Cropland parcels are the basic unit for agricultural production, and their size and shape may change due to human activities, e.g., land consolidation.  Remote sensing has been increasingly used for mapping cropland parcel, yet detecting changes in cropland parcels by wall-to-wall mapping is time-consuming.  This paper proposes a new algorithm to identify whether and where cropland parcel changes have been undertaken without generating complete parcel maps.  We use the number of edge pixels derived from remote sensing imagery as a proxy indicator for cropland parcel changes.  First, we apply a Sobel operator to delineate the total edge pixels of parcels from dual-time images.  Second, we apply the connected-components labeling to remove pseudo-edges arising from non-cropland built structures and transmission towers.  We then perform topological optimization, including morphological dilation and skeleton extraction, to eliminate redundant edge pixels for parcel structure.  Finally, we detect whether parcel changes have been undertaken by counting and comparing the number of edge pixels derived from dual-time images.  We applied this innovative framework in five regions in East Asia where land consolidation has significantly changed cropland parcels.  Our method demonstrated robust detection results, with stable accuracy, precision, recall, and F1-score, all exceeding 0.85.  Screening redundant edge pixels reduced noise and permitted efficient detection of changes in cropland parcels.  Our method extends the traditional detection of semantic change to structural change and can quickly detect cropland parcel changes with high accuracy.  This capability offers the potential to identify hotspot areas of cropland changes on a larger scale without the need to produce full cropland maps, which is particularly useful for monitoring land consolidation programs.

soil organic matter  /  remote sensing zoning  /  feature selection algorithm  /  random forest algorithm  /  prediction