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The urban black and odorous water bodies affect the daily life of urban residents, destroy the aquatic ecosystem and damage the image of the city. This research used the evaluation method of black and odorous water body proposed by the Ministry of Housing and Urban-Rural Development of the People’s Republic of China (MOHURD) to evaluate the water quality of 50 key creeks (53 sections) in Guangzhou in 2016 and 2017 and analyzed the spatial distribution of black and odorous water bodies and their changes over time. The results showed that the water quality of Conghua District, Panyu District, Nansha District, and Yuexiu District was relatively good, while the water quality of Baiyun District, Haizhu District, Huadu District, Huangpu District, Liwan District, Tianhe District, and Zengcheng District was relatively poor. Compared with 2016, the water quality of creeks in Huangpu District and Baiyun District deteriorated in 2017, while the water quality of creeks in Tianhe District, Liwan District, and Huadu District improved. The water quality of creeks in other districts did not change significantly. In 2017, ammonia nitrogen (NH3-N), total phosphorus (TP), and chemical oxygen demand (COD) content decreased, and dissolved oxygen (DO) content increased compared to 2016. In 2017, the number of black-odor water bodies was greatly reduced, and the water quality of creeks was generally improved. The results can provide a reference for the treatment of the black and odorous creeks in Guangzhou.

Horqin Sandy Land is a key area for the construction of northeast shelterbelts. Ecological environment of the area is fragile and vulnerable to damage. However, there is a lack of research on the long time series and continuous system in Horqin Sandy Land. Therefore, it is urgent to grasp the impact of policies of the Three-North Shelterbelt Program on Horqin Sandy Land and analysis the differences in land use patterns in the Horqin Sandy Land before and after implementing restoration projects. In this paper, six counties in Horqin Sandy Land are used as the research areas. Six Landsat TM / ETM + / OLI remote sensing images from 1990 to 2015 are collected. Based on the NDVI, NDWI, NDBI, and the principle of spectral hybrid analysis, a decision tree classification method is used to put the satellite image data divided into seven categories: sandy land, cultivated land, waters, forest land, grassland, residential land, and saline-alkali land. The accuracy of the classification results is verified on the basis of high-resolution images and yearbook data. The results show that: (1) Analysis is made from the perspective of land use dynamics. During the period of 1990–2015, the area of cultivated land and residential land continued to increase. The area of salinized land, water body and grassland fluctuated little, and decreased generally. The forest land and desertification area fluctuated greatly and the overall trend was “increase-decrease-increase”; the overall trend of sandy land was “decrease-increase-decrease”. The areas of grassland, woodland and sandy land showed a negative correlation. (2) Analysis is made from the perspective of spatial distribution pattern of land use types, each of land types has undergone dramatic changes, among which grassland, cultivated land, forest land, and sandy land are the most important types of change. (3) Twenty-five years after the implementation of the Three-Norths Shelter Program, the ecological environment of Horqin Sandy Land has been initially restored. The ecological environment in this area is extremely fragile and requires long-term and continuous protection at a later stage. This study analyzes the change of land use pattern in Horqin Sandy Land in the past 25 years through an effective decision tree classification method, explores the impact of the Three-North Shelterbelt Program on the evolution of the pattern, and provides a recommendation of rationalization for future sustainable land use in Horqin Sandy Land.

The scale research of landscape pattern is an important basis for the study of spatiotemporal evolution of landscape pattern and the scientific and reasonable allocation of landscape pattern. This paper takes the middle and upper reaches of Meijiang river as the research area. Combined with spatial grain size analysis and extent size analysis, the spatial scale effect was studied to select the optimal research scale in this research area. The results show that most of the appropriate grain size of landscape pattern indexes are in range of 90–150 m, and the optimal grain size scale is 150 m in Middle and Upper Reaches of Meijiang River Basin. At the grain size scale of 150 m, the spatial self-correlation of landscape pattern index over 50% is the highest at the extent size of 300 m, and the optimal extent size scale is 300 m in this basin. This paper determines the optimal scale of landscape pattern in the middle and upper reaches of Meijiang river basin, which is of great significance to the ecological balance and sustainable development of the basin.

Economic development, population growth, industrialization, and urbanization dramatically increase urban water quality deterioration, and thereby endanger human life and health. However, there are not many efficient methods and techniques to monitor urban black and odorous water (BOW) pollution. Our research aims at identifying primary indicators of urban BOW through their spectral characteristics and differentiation. This research combined ground in-situ water quality data with ground hyperspectral data collected from main urban BOWs in Guangzhou, China, and integrated factorial data mining and machine learning techniques to investigate how to monitor urban BOW. Eight key water quality parameters at 52 sample sites were used to retrieve three latent dimensions of urban BOW quality by factorial data mining. The synchronically measured hyperspectral bands along with the band combinations were examined by the machine learning technique, Lasso regression, to identify the most correlated bands and band combinations, over which three multiple regression models were fitted against three latent water quality indicators to determine which spectral bands were highly sensitive to three dimensions of urban BOW pollution. The findings revealed that the many sensitive bands were concentrated in higher hyperspectral band ranges, which supported the unique contribution of hyperspectral data for monitoring water quality. In addition, this integrated data mining and machine learning approach overcame the limitations of conventional band selection, which focus on a limited number of band ratios, band differences, and reflectance bands in the lower range of infrared region. The outcome also indicated that the integration of dimensionality reduction with feature selection shows good potential for monitoring urban BOW. This new analysis framework can be used in urban BOW monitoring and provides scientific data for policymakers to monitor it.