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Alpine land cover (ALC) is facing many challenges with climatic change, biodiversity reduction and other cascading ecosystem damage triggered by natural and anthropogenic interference. Although several global land cover products and thematic maps are already available, their mapping accuracy of alpine and montane regions remains unsatisfactory due to the data acquisition, methodology, and workflow design constraints. Therefore, in this paper, a deep convolutional neural network (DCNN) in Google Earth Engine (GEE) was developed to map the ALC types of the Yarlung Zangbo river basin (YZRB) in the Tibetan plateau using multi-source remote sensing data. The DCNN algorithm was offline trained using automatically generating samples and online deployed in the GEE for a large-scale ALC mapping. Moreover, a set of fine land cover classification system (containing 14 ALC types) was also established in accordance with the natural situation of the YZRB. The overall accuracy and kappa were 86.24% and 0.8156, which were higher than traditional classification algorithms. The spatial distribution of ALC types was analyzed in different gradient zones, and a clear altitudinal characteristic was noticed. The terrain of the YZRB from upper-stream to down-stream with an elevation dramatically decreases, and corresponding to vertical zonal changes from glacier and permanent snow/ice, barren gravel land, alpine desert steppe, alpine steppe, alpine meadow, shrubs, to tree cover. The product can provide valuable land cover information to support alpine ecosystem conservation.

The discrete cosine transform is a commonly used technique in the field of signal processing that employs cosine basis functions for signal analysis. Traditionally, the regression coefficients of the cosine basis functions are solely based on frequency information. This paper extends the regression coefficients associated with the cosine basis functions to take into account both frequency and time information, not just frequency information alone. This modification results in an ill-posed linear system, which requires regularization to prevent overfitting. To address this, this paper uses shaping regularization, a technique used to stabilize ill-posed problems. By doing so, the absolute values of these extended coefficients, now exhibiting variations in both frequency and time domains, are defined as the time–frequency distribution of that input signal. The numerical experiments conducted to validate this approach demonstrate that the proposed method yields a commendable time–frequency resolution. Consequently, it proves valuable for interpreting seismic data, showcasing its potential for applications in this field. [ABSTRACT FROM AUTHOR]

Here, we assess land use/land cover (LULC) transitions over the last 30 years in Jakarta, Indonesia. Land cover maps were prepared for 1990, 1995, 2000, 2005, 2010, 2015, and 2020 using seven categories of Landsat satellite image: bare land, built-up, cropland, green area, mangrove, water body, and pond. LULC changes were assessed through intensity analyses at the interval and transition levels. LULC changes were initially rapid (1990–1995) and then more gradual (1995–2000, 2000–2005, and 2005–2010). Unlike in previous intervals, annual changes were uniformly distributed over time in 2010–2015 and 2015–2020. Driven by high population and economic growth, built-up land was identified as an active gainer in all intervals except 2010–2015. Alongside built-up areas, cropland was the main supplier of other categories, including bare land, pond, built-up, and green areas. The largest transition area occurred in pond and green areas during 2005–2010 and in built-up land during 2015–2020. High demand for built-up land was observed in land changes driven by high population growth triggered by economic necessity. Economic and population growth exhibited a positive correlation (R2 = 0.78, t = 9.996). This study elucidates spatiotemporal LULC transition patterns over 30 years in a rapidly growing city. [ABSTRACT FROM AUTHOR]