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While deforestation has traditionally been the focus for forest canopy disturbance detection, forest degradation must not be overlooked. Both deforestation and forest degradation influence carbon loss and greenhouse gas emissions and thus must be included in activity data reporting estimates, such as for the Reduced Emissions from Deforestation and Degradation (REDD+) program. Here, we report on efforts to develop forest degradation mapping capacity in Nepal based on a pilot project in the country’s Terai region, an ecologically complex physiographic area. To strengthen Nepal’s estimates of deforestation and forest degradation, we applied the Continuous Degradation Detection (CODED) algorithm, which uses a time series of the Normalized Degradation Fraction Index (NDFI) to monitor forest canopy disturbances. CODED can detect low-grade degradation events and provides an easy-to-use graphical user interface in Google Earth Engine (GEE). Using an iterative process, we were able to create a model that provided acceptable accuracy and area estimates of forest degradation and deforestation in Terai that can be applied to the whole country. We found that between 2010 and 2020, the area affected by disturbance was substantially larger than the deforested area, over 105,650 hectares compared to 2753 hectares, respectively. Iterating across multiple parameters using the CODED algorithm in the Terai region has provided a wealth of insights not only for detecting forest degradation and deforestation in Nepal in support of activity data estimation but also for the process of using tools like CODED in applied settings. We found that model performance, measured using producer’s and user’s accuracy, varied dramatically based on the model parameters specified. We determined which parameters most altered the results through an iterative process; those parameters are described here in depth. Once CODED is combined with the description of each parameter and how it affects disturbance monitoring in a complex environment, this degradation-sensitive detection process has the potential to be highly attractive to other developing countries in the REDD+ program seeking to accurately monitor their forests.
Raja Aryal; Crystal Wespestad; Robert Kennedy; John Dilger; Karen Dyson; Eric Bullock; Nishanta Khanal; Marija Kono; Ate Poortinga; David Saah; Karis Tenneson. Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal. Remote Sensing 2021, 13, 2666 .
AMA StyleRaja Aryal, Crystal Wespestad, Robert Kennedy, John Dilger, Karen Dyson, Eric Bullock, Nishanta Khanal, Marija Kono, Ate Poortinga, David Saah, Karis Tenneson. Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal. Remote Sensing. 2021; 13 (14):2666.
Chicago/Turabian StyleRaja Aryal; Crystal Wespestad; Robert Kennedy; John Dilger; Karen Dyson; Eric Bullock; Nishanta Khanal; Marija Kono; Ate Poortinga; David Saah; Karis Tenneson. 2021. "Lessons Learned While Implementing a Time-Series Approach to Forest Canopy Disturbance Detection in Nepal." Remote Sensing 13, no. 14: 2666.
Time series land cover data statistics often fluctuate abruptly due to seasonal impact and other noise in the input image. Temporal smoothing techniques are used to reduce the noise in time series data used in land cover mapping. The effects of smoothing may vary based on the smoothing method and land cover category. In this study, we compared the performance of Fourier transformation smoothing, Whittaker smoother and Linear-Fit averaging smoother on Landsat 5, 7 and 8 based yearly composites to classify land cover in Province No. 1 of Nepal. The performance of each smoother was tested based on whether it was applied on image composites or on land cover primitives generated using the random forest machine learning method. The land cover data used in the study was from the years 2000 to 2018. Probability distribution was examined to check the quality of primitives and accuracy of the final land cover maps were accessed. The best results were found for the Whittaker smoothing for stable classes and Fourier smoothing for other classes. The results also show that classification using a properly selected smoothing algorithm outperforms a classification based on its unsmoothed data set. The final land cover generated by combining the best results obtained from different smoothing approaches increased our overall land cover map accuracy from 79.18% to 83.44%. This study shows that smoothing can result in a substantial increase in the quality of the results and that the smoothing approach should be carefully considered for each land cover class.
Nishanta Khanal; Mir Matin; Kabir Uddin; Ate Poortinga; Farrukh Chishtie; Karis Tenneson; David Saah. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL. Remote Sensing 2020, 12, 2888 .
AMA StyleNishanta Khanal, Mir Matin, Kabir Uddin, Ate Poortinga, Farrukh Chishtie, Karis Tenneson, David Saah. A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL. Remote Sensing. 2020; 12 (18):2888.
Chicago/Turabian StyleNishanta Khanal; Mir Matin; Kabir Uddin; Ate Poortinga; Farrukh Chishtie; Karis Tenneson; David Saah. 2020. "A Comparison of Three Temporal Smoothing Algorithms to Improve Land Cover Classification: A Case Study from NEPAL." Remote Sensing 12, no. 18: 2888.
Understanding land cover change dynamics and potential pathways of change is of critical importance for sustainable resource management, to promote food security and resilience on a range of spatial scales. Data scarcity is a key concern, however, with the availability of free Earth Observation (EO) data, such challenges can be suitably addressed. In this research we have developed a robust machine learning (random forest) approach utilizing EO and Geographic Information System (GIS) data, which enables an innovative means for our simulations to be driven only by historical drivers of change and hotspot prediction based on probability to change. We used the Mekong region as a case study to generate a training and validation sample from historical land cover patterns of change and used this information to train a random forest machine learning model. Data samples were created from the SERVIR-Mekong land cover data series. Data sets were created for 2 categories both containing 8 classes. The 2 categories included—any generic class to change into a specific one and vice versa. Classes included the following: Aquaculture; Barren; Cropland; Flooded Forest; Mangroves; Forest; Plantations; Wetlands; and Urban. The training points were used to sample a series of satellite-derived surface reflectance products and other data layers such as information on slope, distance to road and census data, which represent the drivers of change. The classifier was trained in binary mode and showed a clear separation between change and no change. An independent validation dataset of historical change pixels show that all median change probabilities are greater than 80% and all lower quantiles, except one, are greater than 70%. The 2018 probability change maps show high probabilities for the Plantations and Forest classes in the ‘Generic to Specific’ and ’Specific to generic’ category, respectively. A time-series analysis of change probability shows that forests have become more likely to convert into other classes during the last two decades, across all countries. We successfully demonstrated that historical change patters combined with big data and machine learning technologies are powerful tools for predictive change analytics on a planetary scale.
Ate Poortinga; Aekkapol Aekakkararungroj; Kritsana Kityuttachai; Quyen Nguyen; Biplov Bhandari; Nyein Soe Thwal; Hannah Priestley; Jiwon Kim; Karis Tenneson; Farrukh Chishtie; Peeranan Towashiraporn; David Saah. Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region. Remote Sensing 2020, 12, 1472 .
AMA StyleAte Poortinga, Aekkapol Aekakkararungroj, Kritsana Kityuttachai, Quyen Nguyen, Biplov Bhandari, Nyein Soe Thwal, Hannah Priestley, Jiwon Kim, Karis Tenneson, Farrukh Chishtie, Peeranan Towashiraporn, David Saah. Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region. Remote Sensing. 2020; 12 (9):1472.
Chicago/Turabian StyleAte Poortinga; Aekkapol Aekakkararungroj; Kritsana Kityuttachai; Quyen Nguyen; Biplov Bhandari; Nyein Soe Thwal; Hannah Priestley; Jiwon Kim; Karis Tenneson; Farrukh Chishtie; Peeranan Towashiraporn; David Saah. 2020. "Predictive Analytics for Identifying Land Cover Change Hotspots in the Mekong Region." Remote Sensing 12, no. 9: 1472.
During the last few decades, a large number of people have migrated to Kathmandu city from all parts of Nepal, resulting in rapid expansion of the city. The unplanned and accelerated growth is causing many environmental and population management issues. To manage urban growth efficiently, the city authorities need a means to be able to monitor urban expansion regularly. In this study, we introduced a novel approach to automatically detect urban expansion by leveraging state-of-the-art cloud computing technologies using the Google Earth Engine (GEE) platform. We proposed a new index named Normalized Difference and Distance Built-up Index (NDDBI) for identifying built-up areas by combining the LandSat-derived vegetation index with distances from the nearest roads and buildings analysed from OpenStreetMap (OSM). We also focused on logical consistencies of land-cover change to remove unreasonable transitions supported by the repeat photography. Our analysis of the historical urban growth patterns between 2000 and 2018 shows that the settlement areas were increased from 63.68 sq km in 2000 to 148.53 sq km in 2018. The overall accuracy of mapping the newly-built areas of urban expansion was 94.33%. We have demonstrated that the methodology and data generated in the study can be replicated to easily map built-up areas and support quicker and more efficient land management and land-use planning in rapidly growing cities worldwide.
Nishanta Khanal; Kabir Uddin; Mir A. Matin; Karis Tenneson. Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sensing 2019, 11, 2296 .
AMA StyleNishanta Khanal, Kabir Uddin, Mir A. Matin, Karis Tenneson. Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu. Remote Sensing. 2019; 11 (19):2296.
Chicago/Turabian StyleNishanta Khanal; Kabir Uddin; Mir A. Matin; Karis Tenneson. 2019. "Automatic Detection of Spatiotemporal Urban Expansion Patterns by Fusing OSM and Landsat Data in Kathmandu." Remote Sensing 11, no. 19: 2296.
Land cover monitoring efforts are important for resource planning and ecosystem services in many countries. Collect Earth Online (CEO) is a new, free open source and user-friendly software tool for land monitoring. It is the product of a collaborative effort between NASA, Food and Agriculture Organization of the United Nations (FAO), US Forest Service and Google. This paper provides a full overview of CEO's structure and functionality. Based on the cloud, CEO's structure supports simultaneous data entry by multiple users. No desktop installation is required and only an internet connection is required setting minimal requirements for using the software. Google Earth Engine widgets can be created for assisted plot interpretation such as image collection, time series graphs featuring indices such as Normalized Difference Vegetation Index (NDVI) and related statistics. We also provide a case study and related findings from a CEO workshop held in Myanmar.
David Saah; Gary Johnson; Billy Ashmall; Githika Tondapu; Karis Tenneson; Matt Patterson; Ate Poortinga; Kel Markert; Nguyen Hanh Quyen; Khun San Aung; Lena Schlichting; Mir Matin; Kabir Uddin; Raja Ram Aryal; John Dilger; Water Lee Ellenburg; Africa Ixmucane Flores-Anderson; Daniel Wiell; Erik Lindquist; Joshua Goldstein; Nick Clinton; Farrukh Chishtie. Collect Earth: An online tool for systematic reference data collection in land cover and use applications. Environmental Modelling & Software 2019, 118, 166 -171.
AMA StyleDavid Saah, Gary Johnson, Billy Ashmall, Githika Tondapu, Karis Tenneson, Matt Patterson, Ate Poortinga, Kel Markert, Nguyen Hanh Quyen, Khun San Aung, Lena Schlichting, Mir Matin, Kabir Uddin, Raja Ram Aryal, John Dilger, Water Lee Ellenburg, Africa Ixmucane Flores-Anderson, Daniel Wiell, Erik Lindquist, Joshua Goldstein, Nick Clinton, Farrukh Chishtie. Collect Earth: An online tool for systematic reference data collection in land cover and use applications. Environmental Modelling & Software. 2019; 118 ():166-171.
Chicago/Turabian StyleDavid Saah; Gary Johnson; Billy Ashmall; Githika Tondapu; Karis Tenneson; Matt Patterson; Ate Poortinga; Kel Markert; Nguyen Hanh Quyen; Khun San Aung; Lena Schlichting; Mir Matin; Kabir Uddin; Raja Ram Aryal; John Dilger; Water Lee Ellenburg; Africa Ixmucane Flores-Anderson; Daniel Wiell; Erik Lindquist; Joshua Goldstein; Nick Clinton; Farrukh Chishtie. 2019. "Collect Earth: An online tool for systematic reference data collection in land cover and use applications." Environmental Modelling & Software 118, no. : 166-171.
Forests in Southeast Asia are experiencing some of the highest rates of deforestation and degradation in the world, with natural forest species being replaced by cropland and plantation monoculture. In this work, we have developed an innovative method to accurately map rubber and palm oil plantations using fusion of Landsat-8, Sentinel 1 and 2. We applied cloud and shadow masking, bidirectional reflectance distribution function (BRDF), atmospheric and topographic corrections to the optical imagery and a speckle filter and harmonics for Synthetic Aperture Radar (SAR) data. In this workflow, we created yearly composites for all sensors and combined the data into a single composite. A series of covariates were calculated from optical bands and sampled using reference data of the land cover classes including surface water, forest, urban and built-up, cropland, rubber, palm oil and mangrove. This training dataset was used to create biophysical probability layers (primitives) for each class. These primitives were then used to create land cover and probability maps in a decision tree logic and Monte-Carlo simulations. Validation showed good overall accuracy (84%) for the years 2017 and 2018. Filtering for validation points with high error estimates improved the accuracy up to 91%. We demonstrated and concluded that error quantification is an essential step in land cover classification and land cover change detection. Our overall analysis supports and presents a path for improving present assessments for sustainable supply chain analyses and associated recommendations.
Ate Poortinga; Karis Tenneson; Aurélie Shapiro; Quyen Nquyen; Khun San Aung; Farrukh Chishtie; David Saah. Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification. Remote Sensing 2019, 11, 831 .
AMA StyleAte Poortinga, Karis Tenneson, Aurélie Shapiro, Quyen Nquyen, Khun San Aung, Farrukh Chishtie, David Saah. Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification. Remote Sensing. 2019; 11 (7):831.
Chicago/Turabian StyleAte Poortinga; Karis Tenneson; Aurélie Shapiro; Quyen Nquyen; Khun San Aung; Farrukh Chishtie; David Saah. 2019. "Mapping Plantations in Myanmar by Fusing Landsat-8, Sentinel-2 and Sentinel-1 Data along with Systematic Error Quantification." Remote Sensing 11, no. 7: 831.
Remote sensing landscape monitoring approaches frequently benefit from a dense time series of observations. To enhance these time series, data from multiple satellite systems need to be integrated. Landsat image data is a valuable 30-meter resolution source of spatial information to assess forest conditions over time. Together both operational Landsat satellites—7 and 8—provide a revisit frequency of 8 days at the equator. This moderate temporal frequency provides essential information to detect annual large area abrupt land cover changes. However, the ability to measure subtle and short lived intraseasonal changes is challenged by gaps in Landsat imagery at key points in time. The first Sentinel-2 satellite mission was launched by the European Space Agency in 2015. This moderate resolution data stream provides an opportunity to supplement the Landsat data record. The objective of this study is to assess the potential for integrating top of atmosphere Landsat and Sentinel 2 image data archived in the Google Earth Engine compute environment. In this paper we assess absolute and proportional differences in near-contemporaneous observations for six bands with comparable spectral response functions and spatial resolution between the Sentinel-2 Multi Spectral Instrument and Landsat Operational Land Imager and Enhanced Thematic Mapper Plus imagery. We assessed differences using absolute difference metrics and major axis linear regression between over 10,000 image pairs across the conterminous United States and present cross sensor transformation models. Major axis linear regression results indicate that Sentinel MSI data are as spectrally comparable to the two types of Landsat image data as the Landsat sensors are with each other. Root-mean-square deviation (RMSD) values ranging from 0.0121 to 0.0398 were obtained between MSI and Landsat spectral values, and RMSD values ranging from 0.0124 and 0.0372 were obtained between OLI and ETM+. Despite differences in their spatial, spectral, and temporal characteristics, integration of these datasets appears to be feasible through the application of bandwise linear regression corrections.
Robert Chastain; Ian Housman; Joshua Goldstein; Mark Finco; Karis Tenneson. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States. Remote Sensing of Environment 2018, 221, 274 -285.
AMA StyleRobert Chastain, Ian Housman, Joshua Goldstein, Mark Finco, Karis Tenneson. Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States. Remote Sensing of Environment. 2018; 221 ():274-285.
Chicago/Turabian StyleRobert Chastain; Ian Housman; Joshua Goldstein; Mark Finco; Karis Tenneson. 2018. "Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States." Remote Sensing of Environment 221, no. : 274-285.
Historical forest management practices in the southwestern US have left forests prone to high-severity, stand-replacement fires. Reducing the cost of forest-fire management and reintroducing fire to the landscape without negative impact depends on detailed knowledge of stand composition, in particular, above-ground biomass (AGB). Lidar-based modeling techniques provide opportunities to increase ability of managers to monitor AGB and other forest metrics at reduced cost. We developed a regional lidar-based statistical model to estimate AGB for Ponderosa pine and mixed conifer forest systems of the southwestern USA, using previously collected field data. Model selection was performed using Bayesian model averaging (BMA) to reduce researcher bias, fully explore the model space, and avoid overfitting. The selected model includes measures of canopy height, canopy density, and height distribution. The model selected with BMA explains 71% of the variability in field-estimates of AGB, and the RMSE of the two independent validation data sets are 23.25 and 32.82 Mg/ha. The regional model is structured in accordance with previously described local models, and performs equivalently to these smaller scale models. We have demonstrated the effectiveness of lidar for developing cost-effective, robust regional AGB models for monitoring and planning adaptively at the landscape scale.
Karis Tenneson; Matthew S. Patterson; Thomas Mellin; Mark Nigrelli; Peter Joria; Brent Mitchell. Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Remote Sensing 2018, 10, 442 .
AMA StyleKaris Tenneson, Matthew S. Patterson, Thomas Mellin, Mark Nigrelli, Peter Joria, Brent Mitchell. Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA. Remote Sensing. 2018; 10 (3):442.
Chicago/Turabian StyleKaris Tenneson; Matthew S. Patterson; Thomas Mellin; Mark Nigrelli; Peter Joria; Brent Mitchell. 2018. "Development of a Regional Lidar-Derived Above-Ground Biomass Model with Bayesian Model Averaging for Use in Ponderosa Pine and Mixed Conifer Forests in Arizona and New Mexico, USA." Remote Sensing 10, no. 3: 442.