Institute of Remote Sensing and Digital Earth

Research on flood disaster generate ideas and provoke the best solution for disaster management. This work primarily focuses research on monsoon flood due to its frequency and severity in the southern flood plain of Nepal. Here we review the previous studies on flood disaster at the regional and national level and compare with the global context. This facilitates exploring the data and methods that are mostly unexplored, and areas that have not lightened in the field of flood studies in Nepal. Our scope of literature review limited the literature that are accessed through internet. The findings are revised and compared with different contexts. Multi-criteria weighted arithmetic mean have been used to find the spatial severity of flood disaster in 2017. We found several studies carried out on flood in Nepal. They are mostly based on field-based data, except few that have used current state-of-art, remote sensing method, using satellite images. Since the multi-spectral optical satellite imageries have a high cloud effect, it is not very useful in real time flood mapping; and very limited Synthetic-Aperture Radar (SAR) image, has been used in Nepal. In Global context, Support Vector Machine and Random Forest method are used in flood risk assessment; VNG flood V1.0 software has been used in flood forecasting, and Probabilistic Change Detection and Thresholding have widely been used in flood research, which can also be adopted in Nepalese context.

The Himalayan region, a major source of fresh water, is recognized as a water tower of the world. Many perennial rivers originate from Nepal Himalaya, located in the central part of the Himalayan region. Snowmelt water is essential freshwater for living, whereas it poses flood disaster potential, which is a major challenge for sustainable development. Climate change also largely affects snowmelt hydrology. Therefore, river discharge measurement requires crucial attention in the face of climate change, particularly in the Himalayan region. The snowmelt runoff model (SRM) is a frequently used method to measure river discharge in snow-fed mountain river basins. This study attempts to investigate snowmelt contribution in the overall discharge of the Budhi Gandaki River Basin (BGRB) using satellite remote sensing data products through the application of the SRM model. The model outputs were validated based on station measured river discharge data. The results show that SRM performed well in the study basin with a coefficient of determination (R 2 ) >0.880. Moreover, this study found that the moderate resolution imaging spectroradiometer (MODIS) snow cover data and European Centre for Medium-Range Weather Forecasts (ECMWF) meteorological datasets are highly applicable to the SRM in the Himalayan region. The study also shows that snow days have slightly decreased in the last three years, hence snowmelt contribution in overall discharge has decreased slightly in the study area. Finally, this study concludes that MOD10A2 and ECMWF precipitation and two-meter temperature products are highly applicable to measure snowmelt and associated discharge through SRM in the BGRB. Moreover, it also helps with proper freshwater planning, efficient use of winter water flow, and mitigating and preventive measures for the flood disaster.

Global warming threatens ecosystem functions, biodiversity, and rangeland productivity in Mongolia. The study analyzes the spatial and temporal distributions of the Net Primary Production (NPP) and its response to climatic parameters. The study also highlights how various land cover types respond to climatic fluctuations from 2003 to 2018. The Boreal Ecosystem Productivity Simulator (BEPS) model was used to simulate the rangeland NPP of the last 16 years. Satellite remote sensing data products were mainly used as input for the model, where ground-based and MODIS NPP were used to validate the model result. The results indicated that the BEPS model was moderately effective (R2 = 0.59, the Root Mean Square Error (RMSE) = 13.22 g C m−2) to estimate NPP for Mongolian rangelands (e.g., grassland and sparse vegetation). The validation results also showed good agreement between the BEPS and MODIS estimates for all vegetation types, including forest, shrubland, and wetland (R2 = 0.65). The a...

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GB...

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (...

Understanding the response of terrestrial ecosystems to future climate changes would substantially contribute to the scientific assessment of vegetation–climate interactions. Here, the spatiotemporal distribution and dynamics of vegetation in China were projected and compared based on comprehensive sequential classification system (CSCS) model under representative concentration pathway (RCP) RCP2.6, RCP4.5, and RCP8.5 scenarios, and five sensitivity levels were proposed. The results show that the CSCS model performs well in simulating vegetation distribution. The number of vegetation types would increase from 36 to 40. Frigid–perhumid rain tundra and alpine meadow are the most distributed vegetation types, with an area of more than 78.45 × 104 km2, whereas there are no climate conditions suitable for tropical–extra-arid tropical desert in China. Some plants would benefit from climate changes to a certain extent. Warm temperate–arid warm temperate zone semidesert would expand by more...

Drought is a very complex natural hazard and has a negative impact on the global ecosystem as a whole. Recently Bangladesh has been experiencing by different degree of dryness as a consequence of high climate variability, affecting the crop production to a great extent in the last couple of decades. In this context, the present study was made an effort to assess and analyse drought characteristics based on two drought indices, i.e., Standardized Precipitation Index (SPI) and Vegetation Condition Index (VCI), and model agricultural drought risk with Fast-and-frugal decision tree (FFT) model in Bangladesh from 2001 to 2016. We identified drought occurrence and its dynamics with three-time scale, i.e., SPI3J (November-January), SPI3A (February-April) and SPI6A (November-April), and three rice-growing seasons, i.e., Aus (March-July), Aman (June-November), and Boro (November-May) from TRMM (Tropical Rainfall Measuring Mission) and MODIS (Moderate Resolution Imaging Spectroradiometer) data. The results demonstrate that TRMM had good consistency with rain gauge measurement compared to CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) and PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data to derive SPI3J, SPI3A and SPI6A. Overall results confirmed that more drought frequency observed in SPI6A than SPI3J and SPI3A time scale, representing moderate to severe drought throughout the country. Regarding agricultural drought resulting from VCI demonstrated Boro rice-growing season as more vulnerable crop growing season affected by severe to extreme drought event. Validation results of VCI exhibited a high correlation with rice yield data than in-situ soil moisture data. Results of the FFT model show that out of ten predictor variables SPI3J and SPI6A caused agricultural drought with SPI value less than-1.08 and-1.21 respectively. Additionally, the model characterized SPI3J and SPI6A as the most critical driving factors with the highest balanced accuracy triggering agricultural drought risk in Bangladesh.

Heat-health risk is a growing concern in many regions of China due to the more frequent occurrence of extremely hot weather. Spatial indexes based on various heat assessment frameworks can be used for the assessment of heat risks. In this study, we adopted two approaches—Crichton’s risk triangle and heat vulnerability index (HVI) to identify heat-health risks in the Northern Jiangxi Province of China, by using remote sensing and socio-economic data. The Geographical Information System (GIS) overlay and principal component analysis (PCA) were separately used in two frameworks to integrate parameters. The results show that the most densely populated community in the suburbs, instead of city centers, are exposed to the highest heat risk. A comparison of two heat assessment mapping indicates that the distribution of HVI highlights the vulnerability differences between census tracts. In contrast, the heat risk index of Crichton’s risk triangle has a prominent representation for regions w...

Urbanization is an increasing phenomenon around the world, causing many adverse effects in urban areas. Urban heat island is are of the most well-known phenomena. In the present study, surface urban heat islands (SUHI) were studied for seven megacities of the South Asian countries from 2000–2019. The urban thermal environment and relationship between land surface temperature (LST), land use landcover (LULC) and vegetation were examined. The connection was explored with remote-sensing indices such as urban thermal field variance (UTFVI), surface urban heat island intensity (SUHII) and normal difference vegetation index (NDVI). LULC maps are classified using a CART machine learning classifier, and an accuracy table was generated. The LULC change matrix shows that the vegetated areas of all the cities decreased with an increase in the urban areas during the 20 years. The average LST in the rural areas is increasing compared to the urban core, and the difference is in the range of 1–2 (...

Soil salinization is recognized as a key issue negatively affecting agricultural productivity and wetland ecology. It is necessary to develop effective methods for monitoring the spatiotemporal distribution of soil salinity at a regional scale. In this study, we proposed an optimized remote sensing-based model for detecting soil salinity in different depths across the Yellow River Delta (YRD), China. A multi-dimensional model was built for mapping soil salinity, in which five types of predictive factors derived from Landsat satellite images were exacted and tested, 94 in-situ measured soil salinity samples with depths of 30-40 cm and 90-100 cm were collected to establish and validate the predicting model result. By comparing multiple linear regression (MLR) and partial least squares regression (PLSR) models with considering the correlation between predictive factors and soil salinity, we established the optimized prediction model which integrated the multi-parameter (including SWIR1, SI9, MSAVI, Albedo, and SDI) optimization approach to detect soil salinization in the YRD from 2003 to 2018. The results indicated that the estimates of soil salinity by the optimized prediction model were in good agreement with the measured soil salinity. The accuracy of the PLSR model performed better than that of the MLR model, with the R 2 of 0.642, RMSE of 0.283, and MAE of 0.213 at 30-40 cm depth, and with the R 2 of 0.450, RMSE of 0.276, and MAE of 0.220 at 90-100 cm depth. From 2003 to 2018, the soil salinity showed a distinct spatial heterogeneity. The soil salinization level of the coastal shoreline was higher; in contrast, lower soil salinization level occurred in the central YRD. In the last 15 years, the soil salinity at depth of 30-40 cm experienced a decreased trend of fluctuating, while the soil salinity at depth of 90-100 cm showed fluctuating increasing trend.

Monsoon floods are frequent in the Tarai region of Nepal and claim thousands of lives and substantial numbers of properties every year. Certain human activities are more affected than others in the case of the same hazard. This study analyzes vulnerability to flooding among Tharu households. Data were collected by employing household surveys, group discussions, and key informant interviews in the Thapapur Village Development Committee (VDC) of Kailali district, western Tarai, Nepal. The analysis presented in this study is based on the theory that underpins the pressure and release (PAR) and access models. The results show that Tharu people are the major inhabitants in the study area and they prefer to live within their community; many ex-bonded laborers (marginalized people) choose this location for residence. Human causalities have been reduced in recent years due to easy access to cell phones, which has facilitated effective flood warnings with suitable lead times, but agriculture...

Forest regeneration is the process by which new tree seedlings become entrenched after forest trees have been harvested or destroyed by fire, insects, or diseases. The process is key to sustainable forestry and can be accomplished through natural regeneration, which occurs when new seedlings or sprouts are produced by trees left on or near the site. The study is to calculate the regeneration status of the tree species in community-managed forests. The sampling points were generated by using the random table. The sample quadrate size was taken 20m×20m for a tree, 5m×5m for a sapling, and 1m×1m for the seedling. In five community forests, altogether 30 quadrates were taken for a tree, 60 nested quadrates were taken for a sapling and 120 nested quadrates were taken for a seedling. The tree layer, sapling layer and seedling layer were differentiated by taking reference tothe Diameter at Breast Height (DBH) class. An individual having DBH ≥ 5cm is considered a tree, an individual having ...

The government of Nepal has been implementing policies to increase agriculture production by motivating farmers towards farming. This study aims to identify spatially distributed suitable areas for sustainable Arabica coffee production in the Gulmi, Palpa, Arghakhanchi, and Pyuthan districts of Lumbini Province. The multi-criteria analysis was carried out to identify the suitable area for coffee cultivation in the study area, where parameter values were assigned based on Analytical Hierarchy Process (AHP). In the AHP method, the weightage for each parameter for suitability was considered through an expert questionnaire, interview, and field observation. The modeled suitability map was verified through field inventory-based coffee farm points. The result shows different land suitability classes: highly suitable (S1), moderately suitable (S2), marginally suitable (S3), and not suitable (N) cover an area of 869.0 km2 (16.92%), 1949.0 km2 (37.95%), 280.23 km2 (5.46%), 2037.00 km2 and (3...

The escalating global demand for energy has triggered a shift towards cleaner alternatives due to mounting environmental concerns. Emerging as a viable substitute, solar power has gained prominence as fossil fuels' adverse impact becomes evident. Historically reliant on hydropower, Nepal is exploring alternative energy sources to mitigate seasonal output variations. Despite abundant renewable resources like biomass, wind, and solar energy, Nepal's energy sector faces funding and technical expertise challenges. Solar energy presents significant promise as a primary renewable source in Nepal, boasting ample sunlight due to its location. However, the current solar capacity remains limited at around 54.6 MW, comprising less than 2.5% of the total installed capacity. The critical factor is the identification of suitable sites for solar power plants. Geographical Information System (GIS) and Multi-Criteria Decision-Making (MCDM) methods have been employed for suitability analysis. This study employs the Analytic Hierarchy Process (AHP), a robust MCDM technique, to assess site suitability. The research is carried out in the Gandaki province, Nepal, encompassing the Himalayan, Hilly, and Terai regions. Criteria like solar radiation, slope, aspect, land use/land cover, proximity to roads, and substations are considered. These criteria are reclassified into suitability categories based on expert opinions and guidelines. The results indicate areas highly suitable for solar power generation, covering 12.40 km2 (5.64% of the study area), followed by regions least suitable, spanning 7681 km2 (34.93% of the province's area). This research contributes to the effective deployment of solar power by identifying optimal locations for solar power plant construction, thus advancing Nepal's renewable energy goals.

Air pollutant concentration forecasting is an effective method of protecting public health by providing an early warning against harmful air pollutants. However, existing methods of air pollutant concentration prediction fail to effectively model long-term dependencies, and most neglect spatial correlations. In this paper, a novel long short-term memory neural network extended (LSTME) model that inherently considers spatiotemporal correlations is proposed for air pollutant concentration prediction. Long short-term memory (LSTM) layers were used to automatically extract inherent useful features from historical air pollutant data, and auxiliary data, including meteorological data and time stamp data, were merged into the proposed model to enhance the performance. Hourly PM 2.5 (particulate matter with an aerodynamic diameter less than or equal to 2.5 mm) concentration data collected at 12 air quality monitoring stations in Beijing City from Jan/01/2014 to May/28/2016 were used to validate the effectiveness of the proposed LSTME model. Experiments were performed using the spatiotemporal deep learning (STDL) model, the time delay neural network (TDNN) model, the autoregressive moving average (ARMA) model, the support vector regression (SVR) model, and the traditional LSTM NN model, and a comparison of the results demonstrated that the LSTME model is superior to the other statistics-based models. Additionally, the use of auxiliary data improved model performance. For the one-hour prediction tasks, the proposed model performed well and exhibited a mean absolute percentage error (MAPE) of 11.93%. In addition, we conducted multiscale predictions over different time spans and achieved satisfactory performance, even for 13e24 h prediction tasks (MAPE ¼ 31.47%).

Southern Tunisia is one of the most significant areas of historical and archaeological interest as, since the Roman period, it has played a key role as a land bridge controlling the passage from the Saharan plain to plateaus and to the north of the mountain range between Chebika and Metlaoui. Because of the difficult geography, the investigation of ancient sites in this region is not easy and, therefore, satellite-based investigation can play an important role in the detection and documentation of archaeological sites. In this study, we combined high-resolution remote sensing (RS) imagery with in situ investigations to assess the suspected archaeological sites detected using satellite data and historical documentation. The suspected sites were confirmed according to the geometric features of the sites as they appeared in the remotely sensed imagery these sites had not been detected during earlier field investigations. In particular, we aimed to use 1-m spatial resolution WorldView-2 (WV2) imagery of the Wadi El-MelahValley (WMV) in Gafsa (also named Capsa). The satellite data were processed using statistical analysis and unsupervised classification.WV2 satellite imagery of WMV, Southwestern Tunisia, was processed within ArcGIS 10.2 to identify spatial features linked to the sites. The study outlines three different feature-extraction methods (GIS-based, unsupervised classification-based and statistical analysis-based). The satellite-based analysis and archaeological records were evaluated by RS and GIS prospecting in the area of the field survey to confirm the new discoveries. The results of remotely sensed data combined with field survey enabled us to reconstruct the military defense system consisting of is a linear defensive structure (limes) and forts related to the Imperial Roman period. Taken together, the results of our investigations provide new insights into some important sections of the Roman limes in the southern part of the empire. Other concealed remains are still to be found and explored.

In recent years, very high-resolution satellite remote-sensing tools have been progressively used in archaeological prospecting to acquire information and improve documentation. Satellite remote sensing has also benefited from technical improvements, including better spectral and spatial resolution of sensors, which have facilitated the detection and discovery of unknown archaeological areas. This paper focuses on investigations conducted using multi-spectral satellite remote-sensing data of the ancient canal systems of the Wadi el Melah Valley (WMV) in southern Tunisia. The area used to be part of a huge military defense system along the desert border. This paper describes the use of GeoEye-1 and Ziyuan-3 satellite remote-sensing data to reveal ancient Roman canals, which were part of an advanced hydraulic system devised to capture runoff water and cope with the lack of water in the area. In general, this research provides new information on some essential sections of the Roman walled defense system Limes (Fossatum) in the southern part of the empire, where we study previously undetected sites.

The Han Dynasty Great Wall (GH), one of the largest and most significant ancient defense projects in the whole of northern China, has been studied increasingly not only because it provides important information about the diplomatic and military strategies of the Han Empire (206 B.C.-220 A.D.), but also because it is considered to be a cultural and national symbol of modern China as well as a valuable archaeological monument. Thus, it is crucial to obtain the spatial pattern and preservation situation of the GH for next-step archaeological analysis and conservation management. Nowadays, remote sensing specialists and archaeologists have given priority to manual visualization and a (semi-) automatic extraction approach is lacking. Based on the very high-resolution (VHR) satellite remote sensing imagery, this paper aims to identify automatically the archaeological features of the GH located in ancient Dunhuang, northwest China. Gaofen-1 (GF-1) data were first processed and enhanced after image correction and mathematical morphology, and the M-statistic was then used to analyze the spectral characteristics of GF-1 multispectral (MS) data. In addition, based on GF-1 panchromatic (PAN) data, an auto-identification method that integrates an improved Otsu segmentation algorithm with a Linear Hough Transform (LHT) is proposed. Finally, by making a comparison with visual extraction results, the proposed method was assessed qualitatively and semi-quantitatively to have an accuracy of 80% for the homogenous background in Dunhuang. These automatic identification results could be used to map and evaluate the preservation state of the GH in Dunhuang. Also, the proposed automatic approach was applied to identify similar linear traces of other generations of the Great Wall of China (Western Xia Dynasty (581 A.D.-618 A.D.) and Ming Dynasty (1368 A.D.-1644 A.D.)) in various geographic regions. Moreover, the results indicate that the computer-based automatic identification has great potential in archaeological research, and the proposed method can be generalized and applied to monitor and evaluate the state of preservation of the Great Wall of China in the future.