Improving Seasonal Land Cover Maps of Poyang Lake Area in China by Taking into Account Logical Transitions

Abstract

Land cover maps are fundamental materials for resource management and change detection. Remote sensing technology is crucial for fast mapping with low cost. However, besides the inherent classification errors in the land cover products, numerous illogical transitions exist between the neighboring time points. In this study, we introduce a series of logical codes for all the land cover types according to the ecological rules in the study area. The codes represent the transformational logicality of species between different seasons. The classification performance and the codes for all the seasons are imposed on the initial land cover maps which have been produced independently by the conventional hierarchical strategy. We exploit the proposed modified hierarchical mapping strategy to map the land cover of Poyang Lake Basin area, Middle China. The illogical transitions between neighboring seasons and the accuracies based on the labeled samples are calculated for both the initial and modified strategies. The number of illogical pixels have been reduced by 13%-35% for different seasons and the average accuracy has been improved by 9.7% for the specific land cover maps. The accuracy of land cover changes has also presented great improvement of the proposed strategy. The experimental results have suggested the scheme is effective.

Gap-Free Monitoring of Annual Mangrove Forest Dynamics in Ca Mau Province, Vietnamese Mekong Delta, Using the Landsat-7-8 Archives and Post-Classification Temporal Optimization

“…The longitudinal temporal data of the Landsat archives enabled the use of neighboring time points to cross-validate findings, fill in missing data through temporal data fusion, detect and revise illogical land use changes in the post-classification analysis [33,[39][40][41]46,48].…”

“…Similarly, further optimization of classification results can be achieved by taking into account that land use changes usually occur characterized by a logical transition [40,41]. Land use changes and transitions follow ecological rules [40,41]. For instance, the growth of dense mangrove forests takes at least multiple years.…”

“…Current studies tend to focus on gap-filling based on spatially neighboring pixels [35,36], spectral similarity, and/or multi-sensor (source) data fusion [34,37,38], rather than temporal integration. As such, few land use studies have taken full advantage of temporal dependencies to reduce both information gaps and inconsistent land use transitions [13,[39][40][41]. This is a particularly rare undertaking for the monitoring of mangrove forests land use changes, whereas consistent and gap-free time series are crucial to closely monitor mangrove deforestation, degradation, and disturbance [13,15].…”

“…This is a particularly rare undertaking for the monitoring of mangrove forests land use changes, whereas consistent and gap-free time series are crucial to closely monitor mangrove deforestation, degradation, and disturbance [13,15]. Land use changes tend to follow logical temporal land use transitions which can guide the optimization of classification maps [13,40].…”

Using a Hidden Markov Model for Improving the Spatial-Temporal Consistency of Time Series Land Cover Classification

“…Issues with image quality difference, classification algorithm, and category definition can contribute to this inconsistency [9]. Time series land cover maps are usually produced independently at each time point.…”