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1. A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Author

Henry, Matieu, Iqbal, Zaheer, Johnson, Kristofer, Akhter, Mariam, Costello, Liam, Scott, Charles, Jalal, Rashed, Hossain, Md. Akhter, Chakma, Nikhil, Kuegler, Olaf, Mahmood, Hossain, Mahamud, Rajib, Siddique, Mohammad Raqibul Hasan, Misbahuzzaman, Khaled, Uddin, Mohammad Main, Al Amin, Mohammed, Ahmed, Farid Uddin, Sola, Gael, Siddiqui, Md. Baktiar, Birigazzi, Luca, Rahman, Mahmudur, Animon, Ilias, Ritu, Saimunnahar, Rahman, Laskar Muqsudur, Islam, Aminul, Hayden, Heather, Sidik, Frida, Kumar, Mondal Falgoonee, Mukul, Rakibul Hassan, Nishad, Hossain, Belal, Ariful Hoque, Anik, Asif Reza, Khaleque, Abdul, Shaheduzzaman, Md., Hossain, Syed Shahadat, Aziz, Tariq, Rahaman, Md. Tauhidor, Mohaiman, Ruhul, Meyer, Patrick, Chakma, Purnata, Rashid, A. Z. M. Manzoor, Das, Sourav, Hira, Shrabanti, Jashimuddin, Mohammed, Rahman, Mohammad Mahfuzur, Wurster, Karl, Uddin, Sarder Nasir, Azad, Abul Kalam, Islam, S. M. Zahirul, and Saint-André, Laurent

Published
2021
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13. Additional file 2 of A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Author

Henry, Matieu, Iqbal, Zaheer, Johnson, Kristofer, Akhter, Mariam, Costello, Liam, Scott, Charles, Rashed Jalal, Md. Akhter Hossain, Chakma, Nikhil, Kuegler, Olaf, Mahmood, Hossain, Rajib Mahamud, Siddique, Mohammad Raqibul Hasan, Misbahuzzaman, Khaled, Uddin, Mohammad Main, Amin, Mohammed Al, Ahmed, Farid Uddin, Sola, Gael, Md. Baktiar Siddiqui, Birigazzi, Luca, Mahmudur Rahman, Animon, Ilias, Saimunnahar Ritu, Laskar Muqsudur Rahman, Aminul Islam, Hayden, Heather, Sidik, Frida, Mondal Falgoonee Kumar, Rakibul Hassan Mukul, Nishad, Hossain, Ariful Hoque Belal, Anik, Asif Reza, Khaleque, Abdul, Md. Shaheduzzaman, Hossain, Syed Shahadat, Aziz, Tariq, Md. Tauhidor Rahaman, Ruhul Mohaiman, Meyer, Patrick, Purnata Chakma, A. Z. M. Manzoor Rashid, Sourav Das, Shrabanti Hira, Jashimuddin, Mohammed, Rahman, Mohammad Mahfuzur, Wurster, Karl, Sarder Nasir Uddin, Abul Kalam Azad, S. M. Zahirul Islam, and Saint-André, Laurent

Abstract

Additional file 2: Supplementary information 2. Additional tables provided as supplementary information. Table S2.1. Equations used as ratio-to-size estimators to estimate the biophysical indicators. Table S2.2. Total Carbon density by land cover in different zones. Table S2.3. Comparison between the classes identified from the land cover map and field observations.

Published
2021
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14. Additional file 1 of A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Author

Henry, Matieu, Iqbal, Zaheer, Johnson, Kristofer, Akhter, Mariam, Costello, Liam, Scott, Charles, Rashed Jalal, Md. Akhter Hossain, Chakma, Nikhil, Kuegler, Olaf, Mahmood, Hossain, Rajib Mahamud, Siddique, Mohammad Raqibul Hasan, Misbahuzzaman, Khaled, Uddin, Mohammad Main, Amin, Mohammed Al, Ahmed, Farid Uddin, Sola, Gael, Md. Baktiar Siddiqui, Birigazzi, Luca, Mahmudur Rahman, Animon, Ilias, Saimunnahar Ritu, Laskar Muqsudur Rahman, Aminul Islam, Hayden, Heather, Sidik, Frida, Mondal Falgoonee Kumar, Rakibul Hassan Mukul, Nishad, Hossain, Ariful Hoque Belal, Anik, Asif Reza, Khaleque, Abdul, Md. Shaheduzzaman, Hossain, Syed Shahadat, Aziz, Tariq, Md. Tauhidor Rahaman, Ruhul Mohaiman, Meyer, Patrick, Purnata Chakma, A. Z. M. Manzoor Rashid, Sourav Das, Shrabanti Hira, Jashimuddin, Mohammed, Rahman, Mohammad Mahfuzur, Wurster, Karl, Sarder Nasir Uddin, Abul Kalam Azad, S. M. Zahirul Islam, and Saint-André, Laurent

Abstract

Additional file 1: Supplementary information 1. R scripts for the analysis of the first cycle of the Bangladesh Forest Inventory. 1. R-scripts for the data importation. 2. R-scripts for the preparation of databases. 3. R-scripts for the ratio estimations.

Published
2021
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15. Additional file 3 of A multi-purpose National Forest Inventory in Bangladesh: design, operationalisation and key results

Author

Henry, Matieu, Iqbal, Zaheer, Johnson, Kristofer, Akhter, Mariam, Costello, Liam, Scott, Charles, Rashed Jalal, Md. Akhter Hossain, Chakma, Nikhil, Kuegler, Olaf, Mahmood, Hossain, Rajib Mahamud, Siddique, Mohammad Raqibul Hasan, Misbahuzzaman, Khaled, Uddin, Mohammad Main, Amin, Mohammed Al, Ahmed, Farid Uddin, Sola, Gael, Md. Baktiar Siddiqui, Birigazzi, Luca, Mahmudur Rahman, Animon, Ilias, Saimunnahar Ritu, Laskar Muqsudur Rahman, Aminul Islam, Hayden, Heather, Sidik, Frida, Mondal Falgoonee Kumar, Rakibul Hassan Mukul, Nishad, Hossain, Ariful Hoque Belal, Anik, Asif Reza, Khaleque, Abdul, Md. Shaheduzzaman, Hossain, Syed Shahadat, Aziz, Tariq, Md. Tauhidor Rahaman, Ruhul Mohaiman, Meyer, Patrick, Purnata Chakma, A. Z. M. Manzoor Rashid, Sourav Das, Shrabanti Hira, Jashimuddin, Mohammed, Rahman, Mohammad Mahfuzur, Wurster, Karl, Sarder Nasir Uddin, Abul Kalam Azad, S. M. Zahirul Islam, and Saint-André, Laurent

Abstract

Additional file 3: Supplementary information 3. An illustration of differences between land class identification and delineation using the land cover map and field observations. Figure S3.1. An illustration of differences between land class identification and delineation using the land cover map and field observations.

Published
2021
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18. Soil resources of the Ancient Maya at Piedras Negras, Guatemala

Author

Fernandez, Fabian G., Johnson, Kristofer D., Terry, Richard E., Nelson, Sheldon, and Webster, David

Subjects
Soil science -- Research, Agricultural systems -- History, Agricultural systems -- Environmental aspects, Mayas -- History, Mayas -- Environmental aspects, Earth sciences
Abstract

The ancient Maya thrived for centuries in the Peten rain forest of Guatemala. Their impressive architecture and the evidence of highly populated centers attest that the Maya farmers were capable of producing food surpluses. In the eighth to ninth centuries CE the Classic Maya civilization collapsed. The processes leading to its decline are still debated, but unsustainable agricultural practices and exhaustion of natural resources may have contributed. This paper reports on soil formation rates, soil taxonomy, phytolith analysis, and [[delta].sup.13]C values of soil organic matter in a rural area near the ancient city of Piedras Negras. Our objective was to understand ancient Maya rural life by linking soil characteristics to ancient agricultural resources and anthropogenic activities. We found that these soils formed at a rate of approximately 0.09 mm [yr.sup.-1]. All 15 soil profiles belonged to the order Mollisols. The soils of the back-slope locations were shallow (

Published
2005

25. Great Lakes coastal fish habitat classification and assessment

Author

Kovalenko, Katya E., primary, Johnson, Lucinda B., additional, Riseng, Catherine M., additional, Cooper, Matthew J., additional, Johnson, Kristofer, additional, Mason, Lacey A., additional, McKenna, James E., additional, Sparks-Jackson, Beth L., additional, and Uzarski, Donald G., additional

Published
2018
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27. Implications of allometric model selection for county-level biomass mapping

Author

Duncanson, Laura, Duncanson, Laura, Huang, Wenli, Johnson, Kristofer, Swatantran, Anu, McRoberts, Ronald E., Dubayah, Ralph, Duncanson, Laura, Duncanson, Laura, Huang, Wenli, Johnson, Kristofer, Swatantran, Anu, McRoberts, Ronald E., and Dubayah, Ralph

Abstract

Carbon accounting in forests remains a large area of uncertainty in the global carbon cycle. Forest aboveground biomass is therefore an attribute of great interest for the forest management community, but the accuracy of aboveground biomass maps depends on the accuracy of the underlying field estimates used to calibrate models. These field estimates depend on the application of allometric models, which often have unknown and unreported uncertainties outside of the size class or environment in which they were developed. Here, we test three popular allometric approaches to field biomass estimation, and explore the implications of allometric model selection for county-level biomass mapping in Sonoma County, California. We test three allometric models: Jenkins et al. (For Sci 49(1): 12–35, 2003), Chojnacky et al. (Forestry 87(1): 129–151, 2014) and the US Forest Service’s Component Ratio Method (CRM). We found that Jenkins and Chojnacky models perform comparably, but that at both a field plot level and a total county level there was a ~ 20% difference between these estimates and the CRM estimates. Further, we show that discrepancies are greater in high biomass areas with high canopy covers and relatively moderate heights (25–45 m). The CRM models, although on average ~ 20% lower than Jenkins and Chojnacky, produce higher estimates in the tallest forests samples (> 60 m), while Jenkins generally produces higher estimates of biomass in forests < 50 m tall. Discrepancies do not continually increase with increasing forest height, suggesting that inclusion of height in allometric models is not primarily driving discrepancies. Models developed using all three allometric models underestimate high biomass and overestimate low biomass, as expected with random forest biomass modeling. However, these deviations were generally larger using the Jenkins and Chojnacky allometries, suggesting that the CRM approach may be more appropriate for biomass mapping with lidar. These results co

Published
2017

28. The role of driving factors in historical and projected carbon dynamics of upland ecosystems in Alaska

Author

Genet, Hélène, primary, He, Yujie, additional, Lyu, Zhou, additional, McGuire, A. David, additional, Zhuang, Qianlai, additional, Clein, Joy, additional, D'Amore, David, additional, Bennett, Alec, additional, Breen, Amy, additional, Biles, Frances, additional, Euskirchen, Eugénie S., additional, Johnson, Kristofer, additional, Kurkowski, Tom, additional, (Kushch) Schroder, Svetlana, additional, Pastick, Neal, additional, Rupp, T. Scott, additional, Wylie, Bruce, additional, Zhang, Yujin, additional, Zhou, Xiaoping, additional, and Zhu, Zhiliang, additional

Published
2017
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32. Enhancing interoperability to facilitate implementation of REDD+: case study of Mexico

Author

Vargas, Rodrigo, primary, Alcaraz-Segura, Domingo, additional, Birdsey, Richard, additional, Brunsell, Nathaniel A., additional, Cruz-Gaistardo, Carlos O., additional, de Jong, Bernardus, additional, Etchevers, Jorge, additional, Guevara, Mario, additional, Hayes, Daniel J., additional, Johnson, Kristofer, additional, Loescher, Henry W., additional, Paz, Fernando, additional, Ryu, Youngryel, additional, Sanchez-Mejia, Zulia, additional, and Toledo-Gutierrez, Karla P., additional

Published
2017
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35. Additional file 3: of Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA

Author

Wenli Huang, Swatantran, Anu, Johnson, Kristofer, Duncanson, Laura, Tang, Hao, Jarlath O’Neil Dunne, Hurtt, George, and Dubayah, Ralph

Abstract

Table S1. Total Maryland biomass, in Tg, for CMS_RF at 30Â m resolution and FIA calculations (2008â 2012 cycle) by forest and non-forest classification. FIA forest and non-forest definitions do not follow NLCD landcover classes, rather by on the ground plot conditions. Non-forest biomass in the FIA dataset was calculated by methods described in [28].

Published
2015
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37. TV everywhere for everyone

Author

Johnson, Kristofer

Subjects
Infomercials, Streaming media, Streaming media technology, Advertising, marketing and public relations
Abstract

Everyone is familiar with this oft-used colloquialism: 'You can't teach an old dog new tricks.' Most everyone in the marketing industry also knows the famous (or perhaps infamous) John Wanamaker [...]

Published
2017

38. The role of environmental driving factors in historical and projected carbon dynamics of wetland ecosystems in Alaska

Author

Lyu, Zhou, primary, Genet, Hélène, additional, He, Yujie, additional, Zhuang, Qianlai, additional, McGuire, A. David, additional, Bennett, Alec, additional, Breen, Amy, additional, Clein, Joy, additional, Euskirchen, Eugénie S., additional, Johnson, Kristofer, additional, Kurkowski, Tom, additional, Pastick, Neal J., additional, Rupp, T. Scott, additional, Wylie, Bruce K., additional, and Zhu, Zhiliang, additional

Published
2016
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40. Modeling spatial and temporal patterns of soil organic carbon in two montane landscapes: The northern hardwoods, Vermont and the tabonuco forest, Puerto Rico

Author

Johnson, Kristofer Dee and Johnson, Kristofer Dee

Abstract

Forest soils contribute to a significant portion of the world's carbon flux due to both natural and anthropogenic changes. In terms of human management of carbon pools, forest soil organic matter (SOM) is important because it potentially stores carbon more permanently than living vegetation. Yet, this potential is poorly understood or managed for because of the difficulty in measuring changes in SOM pools over time and space. Modeling combined with intensive field sampling can help overcome these limitations because it extracts from empirically observed relationships to account for the components of SOM formation (topography, time, parent material, organisms and climate[fns2] ). This study utilizes intensive field data, statistical models and process-based ecosystem models to investigate the spatial distribution and dynamics of soil organic carbon dynamics in two contrasting ecosystems—the northern hardwood forest in the Green Mountains, VT and the tabonuco forest in the Luquillo Experimental Forest, PR. In both forests landscape position emerged as the dominate factor in explaining SOM distribution. In Vermont, additional variation was explained by aspect and slope and in Puerto Rico additional variation was explained by landscape factors interrelated to soil drainage. Process-based modeling proved to be a useful management and experimental tool in cases were empirical approaches were impractical for both forests. In Vermont, three ecosystem models demonstrated a substantial reduction of soil organic carbon and harvestable biomass due to the removal of woody carbon by logging after 240 years of rotations. In Puerto Rico, the Century model showed that changes in litter quality and quantity were not likely responsible in explaining landscape level SOM differences. Overall, well drained soils located in colder climates stored the highest SOM whereas poorly drained and highly disturbed soils in steep humid climates stored the lowest SOM. This research demonstrates that

Published
2008

50. Local discrepancies in continental scale biomass maps: a case study over forested and non-forested landscapes in Maryland, USA.

Author

Wenli Huang, Swatantran, Anu, Johnson, Kristofer, Duncanson, Laura, Hao Tang, Dunne, Jarlath O'Neil, Hurtt, George, and Dubayah, Ralph

Subjects
BIOMASS, MAPS, LANDSCAPES, FORESTS & forestry, CARBON, HIGH resolution imaging, DECISION making
Abstract

Background: Continental-scale aboveground biomass maps are increasingly available, but their estimates vary widely, particularly at high resolution. A comprehensive understanding of map discrepancies is required to improve their effectiveness in carbon accounting and local decision-making. To this end, we compare four continental-scale maps with a recent high-resolution lidar-derived biomass map over Maryland, USA. We conduct detailed comparisons at pixel-, county-, and state-level. Results: Spatial patterns of biomass are broadly consistent in all maps, but there are large differences at fine scales (RMSD 48.5–92.7 Mg ha−1). Discrepancies reduce with aggregation and the agreement among products improves at the county level. However, continental scale maps exhibit residual negative biases in mean (33.0–54.6 Mg ha−1) and total biomass (3.5–5.8 Tg) when compared to the high-resolution lidar biomass map. Three of the four continental scale maps reach near-perfect agreement at ~4 km and onward but do not converge with the high-resolution biomass map even at county scale. At the State level, these maps underestimate biomass by 30–80 Tg in forested and 40–50 Tg in non-forested areas. Conclusions: Local discrepancies in continental scale biomass maps are caused by factors including data inputs, modeling approaches, forest/non-forest definitions and time lags. There is a net underestimation over high biomass forests and non-forested areas that could impact carbon accounting at all levels. Local, high-resolution lidar-derived biomass maps provide a valuable bottom-up reference to improve the analysis and interpretation of large-scale maps produced in carbon monitoring systems. [ABSTRACT FROM AUTHOR]

Published
2015
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