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Forest Landscape Models: A Review

There’s a new forest landscape model classification and review out there, recently published in Forest Ecology and Management by Hong He. The paper assumes greater familiarity with the topic of forest and disturbance modelling than the paper I recently published with my former advisor, George Perry, and discussion focuses largely on models primarily developed for the study of temperate forest systems in the USA (e.g., JABOWA, SORTIE, LANDIS, ZELIG – exceptions include MAQUIS and FORMOSAIC).


Distinction between deterministic models and stochastic models

He suggests that, generally, ecological models fall in two seemingly exclusive categories, deterministic models and stochastic models, and that either category of model can use physical or empirical approaches, or a combination of both (see figure). However, the classification He presents in the paper is developed according to how models represent

  1. spatial processes,
  2. temporal processes,
  3. site-level succession, and
  4. the intended use of the model.

Models are classified by succession based on whether the model uses succession pathways (i.e., a Markov state-and-transition approach), vital attributes (as I utilised in my PhD modelling), or by coupling landscape models with more detailed stand-level vegetation succession models. The fourth classification criteria above highlights that there are numerous applications of forest landscape models, and that design is strongly related to the desired applications. He suggests applications of forest landscape models generally fall into one of three categories:

  1. spatiotemporal patterns of model objects,
  2. sensitivities of model object to input parameters, and
  3. comparisons of model simulation scenarios.

After developing and presenting the classification, the paper goes on to discuss two dilemmas facing those using forest landscape models. The first is the validation of model results, which has been discussed on numerous occasion elsewhere (including this blog). The discussion on circular reasoning is more novel however, (and related in some ways to what I have written with regards models of human agents):

“It is often difficult to separate expected results from emergent results. A caution against circular reasoning is the caveat often encountered in this situation, where researchers discuss biological or environmental forcing (causes) of their modeled results, whereas the forcing (causes) is actually built in the model formulation to derive such results. It should be pointed out that most model simulations do not lead to new understanding of the modeled processes themselves. The primary and subsequent results simply reflect the relationships used in building the models, which in turn reflect current understanding of the processes. The findings of these models are simply the spatiotemporal variations of the spatial process (discussed in Section 5.1), not the mechanisms that drive the potential changes of the spatial process. Emergent results are generally those resulted from the interactions and feedbacks of model objects.”


The paper concludes by summarizing likely development of forest landscape modelling in the future:

  1. Model development will move from the foci of theoretical and exploratory purposes to the foci of strategic and tactical purposes with increasing model realism, responding to the needs of forest management and planning.
  2. Multiple spatial and temporal resolutions will be implemented for different processes
  3. Standardized module components may emerge as handy utilities that are ready to be plugged into other models. Since component-based models provide non-developers or end users with access to model components, a component-based model can be more rigorously tested, evaluated, and modified than before, and thus, model development processes can be driven not solely by original developers, but by the broader scientific community
  4. Synchronization of multiple ecological processes can be made possible with multiple computer processors. This will help deal with the limitation that ecological processes are simulated in a sequential order as determined by the executable program.
  5. Model memorization will be improved so that a forest landscape model not only memorizes vegetation, disturbance, and management status at the current and previous model iteration, but also the entire temporal sequence. This would allow more effective studies of legacies of forested landscapes responding to various disturbance and management activities.


Here’s the full paper citation and abstract:

He (2008) Forest landscape models: Definitions, characterization, and classification Forest Ecology and Management 254 (3) Pages 484-498

Abstract
Previous model classification efforts have led to a broad group of models from site-scale (non-spatial) gap models to continental-scale biogeographical models due to a lack of definition of landscape models. Such classifications become inefficient to compare approaches and techniques that are specifically associated with forest landscape modeling. This paper provides definitions of key terminologies commonly used in forest landscape modeling to classify forest landscape models. It presents a set of qualitative criteria for model classification. These criteria represent model definitions and key model implementation decisions, including the temporal resolution, number of spatial processes simulated, and approaches to simulate site-level succession. Four approaches of simulating site level succession are summarized: (1) no site-level succession (spatial processes as surrogates), (2) successional pathway, (3) vital attribute, and (4) model coupling. Computational load for the first three approaches is calculated using the Big O Notation, a standard method. Classification criteria are organized in a hierarchical order that creates a dichotomous tree with each end node representing a group of models with similar traits. The classified models fall into various groups ranging from theoretical and empirical to strategic and tactical. The paper summarizes the applications of forest landscape models into three categories: (1) spatiotemporal patterns of model objects, (2) sensitivities of model object to input parameters, and (3) scenario analyses. Finally, the paper discusses two dilemmas related to the use of forest landscape models: result validation and circular reasoning.

Keywords Forest landscape models; Spatially explicit; Spatially interactive; Definitions; Model characterization; Model classification

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New Global High-Resolution Land Cover Map from ESA

“A new global portrait taken from space details Earth’s land cover with a resolution never before obtained. ESA, in partnership with the UN Food and Agriculture Organisation, presented the preliminary version of the map to scientists last week at the 2nd GlobCover User Consultation workshop held in Rome, Italy. Earth’s land cover has been charted from space before, but this map, which will be made available to the public upon its completion in July, has a resolution 10 times sharper than any of its predecessors….There are 22 different land cover types shown in the map, including croplands, wetlands, forests, artificial surfaces, water bodies and permanent snow and ice. For maximum user benefit, the map’s thematic legend is compatible with the UN Land Cover Classification System (LCCS).”

read more | digg story

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US-IALE 2008 – Landscape Change and other CSIS involvement

Today I started thinking in earnest about the 2008 US-IALE Symposium to be held in Madison, Wisconsin early next month.

I’ll be presenting a poster on our early model development work on the USDA deer/timber regeneration project at CSIS. I will also be chairing the Landscape Change session which has presentations discussing change within and across a diverse range of landscapes including, the Great Plains of the US, the Bolivian Andes and Ukrainian Carpathian mountain ranges, Boreal and Tropical forests, and the Congo Basin.

Whilst in Madison I also plan on attending sessions, symposia, workshops and field-trips devoted to Landscape Patterns and Ecosystem Processes, Modeling Forest Landscapes under Climate Change, Multifunctional Agricultural Landscapes, Forest Landscapes, and Fire. At this last session I’m particularly looking forward to the presentation entitled “Ecological complexity produces simple structure: Power laws in low-severity fire regimes” by Don McKenzie, co-convener of the wildfires session at EGU 2008 the following week (but which I will not be attending).

There will be plenty of other activity by members of CSIS. Jack Liu, president-elect of US-IALE, and CSIS PhD student Vanessa Hull are co-organising the H. Ronald Pulliam Symposium: Sources, Sinks, and Sustainability. Mao-Ning Tuanmu (PhD student) will be making a presentation entitled “Detecting understory vegetation using MODIS data: Implications for giant panda habitat evaluations” in the Remote Sensing session, and Wei Liu (also CSIS PhD student) will present “Conservation success leads to human-wildlife conflicts: Spatial patterns of crop damages and livestock depredation in Wolong Nature Reserve for Giant Pandas, China” in the Social Issues session.

And there’s loads more going on so it promises to be an interesting and busy week! If I get online during a spare 5 minutes I’ll see if I can blog an update on how it’s all going…

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Tackling Amazonian Rainforest Deforestation

This week’s edition of Nature devotes an editorial, a special report and an interview to the subject of tropical rainforests and their deforestation. The articles highlight both the proximate causes and underlying driving forces of tropical deforestation, and the importance of human activity as an agent of change (via fire for example), in these socio-ecological systems.

The editorial considers the economics of rainforest destruction, with regards to global carbon emissions. It suggests that deforestation must be integrated into international carbon markets, to reward those countries that have been able to control the removal of forest land (such as India and Costa Rica). Appropriate accounting of tropical rainforest carbon budgets is required however, and the authors point to the importance of carbon budget modelling and the monitoring of (via satellite imagery for example) change in rainforest areas over large spatial extents. Putting an economic price on ‘ecosystem services’ is key to this issue, and the editorial concludes:

One of the oddly positive effects of global warming is that it has given the world the opportunity to build a more comprehensive and inclusive economic model by forcing all of us to grapple with our impact on the natural environment. We are entering a phase in which new ideas can be developed, tested, refined and rejected as necessary. If we find just one that can beat the conventional economic measure of gross domestic product, and can quantify some of the basic services provided by rainforests and other natural ecosystems, it will more than pay for itself.


The special report focuses on the efforts of the Brazilian government to curb the rate of deforestation in the their Amazonian forests. The Brazilian police force is blockading roads, conducting aerial surveys and inspecting agricultural and logging operations, to monitor human activities on the ground. Brazilian scientists meanwhile are monitoring the situation from space, and have developed methodologies and techniques that are leading the way globally in the remote monitoring of forests. The Brazilian government is a keen advocate of the sort of economic approaches to the issues of rainforest destruction highlighted in the editorial outlined above, and sees this rigorous monitoring as key to be able to show how much carbon they can save by preventing deforestation.

Halting the removal of forest cannot simply be left to carbon trading alone, however, and local initiatives need to be pursued. To ensure the forest’s existence is sustainable, local communities need to be able make money for themselves without chopping down the trees – if they can do this it will be their in their interests NOT to remove forest. But developing this incentive has not been straightforward. For example, some researchers have have suggested that as commodity prices for crops such as soya beans have increased (possibly due to increased demand for corn-based ethanol in the US) deforestation has increased as a result. Although the price of soya beans may be a contributing factor to rainforest removal, Ruth DeFries (who will be visiting CSIS and MSU next week as part of the Rachel Carson Distinguished Lecture Series) suggests that it is not the main driver. Morton et al. found that during for the period 2001-04, conversion of forest to agriculture peaked in 2003. This situation makes it clear that there are both proximate causes and underlying driving forces of tropical deforestation. The Nature special report suggests:

If the international community is serious about tackling deforestation, it will probably need to use a hybrid approach: helping national governments such as Brazil to fund traditional policies for enforcement and monitoring and enabling communities to experiment with a market-based approach.


But how long do policy-makers have to discuss this and get these measures in place? One set of research suggests 55% of the Amazon rainforest could be removed over the next two decades, and the complexity of the rainforest system means that a ‘tipping point’ (i.e., an abrupt transition) beyond which the system might not recover (i.e., reforestation would not be possible). The Nature interview with Carlos Nobre highlights this issue – the interactions of climate change with soil moisture and the potential for fire indicate that the there is risk of rapid ‘savannization’ in the eastern to southeastern Amazon as the regional climate changes. When asked what the next big question scientists need to address in the Amazon is, Nobre replies that the role of human-caused fire will be key:

Fire is such a radical transformation in a tropical forest ecosystem that biodiversity loss is accelerated tremendously — by orders of magnitude. If you just do selective logging and let the area recover naturally, perhaps in 20–30 years only a botanist will be able to tell that a forest has been logged. If you have a sequence of vegetation fires going through that area, forget it. It won’t recover any more.


As I’ve previously discussed, considering the feedbacks and interactions between systems is important when examining landscape vulnerabilities to fire. Along with colleagues I have examined the potential effects of changing human activity on wildfire regimes in Spain (recently we had this paper published in Ecosystems and you can see more wildfire work here). However, the integrated study of socio-economic and ecological systems is still very much in its infancy. And the processes of landscape change in the northern Mediterranean Basin and the Amazonian rainforest are very different; practically inverse (increases in forest in the former and decreases in the latter). As always, plenty more work needs to be done on these subjects, and with the potential presence of ‘tipping points’, now is an important time to be doing it.

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utah pics


Last week I took a brief snowboarding trip to Utah. After two days on the fantastic Snowbird slopes we explored the area around Salt Lake City a little. One place we visited was Garr Ranch on Antelope Island, home of the oldest Anglo house still on its original foundation in Utah. Perched in the middle of the Great Salt Lake, it was quite a windswept location and impressive that it’s also the longest Anglo inhabited site in the state. A couple of photos from the trip now on the photos page.

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Google Earth GeoData

Previously, I highlighted work my old colleague and friend Pete Webley has done using Google Earth to model volcanic ash plumes. Another former King’s College colleague (and teacher) has been also been working with Google Earth. Mark Mulligan has posted online a large collection of KML files for a wide variety of geodata including satellite data on cloud climatology, a database of global place names, urban climate data, tropical land use change data, and much more.


KML files are used in Google products, such as Google Earth or Google Maps, to display geographic data. The data Mark has posted on the King’s server are freely accessible to all for non-commercial use. you can visualise the data in Google Earth and, in many cases, links to the actual downloadable GIS files also provided. Many of the datasets are works in progress and new data will continue to be posted in the future, so keep checking back.

The availability of data such as these, and projects such as Pete’s, really show how Google Earth can be used for so much more than virtual tours of other places or previews of you next holiday destination… [Speaking of which, I’m off to Utah snowboarding next week so hopefully I’ll have some new pics to post on my own Google-enabled photos page.]

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shift happens


I like this video. Less because of the message toward the end about the importance of ensuring western countries continue to train adaptable workforces in an increasingly flat world. More because of how it illustrates the speed and unpredictability of change. In hindsight it might seem obvious that this is how the world should end up – contingency matters in the real world after all. But in these contingent, historical, systems how do we generate a model for the future that we can trust with any useful degree of confidence?

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Software Add-ins for Ecological Modelling

During my modelling antics these last couple of days I seem to have been using many of the add-ins I’ve have installed with the software I use regularly. I thought I’d highlight some of them here as they are really useful tools that can expand the modelling and data manipulation possibilities of a standard software install.

Much of the modelling I do is spatial, so I’m regularly using some form of GIS. I’m most familiar with the ESRI products, but have also tinkered with things like GRASS. Two free add-ins that are really useful if you use ArcMap regularly are the Patch Analyst and Hawth’s Tools. Patch Analyst facilitates the spatial pattern analysis (making use of FRAGSTATS) of landscape patches, and the modelling of attributes associated with patches. Hawth’s Tools is an extension for ArcMap that performs a number of spatial analyses and functions that you can’t do with the standard install of ArcMap. Most of the tools are written with ecological analyses in mind, but it’s also be useful for non-ecologists with functions such as conditional point sampling, kernel density estimation and vector layer editing.

Although it is generally frowned upon for statistics (use R – see below), Microsoft Excel isn’t a bad tool for organising small and medium-sized data sets and for doing basic systems modelling (spatial simulation is a little trickier). Developed by some guys at CSIRO, Pop Tools is a free add-in for PC versions of Excel that facilitates analysis of matrix population models and the simulation of stochastic processes. It was originally written to analyse ecological models, but has been used for studies of population dynamics, financial modelling, and the calculation of bootstrap and resampling statistics. Once installed, PopTools puts a new menu item in Excel’s main menu and adds over a hundred useful worksheet functions. Regardless of whether you intend to do any modelling in Excel or not, the ASAP Utilities add-in is a must for automating many frequently required tasks (including those you didn’t even know you wanted to do in Excel!). There are selection tools (such as ‘select cell with smallest number’), text tools (such as ‘insert before current value’), information tools (such as ‘Find bad cell references (#REF!)’) and many more.

If you’re going to be doing any serious statistical analyses the current software of choice is R, the open-source language and environment for statistical computing and graphics. R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, …) and graphical techniques. If you need to analyse or manipulate large datasets R is for you – you are only restricted by the memory available on your computer. For computationally-intensive tasks, C, C++ and Fortran code can be linked and called at run time. R is also highly extensible by installing optional packages that have been written by users from around the world.

Many of the packages I use are from the Spatial and Environmetrics Task views. For example, I use spdep for calculating spatial autocorrelation, VR for computing spatial correlograms or confidence intervals for model parameters, and hier.part for hierarchical partitioning. This week I started thinking about how I will use the yaImpute package to impute the stand vegetation data we have collected at specific points in our study area across the entire landscape ready to initialise our spatial simulation model. Download the R software and the individual packages from a CRAN mirror near you.

Of course, this is just the tip of the iceberg and only a few of the most useful add-ins for the most commonly used software. For a much more complete list of more technical software and programming tools for ecological and environmental modelling see Andrea Emilio Rizzoli’s ‘Collection of Modelling and Simulation Resources on the Internet‘ or the list of Ecological Modelling links by T. Legovic’ and J. Benz.

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Forest Ecology and Management Special Issue: Forest Landscape Modeling

In June 2006 the China Natural Science Foundation and the International Association of Landscape Ecology sponsored an international workshop of forest landscape modelling. The aim of the workshop was to facilitate a discussion on the progress made in the theory and application of forest landscape models. A special issue of Forest Ecology and Management, entitled Forest Landscape Modeling – Approaches and Appplications [Vol. 253, Iss. 3], presents 12 papers resulting from that meeting. In their editorial, He et al. summarise the papers, organising them into three sections that describe current activities in forest landscape modelling: (1) effects of climate change on forest vegetation, (2) forest landscape model applications, and (3) model research and development.

The LANDIS model is used in several of the papers on climate and human management of forest systems. Advances in the representation of processes that propagate spatially, including fire and seed dispersal, are discussed in several of the papers examining model research and development. He et al. conclude their editorial by reiterating why landscape models are a vital tool for better understanding and managing forested regions of the world:

The papers represented in the special issue of forest landscape modeling highlight the advances and applications of forest landscape models. They show that forest landscape models are irreplaceable tools to conduct landscape-scale experiments while physical, financial, and human constraints make real-world experiments impossible. Most of the results presented in this issue would not have been possible without the use of forest landscape models. Forest landscape modeling is a rapidly developing field. Its development and application will continually be driven by the actual problems in forest management planning and landscape-scale research. We hope that the papers contained in this special issue will serve both researchers and managers who are struggling to incorporate large-scale and long-term landscape processes into their management planning or research.