Showing papers by "Feifei Pan published in 2020"

A Comparative Study of Water Indices and Image Classification Algorithms for Mapping Inland Surface Water Bodies Using Landsat Imagery

Abstract: A comparative study of water indices and image classification algorithms for mapping inland water bodies using Landsat imagery was carried out through obtaining 24 high-resolution (≤5 m) and cloud-free images archived in Google Earth with the same (or ±1 day) acquisition dates as the Landsat-8 OLI images over 24 selected lakes across the globe, and developing a method to generate the alternate ground truth data from the Google Earth images for properly evaluating the Landsat image classification results. In addition to the commonly used green band-based water indices, Landsat-8 OLI’s ultra-blue, blue, and red band-based water indices were also tested in this research. Two unsupervised (the zero-water index threshold H0 method and Otsu’s automatic threshold selection method) and one supervised (the k-nearest neighbor (KNN) method) image classification algorithms were employed for conducting the image classification. Through comparing a total of 2880 Landsat image classification results with the alternate ground truth data, this study showed that (1) it is not necessary to use some supervised image classification methods for extracting water bodies from Landsat imagery given the high computational cost associated with the supervised image classification algorithms; (2) the unsupervised classification algorithms such as the H0 and Otsu methods could achieve comparable accuracy as the KNN method, although the H0 method produced more large error outliers than the Otsu method, thus the Otsu method is better than the H0 method; and (3) the ultra-blue band-based AWEInsuB is the best water index for the H0 method, and the ultra-blue band-based MNDWI2uB is the best water index for both the Otsu and KNN methods.

Ground Surface Recognition at Voxel Scale From Mobile Laser Scanning Data in Urban Environment

Abstract: Ground filtering is an essential process for further classification of other features from a point cloud. This letter aims to develop an efficient method for the automatic recognition of a ground surface in urban environments from a point cloud acquired by mobile laser scanning (MLS). The MLS point cloud has large amounts of data. To decrease the calculation effort, the point clouds are first segmented into regular voxels using an octree structure. Then, voxel cloud connectivity segmentation (VCCS) is applied to generate supervoxels, which alleviate the issue of boundary cross between voxels and ground truth. Finally, several geometric features on the voxel scale are selected for a support vector machine (SVM) to label segments as a ground surface and nonground objects. For an objective comparison with other methods, the accuracy of the proposed method is evaluated by public MLS data sets. Experimental results show that all ground type objects can be well recognized with least susceptibility to scenic change.

Potential Evapotranspiration Reduction and Its Influence on Crop Yield in the North China Plain in 1961–2014

Abstract: Climate change has caused uneven changes in hydrological processes (precipitation and evapotranspiration) on a space-temporal scale, which would influence climate types, eventually impact agricultural production. Based on data from 61 meteorological stations from 1961 to 2014 in the North China Plain (NCP), the spatiotemporal characteristics of climate variables, such as humidity index, precipitation, and potential evapotranspiration (ET0), were analyzed. The sensitivity coefficients and contribution rates were applied to ET0. The NCP has experienced a semiarid to humid climate from north to south due to the significant decline of ET0 (−13.8 mm decade−1). In the study region, 71.0% of the sites showed a “pan evaporation paradox” phenomenon. Relative humidity had the most negative influence on ET0, while wind speed, sunshine hours, and air temperature had a positive effect on ET0. Wind speed and sunshine hours contributed the most to the spatiotemporal variation of ET0, followed by relative humidity and air temperature. Overall, the key climate factor impacting ET0 was wind speed decline in the NCP, particularly in Beijing and Tianjin. The crop yield in Shandong and Henan provinces was higher than that in the other regions with a higher humidity index. The lower the humidity index in Hebei province, the lower the crop yield. Therefore, potential water shortages and water conflict should be considered in the future because of spatiotemporal humidity variations in the NCP.

The Change Characteristics and Interactions of Soil Moisture and Temperature in the Farmland in Wuchuan County, Inner Mongolia, China

Abstract: Soil moisture and temperature are both significant factors for crop growth and development. They are also the main influencing factors of regional climate change. In the context of climate change, the relationship between soil moisture and soil temperature is not only important for explaining the mechanism of moisture and temperature interaction, but also provides scientific suggestions for agricultural production. Since the accurate measurement of soil moisture and temperature is difficult, their relationship remains poorly understood. Here, based on real-time field observation in a potato field in Wuchuan County, Inner Mongolia, China, the change characteristics of soil moisture and temperature under different water level treatments were analyzed, and their relationships were disclosed. The results show that there was an inverse proportional relationship between soil moisture and temperature. With an increase of soil moisture, soil temperature decreased. The basic relation between soil moisture and temperature took the form of reciprocal functions (Q = 4.2 × 103 V × (0.2 + W) × ΔT). The decline of soil moisture in the farmland will increase the soil temperature and has a negative impact on land–atmosphere interactions. The results can provide important insight into regional climate change mechanisms.

Land Use Simulation of Guangzhou Based on Nighttime Light Data and Planning Policies

Abstract: With the implementation processes of strategies such as Guangdong-Hong Kong-Macau Greater Bay Area’s coordinated development and “Belt and Road Initiative” initiative, the planning policies had produced a significant influence on land use distributions in Guangzhou. In this paper, we employ nighttime light (NTL) information as a proxy indicator of gross domestic product(GDP), and a future land use simulation model (FLUS) to simulate the land use patterns in Guangzhou from 2015 to 2018 and 2018 to 2035 by incorporating planning policies. The results show that: (1) the accuracy of simulation result from 2015 to 2018 based on National Polar-orbiting Partnership, Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) is higher than that based on GDP; (2) by incorporating planning policies into the model can better identify the potential spatial distribution of urban land and make the simulated results more consistent with the actual urban land development trajectory. This study demonstrates that NTL is a suitable and feasible proxy indicator of GDP for the land use simulations, providing a scientific basis for the development of urban planning and construction policy.