A Multi-Stage Approach Combining Very High-Resolution Satellite Image, GIS Database and Post-Classification Modification Rules for Habitat Mapping in Hong Kong

Abstract

Identification and mapping of various habitats with sufficient spatial details are essential to support environmental planning and management. Considering the complexity of diverse habitat types in a heterogeneous landscape, a context-dependent mapping framework is expected to be superior to traditional classification techniques. With the aim to produce a territory-wide habitat map in Hong Kong, a three-stage mapping procedure was developed to identify 21 habitats by combining very-high-resolution satellite images, geographic information system (GIS) layers and knowledge-based modification rules. In stage 1, several classification methods were tested to produce initial results with 11 classes from a WorldView-2/3 image mosaic using a combination of spectral, textural, topographic and geometric variables. In stage 2, modification rules were applied to refine the classification results based on contextual properties and ancillary data layers. Evaluation of the classified maps showed that the highest overall accuracy was obtained from pixel-based random forest classification (84.0%) and the implementation of modification rules led to an average 8.8% increase in the accuracy. In stage 3, the classification scheme was expanded to all 21 habitats through the adoption of additional rules. The resulting habitat map achieved >80% accuracy for most of the evaluated classes and >70% accuracy for the mixed habitats when validated using field-collected points. The proposed mapping framework was able to utilize different information sources in a systematic and controllable workflow. While transitional mixed habitats were mapped using class membership probabilities and a soft classification method, the identification of other habitats benefited from the hybrid use of remote-sensing classification and ancillary data. Adaptive implementation of classification procedures, development of appropriate rules and combination with spatial data are recommended when producing an integrated and accurate map.

Fire traps in the wet subtropics: New perspectives from Hong Kong

“…Widespread agricultural abandonment, along with the designation of strictly protected Country Parks over 40% of the land area, led to over 70 years of natural succession and assisted regeneration (Abbas et al., 2016). The current vegetation of Hong Kong consists of a mosaic of grasslands, shrublands, secondary forests and plantations (Kwong et al., 2022).…”

Fire traps in the wet subtropics: new perspectives from Hong Kong

“…Widespread agricultural abandonment, along with the designation of strictly protected Country Parks over 40% of the land area, led to over 70 years of natural succession and assisted regeneration (Abbas, Nichol and Fischer, 2016). The current vegetation of Hong Kong consists of a mosaic of grasslands, shrublands, secondary forests, and plantations (Kwong et al ., 2022).…”

The Habitat Map of Switzerland: A Remote Sensing, Composite Approach for a High Spatial and Thematic Resolution Product

“…For example, Sentinel 1 and/or 2 imagery was applied for the Living England habitat map [14] and Scotland's high resolution habitat map [15], while a combination of SPOT, Indian Remote Sensing, Landsat-5, RapidEye and Disaster Monitoring Constellation satellite images as well as aerial photography was used for county-wide habitat mapping in Norfolk, UK [16]. Natura 2000 habitat types were mapped nationwide for Germany with MODIS imagery [17], and WorldView2/3 imagery was used to develop the Hong Kong habitat map [18]. In contrast, distribution modelling approaches based on topographical, climate, geological and land use predictors rather than remote sensing information have been successfully applied in Norway and Britain [19,20].…”

“…In contrast, distribution modelling approaches based on topographical, climate, geological and land use predictors rather than remote sensing information have been successfully applied in Norway and Britain [19,20]. These maps differentiate a limited number of habitat types/classes, e.g., 17 classes for England [14], 18 for Germany [17], 31 for Norway [19] and 21 for Hong Kong [18], and either have higher uncertainty/less detail around areas with finely resolved habitat mosaics (i.e., in urban areas, [14]) or incorporate higher resolution ancillary data in a composite approach [18].…”