Nature Environment and Pollution Technology

Qingkong Cai ¹, Jinbao Jiang ¹, Ximin Cui ¹, Liangliang Tao ²

Affiliations
1 College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, CN
2 State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, CN

Abstract

Leaf area index (LAI) is one of the most basic parameters to characterize the vegetation canopy structure, and is widely used in monitoring crop growth, yield estimation and other fields. Therefore, accurate estimation of LAI has great significance for agricultural precision fertilization and protecting agricultural ecological environment. However, few studies have attempted to estimate LAI of winter wheat using the continuous wavelet analysis (CWA), particularly at different growth stages. This paper aims at studying the spectral estimation of LAI by applying CWA into canopy spectra of 190 samples observed at Guanzhong Plain in China. Two partial least square regression (PLSR) models using six wavelet features and the optimal spectral indices were constructed and compared respectively. Results indicated that the model using wavelet features combination had a considerable improvement than the spectral indices combination for the whole validation dataset. When the validation dataset was separated according to the growth stage, the predictive performance of the wavelet features combination performed well at both growth stages, while the spectral indices combination had not achieved the same effect. The results showed that CWA approach could derive more robust wavelet features to growth stage variation, and wavelet features were more effective than the spectral indices for predicting LAI of winter wheat at different growth stages.

Keywords

Leaf Area Index, Continuous Wavelet Analysis, Winter Wheat, Hyperspectral Remote Sensing, Agricultural Ecological Environment.

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Qingkong Cai ¹, Erjun Li ², Ruibo Jiang ¹

Affiliations
1 College of Civil Engineering, Henan University of Engineering, Zhengzhou 451191, CN
2 College of Human and Social Sciences, Henan University of Engineering, Zhengzhou 451191, CN

Abstract

Rapid urbanization, coupled with the tremendous growth in population and built-up areas in cities, leads to problems associated with global climate change impacts, such as urban heat island effect, which influences regional climate, urban environmental pollution, and socio-economic development. This study aims to detect the spatial variation of land surface temperature (LST) and determine its quantitative relationship with land cover types using the techniques of remote sensing and geographic information system. Taking the city of Zhengzhou as the study area, we utilized four Landsat TM images from 1988 to 2017 to classify land cover types by maximum likelihood classification and retrieve the LST by using a mono-window algorithm. Results show that the urban heat island areas have an expanding trend from 1988 to 2017 in general. Moreover, the spatial distribution pattern and extension of the urban heat islands are consistent with urban sprawl. Changes in land cover type altered the spatial distribution characteristics of LST from 1988 to 2017. In various land cover types, the construction land and bare land showed the highest temperature, and they significantly contributed to the heat island effect. Green land and water can effectively relieve the heat island effect. Finally, constructive strategies and measures to alleviate the urban heat island effect are proposed from different aspects. Therefore, this work provides a scientific reference for decision making to improve the thermal environment and achieve sustainable development in the future.

Keywords

Land Surface Temperature, Land Cover Change, Zhengzhou, Remote Sensing.

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