Urban heat island effects are well known nowadays and observed in cities throughout the World. The main reason behind the effects of urban heat island (UHI) is the transformation of land use/ land cover, and this transformation is associated with UHI through different actions: i) removal of vegetated areas, ii) land reclamation from sea/river, iii) construction of new building as well as other concrete structures, and iv) industrial and domestic activity. In rapidly developing cities, urban heat island effects increases very hastily with the transformation of vegetated/ other types of areas into urban surface because of the increasing population as well as for economical activities. In this research the effect of land use/ land cover on urban heat island was investigated in two growing cities in Asia i.e. Singapore and Johor Bahru, (Malaysia) using 10 years data (from 1997 to 2010) from Landsat TM/ETM+. Multispectral visible band along with indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Build Index (NDBI), and Normalized Difference Bareness Index (NDBaI) were used for the classification of major land use/land cover types using Maximum Likelihood Classifiers. On the other hand, land surface temperature (LST) was estimated from thermal image using Land Surface Temperature algorithm. Emissivity correction was applied to the LST map using the emissivity values from the major land use/ land cover types, and validation of the UHI map was carried out using in situ data. Results of this research indicate that there is a strong relationship between the land use/land cover changes and UHI. Over this 10 years period, significant percentage of non-urban surface was decreased but urban heat surface was increased because of the rapid urbanization. With the increase of UHI effect it is expected that local urban climate has been modified and some heat related health problem has been exposed, so appropriate measure should be taken in order to reduce UHI effects as soon as possible.
Forest biomass estimation is essential for greenhouse gas inventories, terrestrial carbon accounting and climate change
modeling studies. Although a lot of efforts have been made in estimating biomass using both field-based and remote
sensing techniques, no universal and transferable technique has been developed so far to quantify biomass carbon
sources and sinks due to the complexity of the environmental, topographic and biophysical characteristics of forest
ecosystems. This study investigated the potential of SAR (RADARSAT-2 dual polarizations) and optical (AVNIR-2)
image fusion for biomass estimation using wavelet transform. Six different types of wavelets (haar, daubechies, symlet,
coiflet, biorthogonal and discrete meyer) were tested with different rules and three decomposition levels for four
different image combinations of SAR and optical data. The highest accuracy (r) of 0.84 was obtained from the fusion of
NIR and HV polarization data, compared to 0.70 (r) from the NIR band alone. The results indicated a substantial
improvement of biomass estimation from the fused images, and this accuracy is very promising, especially when using
only one fused image in the high biomass situation of the study area, and gives a clear message to the research
community that biomass estimation can be improved using the fusion of SAR and optical data due to their
complementary information. Furthermore this fusion process can significantly reduce the saturation problem of optical
and SAR data for biomass estimation.
Oceans play a significant role in the global carbon cycle and climate change, and the most importantly it is a reservoir for
plenty of protein supply, and at the center of many economic activities. Ocean health is important and can be monitored
by observing different parameters, but the main element is the phytoplankton concentration (chlorophyll–a
concentration) because it is the indicator of ocean productivity. Many methods can be used to estimate chlorophyll–a
(Chl-a) concentration, among them, remote sensing technique is one of the most suitable methods for monitoring the
ocean health locally, regionally and globally with very high temporal resolution.
In this research, long term ocean health monitoring was carried out at the Bay of Bengal considering three facts i.e. i)
very dynamic local weather (monsoon), ii) large number of population in the vicinity of the Bay of Bengal, and iii) the
frequent natural calamities (cyclone and flooding) in and around the Bay of Bengal. Data (ten years: from 2001 to 2010)
from SeaWiFS and MODIS were used. Monthly Chl–a concentration was estimated from the SeaWiFS data using OC4
algorithm, and the monthly sea surface temperature was obtained from the MODIS sea surface temperature (SST) data.
Information about cyclones and floods were obtained from the necessary sources and in-situ Chl–a data was collected
from the published research papers for the validation of Chl-a from the OC4 algorithm. Systematic random sampling was
used to select 70 locations all over the Bay of Bengal for extracting data from the monthly Chl-a and SST maps. Finally
the relationships between different aspects i.e. i) Chl-a and SST, ii) Chl-a and monsoon, iii) Chl-a and cyclones, and iv)
Chl-a and floods were investigated monthly, yearly and for long term (i.e 10 years). Results indicate that SST, monsoon,
cyclone, and flooding can affect Chl-a concentration but the effect of monsoon, cyclone, and flooding is temporal, and
normally reduces over time. However, the effect of SST on Chl-a concentration can't be minimized very quickly
although the change of temperature over this period is not very large.