LAND COVER CHANGE ASSESSMENT GLCN methodological approach Antonio Di Gregorio

Presentation contents: Different Land Cover Change assessment approaches (the GLCN method) The Kenya case study New perspective of the GLCN approach (CTA software)

No internationally accepted definition exists The definition of a change depends on the context we refer to. To characterized a change we must first define the range (values or semantic definition) that define the limit with in which no change exist. We must determine the time within a change/no change take place In the present study we are considering changes- Quantitative Based on the values/semantic definition used in LCCS WATH IS A CHANGE?

The selection of the different of methodological approaches is directly linked to the types of final applications desired: Approach by sample area gives relatively fast statistical tabular information but doesn’t show the location of changes. Its use is limited to applications where a geographic location of changes is not necessary and when statistical information (obtained with standard methods) are not available. Approach “wall to wall” has the advantage to link the tabular information with the geographic representation of the changes. It can be executed in different ways that can be summarized in two main approaches: 1. Automated methods 2. Visual interpretation CHANGE DETECTION APPROACHES

Different types of automated methods exist: Post classification cross tabulation Cross correlation analysis Neural networks Object oriented classification Advantages: Under favourable conditions, faster than the visual interpretation; Rather objective; Detection of changes at level of pixel size. Disadvantages: Needs an heavy pre-processing; Quality of results have big dependence from differences in atmospheric conditions or seasonality; Quality of results related to the types of classes considered; Detection of changes at level of pixel size. CHANGE DETECTION APPROACHES

Different types of visual approaches exist: Map to map comparison Image to image Advantages: Simple technique; Rather independent from differences in atmospheric conditions or seasonality; Large number of L.C. Class types can be evaluated. Disadvantages: Slow process; Quality of results correlated to the skill of photo interpreters; Quality of results related to the types of classes considered; Detection of changes not level of pixel size. CHANGE DETECTION APPROACHES

THE GLCN APPROACH It is a visual method assisted by a specific software. A GLCN software used to perform visual interpretation has been re-adapted to perform the change detection

THE GLCN APPROACH

Advantages Compared to other visual methods easier and faster; Changes are critically analyzed by the expert in a multi window system, eventual mistakes in the original interpretation can be adjusted; Large types of classes can be analyzed; All the changes are spatially localized. No heavy post-processing is needed, the final result is a fully topological vector layer that allow to track back the change history of each polygon.

3 A HR4/HM24 B HL4 Change in field size

A 2SOJ67 A1 2SOJ67/HR HR4/2SOJ67 2SOJ67/2WP6 Change in field density

Critical assesment of changes

IMAGE RESOLUTION

Level of change details

Actual limitation in depicting changes in heterogeneous areas A/B MMU

Nair obi Cent ral Coas t Easte rn North Eastern Nyan za Rift Valley Wester n SUB GROUP%%%% Rainfed herbaceous crops (large to medium, continuous fields) Rainfed herbaceous crops (small, continuous fields) Rainfed herbaceous crops (scattered clustered or scattered isolated fields) Rainfed shrub crops (large to medium, continuous fields) Rainfed shrub crops (small, continuous fields) Rainfed shrub crops (scattered clustered or scattered isolated fields) Rainfed tree crops (small, continuous fields) Rainfed tree crops (scattered clustered or scattered isolated fields) Irrigated herbaceous crops (large to medium, continuous fields) Irrigated herbaceous crops (small, continuous fields) Irrigated tree crops (large to medium, continuous fields) Forest plantation (large to medium, continuous fields) Aquatic agriculture (large to medium, continuous fields) Aquatic agriculture (small, continuous fields) Kenya case study -results

Year 2000 Year 1970 Kenya case study -results

Agriculture density Kenya case study -results Meru District, Kenya – Agriculture Field Density Status 1970’s, 1980’s and 2000

Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 1980’s Kenya case study -results

Meru District, Kenya – Agriculture Field size and Density Change 1980’s – 2000 Kenya case study -results

Meru District, Kenya – Agriculture Field size and Density Change 1970’s – 2000 Kenya case study -results

Meru District, Kenya – Agriculture Field Size and Density Hectare Change Kenya case study -results

THE KENYA CASE STUDY RESULTS CRICTICAL ANALISYS Time/cost Number of polygons analyzed per day for the three dates –depending complexity of the features to be analyzed/ speed of the expert polygons to be analyzed for Kenya. Total time forecast 6-7 man/month work plus final analysis Outputs Overall detection of changes precise and rather objective. Constrains Level of details in depicting and reporting changes in heterogeneous areas linked with the class and cartographic standards adopted. It could be ameliorated. GENERAL CONSIDERATION In general the method is more effective for agricultural/urban/dense natural vegetated areas respect to natural open formations or very fragmented land cover features.

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Improvments of the present approach Reduction of % of the whole work execution for the present approach Improvement on detail analysing heterogeneous areas Development of additional methods to be applied according to level of detail required, time and costs expected

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Reduction of the execution time- Reduction of the GIS and results analysis work new functions that automatically generates tables and vector layers depicting the history, intensity of changes. Optimization of the analysis/detection of changes itself -General improvement in the multi-window analysis functions -Pattern recognition filters to select only polygon were the change has likely occurred -Increasing efficiency in the polygonization of the change (see next slide)

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Improvment on detail analysing eterogeneous areas Use of magic wand simultaneusely on the multiple windows inside a given polygon to detect percentage of different cover features Use of a variable dot grid to asses percentage of different cover features and/or substitute the polygonization MAGIC WAND USE THE POLYGON LIMIT AS ROI The function is activated simultaneusely on one or all the windows MAGIC WAND GIVE % OF THE SELECTED OBJECT INSIDE THE ROI AND MAKE AN AUTOMATIC LINK OF THIS % TO THE POLYGON CODE IN A SEPARATE COLUM

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Development of additional change assessment methods Change assessment with the area frame method Increasing confidence Level of the results Independent from previous L.C. interpretation Execution time- very fast Results- tabular data No localization of the changes No hot spots

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Development of additional change assessment methods Qualitative change assessment on variable geographic grid Indipendent from any L.C. interpretation Execution time- very fast Results- qualitative assessment of changes on grid No localization of the changes Hot spots- localized by grid CHANGE INTENSITY LOW MEDIUM HIGH DIRECTION OF CHANGES Agriculture vs Forest Forest vs Agriculture

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Development of additional change assessment methods Quantitative change assessment on variable dot grid

2000 Class 1 Class 2 Class 3 ….;

THE CTA – CHANGE TREND ANALYSIS SOFTWARE Development of additional change assessment methods Quantitative change assessment on variable dot grid Independent from any L.C. interpretation Execution time- fast Results- quantitative assessment of changes on dot grid Localization of the changes according to the dot grid size Hot spots- localized by dot grid

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2 main types of approaches exist: – By sample area: a statistically valid number of samples is randomly chosen over the study area. The analysis of changes is performed only in the samples areas. The results are shown in form of tabular data with a certain level of confidence. – “Wall to wall”: the change analysis is done over the whole area. The results are shown by tabular data and by geographic location of the changes. CHANGE DETECTION APPROACHES

Meru District, Kenya – Agriculture Field Density Hectare Change Kenya case study -results