kristofer lasko - Academia.edu (original) (raw)

Papers by kristofer lasko

AGU Fall Meeting Abstracts, Dec 1, 2018

Remote Sensing of Agriculture and Land Cover/Land Use Changes in South and Southeast Asian Countries, 2022

This work builds on the original semi-automated land cover mapping algorithm and quantifies impro... more This work builds on the original semi-automated land cover mapping algorithm and quantifies improvements to class accuracy, analyzes the results, and conducts a more in-depth accuracy assessment in conjunction with test sites and the National Land Cover Database (NLCD). This algorithm uses support vector machines trained on data collected across the continental United States to generate a pre-trained model for inclusion into a decision support tool within ArcGIS Pro. Version 2 includes an additional snow cover class and accounts for snow cover effects within the other land cover classes. Overall accuracy across the continental United States for Version 2 is 75% on snow-covered pixels and 69% on snow-free pixels, versus 16% and 66% for Version 1. However, combining the “crop” and “low vegetation” classes improves these values to 86% for snow and 83% for snow-free, compared to 19% and 83% for Version 1. This merging is justified by their spectral similarity, the difference between cro...

Remote Sensing

Multispectral imagery provides unprecedented information on Earth system processes: however, data... more Multispectral imagery provides unprecedented information on Earth system processes: however, data gaps due to clouds and shadows are a major limitation. Normalized-Difference Vegetation Index (NDVI) and Normalized-Difference Water Index (NDWI) are two spectral indexes employed for monitoring vegetation phenology, land-cover change and more. Synthetic Aperture Radar (SAR) with its cloud-penetrating abilities can fill data gaps using coincident imagery. In this study, we evaluated C-band Sentinel-1, L-band Uninhabited Aerial Vehicle SAR (UAVSAR) and texture for gap filling using efficient machine learning regression algorithms across three seasons. Multiple models were evaluated including Support Vector Machine, Random Forest, Gradient Boosted Trees and an ensemble of models. The Gap filling ability of SAR was evaluated with Sentinel-2 imagery from the same date, 3 days and 8 days later than both SAR sensors in September. Sentinel-1 and Sentinel-2 imagery from winter and spring season...

Feature extraction algorithms are routinely leveraged to extract building footprints and road net... more Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the thr...

Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term... more Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowin...

Title of dissertation: CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM US... more Title of dissertation: CHARACTERIZING RICE RESIDUE BURNING AND ASSOCIATED EMISSIONS IN VIETNAM USING A REMOTE SENSING AND FIELDBASED APPROACH Kristofer Lasko, Doctor of Philosophy, 2018 Dissertation directed by: Christopher Justice, Chair and Professor, Department of Geographical Sciences Agricultural residue burning, practiced in croplands throughout the world, adversely impacts public health and regional air quality. Monitoring and quantifying agricultural residue burning with remote sensing alone is difficult due to lack of field data, hazy conditions obstructing satellite remote sensing imagery, small field sizes, and active field management. This dissertation highlights the uncertainties, discrepancies, and underestimation of agricultural residue burning emissions in a small-holder agriculturalist region, while also developing methods for improved bottom-up quantification of residue burning and associated emissions impacts, by employing a field and remote sensingbased approach....

Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environ... more Land cover type is a fundamental remote sensing-derived variable for terrain analysis and environmental mapping applications. The currently available products are produced only for a single season or a specific year. Some of these products have a coarse resolution and quickly become outdated, as land cover type can undergo significant change over a short time period. In order to enable on-demand generation of timely and accurate land cover type products, we developed a sensor-agnostic framework leveraging pre-trained machine learning models. We also generated land cover models for Sentinel-2 (20m) and Landsat 8 imagery (30m) using either a single date of imagery or two dates of imagery for mapping land cover type. The two-date model includes 11 land cover type classes, whereas the single-date model contains 6 classes. The models’ overall accuracies were 84% (Sentinel-2 single date), 82% (Sentinel-2 two date), and 86% (Landsat 8 two date) across the continental United States. The thr...

Biomass Burning in South and Southeast Asia, 2021

Springer Remote Sensing/Photogrammetry, 2018

Biomass burning emissions variation was calculated based on differences in satellite-derived prod... more Biomass burning emissions variation was calculated based on differences in satellite-derived products: MODIS and MERIS-based land cover, MODIS and MERIS-based burned area (BA), as well as global and regionally - averaged emission factors. The products and resulting emissions were compared for 3 years (2006–2008) in Vietnam. They were compared at four spatial scales including: (1) country level; (2) region level; (3) land cover; and (4) grid-cell level. For the different products, we especially focused on BA as it is the major input for emission calculations. The BA products were tested for differences using the mean absolute deviation (MAD), Average Absolute Deviation (AAD), and Student’s t-test for significance. Of the different regions, Central Highlands showed the highest AAD in BAs. At a country level, the MERIS BA amounts were relatively higher than the MODIS BAs and especially during peak biomass burning months. We also found that emissions calculations using MERIS LC were relatively lower than those from MODIS LC. While over croplands, the regional emission factors yielded notably higher emissions compared with the global emission factors suggesting that current large-scale studies may be underestimating the biomass burning emissions. We further addressed the potential impact of emissions on urban air quality in Hanoi City through HYSPLIT trajectory modeling.

Scientific Reports, 2020

In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Opt... more In this study, we characterize the impacts of COVID-19 on air pollution using NO2 and Aerosol Optical Depth (AOD) from TROPOMI and MODIS satellite datasets for 41 cities in India. Specifically, our results suggested a 13% NO2 reduction during the lockdown (March 25–May 3rd, 2020) compared to the pre-lockdown (January 1st–March 24th, 2020) period. Also, a 19% reduction in NO2 was observed during the 2020-lockdown as compared to the same period during 2019. The top cities where NO2 reduction occurred were New Delhi (61.74%), Delhi (60.37%), Bangalore (48.25%), Ahmedabad (46.20%), Nagpur (46.13%), Gandhinagar (45.64) and Mumbai (43.08%) with less reduction in coastal cities. The temporal analysis revealed a progressive decrease in NO2 for all seven cities during the 2020 lockdown period. Results also suggested spatial differences, i.e., as the distance from the city center increased, the NO2 levels decreased exponentially. In contrast, to the decreased NO2 observed for most of the citi...

The purpose of this study is to demonstrate the application potential of using unmanned aerial sy... more The purpose of this study is to demonstrate the application potential of using unmanned aerial systems (UAS) combined with a time series of moderately high-resolution satellite imagery for mapping ecological restoration progress and resulting land cover changes. This technical note addresses a project under the US Army Corps of Engineers Ecosystem Management and Restoration Research Project (EMRRP) focusing on image acquisition and assessment, digital image processing techniques, analytical methodology, geospatial product development, and documentation of best practice for future data acquisition and analysis in support of ecological management efforts. BACKGROUND: This investigation has been developed by the US Army Engineer Research and Development Center (ERDC) Geospatial Research Lab (GRL) in response to a Statement of Need (SON) presented to the 2017 Environmental Research Area Review Group entitled "Evaluation and Optimization of Unmanned Aircraft Systems to Sense, Identify, and Map in Aquatic Systems" (2017-ER-17). Monitoring the distribution of vegetative cover is an integral element in the planning and implementation of both small and large ecosystem restoration projects. Facilitating the expansion of desirable native species, while suppressing the spread of undesirable exotic plants, is critical to restore both aquatic and terrestrial landscapes. Federal, state and local land management agencies need new techniques to monitor and manage ecosystem vegetation communities at multiple spatio-temporal scales (Reif and Theel 2017). Recent advancements in UAS provide high spatial, spectral, and temporal resolution aerial imagery to accurately and precisely monitor the distribution of plant communities. However, UAS have limitations, including (1) restricted areal coverage (< 5,000 acres) by most light UAS vehicles, (2) a lack of spectral bands (e.g., near-infrared and shortwave infrared) needed to accurately differentiate plant species, and (3) substantial data processing and workflow challenges associated with very high spatial resolution (< 5 cm * † pixels) image mosaics. This proposed investigation will address these limitations by integrating satellite multispectral imagery into the vegetation monitoring workflow, demonstrating the critical requirement to deploy UAS sensors that acquire imagery beyond the visible wavelengths and employing best practices in image processing and orthorectification, as well as building practical software tools that simplify the post-processing and classification of multi-band image mosaics. The * For a full list of the spelled-out forms of the units of measure used in this document, please refer to US Government Publishing Office Style Manual, 31st ed.

The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engine... more The U.S. Army Engineer Research and Development Center (ERDC) solves the nation's toughest engineering and environmental challenges. ERDC develops innovative solutions in civil and military engineering, geospatial sciences, water resources, and environmental sciences for the Army, the Department of Defense, civilian agencies, and our nation's public good. Find out more at www.erdc.usace.army.mil. To search for other technical reports published by ERDC, visit the ERDC online library at http://acwc.sdp.sirsi.net/client/default.

Environmental Pollution, 2019

Satellite observations for regional air quality assessment rely on comprehensive spatial coverage... more Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R 2 ¼ 0.81) and VIIRS AOD (R 2 ¼ 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar,

Remote Sensing, 2018

Quantifying emissions from crop residue burning is crucial as it is a significant source of air p... more Quantifying emissions from crop residue burning is crucial as it is a significant source of air pollution. In this study, we first compared the fire products from two different sensors, the Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product (VNP14IMG) and Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km fire product (MCD14ML) in an agricultural landscape, Punjab, India. We then performed an intercomparison of three different approaches for estimating total particulate matter (TPM) emissions which includes the fire radiative power (FRP) based approach using VIIRS and MODIS data, the Global Fire Emissions Database (GFED) burnt area emissions and a bottom-up emissions approach involving agricultural census data. Results revealed that VIIRS detected fires were higher by a factor of 4.8 compared to MODIS Aqua and Terra sensors. Further, VIIRS detected fires were higher by a factor of 6.5 than Aqua. The mean monthly MODIS Aqua FRP was found to be higher than the VIIRS FRP; however, the sum of FRP from VIIRS was higher than MODIS data due to the large number of fires detected by the VIIRS. Besides, the VIIRS sum of FRP was 2.5 times more than the MODIS sum of FRP. MODIS and VIIRS monthly FRP data were found to be strongly correlated (r 2 = 0.98). The bottom-up approach suggested TPM emissions in the range of 88.19-91.19 Gg compared to 42.0-61.71 Gg, 42.59-58.75 Gg and 93.98-111.72 Gg using the GFED, MODIS FRP, and VIIRS FRP based approaches, respectively. Of the different approaches, VIIRS FRP TPM emissions were highest. Since VIIRS data are only available since 2012 compared to MODIS Aqua data which have been available since May 2002, a prediction model combining MODIS and VIIRS FRP was derived to obtain potential TPM emissions from 2003-2016. The results suggested a range of 2.56-63.66 (Gg) TPM emissions per month, with the highest crop residue emissions during November of each year. Our results on TPM emissions for seasonality matched the ground-based data from the literature. As a mitigation option, stringent policy measures are recommended to curtail agricultural residue burning in the study area.

Scientific Reports, 2019

We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2016... more We assessed the fire trends from Moderate Resolution Imaging Spectroradiometer (MODIS) (2003-2016) and Visible Infrared Imaging Radiometer Suite (VIIRS) (2012-2016) in South/Southeast Asia (S/ SEA) at a country level and vegetation types. We also quantified the fire frequencies, anomalies and climate drivers. MODIS data suggested India, Pakistan, Indonesia and Myanmar as having the most fires. Also, the VIIRS-detected fires were higher than MODIS (AQUA and TERRA) by a factor of 7 and 5 in S/SEA. Thirty percent of S/SEA had recurrent fires with the most in Laos, Cambodia, Thailand, and Myanmar. Statistically-significant increasing fire trends were found for India (p = 0.004), Cambodia (p = 0.001), and Vietnam (p = 0.050) whereas Timor Leste (p = 0.004) had a decreasing trend. An increasing trend in fire radiative power (FRP) were found for Cambodia (p = 0.005), India (0.039), and Pakistan (0.06) and declining trend in Afghanistan (0.041). Fire trends from VIIRS were not significant due to limited duration of data. In S/SEA, fires in croplands were equally frequent as in forests, with increasing fires in India, Pakistan, and Vietnam. Specific to climate drivers, precipitation could explain more variations in fires than the temperature with stronger correlations in Southeast Asia than South Asia. Our results on fire statistics including spatial geography, variations, frequencies, anomalies, trends, and climate drivers can be useful for fire management in S/SEA countries. Vegetation fires are a common phenomenon in many different regions of the world including South/Southeast Asia (S/SEA). Fuel type, topography, climate, weather, lightning, and other factors govern fire occurrence and spread 1-4. Of the different natural factors, drought-induced fires due to El Niño-Southern Oscillation (ENSO) in southeast Asia and more specifically Indonesia are most common 5-7. In addition to these natural factors, most of the fires in S/SEA are human initiated. For example, fire is used as a land clearing tool during the slash and burn agriculture in the

Geocarto International, 2019

Wildland fires result in a unique signal detectable by multispectral remote sensing and Synthetic... more Wildland fires result in a unique signal detectable by multispectral remote sensing and Synthetic Aperture Radar (SAR). However, in many regions, such as Southeast Asia, persistent cloud cover and aerosols temporarily obstruct multispectral satellite observations of burned area, including the MODIS MCD64A1 Burned Area Product (BAP). Multiple days between cloud free pre-and post-burn MODIS observations results in burn date uncertainty. We incorporate cloud-penetrating, C-band SAR-with the MODIS MCD64A1 BAP in Southeast Asia, to exploit the strengths of each dataset to better estimate the burn date and reduce the potential burn date uncertainty range. We incorporate built-in quality control using MCD64A1 to reduce erroneous pixel updating. We test the method over part of Laos and Thailand during April 2016 and found average uncertainty reduction of 4.5days, improving 15% of MCD64A1 pixels. A new BAP could improve monitoring temporal trends of wildland fires, air quality studies, and monitoring post-fire vegetation dynamics.