My research focuses on the interactions between dynamic human and natural systems. I use remote sensing and spatio-temporally explicit models to study these processes at spatial and temporal scales typically prohibitive for studies based on ground observations only. Currently, we are developing methods to better quantify the spatial arrangement and temporal dynamics of natural systems using time-series of remote sensing data and examining the interactions between socio-economic and biophysical systems that lead to these dynamics.
I examine the interactions between human activities and natural systems at landscape to regional-level scales with particular interest in studying the impacts of human activities on the spatio-temporal dynamics of natural systems. I use remotely sensed data and spatially explicit models to better understand the interactions between human and natural systems.
**Graduate Student Positions Available**
I am currently seeking high quality PhD students to join my lab who are interested in the application of high resolution hyperspectral and UAV data to the complex questions related to agro-ecosystems. I am particularly interested in students with previous experience in quantitative image processing, hyperspectral applications, or spatial ecology with a background in remote sensing. Graduate student support is available. If you have any questions, please feel free to email me at firstname.lastname@example.org. Additional information on the application process can be found here https://grad.admissions.uiowa.edu/apply.
We have developed a state‐wide program to integrate plot‐level bioenergy research with state‐wide remote sensing analyses through newly available airborne hyperspectral sensor capabilities, the Hawkeye Hyperspectral Imager. Specifically, we are examining the application of a suite of airborne hyperspectral sensors capable of taking detailed measurements of the reflected light from the earth’s surface at a relatively fine spatial resolution (ca. 1m). These capabilities significantly extend our ability to discern vegetation communities (e.g., invasive species and complex patterns of plant communities in floodplains), empirically measure canopy and topsoil characteristics (such as leaf area, nitrogen and water content), and model the interactions of light with canopies. Hyperspectral modeling relationships and data fusion have been used for floodplain conservation planning, to examine agriculture soil characteristics, and improve the characterization of urban ecosystems.
Modeling Human/Environment Interactions
Remote sensing data provide decades of detailed, spatially-explicit information on human impacts. Using declassified spy satellite photography from the 1960’s and civilian satellite imagery from 1970 - 1990’s, for example, we examined human impacts on giant panda habitat over several decades. These data allowed us to estimate not only the quantity of changes, but also to examine the potential impacts from the timing and spatial arrangement of the changes (Linderman et al. 2005a). Measures of habitat quality and landscape indices were used to asses the spatial implications of land-cover changes. To examine the interactions between households and the landscape, I developed a model to study the indirect impacts of the removal of forest cover on understory bamboo dynamics. Bamboo species typically undergo a regular mass seeding and die-off event before returning to pre-die-off quantities and distribution. We found that the timing and spatial distribution of human impacts may have a considerable impact on this natural dynamic of bamboo species through the removal of forest cover and, consequently, species that rely on bamboo such as the giant panda (Linderman et al. 2006).
In addition, I am also examining broader-scale spatio-temporal variability such as the interannual variability of vegetation productivity across sub-Saharan Africa. Using time series of MODIS 1-km resolution data and newly developed indices of interannual variability, we mapped the changes in timing and magnitude of vegetation activity each year between 2000 and 2004 across Africa (Linderman et al. 2005b). We are examining the regional to global factors influencing these changes such as climate, land use, vegetation types, and abiotic factors to determine the pre-disposing factors of interannual variability and the potential significance for local to global-scale processes (Serneels et al. 2007). As shown by Rowhani et al. (2008), these relatively high-frequency dynamics have significant implications for natural systems. We are currently extending this work to examine land-use impacts on interannual variability in prairie and agricultural systems in Iowa and the influence of variability on society such as through health and food security.
Examples of current research:
- GEOG:1020 The Global Environment: An overview of the Earth System including the atmosphere, hydrosphere, lithosphere, and biosphere with an emphasis on the interactions and implications of changes of these systems.
- GEOG:3500 Introduction to Environmental Remote Sensing: An introductory class on the tools and methods of applying aerial and satellite remotely sensed data with the priority being exposure to techniques and applications related to land use issues common to local to federal agencies and geographic research.
- GEOG:3570 Light Detection and Ranging (LiDAR): Principles and Applications: An introduction to the analyses and applications of terrestrial and aerial lidar.
- GEOG:4010 Field Methods in Geography: This course provides an introduction to the background and methods of sampling spatial environmental variables commonly used in GIS, remote sensing, and environmental sciences. The class will introduce basic sampling methodologies and strategies using readings and discussion of the approaches and methods of sampling and testing vegetation, land cover, soils, and more.
- GEOG:6300 Advanced topics in Remote Sensing and Land Use/Land Cover: Various topics are covered that examine the incorporation of remotely sensed data into land use/land cover analyses and spatial models, emerging tools and methods such as applications of high resolution hyperspectral data, object-based approaches, and the fusion of lidar data.
- Basu N and Linderman M. Remote Sensing Based Distributed Hydrologic Modeling in Midwestern Landscapes for Predicting “Tile-to-Tide” Responses. Global and Regional Environmental Research, 2010, $29,997.
- Cowles MK, Segre A, Kusiak A, Bennett D, Stewart K. IGERT: Geoinformatics for Environmental and Energy Modeling and Prediction (GEEMaP), National Science Foundation, Division of Graduate Education, July 11, 2010 – June 30, 2011, Participating Faculty.
- Leicht, K, Kumar N, Linderman M, Chen J, Peters T, and Rice T. Iowa Survey of Public Attitudes: An Optimal Approach, National Science Foundation, Division of Social and Economic Sciences, March 1, 2009 – February 28, 2010, $595,509.
- Linderman, M and Cowles, K. Center for Global and Regional Environmental Research, 2007, “Inter-Calibration of Global Remotely Sensed Vegetation Measures”, $28,450.
- Linderman M, Z. Shen, and G. Malanson. National Science Foundation, Proposal for Visits and Workshops, 2006, “Geography and Regional Science Program Proposal Development on the Social and Ecological Impacts of the Three Gorges dam, China”, $18,320.
- Linderman M. UIRIS OVPR Internal Funding Initiatives Proposal - Social Sciences Funding Program, 2006 - 2007, “Human Impacts on Inter-annual Vegetation Dynamics”, $29,692.
- Linderman M. National Science Foundation (International Research Fellow Program), 2002 - 2005, "The Application of Global Biophysical Fields Derived from Large-Swath Remote Sensing Data to the Detection and Categorization of Land Cover Change" $137,900.
Tayyebi A, Pijanowski BC, Linderman M and Gratton C. Comparing three global parametric and local non-parametric models to simulate land use change in diverse areas of the world. Environmental Modelling & Software 59: 202-221 2014.
Kumar N, Linderman M, Chu A, Tripathi S, Foster AD and Liang D. Environmental Interventions and Air Pollution (Re)distribution in Delhi, India. Social Science Research Network, 2014. dx.doi.org/10.2139/ssrn.2462105.
Porter S and Linderman M. Historic land cover change in the agricultural Midwest using an object-based approach for classification of high-resolution imagery. J. Appl. Remote Sens. 7(1): 073506-073506 2013. doi:10.1117/1.JRS.7.073506.
Linderman M, and Lepczyk CA. Vegetation dynamics and human settlement across the conterminous United States. Journal of Maps 9(2): 198-202 2013.
Hull V, Xu W, Liu W, Zhou S, Viña A, Zhang J, Tuanmu MN, Huang J, Linderman M, Chen X, Huang Y, Ouyang Z, Zhang H, Liu J. Evaluating the efficacy of zoning designations for protected area management: The case of Wolong Nature Reserve and giant pandas. Biological Conservation 144(12): 3028-3037 2012.
Kumar N, Liang D, Linderman M and Chen J, 2011, An Optimal Spatial Sampling for Demographic and Health Surveys. Social Science Research Network, Working Paper Series, 1808947 (http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1808947).
Rowhani P, Linderman M, Lambin E. Global interannual variability in terrestrial ecosystems: sources and spatial distribution using MODIS-derived vegetation indices, social and biophysical factors. International Journal of Remote Sensing 32(19): 5393-5411 2011.
Rowhani, P, DB Lobell, Linderman M and Ramankutty N. Climate variability and crop production in Tanzania. Agricultural and Forest Meteorology 151(4): 449-460 2011.
Linderman, M, Y. Zeng, P. Rowhani. Climate and land-use effects on interannual fAPAR variability from MODIS 250 m data. Photogrammetric Engineering and Remote Sensing, 76(7): 807-817 July 2010.
He, G. M., X. D. Chen, S. Beaer, M. Colunga, A. Mertig, L. An, S. Q. Zhou, M. Linderman, Z. Ouyang, S. Gage, S. X. Li and J. G. Liu. Spatial and temporal patterns of fuelwood collection in Wolong Nature Reserve: Implications for panda conservation, Landscape and Urban Planning, 92(1): 1-9, 2009.
Rowhani P, Lepczyk CA, Linderman MA, Pidgeon AM, Radeloff VC, Culbert PD, and Lambin EF. Variability in energy influences avian distribution patterns across the USA. Ecosystems, 11(6): 854-867 June 2008. PDF (The original publication is available at http://www.springerlink.com/)
Rindfuss RR, B Entwisle, SJ Walsh, L An, N Badenoch, DG Brown, P Deadman, TP Evans, J Fox, J Geoghegan, M Gutmann, M Kelly, M Linderman, J Liu, GP Malanson, CF Mena, JP Messina, EF Moran, DC Parker, W Parton, P Prasartkul, DT Robinson, Y Sawangdee, LK Vanwey, PH Verburg. 2008. Land use change: complexity and comparisons. Journal of Land Use Science, 3(1): 1-10.
Parker DC, B Entwisle, Rindfuss RR, Vanwey LK, Manson SM, Moran E, An L, Deadman P, Evans TP, Linderman M, Rizia SMM and Malanson G. Case studies, cross-site comparisons, and the challenge of generalization: Comparing agent-based models of land-use change in frontier regions. Journal of Land Use Science 3 (1): 41-72 March 2008.
Bearer SL, Linderman M, Huang J, An L, He G, and Liu J. Effects of fuelwood collection and timber harvesting on giant panda habitat use. Biological Conservation 141(2): 385-393 Feb 2008.
Lupo F, Linderman M, Vanacker V, Bartholom E., And Lambin EF. Categorization of land-cover change processes based on phenological indicators extracted from time series of vegetation index Data. International Journal of Remote Sensing 28 (11): 2469-2483 June 2007
Viña A, Bearer S, Chen X, He G, Linderman M, An L, Zhang H, Ouyang Z, and Liu J. Temporal changes in connectivity of giant panda habitat across the boundaries of Wolong Nature Reserve (China). Ecological Applications 17 (4): 1019-1030 June 2007. PDF
Serneels S, Linderman M, and Lambin EF. A multilevel analysis of the impact of land use on interannual land-cover change in East Africa. Ecosystems 10 (3): 402-418 April 2007. PDF (The original publication is available at http://www.springerlink.com/)
Lambin EF and Linderman M. Time series of remote sensing data for land change science. IEEE Transactions on Geoscience and Remote Sensing 44 (7): 1926 1928 July 2006. Abstract
Linderman MA, An L, Bearer S, He G, Ouyang Z, and Liu J. Interactive effects of natural and human disturbances on vegetation dynamics across landscapes. Ecological Applications 16 (2): 452463 April 2006. PDF
Linderman M, Rowhani P, Benz D, Serneels S, Lambin EF. Land-cover change and vegetation dynamics across Africa. Journal of Geophysical Research-Atmospheres 110 (D12): Art. No. D12104 Jun 17 2005. Abstract
Linderman MA, An L, Bearer S, He GM, Ouyang ZY, Liu JG. Modeling the spatio-temporal dynamics and interactions of households, landscapes, and giant panda habitat. Ecological Modelling 183 (1): 47-65 Apr 10 2005. Abstract
Vanacker V, Linderman M, Lupo F, Flasse S, Lambin E, et al. Impact of short-term rainfall fluctuation on interannual land cover change in sub-Saharan Africa. Global Ecology and Biogeography 14 (2): 123-135 Mar 2005
An L, Linderman M, Qi J, Shortridge A, Liu J, et al. Exploring complexity in a human-environment system: An agent-based spatial model for multidisciplinary and multiscale integration. Annals of the Association of American Geographers 95 (1): 54-79 Mar 2005
Linderman M, Bearer S, An L, Tan YC, Ouyang ZY, Liu HG. The effects of understory bamboo on broad-scale estimates of giant panda habitat. Biological Conservation 121 (3): 383-390 Feb 2005. Abstract
Linderman M, Liu J, Qi J, An L, Ouyang Z, Yang J, Tan Y. Using artificial neural networks to map the spatial distribution of understorey bamboo from remote sensing data. International Journal of Remote Sensing 25 (9): 1685-1700 May 2004. Abstract
An L, Lupi F, Liu JG, Linderman MA, Huang JY. Modeling the choice to switch from fuelwood to electricity Implications for giant panda habitat conservation. Ecological Economics 42 (3): 445-457 Sep 2002
Liu JG, Linderman M, Ouyang Z, An L. The pandas' habitat at Wolong Nature Reserve Response. Science 293 (5530): 603-605 Jul 27 2001
An L, Liu JG, Ouyang ZY, Linderman M, Zhou SQ, Zhang HM. Simulating demographic and socioeconomic processes on household level and implications for giant panda habitats. Ecological Modelling 140 (1-2): 31-49 May 30 2001
Liu JG, Linderman M, Ouyang ZY, An L, Yang J, Zhang HM. Ecological degradation in protected areas: The case of Wolong Nature Reserve for giant pandas Science 292 (5514): 98-101 Apr 6 2001. Abstract
Liu, J., L. An, S. Batie, S. Bearer, X. Chen, R. Groop, G. He, Z. Liang, M. Linderman, A. Mertig, O. Zhiyun, J. Qi, H. Zhan, S. Zhou, (2005) Beyond Population Size: Examining Intricate Interactions Among Population Structure, Land Use, and Environment in Wolong Nature Reserve, China. In Population, Land Use, and Environment: Research Directions (B. Entwisle and P. Stern, eds., National Academies Press).
Liu, J., Z. Ouyang, M. Linderman, L. An, S. Bearer, and G. He, (2004). A New Paradigm for Panda Research and Conservation: Integrating Ecology with Human Demography, Behavior, and Socioeconomics. In: Giant Pandas: Biology and Conservation (Donald G. Lindburg and Karen Baragona, editors; University of California Press, Berkeley).
Liu, J., L. An, S. Batie, R. Groop, Z. Liang, M. Linderman, A. Mertig, Z. Ouyang, and J. Qi, (2003). Human Impacts on Land Cover and Panda Habitat in Wolong Nature Reserve: Linking Ecological, Socioeconomic, Demographic, and Behavioral Data. In People and the Environment: Approaches for Linking Household and Community Surveys to Remote Sensing and GIS (Jeff Fox, Vinod Mishra, Ron Rindfuss, and Steve Walsh, eds., Kluwer Academic Publishers).
Linderman, M, V. Vanacker, F. Lupo, A. Wannebo, S. Serneels, D. Benz, E. Swinnen, P. Rowhani, and E. Lambin. Analyzing land-cover/land-use change using coarse resolution imagery. Proceedings of the 2nd International VEGETATION User Conference, Antwerp, Belgium, ed. F. Veroustraete, E. Bartholom and W.W. Verstraeten. 24- 26 March 2004.