Jul 2026· The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences· 0 citations· 3 references
Abstract
Abstract. Vegetation mapping in alpine environments is essential for monitoring ecosystem dynamics and climate change impacts, yet remains challenging when using very high-resolution UAV imagery under limited labeled data. This study proposes a data-centric, pixel- based classification framework for class-level vegetation mapping using multispectral UAV data acquired in an alpine study area. The approach prioritizes improving data representation rather than increasing model complexity. To address label scarcity, a feature-rich dataset was constructed by integrating spectral information, vegetation indices, and lightweight spatial descriptors to enhance class separability. Classification was performed using XGBoost, which is well suited for multispectral tabular data and robust under imbalanced conditions. The results show consistent classification performance across vegetation types and demonstrate the effectiveness of dataset enrichment under limited supervision, highlighting the importance of feature representation in data-scarce scenarios.
Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which...
Javier Marcello, F. Eugenio, A. Mederos-Barrera et al.· Remote Sensing· 0 citations
Estuarine wetlands are highly dynamic ecosystems, and the vegetation serves as a critical indicator of ecological health. Accurate mapping of different vegetation types remains challenging due to spectral similarities and the high dimensionality of time-series data. This research introduces a Google Earth Engine (GEE)-...
Yi-Han Wang, Jin-Xiu Zeng, Ruo-Zeng Wang et al.· Remote Sensing· 0 citations
Accurate crop-type mapping is essential for agricultural monitoring, but pixel-based products often suffer from within-field fragmentation, boundary noise, and limited consistency with field management units. This study developed a parcel-constrained crop classification approach using the HLSS30 product from the Harmon...
Yong Zhang, Qian-Hua Ren, Frank Hang Xu et al.· Remote Sensing· 0 citations
Urban ecological monitoring is increasingly essential in rapidly developing tropical regions. This study investigates land-cover changes and vegetation dynamics in Baubau City, Indonesia, from 2019 to 2025 using an RGB-based unsupervised classification framework. The primary aim is to develop a practical, cloud-enabled...
Afriningsih Harjunianti, S. Syarif, Yuyun Wabula et al.· JOURNAL OF APPLIED INFORMATI...· 0 citations
Remotely sensed land-use/land-cover (LULC) data products are an important tool for understanding landscape processes at all scales. The types of inference researchers can derive from remote sensing data depend on the quality and characterization of these LULC products. Currently, gaps in the detail of widely available...
Sky Mae Gennette· 0 citations
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