An effective visualization and comparison of online terrain draped with multi-sensor satellite images

The objective of this paper is to discuss the effectiveness of visualizing online 3D terrain draped with different satellite imageries. The topographic data of the study area were obtained from the contour maps of Universiti Putra Malaysia, Selangor, Malaysia. The high resolution satellite imageries used in this project involving QUICKBIRD (0.6 m resolution), IKONOS (1 m resolution), and SPOT5 (5 m resolution). R2V software was used for editing the contour data, whereas Arc GIS was used for overlaying the imageries over the 3D terrain data. Then the data were exported into Virtual Reality Markup Language to compare the effectiveness of different satellite imageries based on the data file size, imageries size, number of images tile, loading time during office hours (from 8 a.m. to 5 p.m.) and out of office hours (after 5 p.m.), frame rate per second, and visualization quality. The results revealed that IKONOS satellite imageries are better for an effective online 3D terrain visualization utilizing GIS data even though it has lower resolution compared to QUICKBIRD.     

Regional precipitation trend analysis at the Langat River Basin,Selangor, Malaysia

Various hydrological and meteorological variables such as rainfall and temperature have been affected by global climate change. Any change in the pattern of precipitation can have a significant impact on the availability of water resources, agriculture, and the ecosystem. Therefore, knowledge on rainfall trend is an important aspect of water resources management. In this study, the regional annual and seasonal precipitation trends at the Langat River Basin, Malaysia, for the period of 1982–2011 were examined at the 95 % level of significance using the regional average Mann–Kendall (RAMK) test and the regional average Mann–Kendall coupled with bootstrap (RAMK–bootstrap) method. In order to identify the homogeneous regions respectively for the annual and seasonal scales, firstly, at-site mean total annual and separately at-site mean total seasonal precipitation were spatialized into 5 km?×?5 km grids using the inverse distance weighting (IDW) algorithm. Next, the optimum number of homogeneous regions (clusters) is computed using the silhouette coefficient approach. Next, the homogeneous regions were formed using the K-mean clustering method. From the annual scale perspective, all three regions showed positive trends. However, the application of two methods at this scale showed a significant trend only in the region AC1. The region AC2 experienced a significant positive trend using only the RAMK test. On a seasonal scale, all regions showed insignificant trends, except the regions I1C1 and I1C2 in the Inter-Monsoon 1 (INT1) season which experienced significant upward trends. In addition, it was proven that the significance of trends has been affected by the existence of serial and spatial correlations.     

Livelihood diversification as an adaptation approach to change in the pastoral Hindu-Kush Himalayan region

Migratory livestock raising has been one of the most important livelihood options for people residing in high mountain areas and has made a significant contribution to the economy of the Hindu Kush Himalayan（HKH） region, both in terms of supporting households and in export earnings. However, in recent decades, changes in the socioeconomic situation and increasing climate variability have led to a need to enhance adaptation by building the resilience of local socio-ecological systems, including economic diversification and sustainable management of natural resources. Based on semistructured interviews with pastoral communities in six countries within the pastoral HKH region, this paper discusses the situation, trends and driving forces behind the diversification of pastoral livelihood. For internal diversification the study highlights the need for enhancement of pastoral livelihoods through value-adding activities in the pastoral sectors. For external diversification changes on policy changes are needed to support free out-migration, market exploitation, and multiple resource use. Finally the paper highlights the role of education in determining household adaptation strategies in the face of various socio-ecological pressures and recommends integrating innovative and indigenous knowledge to develop appropriate methods for risk management and resource management in the pastoral HKH region.     

Estimation of Wheat/Rice Residue Burning Areas in Major Districts of Haryana,India, Using Remote Sensing Data

Haryana has emerged as an important state for Rice & Wheat production in India contributing significantly in the central pool. Mechanized combine harvesting technologies, which have become common in Rice Wheat System (RWS) in India, leave behind large quantities of straw in the field for open burning of residue. Besides causing pollution, the burning kills the useful micro flora of the soil causing soil degradation. There is no field survey (Girdawari) data available with the Government for the areas where stubble burning is taking place. The present paper describes the methodology and results of wheat and rice residue burning areas for three districts of Haryana namely Kaithal, Kurukshetra and Karnal for the year 2010 using complete enumeration approach of multi-date IRS-P6 AWiFS and LISS-III data. In season ground truth was collected using hand held GPS and used to identify area of burnt wheat/rice residues, associated crops and land features. After geo-referencing the satellite images, district images were masked-out and multi-date image data stacks were created. Normalized Difference Vegetation Index (NDVI) of each date was generated and used at the time of classification along with other spectral bands. The non-agricultural classes in the image included: forest, wasteland, water bodies, urban/settlement and permanent vegetation etc. The vector of these non-agriculture classes were extracted from the land use, imported and mask was generated. During the classification non-agriculture area was excluded by using mask of these classes. From this the agricultural area could be separated out. The area was estimated by computing pixels under the classified image mask. In season multi-date AWiFS data along with available single-date LISS-III data between third week of April to last week of May are found to be useful for estimation of wheat residue burning areas estimation. The data between second week of October to last week of November is useful for estimation of rice residue burning areas estimation at district level.     

A geospatial assessment of flood hazard in north-eastern depressed basin,Bangladesh

Floods are a frequently occurring calamity in deltaic Bangladesh. This paper aims to assess the temporal expansion of waterbodies during flooding using geospatial techniques. Several water indices were applied to classify the satellite images at various temporal scales. Among them, the Normalized Difference Water Index (NDWI) showed the highest correlation (r = 0.831; where p = 0.01) with rainfall data. Specifically, the NDWI results showed that perennial waterbodies measured 37 km² and 60 km² in Sunamganj District in 2017 and 2019, respectively. The area of waterbodies notably increased 52-fold from March to April (37 km² to 1958 km²) during the pre-monsoon flash flood of 2017. During the July 2019 monsoon flood, waterbodies started to extend after May and flooded 2784 km² in area. NDVI analysis showed that in 2019, floodwater submerged 361.7 km² of vegetation cover. At the same time, the Surma River's flooding resulted in a 73.9 per cent inundation of the total area of the Sunamganj District. We hope that this study will provide better understanding of the varying nature of floods that occur in the low lying bowl shaped Haor region which will in turn assist the government with flood mitigation.     

FAO 56 Model and Remote Sensing for the Estimation of Crop-Water Requirement in Main Branch Canal of the Bhadra Command area, Karnataka State

Remote sensing and FAO 56 crop water model are used for estimating crop water requirement for paddy crop located in the main branch canal of Bhadra Command Area in Karnataka, India. The estimation of crop-water requirement depends on the meteorological factors, soil type and crop coefficients. The result obtained showed that water requirements of rabi crops higher than those of the kariff crops. The total irrigated area estimated from the IRS image is 29,353 ha. It is found that the total paddy crop acreage is 18,257 ha covering 62 % in the total irrigated area of the command area, Arecanut 20 %, coconut 15 % and sugarcane with other crops 3 %. The water requirement for paddy is 1180.4 mm for its entire growth period. The total water requirement for irrigation supply for crops in the entire command area is 5,790 at a demand of 0.10501 cusecs per ha.     

Characteristics of narrow bipolar pulses observed in Malaysia

Narrow bipolar pulses (NBPs) are considered as isolated intracloud events with higher peak amplitude and strong high frequency emission compared to the first return strokes and other intracloud discharges. From 182 NBPs recorded in Malaysia in the tropic, 75 were narrow negative bipolar pulses (NNBPs) while 107 were narrow positive bipolar pulses (NPBPs). The mean duration of NNBPs was 24.6±17.1 μs, while 30.2±12.3 μs was observed for NPBPs. The mean full-width at half-maximum (FWHM) was 2.2±0.7 and 2.4±1.4 μs for NNBPs and NPBPs, respectively. The mean peak amplitude of NPBPs normalized to 100 km was 22.7 V/m, a factor of 1.3 higher than that of NNBPs which is 17.6 V/m. In contrast to the previous studies, it was observed that the electric field change was characterized by a bipolar pulse with a significant amount of fine structures separated by a few tens of nanoseconds intervals, embedded on it.     

Comparison of BIAS correction techniques for GPCC rainfall data in semi-arid climate

Long-term historical precipitation data are important in developing metrics for studying the impacts of past hydrologic events (e.g., droughts) on water resources management. Many geographical regions around the world often witness lack of long term historical observation and to overcome this challenge, Global Precipitation Climatology Center (GPCC) datasets are found to be useful. However, the GPCC data are available at coarser scale (0.5° resolution), therefore bias correction techniques are often applied to generate local scale information before it can be applied for decision making activities. The objective of this study is to evaluate and compare five different bias correction techniques (BCT’s) to correct the GPCC data with respect to rain gauges in Iraq, which is located in a semi-arid climatic zone. The BCT’s included in this study are: Mean Bias-remove (B) technique, Multiplicative Shift (M), Standardized-Reconstruction (S), Linear Regression (R), and Quantile Mapping (Q). It was observed that the Performance Index (PI) of BCT’s differs in space (i.e., precipitation pattern) and temporal scale (i.e., seasonal and monthly). In general, the PI for the Q and B were better compared to other three (M, S and R) bias correction techniques. Comparatively, Q performs better than B during wet season. However, both these techniques performed equally well during average rainy season. This study suggests that instead of using a single bias correction technique at different climatic regimes, multiple BCT’s needs to be evaluated for identifying appropriate methodology that suits local climatology.