Hola,

These papers may be useful to you. Good luck!

Clark et al. 2012

http://www.sciencedirect.com/science/article/pii/S0034425712003252

Sanchez-Cuervo et al. 2012

http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0043943

Cheers!

I would highly recommend the method proposed by Pontius.

Put attention to the difference between "systematic" vs "random" change detection, and intensity assessment of LUCC.

https://sites.google.com/site/intensityanalysis/publications

Personal case study: Romero-Ruiz, M. H., S. G. A. Flantua, K. Tansey and J.C Berrio (2011) Landscape transformations in savannas of northern South America: Land use/cover changes since 1987 in the Llanos Orientales of Colombia. Applied Geography 32: 766-776.

There are many techniques for quantification of land use changes but all depends on the required level of details. You may go for image processing based techniques of remotely sensed data. It is suggested to use ERDAS Imagine OR ENVI OR Geomatica software where you will find many tools with the same header 'change detection', which will provide you outcomes in qualitative as well as quantitative formats.

I spent a lot of the middle part of my professional life attempting to assess tropical land use change for carbon release. I wrote a bunch of papers of varying quality. you might google "Charles Hall energy" and click on my home page and then my publications. If you see anything on the 1980-s -- 2000 that you want make a list. I am probably meeting with a Ghanian (My former grad student Amos Quaye) and could get you whatever might seem interesting to you within a month or so.

Humans trying to understand the current state or predict the future condition of social-ecological landscapes (SELs), as for instance driven by climate change, regularly resort to simple, easily interpreted surrogates as parts of the whole complexity that can be understood and used by non-scientists to make planning and management decisions. Yet, the overall information we can gain from a set of indicators will never match that of the whole system, since each individual indicator carries only partial information. Thus, the set of indicators needs to be constantly re-evaluated and re-interpreted in the light of the increasing understanding of the whole organization and functioning of systems. Fortunately, the complexity of living systems of people and nature emerges not from a random association of a large number of interacting factors but rather from a smaller number of key-controlling processes (Holling 2001; Gunderson and Holling 2002). Much of the fundamental nature of systems can often be captured and described by single key variables, as many features of the system’s state tend to shift in concert with a few important key-state variables (Holling 2001). Remote sensing is a primary source of information to study the complexity of SELs in terms of dynamics at multiple spatial and temporal scales. It has become a proven tool for scientists to monitor synoptically and globally, and to understand major disturbance events and their historical regimes at regional and global scales (Kerr and Ostrovsky 2003; Potter et al. 2003; Zurlini et al. 2006). It has provided valuable indices to describe and quantify natural and human-related land-cover transformations and processes and ecosystem service provisioning. One, in particular, has been widely used: the Normalized Difference Vegetation Index (NDVI). For a comprehensive review of NDVI applications, see Kerr and Ostrovsky (2003) and Pettorelli et al. (2005). Briefly, NDVI can be used to quantify annual net primary productivity (Young and Harris 2005), which is a main supporting ecosystem service (MEA 2005; Costanza et al. 2007) and it responds to temperature and water availability. NDVI is broadly recognized as a spatially explicit robust indicator of vegetation photosynthesis related to social–ecological processes such as habitat-land use conversion (e.g., urban sprawling) or crop rotation (Guerschman et al. 2003; Potter et al. 2003; Young and Harris 2005). It is used to identify and assess the impact of disturbances such as drought, fire, flood, frost (Potter et al. 2003; Mildrexler et al. 2007), or other human-driven disturbances (Guerschman et al. 2003; Zurlini et al. 2012; Wylie et al. 2008; Zaccarelli et al. 2008). Thus, NDVI related indices are, I believe, the most appropriate indicator to gauge land use change and its impact on migrant birds.

you can use many methods such as Markov chain and CA models, cross tabulation.

if you r concerning about only one class u can you more models like statistical methods to find probable change

Also find useful attached article

I am highly recommended "Change Vector analysis method" that gives you two indicators: the magnitude of change and the direction of change during time one and time two. ypu can apply this method using ERDAS Imagine.

You can use GIS technique, with suitable software, as a tool to overlay maps of land uses for periods of your studies. The detail of your land use maps, and number of periods or maps, the detail of your output for your analysis.

GIS model builders using weighted spatial analysis is a great tool, provided you have your own land cover data, or some in the public domain like UN Data and EGU (can provide links). Both Esri and QGIS provide tools for that. It's called Model Builder in Esri and Sextante plugins for QGIS. If you don't have Esri you can get it for $99/yr for research from local distributor, and QGIS is free. Look for resources.arcgis.com and www.qgis.org. Alterantely look for anitagraser.com and Google+ groups for QGIS help, and my blog.zolnai.ca (search for RMOTC) and slideshare.net/azolnai (my first / oldest and so far most popular powepoint introduces model builder at the end, or ask me).

to assess land use change, you can use Remote sensing (ERDAS, ENVI soft wares are very good) . but accuracy depends on your expertise. now a days very high spatial resolution RS images are available.

The use of remote sensing is well indicated. The idea behind is classifying satellite images of different dates (five dates) with one as a control point. The software generates areas occupied by each land use.

Thank you all for your contributions they have been very helpful and instructive

more on this

Clark Labs of Clark University Receives Grant from Environmental Systems Research Institute, Inc. (Esri) to Implement its Land Change Modeler Extension to the Esri Cloud Platform

http://bit.ly/1cUPBJM

As a broad point of view LUCC assessment can be divided in monitoring the changes (mainly using remote sesning change detection techniques or map comparison), identifying the drivers of change which can be done by GIS analysis or more field oriented approaches and LUCC modelling. In ecah one of the 3 topics there is a graet variety of approaches and methods

my thesis is about land use and land cover change detection and simulation, i gather a lot of papers , thesises and books....

If you have any questions please feel free to contact me;

Dear Roberts

the land change modeler in IDRISI selva is useful for you

Vahid Amini Parsa, we seem to be working on similar topics, please send me authors and links to search for. Thanks in advance

There are a lot of methods for land use change detection such as Image rationing, Image difference, Change vector analysis, Image regression, Composite analysis and Post­-classification comparison. One of the most widely used change detection methods is post-classification comparison method. Post-classification comparison method use separate classifications of images acquired at different times to produce difference maps from which ‘‘from–to’’ change information can be generated.

Vahid Amini Parsa, i'm working on the impact of land use changes on residential property rental values. Please send me authors and links to search for. Thanks in advance

Please i am researching into the application of geoinformation techniques in land use and land cover change analysis. How do i go about the methodology? i need help

At present, the prediction models of land use change mainly include driving force models, Cellular Automata models, Markov models, CA-Markov models, CLUE models, and so on. Many scholars have conducted a lot of in-depth and meticulous studies in this field. For instance, Niu et al. proposed an activity-based land use/transport interaction (LUTI) model to predict the impact of land use policies for urban activities [20]. Valerio Amici et al. used the MaxEnt algorithm in studies of land cover classification and land use change [21]. Zhao et al. extended the LandSys I model to introduce artificial neural networks (ANNs) into the framework of CA, multi-agents, and geographic information systems (GIS) to predict land use changes [22]. Huang et al. used a Markov model to simulate land use prediction based on the Markov model [23]. Anputhas et al. used the CLUE-S model to predict land-use conversions and their impact on small areas [24] The studies of land use change in China are mostly based on CA-Markov models, neural networks, GIS, and logistic models. For example, Li et al. used the CA-Markov model to predict land use changes in Harbin [25]. Huang et al. used the CA-Markov model to study land use changes in Qingjiang [26]. Zhang et al. simulated and predicted the land use evolution based on MCE-CA-Markov in the Three Gorges reservoir area [27]. Doumetana et al. analyzed land use changes in the Cullen County based on GIS and CA-Markov model [28]. The dynamic changes of land use in the Balikun Lake Basin were analyzed and forecasted based on the CA-Markov model by Lune et al. [29]. Wang et al. presented a genetic Back Propagation (BP) neural network model to predict land use changes [30]. Zhang et al. carried out the predictive analysis of land use changes in Ganzhou District based on CA-Markov model [31]. Yu et al. studied and applied land use structural changes based on the logistic–Markov method [32]. Many experts and scholars have done a lot of research works in the field of land use. However, in most of these studies, conditional probability or a single ecological factor is used as the rules for the conversion of cellular automata, which results in a conversion rule that is too simple, and that lacks consideration for the impact of socio-economic factors on land use changes

Refrence (( Application of an Improved ABC Algorithm in Urban Land Use Prediction ))