In order to reveal the characteristics of spatial distribution and dynamic change of the forest in the Wuyishan Nature Reserve from 2000 to 2019, the recently proposed shadow-eliminated vegetation index (SEVI) and the method of Sen+Mann-Kendall were used together in this study. The results show a trend of “decreasing first and then increasing” of vegetation cover change of the Reserve during the sub-periods of 2000-2011 and 2012-2019, but, as a whole, the vegetation cover of the Reserve was improved during 2000-2019, with 43.76% slight improvement and 55.18% significant improvement, approximately.

Abstract:Agricultural water resource security is faced with serious challenges in the background of climate change. It is important to study the trend of evapotranspiration (ET) for future planning and management of agricultural water resources. On the basis of the 1961-2004 weather data, the trends of six meteorological variables were analysed. Also, the potential ET (PET) of maize (Zea mays L.) in the growing season was simulated by the World Food Study (WOFOST) model. Results indicated that the maximum and minimum temperatures increased significantly, and the daily minimum temperature increased much faster than the daily maximum temperature, which resulted in the decrease in the diurnal temperature range (DTR). The precipitation showed an increasing trend, whereas the sunshine duration and wind speed (WS) showed a declining trend. No remarkable linear tendency was found on vapour pressure. As a result, the simulated maize PET showed a significant decrease by 1 mm year 1 , and it was mainly as a result of decreased sunshine duration and WS. This may provide valuable information on the aspect of water resource management in the Yellow River basin.

The mountainous vegetation is important to regional sustainable development. However, the topographic effect is the main obstacle to the monitoring of mountainous vegetation using remote sensing. Aiming to retrieve the reflectance of frequently-used red–green–blue and near-infrared (NIR) wavebands of rugged mountains for vegetation mapping, we developed a new integrated topographic correction (ITC) using the SCS + C correction and the shadow-eliminated vegetation index. The ITC procedure consists of image processing, data training, and shadow correction and uses a random forest machine learning algorithm. Our study using the Landsat 8 OLI multi-spectral images in Fujian province, China, showed that the ITC achieved high performance in topographic correction of regional mountains and in transferability from the sunny area of a scene to the shadow area of three scenes. The ITC-corrected multi-spectral image with an NIR–red–green composite exhibited flat features with impressions of relief and topographic shadow removed. The linear regression of corrected waveband reflectance vs. the cosine of the solar incidence angle showed an inclination that nearly reached the horizontal, and the coefficient of determination decreased to 0.00~0.01. The absolute relative errors of the cast shadow and the self-shadow all dramatically decreased to the range of 0.30~6.37%. In addition, the achieved detection rate of regional vegetation coverage for the three cities of Fuzhou, Putian, and Xiamen using the ITC-corrected images was 0.92~6.14% higher than that using the surface reflectance images and showed a positive relationship with the regional topographic factors, e.g., the elevation and slope. The ITC-corrected multi-spectral images are beneficial for monitoring regional mountainous vegetation. Future improvements can focus on the use of the ITC in higher-resolution imaging.

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