Land Use Modelling - Science topic

You can check this article it will be helpful for your study

Kory Postma thank you very much for your response.

Hope you get some idea from the following paper

Yes indeed.

Qin Liu came across the same question with few answers on Research Gate and felt like sharing with you and the experts above

Hope so this will be helpfull

Todo índice es util siempre y cuando según la tecnología disponible se puedan resolver problrmas tecnicos.

Dear Bastian Bertsch-Hörmann

Have you explored the WEAP model?

Dear Pritiranjan Das

Here are some of the most prominent models for simulating urban growth. Besides, Jiří Kroc has provided a very good approach as well.

Thanks and regards

Gowhar Meraj

http://bhuvan.nrsc.gov.in/data/download/index.php

From this link at first register yourself in Bhuban portal then you can buy the IRS LISS IV satellite imageries....

Hi Jake,

It is a daunting task and not very easy to answer. We have been struggling with it for a while now. From our experiences it comes back to two basic challenges. The first one is how you define land degradation (see our recent publication below), but of course also to obtain the proper datasets. In our research we are focusing on creating a time series of a range of soil properties. We are currently aiming to at a time series of soil conditions for the UN statistics division in the context of their national accounting.

Cheers Jetse

Sterk, G., Stoorvogel, J.J., 2020. Desertification–Scientific Versus Political Realities. Land 2020, 9, 156; doi:10.3390/land9060156.

Stoorvogel, J.J., Bakkenes, M., ten Brink, B.J.E., and Temme, A.J.A.M., 2017. To what extent did we change our soils? A global comparison of natural and current conditions.. Land Degrad. Develop., doi: 10.1002/ldr.2721.

Stoorvogel, J. J., Bakkenes, M., Temme, A.J.A.M., Batjes, N.H., and ten Brink, B.J.E., 2017. S-World: A Global Soil Map for Environmental Modelling. Land Degrad. Develop., 28: 22–33. doi: 10.1002/ldr.2656.

Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).

Dear Alemsha,

The MEDALUS formula can be applied anywhere in the world because it is based on indicators of climate, soil, vegetation, and management quality.

This research may be useful to you:

In ENVI, image differencing and thematic change detection methods are available which are very easy to use. The image differencing method is used for change detection using raster images such as raster indices images.

Thematic change detection method uses classified initial and final mages.

It is reported in different studies that this method provides better accuracy.

Both methods provides detail change statistics.

See for comprehensive overview and http://docs.momepy.org/en/latest/ for the implementation of the large number of measurable indicators.

Minh Nguyen

Yes! If you are aiming for a fairly simple CA simulations (as referred code above). It can be effectively implemented by using Focal operators (https://developers.google.com/earth-engine/image_morph) and conditional operators (https://developers.google.com/earth-engine/image_relational) and loops (https://developers.google.com/earth-engine/tutorial_js_03) together. All you need is to find a way to combine these (into Geo algebra) to simulate the UG.

A beautiful paper, which explains Geo algebra is

However, creating a good CA model for land use is a difficult task, because we are dealing with a complex adaptive systems which are very difficult understand and simulate. Please go through

I feel, any simulation model should be useful to guide us for better understanding and management of system under investigation.

"Simulation models are often used to inform rather than predict. Thus, the practical utility of an urban growth simulation model hinges on its applicability to assess the impact of planning measures or to devise strategic plans to avoid undesirable effects (KantaKumar, Kumar, Schneider 2019)"

Hope this will help you

Regards

Hello,

Geoprospective is a difficult discipline to manipulate, not because it deals with the future, but because humans are like the Nature they are from : unpredictable. So, there is a possible solution : using together the nomothetic (spatial analysis) which explains us what is permanent through time (and so, we can suppose it will still be the case in 50 or 100 years from now) but also axiology which deals with ethics, esthetic but also (that's a personnal opinion) with cognitive science. Axiology is not permanent, even if scientific methods were tried on it, ethics change through time and between societies, esthetic too and i don't talk even talk of minds (the cultural matter) ! ... The mixing of those two sides should produce something interesting, as long as there is a perfect logical, reasoning and objective work.

See you here or there.

Térence

Hi Phan Cao,

I think that ENVI (Environment for Visualizing Imagery) has a low entry level which allows you to get to image classification fast. The algorithms are state-of-the art for many applications including land use and land cover classification in supervised and unsupervised classification modes.

Hence have a look at the follwinf site to know more about this software, which I have been using for years now in remote sensing applications. It can also include Interactive Diagramming Language (IDL) when you need to program very specific appliations.

Here is the website of ENVI:

Lots of success,

Frank

Dear

THis is fairly common problem in LULC mapping. It is always possible to validate a recent map, since the land cover/land use can be checked for accuracy through field visit. However, for past LULC maps, all you can do is to use the maximum trusted data source you might have at your disposal which can be checked against your past remotely sensed maps. It can either be aerial photographs, reference LULC maps, ancillary data.

A common approach people often use is sampling from Google Earth satellite images. They are free but you cannot download them, but rather just browse them. However, since Google is providing a timeline feature for free, aiming at your area of interest, you can freely access old picture and sample data for each class of interest. Lots of published papers use this approach for validation when ancillary sources/aerial photographs or reference datasets for the past years are not available, yet it has proven to be a realiable method (see http://adsabs.harvard.edu/abs/2015SPIE.9644E..1NR and ). I can direct you to a tutorial here (https://ibis.geog.ubc.ca/courses/geob373/labs/IGETT_Exercises/google_earth_for_ground_truthing.html).

Also find attached to this answer the FAO Map Accuracy Assessment and Area Estimation practical guide advocating the same method for sampling data from Google Earth for map validation, along with formulas and a link to an interesting plugin developed to this end, CollectEarth (http://www.openforis.org/tools/collect-earth.html).

Hope this helps.

Hi Naveed Ahmed. I normally analyze LU change scenarios as follows. First I calibrate and validate SWAT with the current LU scenario or whatever your baseline scenario is. Next I will re-run the model but modifying the LU input raster according with the scenarios of interest, and using the calibrated parameters that you came up with during the Calibration process. This approach implies that you create your scenarios of interest out of the model according with the problem that you want to analyze. One simple example would be analyzing a deforestation risk map you may identify future scenarios. SWAT also allows you to analyze BMP inside the model.

Tahnk you Mr J. C. Tarafdar for your help

For the specific case of a far-away island covered by forest we could use the woody biomass for other purposes than biochar if supply is greater than demand. It can be used as a construction material, furniture, cooking utensils, as fuel for electricity generation and cooking. We can produce paper from it, hydrolyse it and make bioethanol for cars or for parties.

Hi Aydin,

I recommend this tutorial to quantify Landcover's changes in space and time and the use of markov chains: http://benbestphd.com/landscape-ecology-labs/lab2.html

Also, for this purpose you can use the lulcc package : https://spiral.imperial.ac.uk/bitstream/10044/1/41199/3/gmd-8-3215-2015.pdf

And this paper:

Dear Jasasvini Peranantham

Please have a look at these useful RG links.

Good luck!

What have you decided to do: using openCv or GIS softwares? Did you decide to use Weka?

There are several LUCC models for free. One quite widespread and famous is Dinamica Ego (http://csr.ufmg.br/dinamica/). You also have the CLUE model (http://www.ivm.vu.nl/en/Organisation/departments/spatial-analysis-decision-support/Clue/index.aspx) developed by a research team in Netherlands.

Also in this paper ( An open and extensible framework for spatially explicit land use change modelling: The lulcc R package) you can find the presentation of a R package which includes two LUCC models, being CLUE one of them if I am remember well.

For a general overview, you have short presentations of several LUCC models in the last part of this book: http://www.springer.com/us/book/9783319608006

I have given you above the links to some of them. However, I am sure there are another ones different which are also for free.

Hi there. The following is my opinion, references mostly from memory, caveat emptor!

1) agent-based models;

hard to calibrate, especially if there are many agents, not always spatially explicit. Probably better for understanding the process of change rather than creating future simulations, especially if the main goal is to understand the interaction of the agents of change. But recent work is beginning to address these limitations, e.g. see the CRAFTY model (Murray-Rust et al)

2) artificial neural networks;

rules of transition are calculated by the neural network and are invisible to the user - the model is effectively a "black box", these models have a tendency to overfit to the calibration date. On the other hand, they do produce good simulations by standard goodness-of-fit measures. See Pijanowski and collaborators, from 2005 (Grand Traverse Bay Watershed Michigan and numerous other cases up to the present day)

3) cellular automata;

most practical examples rely on trial and error calibration, rather than empirical evidence of association between land use and drivers. But transition rules are transparent (unlike ANN) since the user must explicitly define them. The modeller achieves a fuller understanding of the change processes and they perform well on pattern-based or cell-by-cell statistical measures. Convincingly model the dynamics of urban growth. See work by Keith Clarke and collaborators (1997, 1998 etc), White and Engelen 1993, White et al 1997 (city of Cincinnati). See also work on automatic or semi-automatic calibration of these models - Straatman, B., White, R., & Engelen, G. (2004). Towards an automatic calibration procedure for constrained cellular automata. Computers, Environment and Urban Systems, 28(1), 149-170, and also more recent work (see esp. Barreira-Gonzalez et al 2015, and Norte Pinto and Pais Antunes 2010)

4) economics-based models; and

a range of econometric models exist that claim to reliably associate the size of cities with three key drivers, distance from central business district, agricultural land value, and population. Problem is that this strong association at small spatial scales (e.g. all US cities) breaks down at large spatial scales (e.g within an individual region). Also does not really explain the process or pattern of change. See Brueckner and Fansler 1984, etc etc also critique by Bockstael and Irwin 1999

We talked about some of these issues in a recent working paper, maybe its of interest.

5) Markov chains;

Don't know, I've not looked into these in detail.

Hope this helps!

future pridiction can be done by markov chain analysis.

good a day

there is many software and many extensions , so the classify vector format or files inside vector format that mean Raster or what? therefore please  clearly and directly ask what you wanted? 

Our basic assumption is that there is a possibility of one class changing into other classes (for which reclass tool is used mentioning what classes can change into what other classes) and that influence of each pixel on target pixel decreases with the increase in distance between the pixels, i.e) near pixels are influenced more than farther pixels (for which user-defined filter is used). Thus output of cell automation will be what are the pixels changed to other classes, which gives finally the suitability image.

Hello

For Sure Using MATLAB, you can implement GA, ACO or PSO Techniques for many applications

Hi Bibi - Its good to take as much as possible points for high accuracy and the number of pixels in one point must be n(n+1), here n is number of classes.

Well, to start with the basic SAR processing, you may consider the tool available at JAXA's platform for ALOS-2 processing. Please refer to:

 http://www.eorc.jaxa.jp/ALOS/en/doc/tool.htm

You may also use SARSCAPE within ENVI 4.8 or ENVI 5.0. There are some commercial software available as well if you want to consider. For example, GAMMA software available at this link:

However, it is good to go with open source tools available initially. Some of them are:

ESA's PolSARPro:

You may also try ESA's NEST SAR Toolbox:

For Interferometric (InSAR) and Polarimetric (PolSAR) processing, you may also consider:

Hope this helps you!

I would recommend ht-index, as an alternative metric to fractal dimension, to characterize landscape fragmentation. It help overcome some limitations of fractal dimension, yet is able to capture the underlying complexity of geographic features.

What underlies the notion of ht-index is a fractal way of thinking:

Hi Namita, the LandSHIFT modelling framework was developed at the University of Kassel. One of the authors of the attached paper / links should be able to help you.

Regards, John

 I will advise you to same way: Erdas imagine Accuracy assessment tools to evaluate your LU/LC also you can use kappa index but it is not usefull at latest studies. You will use maximum likelihood algorithm and accuracy measures like 0.82, 0.90  ....

This is quite difficult, if your study area belong to dry region. I would suggest to use Knowledge based approach, you need to derive the rules by examining the spectral information data.

If you have high resolution image use object orient classification in IDRISI. Builtup has rough texture while barren land has smooth texture.

You can use EGO Dynamica and SLEUTH for this.

regards

Please elaborate, what do you by regional delineation?

SLEUTH model might be another option, there is road gravity factor in that model.  Check this http://www.ncgia.ucsb.edu/projects/gig/

I'd be prepared to accept a partial equilibrium approach given the difficulty you would likely face doing a general equilibrium model. So, demand drives the model. Land use is converted. We have a new (partial equilibrium) or balance where the land is now allocated according to the demand. If you wanted to guess for example the ultimate impact of this on relatively unrelated sectors (say energy impact due to land change) I think there would be a need for very broad equations. In the more partial scheme, work in California by LANDIS and co-workers does a good job on simulating land use conversion. Let me know if you need some references to Landis' work.

Dear Partha,

Two days ago I was in Jhansi and I had a short visit to your institute. We could have met if I known you were there.

Your question could be formulated better. As I understood it right, it has to do with biophysical criteria affecting grasslands. In addition to what has been suggested, you need more detailed information. For example many colleagues mentioned precipitation. In fact annual precipitation does not say much. You need information on the intensity and spread over time. Similar for climate. Slope is crucial as it is strongly correlated to soil and water erosions. In addition aspect is usually neglected though it has great influence on the ETP as direct sun light soil gets drier quickly and natural vegetation will not be the same for South facing versus North facing. You need to also consider species composition, any invasive species coming, past disturbance, etc. Grassland ecology is complex as several factors either linked to environment or to human affect condition and trend.

Best, Mounir

may the following link and attachment are useful for you to understand accuracy assessment process

Forgot, report may be useful on documented changes in plant types specific to salinity changes in the Santee River Delta due to reduced instream base flows from upstream reservoir. Other biological indicators such as are changes in fish species, as many freshwater species and perhaps aquatic macrointertebrates cannot handle saline or brackish waters.

Dear Sigit,

The first question is the scale. Do you plan to map at a 1:10 000 scale or coarser? The mapping depends on your goals, how detailed map do you need? Do you have the sufficient time and resources for such a detailed map? What is the average depth of the peaty layer? Does it go as far as 11 meters or it is "only" 1-2 meters deep?

I would definitely make a field trip to find out the average depth of the peaty layer and the heterogeneity of the landscape.

A relief map would be very useful. Are you mapping on a lowland/highland condition? It is possible that a relief map can help you to outline how far the peaty areas stretch e.g. from/above the water sources that resulted in their formation. Making some cross sections (small catenas) by an auger might help you getting an idea what is the situation on your peatland.

So, generally, I would follow a normal soil mapping methodology that mainly differs in the frequency of the sampling based on the scale you choose.

Best regards, Csaba

Dear Woko

The previous answers have good information.

I will add a quick comment on the governance arrangements.

In Australia, the preferred body to conduct a land assembly-then-land re-allocation exercise would be a statutory authority, rather than a ministerial department of government. Statutory authorities, while still entirely non-profit and entirely dedicated to the public interest rather than commercial interest, are established as corporations with more flexibility in buying and selling land, letting contracts and recruiting staff than the pure ministerial department. However, special safeguards need to be built in to ensure that they aren't corrupted by vested interests. For example, the board members and chief executive must be selected carefully to ensure that they have a public interest mindset and no conflict of interest.

I will also add a quick comment about the final paragraph of Peter Colwell's response above.

I agree that once the new settlement layout is designed and the new titles are issued, subsequent development (and possible subdivision) should be left to the new private landowners through private markets, so long as there are effective zoning regulations in place to prevent excessive intensification that degrades the liveability of the area.

I would add two points. First, please note the difference between the reallocation and sale of land (which is a transfer of ownership from the authority to the new owners); and the regulation of development through zoning (which draws on a different head of power, the sovereign power to pass legislation). Zoning regulation modifies or restricts the property rights that otherwise were inherent in the land title.

My second point is that the act of redesigning the settlement and establishing new allotments usually creates additional economic value. This value rightly belongs to the community and should be captured by the authority that is selling the allotments in the new layout. It can do this by selling at a fair market price through transparent methods; and by ensuring that the new allotments are a suitable size to match the proposed land uses. If the new allotments are very large, and require additional subdivision by the new private owners, then much of the additional value will be captured by the new owners.

Hello Aldrin Mwilima, you may follow my research articles on land use conflicts (through the links bellow)....., will help you for both qualitative and quantitative analyses.

Habib

You can contact Lindo Systems in Chicago and request a free student license if you are still a student. The license provides you with the unlimited variable version that should be suitable for any linear or mixed integer problem. Therefore, I agree with Sajal. Lingo allows one to develop an intuitive problem (in equation form), as do other solvers, but it is easy to use and the output is easy to interpret.

Although I am not familiar with the CGE Model, I recommend you use a land-use capability classification to define sustainable use categories based on a broad array of soil properties such as fertility, slope, depth, erosion susceptability, inter alia.  Categories should go from most intensive (e.g., annual crops) to least intensive (i.e., protection forests).

So, you've gathered qualitative data on land-user's benefits and you need to conduct a "test"? So what exactly are you trying to achieve? A test or to assess the benefits of various land-user's of your study area?

The kind of qualitative analysis you can conduct, first depends on the kind of qualitative data you've collected. If you provide some more details relative to the research objective plus the kind of qualitative data you have, I could provide some suggestions.

Here is a nice report in this area, in particular on the necessity to clearly specify what is needed or not - although it appears that some of the math is missing:

Dear Luis I do not have enough experience on spatial analysis but I do in AHP. The reasons that supports Saaty's scale are borrowed from psychological literature. One of them is The Magic Number Seven (attached) that is the famous nine levels but when you research on original Saaty's sources you grasp that really there are only five levels (1=equal, 3=somewhat more important, 5=much more important, 7=very much more important, and 9=absolutely more important)

The even numbers are original used to solve controversies in group judgement when consenus fail to be reached. In any case AHP is a methodological tool and in this vein it is never overcomes the expert knowledge of the application context.

Good day Luiz and Niels.  I think we should be able to have models of similar structure linked together from local, to provincial, to regional, to national, to blocks of countries, and one global, which can then be linked to global programs and banks and global institutions in a way that we create links of what it is needed globally with what it happening on the ground and be able to monitore it in real time yearly and ongoing...so each locality, province, region, block and the world know what they need to do based on sustainability land use planning and that others can see what they are doing....all from the deforested area/deforested area point of view...It can also help land users to connect directly with kyoto programs, global warming efforts....and so on.

I will bring together the ideas in the following papers:

Traditional Forestry, Sustainable Forestry, and Forestry Sustainability: Expressing Evolving Forestry Practices Using Qualitative Comparative Conjunctural Interactions

Beyond traditional sustainable development: Stating specific and general sustainability theory and sustainability indices using ideal present-absent qualitative comparative conditions

Linking Sustainable Development Indicators by Means of Present/Absent Sustainability Theory and Indices: The Case of Agenda 21

Dear Alfred,

Write your keywords in fallowing link and search;

There are some wonderful new tools for doing this.  But just a caution not to get so caught up in the data that you forget the theory you want to apply.  For example, you might identify several great habitat areas in the region you are analyzing, but you need to grapple with the classic "one large vs. several small areas" issue that is fundamental in the conservation biology literature.  Also, questions of corridors, edge vs. interior habitat (the latter a function of area size), crops or trees that act as barriers to movement of target species vs. ones that are permeable.  Even if you are only looking at agriculture or forestry, you have to consider minimum sizes of fields/plantation areas and length and location of haul roads.  For some applications, you might want to go back to the 50 year old ur-source for suitability analysis, Ian McHarg's Design with Nature for a general framework.  The idea of overlays and assigning suitability points to given areas has much to commend it.  I'm encountering some of these issues in trying to analyze the suitability for development of a 40 acre suburban parcel where more than 200 townhouses have been proposed. The area contains a stream floodway (and floodway fringe) and large trees.  Among the constraints are locating a long distance trail through the property and the demands of neighbors on one side for as large a vegetated buffer as possible.  Computer mapping can be helpful, but it doesn't give a single correct answer.  One has to run through various scenarios, with some constraints absolute (no building in the floodway) and others involving tradeoffs (saving big trees away from the neighbors vs. buffering the neighbors from the new building).

Hello

This is the abstract of one paper of mine written in Persian with English abstract which fulfils some parts of your need.

Best Wishes

Response of Sedimentgraphs and Sediment Rating Loops to Land Use Type and Spatial Pattern

Abstract

Assessment of suspended sedimentgraph (SG) and rating loops are important tools for analyzing the behavior of watersheds and successful implementation of watershed management measures in reducing sediment. However, the development and analysis of SGs and rating loops and influencing factors need more attention in watershed-scale. Therefore, the present study aimed to conduct the analysis of 13 observed and simulated sedimentgraphs (SGs) during October 2011 and November 2012, in Galaz-Chai Watershed with an area of 103km2 located in West-Azarbaijan Province, Iran. Towards this attempt, the SG of observed events was derived using instantaneous unit sediment graph (IUSG) concept, and the effects of different land uses were investigated. Also, the variation and patterns of the simulated sedimentgraphs (SGs) and rating loops were plotted and interpreted with the respective land use types. Based on the results, an early peak occurred in SGs with respect to the observed hydrograph peak rates and consequently had clock-wise pattern. Increase in falling limb of hydrograph could be attributed to the participation of sediment production from agricultural land in watershed upstream. The model predicted SGs with good accuracy according to the Nash-Sutcliffe criterion (70%). Analysis of results indicated that the type and distribution pattern of different land uses had significant effect on the shape of SGs and sediment rating loops.

Key words: Land use spatial pattern, Temporal variation of suspended sediment load, Sediment yield, Spatial location of land use, Sediment availability

You must be meaning MOLUSCE: never fully tried it myself but their quick help (accessible from the menu and in the QGIS wiki) is rather explicit on the meaning and contents of the tabs, that should be used sequentially:

There are many pathways for the last step. But MOLUSCE seems to be sharing most of its algorithms with existing frameworks. You might have a look at the Mas & al paper, where they describe and compare in detail the modelling approaches of IDRISI's modules, Dinamica and CLUE-S.  

Dear Mateus,

If you want to make a 3D city, as a wire frame or solid masses the best program is Auto CAD (3D). If you want to add textures and materials, then using SketchUp program is the best.

Dear friend Anibariki Ngonyoka,

One method, You have choose tsetse fly flight range and make a GIS based grid on the study area and select a sampling nearer to the intersection points or nearer to the intersection points. you can get total sampling size.

second method, include the transmission of trypanosomiasis cycle involves four interacting organisms: the human host, the insect vector, the pathogenic parasite and the domestic resting places), and wild animal reservoirs.

Third, based on the clinical data of patients and their household locations and the number of patients, select a 30 to 40 % of sampling size randomly, and which  must be representation or cover the entire study areas.

Fourth, includes socioeconomic and the environmental variables for the spatial cognition, recognition or mathematical morphological analysis.

M.Palaniyandi

These link will be useful for you.

 

I think there are studies that specifies the precision of the CLC for particular areas / countries. For example, the accuracy of CLC for Poland is estimated from 92.7%. Everything depends on the precision that you assumed for your studies. But Andreas Paul is right some errors are inevitable.

Most meta-analyses are based on one of two statistical models:-

 Fixed-effect model

This model assumes that there is one true effect size; which underlies all the studies in the analysis, and that all differences in observed effects are due to sampling error. While we follow the practice of calling this a fixed-effect model, a more descriptive term would be a common-effect model. In either case, we use the singular (effect) since there is only one true effect.

 Random-effects model

Under this random-effects model we allow that the true effect could vary from study to study. For example, the effect size might be higher or lower in studies. Because studies will differ in the mixes of participants and in the implementations of interventions, among other reasons, there may be different effect sizes underlying different studies. If it were possible to perform an infinite number of studies (based on the inclusion criteria for analysis), the true effect sizes for these studies would be distributed about some mean. The effect sizes in the studies that actually were performed are assumed to represent a random sample of these effect sizes (hence the term random effects).

I thank everyone for the suggestions.

Hello Thamuka Moyo,

Urban Change Processes

For the evaluation of operational urban models, the urban change processes to be modelled are identified. Eight types of major urban subsystem are distinguished. They are ordered by the speed by which they change, from slow to fast processes:

- Very slow change: networks, land use. Urban transport, communications and utility networks are the most permanent elements of the physical structure of cities. Large infrastructure projects require a decade or more, and once in place, are rarely abandoned. The land use distribution is equally stable; it changes only incrementally.

- Slow changes: workplaces, housing. Buildings have a life-span of up to one hundred years and take several years from planning to completion. Workplaces(non-residential buildings) such as factories, warehouses, shopping centres or offices, theatres or universities exist much longer than the firms or institutions that occupy them, just as housing exists longer than the house-holds that live in it.

- Fast change: employment, population. Firms are established or closed down, expanded or re-located; this creates new jobs or makes workers redundant and so affects employment. House-holds are created, grow or decline and eventually are dissolved, and in each stage in their life cycle adjust their location and motorisation to their changing needs; this determines the distribution of population and car ownership.

- Immediate change: goods transport, travel. The location of human activities in space gives rise to a demand for spatial interaction in the form of goods transport and travel. These inter-actions are the most flexible phenomena of spatial urban development; they can adjust in minutes or hours to changes in congestion or fluctuations in demand, though in reality adjustment may be retarded by habits, obligations or subscriptions.

There is a ninth subsystem, the urban environment. Its temporal behaviour is more complex. The direct impacts of human activities, such as transport noise and air pollution are immediate; other effects such as water or soil contamination build up incrementally over time, and still others such as long-term climate effects are so slow that they are hardly observable. All other eight sub-systems affect the environment by energy and space consumption, air pollution and noise emission, whereas only locational choices of housing investors and households, firms and workers are co-determined by environmental quality, or lack of it. All nine subsystems are partly market-driven and partly subject to policy regulation.

In the 1950s first efforts were made in the USA to study the interrelationship between trans-port and the spatial development of cities systematically. Hansen (1959) demonstrated for Washington, DC that locations with good accessibility had a higher chance of being developed, and at a higher density, than remote locations ("How accessibility shapes land use").

The recognition that trip and location decisions co-determine each other and that therefore transport and land use planning needed to be co-ordinated, quickly spread among American planners, and the 'land-use transport feedback cycle' became a commonplace in the American planning literature. The set of relationships implied by this term can be briefly summarised as follows:

Figure 1. The 'land-use transport feedback cycle'.

- The distribution of land uses, such as residential, industrial or commercial, over the urban area determines the locations of human activities such as living, working, shopping, education or leisure. - The distribution of human activities in space requires spatial interactions or trips in the transport system to overcome the distance between the locations of activities.

- The distribution of infrastructure in the transport system creates opportunities for spatial interactions and can be measured as accessibility.

- The distribution of accessibility in space co-determines location decisions and so results in changes of the land use system.

Dear David,

Being a Domain Model, there are already a few prototype implementations in several countries, some will be soon oficial (expectedly) like in Cyprus or in Cabo Verde. Each country usually elaborates on the domain model to create an implementation which is specific for the legal / cadastral and administration situation in the country.

If you want to start working in a solution for your country, based on open source, then I strongly recommend the LADM implementation from FAO's named SOLA. Is an Entity-Relationship model, and it is implemented in PostgreSQL. You can find more (and the most recent updates) at www.flossola.org

Caro Joao,

To the question "I would like to know if there is some publication which reports yearly allocation of deforestation to land use for each state or, if that is not available, then whether a GIS time series of land use in the Amazon can be found somewhere. I'm not a GIS expert and I need this data as an input to a different study. ", I think the answer is negative for annual state-level stats.

You've already got hold of the most recent publications; I would also check the World Bank site for more analyses (usually their analyses lack sophisticated spatial methods, but they tend to be broad-scale and coherent in theoretical terms, even if they can be disputed in some cases; in contrast, you may find most large-area analyses derived from satellite imagery theoretically poor, but spatially adequate).

As you know well, to correlate Agric production and Deforestation, you have to adapt your methods to data availability, depending on the exact objectives and scales of your analyses. In general, I think you should take into account that (i) yearly, spatially explicit, large-area deforestation data (conf. PRODES, apud Espindola, apud TERRACLASS, cited by you) are available ONLY for what is roughly called 'forest' areas, leaving unmapped all of the cerrado - roughly 20% of the Legal Amazonia, where a large part of agriculture is concentrated - (ii) there are probably no datasets for defor or agric production presenting adequate assessments of uncertainties for their stats - this can make these data nearly useless for several classes of problems: you might want to assess that, in particular, if your problem is defined in terms of interannual variations (iii) data from the Agricultural Censuses are not appropriate if you need annual stats, and frequently they are difficult to intercompare, due to significant changes in data categories and collection methods; however, they are reported at the municipal level for all of the Amazonia (and Brasil), and you can download them from the IBGE site (www.ibge.gov.br; also www.sidra.ibge.gov.br, I think).

You may detect land cover classes by using remotely sensed data, especially Landsat 8 (30 m spatial resolution). You should pay attention at the difference between "land use" and "land cover" definitions.

You have formulated your question rather broadly. Are you looking for land use change models? At what scale is your interest?

In any case, I recommend to look at the work from the LUCC (Land Use and Cover Change) network, and at its successor GLP (Global Land Project; www.globallandproject.org).

Apart from that, good starting points are a (somewhat outdated...) special issue in Agriculture, Ecosystems and Environment "Predicting Land Use Change", edited by Eric Lambin and Tom Veldkamp. A bok with the final findings from the LUCC from 2006 (Land-Use and Land-Cover Change: Local Processes and Global Impacts; Lambin and Geist editors).

If it is models that you are looking for, there are about a dozen good ones around. A good starting point for that is a paper from 2008:

Pontius Jr, R.G., Boersma, W., Castella, J.-C., Clarke, K., De Nijs, T., Dietzel, C., Duan, Z., Fotsing, E., Goldstein, N., Kok, K., Koomen, E., Lippitt, C.D., McConnell, W., Pijanowski, B., Pithadia, S., Sood, A.M., Sweeney, S., Trung, T.N., Veldkamp, A., Verburg, P.H. 2008. Comparing the input, output, and validation maps for several models of land change. Annals of Regional Science 42(1) p.11-47

there is much more..