Rajesh Bahadur Thapa

Examining Spatiotemporal Urbanization Patterns in Kathmandu Valley, Nepal: Remote Sensing and Spatial Metrics Approaches

This paper examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on 33-years of time series data compiled from satellite images. Along with new developments within the city fringes and rural villages in the valley, shifts in the natural environment and newly developed socioeconomic strains between residents are emerging. A highly dynamic spatial pattern of urbanization is observed in the valley. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The urbanization process has developed fragmented and heterogeneous land use combinations in the valley. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades.

Comparative Assessment of Supervised Classifiers for Land Use–Land Cover Classification in a Tropical Region Using Time-Series PALSAR Mosaic Data

Numerous classification algorithms have been proposed to create accurate classification maps using optical remote sensing data. However, few comparative studies evaluate the performance of classification algorithms with focus on tropical forests due to cloud effects. Advances in synthetic aperture radar (SAR) techniques and spatial resolution, mapping, and comparison of classification algorithms are possible. This research investigated the accuracy and processing speeds of five supervised classifiers, including Naïve Bayes, AdaBoost, multi-layer perceptron, random forest (RF), and support vector machine, for land use-land cover (LULC) classification in a tropical region using time-series Advanced Land Observing Satellite-phased array type L-band SAR (ALOS-PALSAR) 25-m mosaic data. The study area is located in central Sumatra, Indonesia, where abundant forest-related carbon stocks exist. This investigation was intended to aid the implementation of a classification algorithm for the automatic creation of LULC classification maps. We perform object-based and pixel-based analyses to investigate the ability of the classifiers and their accuracies, respectively. RF had the best classification accuracy and processing speed in which the accuracies for 10 classes and 2 classes were 64.07% and 90.22% for pixel-based and 82.94% and 86.23% for object-based evaluations, respectively. These results indicate that RF is a useful classifier for the analysis of PALSAR mosaic data and that the automatic creation of highly accurate classification maps is possible by using time-series data. The outcome of this research will be valuable resources for biodiversity and global-warming mitigation efforts in the region.

SAR interferometry using ALOS-2 PALSAR-2 data for the Mw 7.8 Gorkha, Nepal earthquake

The Advanced Land Observing Satellite-2 (ALOS-2, “DAICHI-2”) has been observing Nepal with the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) in response to an emergency request from Sentinel Asia related to the Mw 7.8 Gorkha earthquake on April 25, 2015. PALSAR-2 successfully detected not only avalanches and local crustal displacements but also continental-scale deformation. Especially, by the use of the ScanSAR mode, we are able to make interferograms that cover the entire displacement area of the earthquake. However, we did encounter some fundamental problems with the ScanSAR and incorrect settings of PALSAR-2 operation that have now been fixed. They include (1) burst overlap misalignment between two ScanSAR observations, which limits the number of pairs available and the quality of the interferogram, (2) non-crustal fringes which are derived from co-registration error and/or ionospheric effect and, (3) incorrect setting of the center frequency in the Stripmap beam F2-6. In this paper, we describe their problems and solutions. The number of interferometric pairs are limited by (1) and (3). The accuracy of the interferograms are limited by (2) and (3). The experimental results showed that current solutions for (2) and (3) work appropriately.

Multidisciplinary cooperation in gis education: A case study of us colleges and universities

This paper examines the degree of multidisplinary cooperation for Geographic Information Science (GIS) education programs that award GIS-related degrees or certificates at US colleges and universities. We classified departments and courses into ten major disciplines using Dewey Decimal Classification. In the 2007–2008 academic year, approximately 40 per cent of GIS education programs related to multiple disciplines and nearly 20 per cent were involved with more than three disciplines. Geography was the major provider of GIS education programs, but the ratio between geography-related discipline and other disciplines combined was approximately 1:3. Fostering multidisciplinary GIS education programs should strengthen geography in general as well as GIS education.

Calibration of Aboveground Forest Carbon Stock Models for Major Tropical Forests in Central Sumatra Using Airborne LiDAR and Field Measurement Data

Despite substantial policy attention, tropical forests in Southeast Asian region are releasing large amount of carbon to the atmosphere due to accelerating deforestation. Accurately determining forest statistics and characterizing aboveground forest carbon stocks (AFCSs) are always challenging in the region. In order to develop more accurate estimates of AFCS, the present study collected airborne LiDAR and field measurements data and calibrated AFCS models to estimate carbon stock in the tropical forests in central Sumatra. The study region consists of natural forests, including peat swamp, dry moist, regrowth, and mangrove, and plantation forests, including rubber, acacia, oil palm, and coconut. To cover the different forest types, 60 field plots of 1 ha in size were inventoried. Eight transects crossing these field plots were acquired to calibrate the LiDAR to AFCS models. The AFCS values for the field plots ranged from 4 to 161 Mg ha-1. General models were fitted without considering forest types, whereas a specific model was fitted for each specific forest type. Five alternative general models with different LiDAR metrics were calibrated with model performance expressed as R2 ranging from 0.73 to 0.87 and root-meansquare error (RMSE) values ranging from 17.4 to 25.0 Mg ha-1 . Seven forest-specific AFCS models were calibrated for different forest types, with R2 values ranging from 0.72 to 0.97 and RMSE values ranging from 1.4 to 10.7 Mg ha-1. The performance of each model was cross-validated by iteratively removing one data point. While forest-specific models provide better AFCS estimates, the general models are still useful when forest types are ambiguous.

Multitemporal Fluctuations in L-Band Backscatter From a Japanese Forest

The temporal variations (diurnal and annual) in arboreal (ε_Tree) and bare soil (ε_Soil) dielectric constants and their correlation with precipitation were examined for several trees in Japan. A significant (1 σ (standard deviation) and 2 σ) ε_Tree increase is observed after rainfall at 89.8% and 90.5% probability. However, rainfall does not always induce significant ε_Tree increases. Rainfall of more than 5 mm/day can induce 1 σ ε_Tree Tree increase at a 59.6% probability. In order to examine whether the increase in εTree affects the L-band σ⁰ variation in a forest, the four-year temporal variation of the L-band backscattering coefficient (σ⁰) was estimated from observations by the Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar. Observed maximum absolute deviations from the mean over the forest area were 1.0 and 1.2 dB for σ_HH⁰ and σ_HV⁰, respectively, and 4.0 and 3.0 dB over open land. σ⁰ and rainfall correlations show that ε_Tree and σ_Forest⁰ are proportional to precipitation integrated over seven or eight days; ε_Soil and σ_{Open land}⁰ are proportional to precipitation integrated over three days. This finding indicates that ε_Tree variations influence σ_{Forest areas}⁰. A stronger correlation between σ_HV⁰ and precipitation is observed in several sites with low σ_HV⁰, where less biomass is expected, and several sites with high σ_HV⁰, where more biomass is expected. A weaker correlation between σ_HV⁰ and precipitation is observed for several sites with high σ_HV⁰. These differences may be explained by the different contributions of double bounce scattering and potential transpiration, which is a measure of the ability of the atmosphere to remove water from the surface through the processes of transpiration. The two other results were as follows: 1) The functional relation between aboveground biomass and σ⁰ showed dependence on precipitation data, this being an effect connected with seasonal changes of the ε_Tree. This experiment reinforces the fact that the dry season is preferable for retrieval of woody biomass from inversion of the functional dependence of SAR backscatter and for avoiding the influence of rainfall. 2) The complex dielectric constant for a tree trunk, which is measured between 0.2 and 6 GHz, indicates that free water is dominant in the measured tree.

Dependency of forest biomass on full Polarimetric parameters obtained from L-band SAR data for a natural forest in Indonesia

Aboveground (AG)-biomass was estimated from a field biomass collection and LiDAR observations for a natural forest in Indonesia. The derived AG-biomass data were plotted against full polarimetric parameters calculated from Polarimetric and Interferometric Airborne Synthetic Aperture Radar L2 (Pi-SAR-L2) and PALSAR data. The α°-AG-biomass curve shows saturation by around 100 tons/ha, while for entropy, correlation is observed up to 200 tons/ha. The largest coefficient of determination (R2 = 0.2348) was observed for the range with AG-biomass of more than 100 tons/ha for the relation between AG-biomass and entropy. The α°HV-biomass plot derived from PALSAR data shows smaller variance in the dry season than in other seasons, indicating that dry season data is preferable for a more accurate estimate of AG-biomass.

Spatiotemporal Patterns of Urbanization: Mapping, Measurement, and Analysis

This chapter examines the spatiotemporal pattern of urbanization in Kathmandu Valley using remote sensing and spatial metrics techniques. The study is based on time series data compiled from satellite images acquired in the last four decades. A five-step hybrid technique is presented to create land use and land cover maps from remote sensing imagery. Urban built-up areas had a slow trend of growth in the 1960s and 1970s but have grown rapidly since the 1980s. The metrics of the urbanization process has confirmed that the landscape in the valley consists of fragmented and heterogeneous land use combinations. However, the refill type of development process in the city core and immediate fringe areas has shown a decreasing trend in the neighborhood distances between land use patches, and an increasing trend towards physical connectedness, which indicates a higher probability of homogenous landscape development in the upcoming decades.

Examining High-Resolution PiSAR-L2 Textures for Estimating Tropical Forest Carbon Stocks

This study examines the potential of airborne PiSARL2 data for estimating forest carbon stocks in central Sumatra. Polarimetric interferometric synthetic aperture radar L-band-2 (PiSAR-L2) is a second-generation airborne sensor developed by JAXA. We acquired full-polarimetric data at a fine spatial resolution of 2.5 m during the PiSAR-L2 flight campaign in August 2012. A total of 59 field measurement plots for aboveground forest carbon stocks (AFCSs) were established in same year where AFCS ranged between 4.8 and 253.5 Mg C ha-1. The plots comprised natural and plantation forests. These plot-level field data were used for calibrating and validating AFCS estimation models with the SAR data. Various possibilities including direct sigma naught backscatters and their ratios and various types of textures obtained from HH, HV, and VV polarizations were examined by applying regression modeling. The main indicators used for the selection of best potential models in the calibration phase were R2, variable inflation factor (VIF), p-value, and root-meansquared errors (RMSEs). The potential models were validated using the leave-one-out (LOO) method. The results indicated that a simple combination of backscatters and their ratios provides an AFCS estimate with an RMSE of 42.37 Mg C ha-1 and an R2 of 0.65. Inclusion of SAR textural parameters improved the AFCS estimates with an RMSE of 30.93 Mg C ha-1 and an R2 of 0.80. This indicates that the airborne PiSAR-L2 full-polarimetric data have the potential to estimate forest carbon stocks with an improved accuracy in the tropical region.

Geographically Weighted Regression in Geospatial Analysis

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial non-stationarity. The assumption in GWR is that observations nearby have a greater influence on parameter estimates than observations at a greater distance. This is very close to Tobler’s first law of geography—everything is related to everything else, but near things are more related than distant things (Tobler 1970). GWR was developed on the basis of the traditional regression framework which incorporates local spatial relationships into the framework in an intuitive and explicit manner (Brunsdon et al. 1996; Fotheringham and Brunsdon 1999; Fotheringham et al. 2002).