Urban green space (e.g., parks, farmland, gardens, etc.) design in different urban functional regions (e.g., residential land, commercial land, etc.) depends on different planning purposes. The changes in urban green spaces are highly related to urban land-use changes (e.g., from residential land to commercial land). However, the investigation of urban green space patterns in response to urban land-use changes has been ignored. This research takes Hangzhou city, a typical example in terms of urbanization, population growth, economic development, and land-use changes, as the study site, aiming to explore the landscape patterns of urban green space changes with different urban land-use changes. The results showed that urban green spaces increased from 2017 to 2021, and the growth was mainly concentrated in the urban core area, indicating that the city has made remarkable achievements in the planning of green spaces. Specifically, the increase in urban green spaces in the first ring belt was mainly related to the old town transformation program in the residential land. The change from the residence parcels to the business parcels determined the increase in green spaces in the second and third ring belts, probably because of the attractiveness of customers. In addition, a large number of open-space parcels have changed to business parcels around the urban periphery, which might be due to the transformation from farmland to impervious surfaces. Combined with the urban land-use and green-space policies, the findings highlighted that a reasonable urban land-use layout can promote the optimization and layout of urban green spaces. The private sector (e.g., shopping malls) can also contribute to the increase in green spaces. The understanding of urban green landscapes with different urban land-use changes can provide references for analyzing and optimizing green space in other cities experiencing rapid urban land-use changes.

Lake systems on the Tibetan Plateau (TP) are important for the supply and storage of fresh water to billions of people. However, previous studies on the dynamics of these lakes focused on monitoring on multi-year scales and therefore lack sufficient temporal information. Here we present a new dataset comprising annual maps of big lakes (>10 km²) on the TP for 1991–2018, generated by utilizing all available Landsat images in conjunction with Google Earth Engine. The annual lake maps with high overall accuracy (~96%) highlight distinctive lake distribution and lake changes: (1) about 70% number and area of lakes concentrated in the Inner basin; (2) generally increasing trends in both the area (by 33%) and number (by 30%) of lakes from 1991 to 2018; (3) the total area changes were dominated by larger lakes (>50 km²) while more fluctuations in the lake number changes were found in medium lakes (10−50 km²). Our dataset infills temporal gaps in long-term inter-annual variations of big lakes, contributing towards enhanced knowledge of TP lake systems.

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

Topographic data on The Tibetan Plateau (TP) terrain are fundamental for geoscientific research, but are difficult to obtain. The Shuttle Radar Topographic Mission (SRTM) Digital Elevation Model (DEM) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) are two commonly used GDEM data. Verifying the accuracy of the two dataset for the TP mountain areas provides a reference point for the application of both DEMs. For evaluating the elevation accuracy and topographic information, we used 8242 field measurements from Differential Global Positioning System (DGPS) points and DEM data generated from 1:100,000 topographic maps to examine the accuracy of ASTER GDEM V2 and SRTM3 V4.1 elevation results. The average RMSE for elevation differences between DGPS and ASTER GDEM across the study areas was 18.56m while the average RMSE between DGPS and SRTM3 was 10.39m. The average RMSEs of ASTER GDEM and SRTM3 in glaciated areas were 8.55m and 5.87m, respectively. The vertical accuracy of SRTM3 is better than that of ASTER GDEM. The vertical accuracy of both DEMs do not vary with altitude, but is related to aspect and slope.

Northeast China, a crucial agricultural region contributing one-third of China’s commodity grain production, lacks detailed, high-resolution crop maps pre-2013 due to limited satellite observations. To bridge this gap, this study developed annual 30 m crop type maps for Northeast China (2001–2022) using all available Landsat and MODIS imagery and the Highly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm. The accuracy of crop-type maps was assessed using three complementary approaches. First, validation against ground-truth data (2017–2022) yielded overall accuracies of 80.7%–91%. Second, validation using ‘trusted pixels’ from existing crop-mapping products (2001–2020) produced overall accuracies of 85%–95%. Third, comparison with government statistics (2001–2022) showed average R² of 0.98 (paddy rice), 0.83 (maize), and 0.90 (soybean). The generated maps were found to be the most consistent with government statistics compared to pre-existing crop maps, while providing comprehensive spatial and temporal details. This dataset is an important contribution to long-term fine-resolution crop mapping at the regional scale in China, which provide valuable guidance for sustainable agriculture practices in China’s primary grain-producing region.