I obtained my Bachelor of the Science of Engineering Degree and research based Master of Philosophy degree from the University of Moratuwa, Sri Lanka in 2006 and 2012 respectively. My MPhil research was on ‘Site Suitability Analysis for Water Harvesting Structures in Suriyawewa, Hambantota District Using GIS Techniques’.
After graduation, I worked as a lecturer at the Department of Earth Resources Engineering at my Alma mater for five years and as a research engineer at the Arthur C Clarke Institute for Modern Technologies, Sri Lanka for one and a half years. My main research interests are on environmental planning and natural resource management through remote sensing and GIS based approaches.
Currently, I am working on characterization of spatio-temporal variability of soil moister at catchment scales and disaggregating satellite soil moisture retrievals using multivariable approach as a part of my PhD research. Supervisors: Dr. In-Young Yeo and Prof. Garry Willgoose
Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposit... more Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence ...
Semi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate ... more Semi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate unique hydro‐climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non‐linear relationship between these factors and soil moisture, and the lack of long‐term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south‐east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in‐situ dataset. This unique in‐situ soil moisture monitoring network is established over a semi‐arid to temperate catchment representing typical south‐east Australian agricultural landscape and the data record has captured some major climatic events. Tim...
2019 Moratuwa Engineering Research Conference (MERCon)
Smartphones have become an essential companion in most of the communities. Yet, we may not be qui... more Smartphones have become an essential companion in most of the communities. Yet, we may not be quite aware of the capabilities and services that these devices could provide. As a result, features such as location services are under-utilized and mostly used for navigation and location sharing. This study explores the limitations of embedded GPS receivers in smartphones with reference to the performance of a consumer-grade hand-held GPS device. The location coordinates obtained with the GPS unit and six smartphones on five locations over ten consecutive days revealed that over 70% of smartphone records provide the location coordinates within 0 to 10 m accuracy. Furthermore, at certain locations, over 75% of records have maintained the coordinate accuracy within 0 to 5 m. Hence, the use of smartphone location information in place of standalone GPS readings, can be recommended for moderate location accuracy requirements, such as geo-tagged data collection. Nevertheless, hand-held GPS units provide better approximations than the smartphones, for elevation readings at the studied locations. Accordingly, further investigations are recommended, to evaluate the discrepancies in elevation records, provided the ambiguities generated while recording the elevation measurements from the hand-held GPS units are minimized.
Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is ... more Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is important for hydrologic and climatic modelling, and for agricultural applications. However, the available point-scale in-situ observations and coarse resolution (10s of km) satellite soil moisture retrievals are unable to capture the spatial variability of soil moisture at high spatial resolutions as required by many of the local and regional scale applications. The lack of high resolution soil moisture data has resulted uncertainties in the model outcomes in these applications. Downscaling coarse spatial resolution L-band microwave soil moisture products using high spatial resolution optical/thermal satellite data appears to be a feasible option to estimate near surface (~top 5 cm) soil moisture at a high spatial resolution. The objective of this study is to develop a model to estimate soil moisture at 1 km spatial resolution by downscaling coarse resolution satellite soil moisture products. The downscaling model was built by using in-situ observations from a long term monitoring network (2004-2015) over two sub-catchments, Krui and Merriwa River, located in the Upper Hunter Region of south-eastern Australia. The model was based on the soil thermal inertia relationship between the diurnal temperature difference (ΔT) and daily mean soil moisture (μSM). A regression tree (RT) was built between ΔT and μSM and classified based on the season, vegetation density and soil clay content. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) values at 1 km spatial resolution were input to the model to estimate soil moisture at 1 km resolution. Subsequently, SMAP Enhanced 9 km (SMAP L3_SM_P) and SMOS 25 km grid (SMOS CATDS L3 SM 3-DAY) soil moisture products over the study area were downscaled and then compared against in-situ observations. Further, the downscaling algorithm was applied to aggregated soil moisture observations from National Airborne Field Experiment 2005 (NAFE’05) over a 40 x 40 km land area over Krui and Merriwa River catchments. The downscaled products were validated using 1 km airborne data. The SMAP L3_SM_P and SMOS CATDS L3 SM 3-DAY products show unbiased root mean square errors (ubRMSEs) of 0.068 and 0.051 cm3/cm3 (R2 values of 0.40 and 0.61), respectively, against in-situ observations. The downscaled airborne soil moisture data shows an RMSE of 0.07 cm3/cm3 (with R value of 0.4). The model performed well in dry conditions compared to wet conditions. This method shows a good potential in developing a long time series of high spatial resolution soil moisture information over arid and semi-arid regions using SMOS and SMAP soil moisture products.
High spatial resolution soil moisture information is important for hydrological, climatic and agr... more High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. The downscaling method based on the soil thermal inertia relationship between the diurnal soil temperature difference (ΔT) and the daily mean soil moisture (μ) has showed a good potential in providing accurate high resolution soil moisture estimations over arid and semi-arid regions. The aim of this study is to evaluate the performance of in-situ data based and model-based ΔT-μ regression algorithms to downscale satellite soil moisture products in a semiarid catchment of southeast Australia. Regression trees between ΔT and μ will be developed by using a long term in-situ dataset and coarse resolution land surface model estimates. The radiometric satellite soil moisture products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) will be disaggregated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperatures (1 km resolution) as input into the regression tree models. The high-resolution soil moisture estimated from the two regression trees will be compared against high resolution L-band airborne soil moisture retrievals and in-situ soil moisture observations. This comparative analysis will help to identify the potential limitations of using commonly available land surface model outputs and the radiometric soil moisture products to derive a high resolution soil moisture and provide useful suggestion to account for the soil moisture variability induced by the local physical factors. SM SM SM Authors Indishe Prabath Senanayake In-Young Yeo
Post-mining landscape rehabilitation is an eco-sensitive process over large scale open-cast mines... more Post-mining landscape rehabilitation is an eco-sensitive process over large scale open-cast mines. Mining and mine rehabilitation processes affect plant water availability and hydrology of the area. Soil moisture plays an important role in monitoring the restoration process of native vegetation and changes in hydrology in mine rehabilitation. Therefore, monitoring the sub-watershed scale soil moisture dynamics is useful in assessing the post-rehabilitation impacts of open-cast mines. Satellite derived L-band microwave soil moisture products provide a feasible method to measure surface soil moisture at a frequent temporal resolution (~3-days). However, the coarser resolution of these products at 10s of kms limit their applicability at sub-watershed scale monitoring processes. This study introduces a model to estimate soil moisture at 1-km spatial resolution by downscaling satellite soil moisture products based on the thermal inertia of soils. Simulated satellite observations over an area of approximate coarse-resolution satellite soil moisture pixel retrieved from an airborne L-band radiometer was used to validate the downscaled model. The downscaled soil moisture products were able to successfully capture the spatial patterns of soil moisture with the average root mean square error (RMSE) of 0.070 cm3/cm3 over 3 days, whereas a higher accuracy (RMSE= 0.046 cm3/cm3) was observed at dryer conditions.
Industrial grade Kaolin deposits in Sri Lanka are located in Boralesgamuwa and Meetiyagoda. Two d... more Industrial grade Kaolin deposits in Sri Lanka are located in Boralesgamuwa and Meetiyagoda. Two distinct grades of Kaolin are produced at Meetiyagoda refinery, based on the whiteness index (WI). The presence of iron oxides significantly affects the WI of the Kaolin. Upgrading of the whiteness will result in a huge increase in the product prices as high as 35%. However, no effective methods are currently in place for the whiteness improvement at Meetiyagoda Kaolin refinery. Hence, this study was carried out to assess the whiteness enhancement capability of Meetiyagoda Kaolin by chemical leaching process using oxalic and citric acids. The treatment time (0–120 min) in steps and concentration of acids (0.01, 0.1, 0.2, 0.3, 0.4 and 0.5 M) were considered as the main variables of this study. Leachate and treated samples were analysed by using spectrophotometric and XRF methods respectively. The results depict that the oxalic acid is more effective in leaching iron oxides from Kaolin compared to citric acid for the Kaolin at Meetiyagoda. It was observed that 25% and 14% of Fe in Kaolin filter cakes can be removed by 0.4 M oxalic and 0.5 M citric acid respectively, within a treatment time of 120 mins at the room temperature
In Kalutara district, Sri Lanka a significant number of rock quarries has been opened during the ... more In Kalutara district, Sri Lanka a significant number of rock quarries has been opened during the last decade: from 2005 to 2015. The main reason behind this rapid increase of the number of Rock quarries was the development projects, such as port expansion project, highway projects, port city and airport, which were carried out simultaneously. In general, mining is considered as a destructive industry which affects the environment and surrounding echo-systems; and hence it is useful to assess the resulted environmental impact. The removal of the vegetation cover is often considered as one of the worst detrimental aftermaths of mining. However, assessing the impact on the vegetation cover due to mining is a difficult task. This study describes assessment of the devegetation caused by new mines started in the time period of 2005-2015. The objective of the study is to assess the area of the uncovered vegetation by using Google Earth imagery. A remote sensing and geospatial approach is adopted with an image processing technique to analyse the discrepancy resulted in the vegetation cover. Google Earth images, acquired from 2005 to 2010, are used to monitor the change in vegetation cover due to mining activities. Initially, locations of 10 rock quarries, opened during the specified time period, are identified by visually inspecting the Google Earth images. Since the Google Earth imagery consists of high resolution satellite imagery, the clarity is significant enough to visually demarcate the boundaries of vegetation and quarry sites. Images acquired, in the time period of January to March from the two bounding years are selected for each quarry. Two images, representing 2005 and 2015, for each quarryare exported to a GIS platform and georectified. The georectified images are then classified into two classes (namely vegetated area and non-vegetated area) and subsequently assigned with pixel values of 1 and 0 respectively. Changes of vegetation cover due to rock quarrying in the given time period of the study area are delineated by computing the arithmetic difference of the two images acquired in the bounding years through a raster calculation. The result depicts that an average of 16998.4 m2 of vegetation has been removed by a rock quarry in Kalutara district over the time period of 2005-2015. Given the fact that Google Earth images are freely available, and frequently the environmental impacts are assessed by the authorities, the approach utilized in this study has the potential for monitoring and assessing the devegetation over much broader scale than demonstrated in this study. Consequentially, the results can be included for the interpretations when the authorities define the guidelines.
Abstract Lack of high spatial resolution soil moisture data is a major limitation in many regiona... more Abstract Lack of high spatial resolution soil moisture data is a major limitation in many regional scale hydrologic, climatic and agricultural applications. The available satellite soil moisture data products are too coarse and unable to cater for this resolution requirement. Downscaling coarse spatial resolution satellite soil moisture retrievals is a feasible option to meet the required level of spatial resolution for those applications. The main focus of this study is to compare two soil thermal inertia-based downscaling models, built by using long-term time records of (i) point scale in-situ data and, (ii) 25 km resolution Global Land Data Assimilation System (GLDAS) land surface model outputs. The developed models were tested over Goulburn River catchment in the Upper Hunter Region of NSW, Australia to downscale Soil Moisture Active Passive (SMAP) 36 km radiometric products into 1 km resolution. The downscaled SMAP products from both models produced encouraging results with unbiased root mean square errors (ubRMSEs) of 0.07 - 0.10 cm3/cm3, against in-situ field data, and an average ubRMSEs of 0.07 cm3/cm3 when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.
Background. Acid mine drainage (AMD) is a major environmental impact associated with the mining i... more Background. Acid mine drainage (AMD) is a major environmental impact associated with the mining industry. Elevated acidic conditions resulting from the discharge of AMD into the surrounding environment can cause heavy metals to dissolve and transport through water streams and accumulate in the aquatic environment, posing a risk to the health of living organisms. There have been several novel approaches in the remediation of AMD involving passive treatment techniques. The constructed treatment wetland approach is a passive remediation option that has proven to be a cost effective and long-lasting solution in abating toxic pollutant concentrations. Objectives. The present study investigates the applicability of water hyacinth (Eichhornia crassipes), a tropical aquatic plant with reported heavy metal hyper-accumulation in microcosm floating wetland treatment systems designed to remediate AMD with copper (Cu) and cadmium (Cd) concentrations exceeding threshold limits. Methods. Twelve wa...
Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposit... more Mineral prospectivity mapping is a crucial technique for discovering new economic mineral deposits. However, detailed knowledge-based geological exploration and interpretations generally involve significant costs, time, and human resources. In this study, an ensemble machine learning approach was tested using geoscience datasets to map Cu-Au and Pb-Zn mineral prospectivity in the Cobar Basin, NSW, Australia. The input datasets (magnetic, gravity, faults, electromagnetic, and magnetotelluric data layers) were chosen by considering their association with Cu-Au and Pb-Zn mineralization patterns. Three machine learning algorithms, namely random forest (RF), support vector machine (SVM), and maximum-likelihood (MaxL) classification, were applied to the input data. The results of the three algorithms were ensembled to produce Cu-Au and Pb-Zn prospectivity maps over the Cobar Basin with improved classification accuracy. The findings demonstrate good agreement with known mineral occurrence ...
Semi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate ... more Semi‐arid to temperate south‐east Australian catchments with agricultural landscapes demonstrate unique hydro‐climatic characteristics. Understanding the behaviour of soil moisture over such catchments and the influence of driving factors are crucial for hydrologic, climatic and agricultural applications. However, this is challenging due the complex, non‐linear relationship between these factors and soil moisture, and the lack of long‐term catchment scale data records. To address this, spatial and temporal patterns of soil moisture over two south‐east Australian river catchments (i.e., Krui and Merriwa) and the influence of soil texture, topography, vegetation and rainfall on soil moisture variability were evaluated using a decadal in‐situ dataset. This unique in‐situ soil moisture monitoring network is established over a semi‐arid to temperate catchment representing typical south‐east Australian agricultural landscape and the data record has captured some major climatic events. Tim...
2019 Moratuwa Engineering Research Conference (MERCon)
Smartphones have become an essential companion in most of the communities. Yet, we may not be qui... more Smartphones have become an essential companion in most of the communities. Yet, we may not be quite aware of the capabilities and services that these devices could provide. As a result, features such as location services are under-utilized and mostly used for navigation and location sharing. This study explores the limitations of embedded GPS receivers in smartphones with reference to the performance of a consumer-grade hand-held GPS device. The location coordinates obtained with the GPS unit and six smartphones on five locations over ten consecutive days revealed that over 70% of smartphone records provide the location coordinates within 0 to 10 m accuracy. Furthermore, at certain locations, over 75% of records have maintained the coordinate accuracy within 0 to 5 m. Hence, the use of smartphone location information in place of standalone GPS readings, can be recommended for moderate location accuracy requirements, such as geo-tagged data collection. Nevertheless, hand-held GPS units provide better approximations than the smartphones, for elevation readings at the studied locations. Accordingly, further investigations are recommended, to evaluate the discrepancies in elevation records, provided the ambiguities generated while recording the elevation measurements from the hand-held GPS units are minimized.
Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is ... more Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is important for hydrologic and climatic modelling, and for agricultural applications. However, the available point-scale in-situ observations and coarse resolution (10s of km) satellite soil moisture retrievals are unable to capture the spatial variability of soil moisture at high spatial resolutions as required by many of the local and regional scale applications. The lack of high resolution soil moisture data has resulted uncertainties in the model outcomes in these applications. Downscaling coarse spatial resolution L-band microwave soil moisture products using high spatial resolution optical/thermal satellite data appears to be a feasible option to estimate near surface (~top 5 cm) soil moisture at a high spatial resolution. The objective of this study is to develop a model to estimate soil moisture at 1 km spatial resolution by downscaling coarse resolution satellite soil moisture products. The downscaling model was built by using in-situ observations from a long term monitoring network (2004-2015) over two sub-catchments, Krui and Merriwa River, located in the Upper Hunter Region of south-eastern Australia. The model was based on the soil thermal inertia relationship between the diurnal temperature difference (ΔT) and daily mean soil moisture (μSM). A regression tree (RT) was built between ΔT and μSM and classified based on the season, vegetation density and soil clay content. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) values at 1 km spatial resolution were input to the model to estimate soil moisture at 1 km resolution. Subsequently, SMAP Enhanced 9 km (SMAP L3_SM_P) and SMOS 25 km grid (SMOS CATDS L3 SM 3-DAY) soil moisture products over the study area were downscaled and then compared against in-situ observations. Further, the downscaling algorithm was applied to aggregated soil moisture observations from National Airborne Field Experiment 2005 (NAFE’05) over a 40 x 40 km land area over Krui and Merriwa River catchments. The downscaled products were validated using 1 km airborne data. The SMAP L3_SM_P and SMOS CATDS L3 SM 3-DAY products show unbiased root mean square errors (ubRMSEs) of 0.068 and 0.051 cm3/cm3 (R2 values of 0.40 and 0.61), respectively, against in-situ observations. The downscaled airborne soil moisture data shows an RMSE of 0.07 cm3/cm3 (with R value of 0.4). The model performed well in dry conditions compared to wet conditions. This method shows a good potential in developing a long time series of high spatial resolution soil moisture information over arid and semi-arid regions using SMOS and SMAP soil moisture products.
High spatial resolution soil moisture information is important for hydrological, climatic and agr... more High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. The downscaling method based on the soil thermal inertia relationship between the diurnal soil temperature difference (ΔT) and the daily mean soil moisture (μ) has showed a good potential in providing accurate high resolution soil moisture estimations over arid and semi-arid regions. The aim of this study is to evaluate the performance of in-situ data based and model-based ΔT-μ regression algorithms to downscale satellite soil moisture products in a semiarid catchment of southeast Australia. Regression trees between ΔT and μ will be developed by using a long term in-situ dataset and coarse resolution land surface model estimates. The radiometric satellite soil moisture products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) will be disaggregated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperatures (1 km resolution) as input into the regression tree models. The high-resolution soil moisture estimated from the two regression trees will be compared against high resolution L-band airborne soil moisture retrievals and in-situ soil moisture observations. This comparative analysis will help to identify the potential limitations of using commonly available land surface model outputs and the radiometric soil moisture products to derive a high resolution soil moisture and provide useful suggestion to account for the soil moisture variability induced by the local physical factors. SM SM SM Authors Indishe Prabath Senanayake In-Young Yeo
Post-mining landscape rehabilitation is an eco-sensitive process over large scale open-cast mines... more Post-mining landscape rehabilitation is an eco-sensitive process over large scale open-cast mines. Mining and mine rehabilitation processes affect plant water availability and hydrology of the area. Soil moisture plays an important role in monitoring the restoration process of native vegetation and changes in hydrology in mine rehabilitation. Therefore, monitoring the sub-watershed scale soil moisture dynamics is useful in assessing the post-rehabilitation impacts of open-cast mines. Satellite derived L-band microwave soil moisture products provide a feasible method to measure surface soil moisture at a frequent temporal resolution (~3-days). However, the coarser resolution of these products at 10s of kms limit their applicability at sub-watershed scale monitoring processes. This study introduces a model to estimate soil moisture at 1-km spatial resolution by downscaling satellite soil moisture products based on the thermal inertia of soils. Simulated satellite observations over an area of approximate coarse-resolution satellite soil moisture pixel retrieved from an airborne L-band radiometer was used to validate the downscaled model. The downscaled soil moisture products were able to successfully capture the spatial patterns of soil moisture with the average root mean square error (RMSE) of 0.070 cm3/cm3 over 3 days, whereas a higher accuracy (RMSE= 0.046 cm3/cm3) was observed at dryer conditions.
Industrial grade Kaolin deposits in Sri Lanka are located in Boralesgamuwa and Meetiyagoda. Two d... more Industrial grade Kaolin deposits in Sri Lanka are located in Boralesgamuwa and Meetiyagoda. Two distinct grades of Kaolin are produced at Meetiyagoda refinery, based on the whiteness index (WI). The presence of iron oxides significantly affects the WI of the Kaolin. Upgrading of the whiteness will result in a huge increase in the product prices as high as 35%. However, no effective methods are currently in place for the whiteness improvement at Meetiyagoda Kaolin refinery. Hence, this study was carried out to assess the whiteness enhancement capability of Meetiyagoda Kaolin by chemical leaching process using oxalic and citric acids. The treatment time (0–120 min) in steps and concentration of acids (0.01, 0.1, 0.2, 0.3, 0.4 and 0.5 M) were considered as the main variables of this study. Leachate and treated samples were analysed by using spectrophotometric and XRF methods respectively. The results depict that the oxalic acid is more effective in leaching iron oxides from Kaolin compared to citric acid for the Kaolin at Meetiyagoda. It was observed that 25% and 14% of Fe in Kaolin filter cakes can be removed by 0.4 M oxalic and 0.5 M citric acid respectively, within a treatment time of 120 mins at the room temperature
In Kalutara district, Sri Lanka a significant number of rock quarries has been opened during the ... more In Kalutara district, Sri Lanka a significant number of rock quarries has been opened during the last decade: from 2005 to 2015. The main reason behind this rapid increase of the number of Rock quarries was the development projects, such as port expansion project, highway projects, port city and airport, which were carried out simultaneously. In general, mining is considered as a destructive industry which affects the environment and surrounding echo-systems; and hence it is useful to assess the resulted environmental impact. The removal of the vegetation cover is often considered as one of the worst detrimental aftermaths of mining. However, assessing the impact on the vegetation cover due to mining is a difficult task. This study describes assessment of the devegetation caused by new mines started in the time period of 2005-2015. The objective of the study is to assess the area of the uncovered vegetation by using Google Earth imagery. A remote sensing and geospatial approach is adopted with an image processing technique to analyse the discrepancy resulted in the vegetation cover. Google Earth images, acquired from 2005 to 2010, are used to monitor the change in vegetation cover due to mining activities. Initially, locations of 10 rock quarries, opened during the specified time period, are identified by visually inspecting the Google Earth images. Since the Google Earth imagery consists of high resolution satellite imagery, the clarity is significant enough to visually demarcate the boundaries of vegetation and quarry sites. Images acquired, in the time period of January to March from the two bounding years are selected for each quarry. Two images, representing 2005 and 2015, for each quarryare exported to a GIS platform and georectified. The georectified images are then classified into two classes (namely vegetated area and non-vegetated area) and subsequently assigned with pixel values of 1 and 0 respectively. Changes of vegetation cover due to rock quarrying in the given time period of the study area are delineated by computing the arithmetic difference of the two images acquired in the bounding years through a raster calculation. The result depicts that an average of 16998.4 m2 of vegetation has been removed by a rock quarry in Kalutara district over the time period of 2005-2015. Given the fact that Google Earth images are freely available, and frequently the environmental impacts are assessed by the authorities, the approach utilized in this study has the potential for monitoring and assessing the devegetation over much broader scale than demonstrated in this study. Consequentially, the results can be included for the interpretations when the authorities define the guidelines.
Abstract Lack of high spatial resolution soil moisture data is a major limitation in many regiona... more Abstract Lack of high spatial resolution soil moisture data is a major limitation in many regional scale hydrologic, climatic and agricultural applications. The available satellite soil moisture data products are too coarse and unable to cater for this resolution requirement. Downscaling coarse spatial resolution satellite soil moisture retrievals is a feasible option to meet the required level of spatial resolution for those applications. The main focus of this study is to compare two soil thermal inertia-based downscaling models, built by using long-term time records of (i) point scale in-situ data and, (ii) 25 km resolution Global Land Data Assimilation System (GLDAS) land surface model outputs. The developed models were tested over Goulburn River catchment in the Upper Hunter Region of NSW, Australia to downscale Soil Moisture Active Passive (SMAP) 36 km radiometric products into 1 km resolution. The downscaled SMAP products from both models produced encouraging results with unbiased root mean square errors (ubRMSEs) of 0.07 - 0.10 cm3/cm3, against in-situ field data, and an average ubRMSEs of 0.07 cm3/cm3 when compared to the National Airborne Field Experiment 2005 (NAFE'05) soil moisture retrievals. Both models showed promising results over semi-arid regions in estimating soil moisture at a high spatial resolution, but with their own strengths and weaknesses. The findings here provide useful insights on the robustness of the soil thermal inertia relationship across scales and the effects of the model resolution to the downscaled soil moisture estimates. The approach demonstrated encouraging results over semi-arid regions in estimating soil moisture at a high spatial resolution.
Background. Acid mine drainage (AMD) is a major environmental impact associated with the mining i... more Background. Acid mine drainage (AMD) is a major environmental impact associated with the mining industry. Elevated acidic conditions resulting from the discharge of AMD into the surrounding environment can cause heavy metals to dissolve and transport through water streams and accumulate in the aquatic environment, posing a risk to the health of living organisms. There have been several novel approaches in the remediation of AMD involving passive treatment techniques. The constructed treatment wetland approach is a passive remediation option that has proven to be a cost effective and long-lasting solution in abating toxic pollutant concentrations. Objectives. The present study investigates the applicability of water hyacinth (Eichhornia crassipes), a tropical aquatic plant with reported heavy metal hyper-accumulation in microcosm floating wetland treatment systems designed to remediate AMD with copper (Cu) and cadmium (Cd) concentrations exceeding threshold limits. Methods. Twelve wa...
Japan Geoscience Union Meeting 2019 (JpGU 2019) , 2019
Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is ... more Soil moisture is a key variable in the hydrologic cycle. Therefore, soil moisture information is important for hydrologic and climatic modelling, and for agricultural applications. However, the available point-scale in-situ observations and coarse resolution (10s of km) satellite soil moisture retrievals are unable to capture the spatial variability of soil moisture at high spatial resolutions as required by many of the local and regional scale applications. The lack of high resolution soil moisture data has resulted uncertainties in the model outcomes in these applications. Downscaling coarse spatial resolution L-band microwave soil moisture products using high spatial resolution optical/thermal satellite data appears to be a feasible option to estimate near surface (~top 5 cm) soil moisture at a high spatial resolution. The objective of this study is to develop a model to estimate soil moisture at 1 km spatial resolution by downscaling coarse resolution satellite soil moisture products. The downscaling model was built by using in-situ observations from a long term monitoring network (2004-2015) over two sub-catchments, Krui and Merriwa River, located in the Upper Hunter Region of south-eastern Australia. The model was based on the soil thermal inertia relationship between the diurnal temperature difference (ΔT) and daily mean soil moisture (μSM). A regression tree (RT) was built between ΔT and μSM and classified based on the season, vegetation density and soil clay content. Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and Normalized Difference Vegetation Index (NDVI) values at 1 km spatial resolution were input to the model to estimate soil moisture at 1 km resolution. Subsequently, SMAP Enhanced 9 km (SMAP L3_SM_P) and SMOS 25 km grid (SMOS CATDS L3 SM 3-DAY) soil moisture products over the study area were downscaled and then compared against in-situ observations. Further, the downscaling algorithm was applied to aggregated soil moisture observations from National Airborne Field Experiment 2005 (NAFE’05) over a 40 x 40 km land area over Krui and Merriwa River catchments. The downscaled products were validated using 1 km airborne data. The SMAP L3_SM_P and SMOS CATDS L3 SM 3-DAY products show unbiased root mean square errors (ubRMSEs) of 0.068 and 0.051 cm3/cm3 (R2 values of 0.40 and 0.61), respectively, against in-situ observations. The downscaled airborne soil moisture data shows an RMSE of 0.07 cm3/cm3 (with R value of 0.4). The model performed well in dry conditions compared to wet conditions. This method shows a good potential in developing a long time series of high spatial resolution soil moisture information over arid and semi-arid regions using SMOS and SMAP soil moisture products.
High spatial resolution soil moisture information is important for regional-scale hydrologic, cli... more High spatial resolution soil moisture information is important for regional-scale hydrologic, climatic and agricultural applications. However, available point-scale in-situ measurements and coarse-scale (∼10s of km) satellite soil moisture retrievals are unable to capture hillslope to sub-catchment level spatial variability of soil moisture as required by many of these applications. Downscaling L-band satellite soil moisture retrievals appears to be a viable technique in estimating near surface (∼ top 5 cm) soil moisture at a high spatial resolution. Among different downscaling approaches, thermal data based methods exhibits a good potential over arid and semi-arid regions, i.e. in many parts of Australia. This study investigates three downscaling approaches based on soil thermal inertia to estimate near surface soil moisture at high spatial resolution (1 km) over Krui and Merriwa River catchments in the Upper Hunter region of New South Wales, Australia. These methods are based upon the relationship between the diurnal soil temperature difference (∆T) and daily mean soil moisture content (µSM). Regression tree models between ∆T and µSM were developed by using in-situ observations (in the first approach) and using land surface model (LSM) based estimates (in the second approach). The relationship between ∆T and µSM was modulated by the vegetation density and the Austral season. In the in-situ data based approach, soil texture was also employed as a modulating factor. These in-situ datasets were obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network and model-based estimates from the Global Land Data Assimilation System (GLDAS). Moderate Resolution Imaging Spectroradiometer (MODIS) derived Normalized Difference Vegetation Index (NDVI) products were used to define vegetation density. An ensemble machine-learning model was employed in the third approach using ∆T, NDVI and Austral season as predictors and µsm values as responses. Aggregated airborne soil moisture retrievals were used as the coarse resolution soil moisture products. These coarse resolution soil moisture simulations were downscaled to 1 km by employing the above three approaches using MODIS-derived ∆T and NDVI values. The results from the three downscaling methods were compared against the 1 km soil moisture retrievals from the National Airborne Field Experiment 2005 (NAFE'05) over 3 days in November 2005. The results from both in-situ data and GLDAS-based regression tree models show RMSEs of 0.07 cm 3 /cm 3 when compared against the high resolution NAFE'05 airborne soil moisture observations. The GLDAS-based model can be applied over a larger extent, whereas the in-situ data based model is catchment specific. These results were compared with the results from the machine-learnt model. A combination of these methods with additional forcing factors such as topography, meteorology, etc. can be utilized to develop an improved downscaling model. Such a model has a good potential in developing a time record of high resolution soil moisture products over southeastern Australia from 2010 onwards by using the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite soil moisture products.
High spatial resolution soil moisture information is important for hydrological, climatic and agr... more High spatial resolution soil moisture information is important for hydrological, climatic and agricultural applications. The lack of high resolution soil moisture data over large areas at the required accuracy is a major impediment for such applications. The downscaling method based on the soil thermal inertia relationship between the diurnal soil temperature difference (ΔT) and the daily mean soil moisture (μ) has showed a good potential in providing accurate high resolution soil moisture estimations over arid and semi-arid regions. The aim of this study is to evaluate the performance of in-situ data based and model-based ΔT-μ regression algorithms to downscale satellite soil moisture products in a semiarid catchment of southeast Australia. Regression trees between ΔT and μ will be developed by using a long term in-situ dataset and coarse resolution land surface model estimates. The radiometric satellite soil moisture products from the Soil Moisture Active Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) will be disaggregated using the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperatures (1 km resolution) as input into the regression tree models. The high-resolution soil moisture estimated from the two regression trees will be compared against high resolution L-band airborne soil moisture retrievals and in-situ soil moisture observations. This comparative analysis will help to identify the potential limitations of using commonly available land surface model outputs and the radiometric soil moisture products to derive a high resolution soil moisture and provide useful suggestion to account for the soil moisture variability induced by the local physical factors. SM SM SM Authors Indishe Prabath Senanayake In-Young Yeo
1. The downscaled SMAP 36 km, SMAP-Enhanced 9 km and SMOS 25 km soil moisture products showed unb... more 1. The downscaled SMAP 36 km, SMAP-Enhanced 9 km and SMOS 25 km soil moisture products showed unbiased root mean square errors of 0.06, 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. 2. An RMSE of 0.07 cm3/cm3was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. 3. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture.
Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrol... more Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions (~ several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.
Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrol... more Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions (~ several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the south-eastern Australia.
1. The downscaled SMAP 36 km, SMAP-Enhanced 9 km and SMOS 25 km soil moisture products showed unb... more 1. The downscaled SMAP 36 km, SMAP-Enhanced 9 km and SMOS 25 km soil moisture products showed unbiased root mean square errors of 0.06, 0.07 and 0.05 cm3/cm3, respectively, against the in-situ data. 2. An RMSE of 0.07 cm3/cm3was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals. 3. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture.
American Geophysical Union (AGU) Fall Meeting 2017, 2017
Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrol... more Long-term soil moisture datasets at high spatial resolution are important in agricultural, hydrological, and climatic applications. The soil moisture estimates can be achieved using satellite remote sensing observations. However, the satellite soil moisture data are typically available at coarse spatial resolutions (~ several tens of km), therefore require further downscaling. Different satellite soil moisture products have to be conjointly employed in developing a consistent time-series of high resolution soil moisture, while the discrepancies amongst different satellite retrievals need to be resolved. This study aims to downscale three different satellite soil moisture products, the Soil Moisture and Ocean Salinity (SMOS, 25 km), the Soil Moisture Active Passive (SMAP, 36 km) and the SMAP-Enhanced (9 km), and to conduct an inter-comparison of the downscaled results. The downscaling approach is developed based on the relationship between the diurnal temperature difference and the daily mean soil moisture content. The approach is applied to two sub-catchments (Krui and Merriwa River) of the Goulburn River catchment in the Upper Hunter region (NSW, Australia) to estimate soil moisture at 1 km resolution for 2015. The three coarse spatial resolution soil moisture products and their downscaled results will be validated with the in-situ observations obtained from the Scaling and Assimilation of Soil Moisture and Streamflow (SASMAS) network. The spatial and temporal patterns of the downscaled results will also be analysed. This study will provide the necessary insights for data selection and bias corrections to maintain the consistency of a long-term high resolution soil moisture dataset. The results will assist in developing a time-series of high resolution soil moisture data over the southeastern Australia.
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Papers by indishe senanayake
2. An RMSE of 0.07 cm3/cm3was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals.
3. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture.
2. An RMSE of 0.07 cm3/cm3was observed when comparing the downscaled soil moisture against the passive airborne L-band retrievals.
3. The findings here auger well for the use of satellite remote sensing for the assessment of high resolution soil moisture.