Accuracy assessment for remote sensing classification is commonly based on using an error matrix, or confusion table, which needs reference, or ‘ground truthing’, data to support. When undertaking change detection using numerous multi-temporal images, it is often difficult to make the accuracy assessment by the ‘traditional’ method, which typically requires simultaneous collection of reference data. In this study, we propose a new approach by arguing change rationality with post-classification comparison. Multi-temporal Landsat TM images were classified for land use in an urban fringe area of Beijing, China and the post-classification comparison of these classified images shows change trajectories through the time series. These change trajectories were then analysed by assessing their rationality against a set of logical rules to separate cases of ‘real land use change’ and possible classification errors. The analysis results show that the overall accuracy for land use change in the urban fringe area was 86%, with a fuzziness of 7%. Although it is argued that the uncertainty still exists on classification accuracy assessed by this method, it nevertheless provides an alternative approach for more reasonable assessment when ideal simultaneous ‘ground truthing’ is not available.

The arid desert ecosystem is very fragile, and the change of its river discharge has a direct impact on irrigation and natural environment. River discharge attenuation coefficients is a key index to reveal the stability of desert river ecosystem. However, due to the harsh conditions in desert areas, it is difficult to establish a hydrological station to obtain data and calculate the attenuation coefficients, so it is urgent to develop new methods to master the attenuation coefficients of rivers. In this study, Taklamakan desert river was selected as the research area, and the river discharge of the desert river were estimated by combining low-altitude UAV and satellite remote sensing technology, so as to calculate the attenuation status of the river in its natural state. Combined with satellite remote sensing, the surface runoff in the desert reaches of the Hotan River from 1993 to 2017 were estimated. The results showed that the base of runoff attenuation in the lower reaches of the Hotan River is 40%. Coupled UAV and satellite remote sensing technology can provide technical support for the study of surface runoff in desert rivers within ungauged basins. Using UAV and satellite remote sensing can monitor surface runoff effectively providing important reference for river discharge monitoring in ungauged catchments.

Outdoor lighting is becoming increasingly widespread, and residents are suffering from serious light pollution as a result. Residents’ awareness of their rights to protection has gradually increased. However, due to the sometimes-inaccessible nature of residential vertical light incidence intensity data and the high cost of obtaining specific measurements, there is no appropriate hierarchic compensation for residents suffering from different degrees of light pollution. It is therefore important to measure light pollution levels and their damage at the neighborhood scale to provide residents with basic materials for proper protection and to create more politically-suitable solutions. This article presents a light pollution assessment method that is easy to perform, is low-cost and has a short data-processing cycle. This method can be used to monitor residential zone light pollution in other cities. We chose three open areas to test the spatial variation pattern of light intensity. The results are in accordance with spatial interpolation patterns and can be fit, with high precision, using the inverse distance weighted interpolation (IDW) method. This approach can also be used in three dimensions to quantitatively evaluate the distribution of light intensity. We use a mixed-use zone in Beijing known as The Place as our case study area. The vertical illumination at the windows of residential buildings ranges from 2 lux to 23 lux; the illumination in some areas is far higher than the value recommended by CIE. Such severe light pollution can seriously interfere with people's daily lives and has a serious influence on their rest and health. The results of this survey will serve as an important database to assess whether the planning of night-time lighting is scientific, and it will help protect the rights of residents and establish distinguished compensation mechanisms for light pollution.

Accurate information on cropland changes is critical for food production and security, sustainable cropland management, and global change studies. The common change detection methods bi-temporal based, using remotely sensed imagery easily generate pseudo changes due to phenological or seasonal differences. Cropland exhibits a distinctive phenological trajectory that has strong periodic characteristics and seasonal paths. This paper proposes the use of phenological trajectory similarity to search for the overall changes between two time-series images instead of single change events between two dates of imagery. Due to the complex spectral–temporal characteristic of cropland, a phenological trajectory was constructed using a multi-harmonic model for capturing intra-annual variations. Then, phenological trajectory similarity was measured using coefficient vector difference (CVD), and used for detecting change/no-change areas when considering both the amplitude and phase difference. Finally, instead of the traditional classification method based on original images, we used the coefficient ratio vector (CRV) as the input for change type discrimination. The distance between the coefficient ratio vector (CRV) of the change pixel and of the reference change type was calculated to identify the exactly changed types. The performance of this proposed approach was tested using two sets of Landsat time-series images from 2010 and 2015. Moreover, the change area detection results of three other methods, namely, the continuous change detection and classification (CCDC), change vector analysis (CVA), and post-classification comparison (PCC), were also calculated for comparison and analysis. The results indicated that the proposed approach acquired the highest accuracy with an overall accuracy of 98.58% and a kappa coefficient of 0.82, which demonstrated that the method provides the capacity to detect real changes and estimate pseudo changes caused by season differences.

Obtaining the water volume of small- and medium-sized lakes in enclosed watersheds with scarce data is a global focus of research. River flow into a lake is an important factor affecting the water volume. However, most river flow measurement methods involve long cycles, low efficiency, and transdisciplinary expertise, making rapid assessments in ungauged basins impossible. This paper proposes a remote sensing flow estimation method based on multi-source remote sensing data, which quickly assesses river flow and provides important input data for lake water volume simulation. The cross-section flow was estimated by extracting the river width. The calculated results were consistent with the measured data, with accuracy greater than 90%. The results compared with daily data measured at hydrological stations, and the Nash coefficient was greater than 0.9. Additionally, the simulation method for lake area, water volume, and water level was constructed using river inflow input data, greatly reducing the parameters required by the conventional lake water volume simulation method. Based on the remote sensing discharge estimation method, we quickly and conveniently obtained changes in river flow into the lake, simulated lake water volume, and provided the basis for water resource management in terminal lake basins with scarce data.

The accuracy of machine learning models is affected by hyperparameters when classifying different types of urban green spaces. To investigate the impact of hyperparametric algorithms on model optimization, this study used the Marine Predators Algorithm (MPA) to optimize three models: K-Nearest Neighbor (KNN), Support Vector Machines (SVM), and Random Forest (RF). The feasibility of the algorithm was illustrated by extracting and analyzing park green space and attached green spaces within the fifth-ring road of Beijing. A dataset of urban green space type labels was constructed using SPOT6. Three optimized models, MPA-KNN, MPA-SVM and MPA-RF, were constructed. The optimum hyperparameter combination was chosen based on the accuracy of the validation set, and the three optimized models were compared in terms of the Area Under Curve (AUC) value, accuracy on the test set, and other indicators. The results showed that applying MPA improves the accuracy of the validation set of the KNN, SVM, and RF models by 4.2%, 2.2%, and 1.2%, respectively. The MPA-RF model had an AUC value of 0.983 and a test set accuracy of 89.93%, indicating that it was the most accurate of the three models.