Hello Nancy Mwangi.

I believe you can get this data through the use of eg satellite images: Landsat. With the use of his bands it is possible to know you hear change in an area caused by deforestation for timber harvesting, planting some culture or other type of change.

Regards,

Hi Nancy Mwangi, Good Morning

Basically you are looking for land transformation studies over the period of one or two decades. For Remote Sensing and GIS are the best solutions. For example, you can monitor the land use changes by analysis Multi-temporal Satellite data over the specific period. 

Land transition may be because of many factors like de-forestation/re-forestation, improvement in canal irrigation thereby increasing area as well as productivity of agricultural land. Converting waste lands into agriculture or forests. Once the land transformation is monitored, then region specific drivers can be identified based on field studies.

Regards, Manik Kalubarme, BISAG, India

To begin with, there is a very simple method. I recommend that you use the Cramer’s V. In general, the variables that have a Cramer’s V of about 0.15 or higher are useful.

Hi Nancy,

It depends on which transitions you are working, but here is a start with two very different approaches:

Anwar, S., Stein, A. (promoter) and Bijker, W. (assistant promoter)  (2014) Spatial point process modelling of land use and land cover (LULC) change. Enschede, University of Twente Faculty of Geo-Information and Earth Observation (ITC), 2014. ITC Dissertation 257, ISBN: 978-90-365-3787-3.

Yemefack, M., Bijker, W. and de Jong, S.M. (2006) Investigating relationships between LANDSAT - 7 ETM+ data and spatial segregation of LULC types under shifting agriculture in southern Cameroon. In: International Journal of Applied Earth Observation and Geoinformation : JAG, 8 (2006)2 pp. 96-112.

Kind regards,

Wietske

The drivers of land change are very challenging to disentangle. People's choices can often be based on a myriad of drivers, one driver, or any combination of driving forces (i.e. climate, economics, policy changes, etc.). The U.S. Geological Survey has published many reports on multi-temporal Landsat-based analysis of land use and land cover change and their associated drivers: http://landcovertrends.usgs.gov/

One of my papers uses graphical analysis of known drivers (e.g. drought driven agricultural contraction) in the California Central Valley: https://www.researchgate.net/publication/257066490_Recent_land-useland-cover_change_in_the_Central_California_Valley

Another method you could use would be multi-variate statistical analysis such as principal components analysis if you have your suite of drivers quantified. 

Best,

Tamara

Article Recent land-use/land-cover change in the Central California Valley

Attached two articles on land cover transitions, their drivers and subsequent forecasts. Hope you will be inspired. 

Article What Drives Conversion of Tropical Forest in Carrasco Provin...

Article Forecasting the pattern and pace of Fagus forest expansion i...

Hi Nancy - You just take two or more dates data and do change detection and later on make cross table to know all land cover trajectories. 

Hi Nancy

Please study this paper

https://www.researchgate.net/publication/291972705_Integrating_cellular_automata_artificial_neural_network_and_fuzzy_set_theory_to_simulate_threatened_orchards_application_to_Maragheh_Iran?ev=prf_pub

Article Integrating cellular automata, artificial neural network, an...

Hi Nancy 

its depends on your study area is it urban than you can use urban sprawl matrix for land cover transition or if it is forest than you can use forest fragmentation model for land cover transition. you can follow this article 

http://link.springer.com/article/10.1007/s10584-013-0926-x

http://www.tandfonline.com/doi/pdf/10.1080/13658816.2013.770517

https://www.researchgate.net/publication/285574324_Assessing_spatio-temporal_health_of_forest_cover_using_forest_canopy_density_model_and_forest_fragmentation_approach_in_Sundarban_reserve_forest_India

Article Assessing spatio-temporal health of forest cover using fores...