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InfluencIas del cambIo clImátIco y la varIabIlIdad clImátIca sobre el recurso hídrIco, bIodIversIdad y servIcIos ecosIstémIcos en el departamento del magdalena

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Effects of climate change on vegetation greenness have attracted considerable attention in the context of global change; however, the dependence of such climatic effects on elevation remains poorly understood. In this study, we examine the relationship between vegetation greenness change and climate change and, in particular, characterize how this relationship changes with elevation in the high mountains of southwest China by using the remotely sensed normalized difference vegetation index (NDVI), and observed temperature and precipitation data sets for the period of 1982–2013. The results show that vegetation exhibited a greening trend (slope: 0.0008 year−1, p < 0.01) under climate warming (slope: 0.04 °C year−1, p < 0.01) and drying (slope: −2.47 mm year−1, p > 0.05). The vegetation greening and climate warming trends were stronger in the higher elevation plateaus than in the lower elevation mountains. Statistical analysis showed that temperature was the main driving factor on vegetation greening, and the driving effect was elevation‐dependent. A substantially more significant correlation between climate warming and vegetation greening was found in the higher elevation plateaus, which reveals a higher temperature sensitivity of these plateaus. In addition, a significant correlation between inter‐annual standard deviations of NDVI and precipitation during 1982–2013 was tracked over the entire study area.

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Cropland redistribution to marginal land has been reported worldwide; however, the resulting impacts on environmental sustainability have not been investigated sufficiently. Here we investigated the environmental impacts of cropland redistribution in China. Due to urbanization-induced loss of high-quality croplands in south China (∼8.5 t ha–1), croplands expanded to marginal lands in northeast (∼4.5 t ha–1) and northwest China (∼2.9 t ha–1) during 1990–2015 to pursue food security. However, the reclamation in these low-yield and ecologically vulnerable zones considerably undermined local environmental sustainability, e.g. increasing wind erosion (+3.47%), irrigation water consumption (+34.42%), fertilizer use (+20.02%), and decreasing natural habitats (−3.11%). Forecasts show that further reclamation in marginal lands per current policies would exacerbate environmental costs by 2050. The future cropland security risk will be remarkably intensified due to the conflict between food production and environmental sustainability. Our research suggests that globally emerging reclamation of marginal lands should be restricted and crop yield boost should be encouraged for both food security and environmental benefits.

With more and more crowdsourcing geo-tagged field photos available online, they are becoming a potentially valuable source of information for environmental studies. However, the labelling and recognition of these photos are time-consuming. To utilise such information, a land cover type recognition model for field photos was proposed based on the deep learning technique. This model combines a pre-trained convolutional neural network (CNN) as the image feature extractor and the multinomial logistic regression model as the feature classifier. The pre-trained CNN model Inception-v3 was used in this study. The labelled field photos from the Global Geo-Referenced Field Photo Library (http://eomf.ou.edu/photos) were chosen for model training and validation. The results indicated that our recognition model achieved an acceptable accuracy (50.34% for top-1 prediction and 78.20% for top-3 prediction) of land cover classification. With accurate self-assessment of confidence, the model can be applied to classify numerous online geo-tagged field photos for environmental information extraction.

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