (“Forest”, “Agriculture”, “Water Surface”, “Residential Area-
Bare Soil”, “Reforestation” and “Other”). Accuracy assessment was
performed by checking the accuracy of 120 randomized points for
each LULC maps. The best overall accuracy and Kappa statistic
values (90.83%, 0.8791% respectively) were found for PCA images
which were generated from 14-bands combined images called 3-
B/JA.
Digital Elevation Model (DEM) with 15 m spatial resolution
(ASTER) was used to consider topographical characteristics. Soil
properties were obtained by digitizing 1:25000 scaled soil maps of
Rural Services Directorate General. Potential Agricultural Lands
(PALs) were determined using Geographic information Systems
(GIS). Procedure was applied considering that “Other” class of
LULC map may be used for agricultural purposes in the future
properties. Overlaying analysis was conducted using Slope (S), Land
Use Capability Class (LUCC), Other Soil Properties (OSP) and Land
Use Capability Sub-Class (SUBC) properties.
A total of 901.62 ha areas within “Other” class (15798.2 ha) of
LULC map were determined as PALs. These lands were ranked as
“Very Suitable”, “Suitable”, “Moderate Suitable” and “Low
Suitable”. It was determined that the 8.03 ha were classified as “Very
Suitable” while 18.59 ha as suitable and 11.44 ha as “Moderate
Suitable” for PALs. In addition, 756.56 ha were found to be “Low
Suitable”. The results obtained from this preliminary study can serve
as basis for further studies.
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: Maize constitutes a major agrarian production for use
by the vast population but despite its economic importance; it has not
been produced to meet the economic needs of the country. Achieving
optimum yield in maize can meaningfully be supported by land
suitability analysis in order to guarantee self-sufficiency for future
production optimization. This study examines land suitability for
maize production through the analysis of the physicochemical
variations in soil properties and other land attributes over space using
a Geographic Information System (GIS) framework.
Physicochemical parameters of importance selected include slope,
landuse, physical and chemical properties of the soil, and climatic
variables. Landsat imagery was used to categorize the landuse,
Shuttle Radar Topographic Mapping (SRTM) generated the slope and
soil samples were analyzed for its physical and chemical components.
Suitability was categorized into highly, moderately and marginally
suitable based on Food and Agricultural Organisation (FAO)
classification, using the Analytical Hierarchy Process (AHP)
technique of GIS. This result can be used by small scale farmers for
efficient decision making in the allocation of land for maize
production.
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: 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: Satellite images interpretation and analysis assist geologists by providing valuable information about geology and minerals of an area to be surveyed. A test site in Fatejang of district Attock has been studied using Landsat ETM+ and ASTER satellite images for lithological mapping. Five different supervised image classification techniques namely maximum likelihood, parallelepiped, minimum distance to mean, mahalanobis distance and spectral angle mapper have been performed upon both satellite data images to find out the suitable classification technique for lithological mapping in the study area. Results of these five image classification techniques were compared with the geological map produced by Geological Survey of Pakistan. Result of maximum likelihood classification technique applied on ASTER satellite image has highest correlation of 0.66 with the geological map. Field observations and XRD spectra of field samples also verified the results. A lithological map was then prepared based on the maximum likelihood classification of ASTER satellite image.
Abstract: Structural interpretation of aeromagnetic data and Landsat imagery over the Middle Benue Trough was carried out to determine the depth to basement, delineate the basement morphology and relief, and the structural features within the basin. The aeromagnetic and Landsat data were subjected to various image and data enhancement and transformation routines. Results of the study revealed lineaments with trend directions in the N-S, NE-SW, NWSE and E-W directions, with the NE-SW trends been dominant. The depths to basement within the trough were established to be at 1.8, 0.3 and 0.8km, as shown from the spectral analysis plot. The Source Parameter Imaging (SPI) plot generated showed the centralsouth/ eastern portion of the study area as being deeper in contrast to the western-south-west portion. The basement morphology of the trough was interpreted as having parallel sets of micro-basins which could be considered as grabens and horsts in agreement with the general features interpreted by early workers.
Abstract: This paper regards the phenomena of intensive suburbanization and urbanization in Olomouc city and in Olomouc region in general for the period of 1986–2009. A Remote Sensing approach that involves tracking of changes in Land Cover units is proposed to quantify the urbanization state and trends in temporal and spatial aspects. It actually consisted of two approaches, Experiment 1 and Experiment 2 which implied two different image classification solutions in order to provide Land Cover maps for each 1986–2009 time split available in the Landsat image set. Experiment 1 dealt with the unsupervised classification, while Experiment 2 involved semi- supervised classification, using a combination of object-based and pixel-based classifiers. The resulting Land Cover maps were subsequently quantified for the proportion of urban area unit and its trend through time, and also for the urban area unit stability, yielding the relation of spatial and temporal development of the urban area unit. Some outcomes seem promising but there is indisputably room for improvements of source data and also processing and filtering.
Abstract: Retrieval of the surface reflectance is important in the
remotely sensed data analysis to obtain the atmospheric reflectance or
atmospheric correction. The relationship between visible and mid
infrared reflectance over land was investigated and developed in this
study. The surface reflectances of the two visible bands were
measured using a handheld spectroradiometer collected around
Penang Island. In this study, we use the assumption that the 2.1 μm
band is not affected by aerosol and it is transparent to most aerosol
types (except dust). Therefore the satellite observed signal is the
same as the surface signal in 2.1 μm band. The correlation between
the surface reflectance measured by the spectroradiometer in the blue
and red region and the 2.1 μm observed by the satellite has been
established. We investigate five dates of Landsat TM scenes in this
study. The finding obtained by this study indicates that the surface
reflectance can be retrieved from the 2.1 μm band.
Abstract: One of the most important parameters to develop and
manage urban areas is appropriate selection of land surface to
develop green spaces in these areas. In this study, in order to identify
the most appropriate sites and areas cultivated for ornamental species
in Jiroft, Landsat Enhanced Thematic Mapper Plus (ETM+) images
due to extract the most important effective climatic and adaphic
parameters for growth ornamental species were used. After geometric
and atmospheric corrections applied, to enhance accuracy of multi
spectral (XS) bands, the fusion of Landsat XS bands by IRS-1D
panchromatic band (PAN) was performed. After field sampling to
evaluate the correlation between different factors in surface soil
sampling location and different bands digital number (DN) of ETM+
sensor on the same points, correlation tables formed using the best
computational model and the map of physical and chemical
parameters of soil was produced. Then the accuracy of them was
investigated by using kappa coefficient. Finally, according to
produced maps, the best areas for cultivation of recommended
species were introduced.
Abstract: Megalopolis is a group of densely populated metropolitan areas that combine to form an urban complex. Since China introduced the economic reforms in late 1970s, the Chinese urban system has experienced unprecedented growth. The process of urbanisation prevailed in the 1980s, and the process of predominantly large city growth appeared to continue through 1990s and 2000s. In this study, the magnitude and pattern of urbanisation in China during 1990s were examined using remotely sensed imagery acquired by TM/ETM+ sensor onboard the Landsat satellites. The development of megalopolis areas in China was also studied based on the GIS analysis of the increases of urban and built-up area from 1990 to 2000. The analysis suggests that in the traditional agricultural zones in China, e.g., Huang-Huai-Hai Plains, Changjiang River Delta, Pearl River Delta and Sichuan Basin, the urban and built-up areas increased by 1.76 million hectares, of which 0.82 million hectares are expansion of urban areas, an increase of 24.78% compared with 1990 at the national scale. The Yellow River Delta, Changjiang River Delta and Pearl River Delta also saw an increase of urban and built-up area by 63.9%, 66.2% and 83.0% respectively. As a result, three major megalopolises were developed in China: the Guangzhou-Shenzhen-Hong Kong- Macau (Pearl River Delta: PRD) megalopolis area, the Shanghai- Nanjing-Hangzhou (Changjiang River Delta: CRD) megalopolis area and the Beijing-Tianjing-Tangshan-Qinhuangdao (Yellow River Delta-Bohai Sea Ring: YRD) megalopolis area. The relationship between the processed of megalopolisation and the inter-provincial population flow was also explored in the context of social-economic and transport infrastructure development in Post-reform China.
Abstract: Land surface temperature (LST) is an important
parameter to study in urban climate. The understanding of the
influence of biophysical factors could improve the establishment of
modeling urban thermal landscape. It is well established that climate
hold a great influence on the urban landscape. However, it has been
recognize that climate has a low priority in urban planning process,
due to the complex nature of its influence. This study will focus on
the relatively cloud free Landsat Thematic Mapper image of the study
area, acquired on the 2nd March 2006. Correlation analyses were
conducted to identify the relationship of LST to the biophysical
factors; vegetation indices, impervious surface, and albedo to
investigate the variation of LST. We suggest that the results can be
considered by the stackholders during decision-making process to
create a cooler and comfortable environment in the urban landscape
for city dwellers.
Abstract: In order to develop forest management strategies in
tropical forest in Malaysia, surveying the forest resources and
monitoring the forest area affected by logging activities is essential.
There are tremendous effort has been done in classification of land
cover related to forest resource management in this country as it is a
priority in all aspects of forest mapping using remote sensing and
related technology such as GIS. In fact classification process is a
compulsory step in any remote sensing research. Therefore, the main
objective of this paper is to assess classification accuracy of
classified forest map on Landsat TM data from difference number of
reference data (200 and 388 reference data). This comparison was
made through observation (200 reference data), and interpretation
and observation approaches (388 reference data). Five land cover
classes namely primary forest, logged over forest, water bodies, bare
land and agricultural crop/mixed horticultural can be identified by
the differences in spectral wavelength. Result showed that an overall
accuracy from 200 reference data was 83.5 % (kappa value
0.7502459; kappa variance 0.002871), which was considered
acceptable or good for optical data. However, when 200 reference
data was increased to 388 in the confusion matrix, the accuracy
slightly improved from 83.5% to 89.17%, with Kappa statistic
increased from 0.7502459 to 0.8026135, respectively. The accuracy
in this classification suggested that this strategy for the selection of
training area, interpretation approaches and number of reference data
used were importance to perform better classification result.
Abstract: This study aimed at developing visualization tools for integrating CloudSat images and Water Vapor Satellite images. KML was used for integrating data from CloudSat Satellite and GMS-6 Water Vapor Satellite. CloudSat 2D images were transformed into 3D polygons in order to achieve 3D images. Before overlaying the images on Google Earth, GMS-6 water vapor satellite images had to be rescaled into linear images. Web service was developed using webMathematica. Shoreline from GMS-6 images was compared with shoreline from LandSat images on Google Earth for evaluation. The results showed that shoreline from GMS-6 images was highly matched with the shoreline in LandSat images from Google Earth. For CloudSat images, the visualizations were compared with GMS-6 images on Google Earth. The results showed that CloudSat and GMS-6 images were highly correlated.
Abstract: An end-member selection method for spectral unmixing that is based on Particle Swarm Optimization (PSO) is developed in this paper. The algorithm uses the K-means clustering algorithm and a method of dynamic selection of end-members subsets to find the appropriate set of end-members for a given set of multispectral images. The proposed algorithm has been successfully applied to test image sets from various platforms such as LANDSAT 5 MSS and NOAA's AVHRR. The experimental results of the proposed algorithm are encouraging. The influence of different values of the algorithm control parameters on performance is studied. Furthermore, the performance of different versions of PSO is also investigated.
Abstract: This paper presents a new system developed in Java®
for pattern recognition and pattern summarisation in multi-band
(RGB) satellite images. The system design is described in some
detail. Results of testing the system to analyse and summarise
patterns in SPOT MS images and LANDSAT images are also
discussed.