Hey Yannick,

Thanks for your methods, they are producing closer results. I want to fiddle around with some of the inputs. I am not familiar with the OBIA expressions. Where can I find other expressions?

My PI wants to replicate these methods closely:

DutrieuxSegmentedFields.pdf (873.7 KB)

I believe he may ask me to segment using LSMS as the authors did (page 117: 2.5. Spatial Segmentation)

Quote from page 117:

“We used a mean shift segmentation algorithm, as implemented in the Orfeo ToolBox (www.orfeo-toolbox.org) (version 4.2.0) to perform the multi-temporal segmentation (Fukunaga and Hostetler, 1975; Inglada and Christophe, 2009). **A NDMI time-series,** which we assembled by building **annual mean value composites resulting in one average NDMI value per year,** was used as input data. **… The resulting NDMI stack used as input for the segmentation contains 25 layers spanning the 1986–2014 period.** The mean shift segmentation algorithm requires as input parameters a range radius, a spatial radius and a minimum object size. We chose for the range radius, which is the similarity (Euclidean distance) measure for a pair of pixel profiles, a value of 0.14. Such value exceeds the natural variability between two similar pixels while successfully differentiating two pixels with different land use trajectories. A too small range radius value has the effect of over-segmenting the area, while larger values have the opposite effect. **Based on a-priori knowledge of the system, we set the spatial radius to three pixels and the minimum object size to three pixels** (Jakovac et al., 2015). This initial segmentation step results in many segments, some of which are not swidden agriculture. Since we are only interested in the swidden agriculture fields, we applied a set of rules to filter and keep only those polygons that delineate areas of swidden agriculture. First, we discarded objects larger than 15 ha, which is larger than the average field size of 1 ha, usually found in the area (Jakovac et al., 2015). Following that we used the NDMI time-series associated to the segments in order to filter out remaining segments of stable forest (NDMI never falling below 0.3), and urban areas, permanent agriculture and wetlands (NDMI never exceeding 0.3).”

This output using LSMS produced this segmentation:

Do you have a suggestion for how this might have been achieved using LSMS?

I used the same inputs of the NDMI multiyear stack, 0.14 range radius, 3 pixel spatial radius, and 3 pixel minimum object size

Thanks so much.