The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algor... more The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all...
Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technol... more Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using a Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agricul...
Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unpla... more Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.
The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-lear... more The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs....
Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lin... more Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lines and show planimetric details. Various other maps like geomorphological maps, contour maps, and land use planning maps are derived from topographical maps. In this constantly changing world, the update of TBMs is indispensable. In Nepal, their update and maintenance are done by the Survey Department (SD) as a national mapping agency. This paper presents the history of topographical mapping and the reasons for the lack of updates. Currently, the SD is updating the TBM database using panchromatic and multispectral images from the Zi Yuan-3 (ZY-3) satellite with a resolution of 2.1 and 5.8 m, respectively. The updated methodology includes the orthorectification of images, the pansharpening of images, field data collection, digitization, change detection, and updating, the overlay of vector data and field verification, data quality control, and printing map production. A TBM in the Dang di...
Proceedings of 6th International Electronic Conference on Sensors and Applications
In general, water quality mapping is done by interpolation of in-situ measurement samples. Often,... more In general, water quality mapping is done by interpolation of in-situ measurement samples. Often, these parameters change with time. Due to geographic variability and lack of budget in Nepal, such measurements are done less often. Remote sensors which collect spectral information continually can be very useful in regular monitoring of water quality parameters. Landsat OLI bands have been used to estimate water quality parameters. In this work, we model two water quality parameters: Chlorophyll-a (Chl-a) and Dissolved Oxygen (DO) using Sequential Minimal Optimization Regression (SMOreg) which implements Support Vector Machine (SVM) algorithm and Recursive Partitioning Tree (REPTree) regressions. A total of 19 measurements were taken from Phewa Lake, Nepal and various secondary bands were derived from using Landsat 8 Operational Land Imager (OLI) bands. These bands undergo feature selection and regression models were created based on selected bands and sample data. The results showed satisfactory modelling of water quality parameters using Landsat 8 OLI bands in Phewa Lake. Due to a limited number of data cross-validation was done with 10 folds. SVM showed a better result than REPTree regression. For future studies, the performance can be further evaluated in large lakes with larger sample numbers and other water quality parameters.
The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algor... more The study proposes Secondary Precipitation Estimate Merging using Machine Learning (SPEM2L) algorithms for merging multiple global precipitation datasets to improve the spatiotemporal rainfall characterization. SPEM2L is applied over the Krishna River Basin (KRB), India for 34 years spanning from 1985 to 2018, using daily measurements from three Secondary Precipitation Products (SPPs). Sixteen Machine Learning Algorithms (MLAs) were applied on three SPPs under four combinations to integrate and test the performance of MLAs for accurately representing the rainfall patterns. The individual SPPs and the integrated products were validated against a gauge-based gridded dataset provided by the Indian Meteorological Department. The validation was applied at different temporal scales and various climatic zones by employing continuous and categorical statistics. Multilayer Perceptron Neural Network with Bayesian Regularization (NBR) algorithm employing three SPPs integration outperformed all...
Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technol... more Land cover (LC) is a crucial parameter for studying environmental phenomena. Cutting-edge technology such as remote sensing (RS) and cloud computing have made LC change mapping efficient. In this study, the LC of Rupandehi District of Nepal were mapped using a Landsat imagery and Random Forest (RF) classifier from 2005 to 2020 using Google Earth Engine (GEE) platform. GEE eases the way in extracting, analyzing, and performing different operations for the earth’s observed data. Land cover classification, Centre of gravity (CoG), and their trajectories for all LC classes: agriculture, built-up, water, forest, and barren area were extracted with five-year intervals, along with their Ecosystem service values (ESV) to understand the load on the ecosystem. We also discussed the aspects and problems of the spatiotemporal analysis of developing regions. It was observed that the built-up areas had been increasing over the years and more centered in between the two major cities. Other agricul...
Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unpla... more Water is an integral aspect of the world necessary for living creatures to thrive. Owing to unplanned urbanization, rapid industrialization, and uncontrollable human intervention, water quality is gradually degrading. This affects not only marine animals but also humans. Thus, the quality of water should be examined regularly. Water quality parameters should be estimated to monitor water quality. In general, water quality parameters are measured by in situ measurements. Although these measurements are accurate, they are costly and do not provide real-time spatial and temporal changes in water quality. To overcome this limitation, water quality parameters can be estimated using machine learning (ML) along with remote sensing (RS) data. A combination of ML and RS data is a powerful approach for the routine assessment of spatial and temporal variations in water quality parameters. In this paper, some articles based on this approach are reviewed. By analyzing the literature, it was found that the integrated use of RS-based geospatial data with ML helps to produce an accurate result. Most of the authors used the regression algorithm in the estimation of the water quality parameters, with a support vector machine (SVM) regression intensively used. The artificial neural network (ANN) algorithm was the most used algorithm of ML in many of the studies. The researchers used multispectral images for their study. By applying ML to RS data, water quality monitoring systems are evolving into real-time artificial intelligence (AI)-enabled models that provide valuable recommendations and insights to support farmers to make decisions and take action in aquaculture.
The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-lear... more The study deals with the application of Google Earth Engine (GEE), Landsat data and ensemble-learning methods (ELMs) to map land cover (LC) change over a decade in the Kaski district of Nepal. As Nepal has experienced extensive changes due to natural and anthropogenic activities, monitoring such changes are crucial for understanding relationships and interactions between social and natural phenomena and to promote better decision-making. The main novelty lies in applying the XGBoost classifier for LC mapping over Nepal and monitoring the decadal changes of LC using ELMs. To map the LC change, a yearly cloud-free composite Landsat image was selected for the year 2010 and 2020. Combining the annual normalized difference vegetation index, normalized difference built-up index and modified normalized difference water index, with elevation and slope data from shuttle radar topography mission, supervised classification was performed using a random forest and extreme gradient boosting ELMs....
Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lin... more Topographic Base Maps (TBMs) are those maps that portray ground relief as the form of contour lines and show planimetric details. Various other maps like geomorphological maps, contour maps, and land use planning maps are derived from topographical maps. In this constantly changing world, the update of TBMs is indispensable. In Nepal, their update and maintenance are done by the Survey Department (SD) as a national mapping agency. This paper presents the history of topographical mapping and the reasons for the lack of updates. Currently, the SD is updating the TBM database using panchromatic and multispectral images from the Zi Yuan-3 (ZY-3) satellite with a resolution of 2.1 and 5.8 m, respectively. The updated methodology includes the orthorectification of images, the pansharpening of images, field data collection, digitization, change detection, and updating, the overlay of vector data and field verification, data quality control, and printing map production. A TBM in the Dang di...
Proceedings of 6th International Electronic Conference on Sensors and Applications
In general, water quality mapping is done by interpolation of in-situ measurement samples. Often,... more In general, water quality mapping is done by interpolation of in-situ measurement samples. Often, these parameters change with time. Due to geographic variability and lack of budget in Nepal, such measurements are done less often. Remote sensors which collect spectral information continually can be very useful in regular monitoring of water quality parameters. Landsat OLI bands have been used to estimate water quality parameters. In this work, we model two water quality parameters: Chlorophyll-a (Chl-a) and Dissolved Oxygen (DO) using Sequential Minimal Optimization Regression (SMOreg) which implements Support Vector Machine (SVM) algorithm and Recursive Partitioning Tree (REPTree) regressions. A total of 19 measurements were taken from Phewa Lake, Nepal and various secondary bands were derived from using Landsat 8 Operational Land Imager (OLI) bands. These bands undergo feature selection and regression models were created based on selected bands and sample data. The results showed satisfactory modelling of water quality parameters using Landsat 8 OLI bands in Phewa Lake. Due to a limited number of data cross-validation was done with 10 folds. SVM showed a better result than REPTree regression. For future studies, the performance can be further evaluated in large lakes with larger sample numbers and other water quality parameters.
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Papers by Nimisha Wagle