As spatial resolution increases in remote-sensing imagery, the challenge of semantic segmentation intensifies due to the need to discern intricate changes in terrain. Terrain, a composite of diverse geographic elements arranged in specific spatial patterns, demands a higher level of abstraction in semantic categorization. Achieving accurate semantic segmentation in high-resolution remote-sensing images necessitates a profound understanding of the semantic structures within complex scenes. In response to this imperative, this article introduces the object Markov random field with hierarchical semantics (OMRF-HSs) method. The primary contributions of this work are twofold: 1) effective representation of layered semantic information within images is achieved, enhancing the understanding of complex scenes and 2) unified under an object Markov random field (MRF) model, the method enables the joint modeling of structured semantics and spatial context information, facilitating more robust segmentation outcomes. Experimental evaluations conducted on multiscene high-resolution remote-sensing images from the aerial sensor, SPOT5, GeoEye, and Gaofen-2 satellites demonstrate that the proposed method outperforms state-of-the-art techniques, yielding superior segmentation accuracy. The availability of code and example data further facilitates the reproducibility and adoption of the OMRF-HS method, accessible athttps://github.com/FHY-146/OMRF-HS.

Remote sensing images have a large imaging field, covering a wide range of land surfaces and encompassing complex geographical scenes. More diverse land cover types and detailed structural information within geographical scenes can be observed with improved image spatial resolution. However, due to the high cost of annotating fine semantic scales, only a few semantic segmentation methods can fully utilize multi-level semantic information. This paper introduces a novel multi-semantic Markov Random Field (MRF-MS) model for semantic segmentation of high-resolution remote sensing images. This method extends the classical object-level Markov Random Field model by introducing a sub-label field to capture semantic hierarchical information in high-resolution images. Assuming the initial categories follow a mixture of Gaussian distribution, this method models the land cover types or structural details of geographical objects within the scene by decomposing the mixture of Gaussian distribution into sub-components. Furthermore, it establishes an interactive contextual structure between the sub-label and initial label fields to model the interaction across different semantic scales. Experimental results on multi-scene high-resolution remote sensing images demonstrate that this approach yields more accurate classification results compared to the latest MRF methods.

Camellia sinensis var.assamica is a unique product of Geographical Indication in Yunnan province. Centennial tea plantations within plantations of C.sinensis var.assamica can yield high-quality ecological tea. However, the limited production of ecological tea is insufficient to meet the widespread demand for high-quality tea leaves. The optimized MaxEnt model exhibits better accuracy and reliability in predicting the potential suitable distribution of small-sample species. Therefore, this study based on 80 sample data and 16 key environmental variables, optimize the MaxEnt model using the ENMeval package. The distribution and changes of the potential suitable areas of ecological tea plantations of C.sinensis var.assamica in Yunnan province were extracted for the periods 1981-2010(History), 2011-2040(Current), and 2041-2070(Future). The research results indicate that the performance of the MaxEnt model has been enhanced, with the 10P value reduced to 0.1125 and Training AUC and Test AUC reaching 0.9516 and 0.9422, respectively. In the three periods, the potential suitable areas are mainly concentrated in the western, the south and southwest regions of Yunnan Province. The area of high suitability has decreased by a total of 259 km2, while the area of moderate suitability has increased by 4031 km2. There is potential for developing ecological tea plantations of C.sinensis var.assamica in the western, the southern and southwestern parts of Yunnan Province. The managers and planners of tea plantations can further plan for the transformation and evaluation of tea gardens based on local policies. The results of this study will contribute to driving tea industry in Yunnan Province towards a more sustainable development path.

This study used multispectral satellite imagery (Sentinel-2 MSI) to evaluate forest type mapping capabilities over a mountainous area (Shangri-La, Yunnan Province, China) at regional level. Coupled with the cloud computing platform of Google Earth Engine, the sentinel-2 satellite images were used to extract multi-temporal and spectral information, and then combined with terrain information. The random forest algorithm was adopted to identify the typical forest types. Firstly, the area is classified into forest and non-forest types. Secondly, the forest cover was sub-classified into coniferous forest and broad-leaved forest. In the end, eight types of coniferous forests (Cupressus funebris forest, Abies forest, Pinus densata forest, Picea forest, Pinus yunnanensis forest, Larix forest, Pinus armandi forest, Tsuga dumosa forest) were identified within the cover of coniferous forest. As for the whole area, the overall accuracy of forest and non-forest was 95.76%, and the Kappa coefficient was 91.34%. Within the forest coverage, the overall accuracy of the coniferous forest and the broadleaf forest was 89.74%, and the Kappa coefficient was 79.26%. And within the coniferous forest, the overall accuracy of the eight types of coniferous forest was 91.59%, and its Kappa coefficient was 90.33%. The classification results indicated that topographic information is beneficial to the extraction of forest type information, and multi-temporal Sentinel-2 imagery has great potential to accurately identify forest type at regional level.

An accurate forest type map is important for the forestry resources monitor and management. Forest type mapping over a large and complicated mountain area is full of challenge due to complex forest type compositions, similar spectral characteristics between various forest types, lack of high quality images caused by clouds or cloud shadows, and the difficulties in managing and processing large amount data. This study aims to explore forest-type mapping methods over a mountain region with strong geographic and climate heterogeneity characteristic landscape (Yunnan Province, China). Based on Landsat OLI dense time series data, four median seasonal composites consist of 7 spectral bands and 5 vegetation indexes were derived on Google Earth Engine cloud platform. The Random Forest classifier was used in two-level classification, which includes the classification of forest/non-forest and the classification of forest-type. In two-level classification, three types of feature combination, single-seasonal composite in four seasons, multi-seasonal composite and the combination of multi-seasonal composite and three environmental factors, were set, respectively. We also compared our method with two commonly used methods. The resultant forest map was evaluated by using overall accuracy and Kappa and compared to the ground survey data and four public forest products. The results show that multi-seasonal multispectral variables and the combination with environment factors improved accuracy of forest type classification. Our result is also superior to stat-of-the-art result, FCS2020, in the research area.