Rice is one of the most important grain crops in Northeast China(NEC)and its cultivation is sensitive to climate change.This study aimed to explore the spatio-temporal changes in the NEC rice planting area over the period of 1980-2010 and to analyze their relationship to climate change.To do so,the CLUE-S(conversion of land use and its effects at small region extent)model was first updated and used to simulate dynamic changes in the rice planting area in NEC to understand spatio-temporal change trends during three periods:1980-1990,1990-2000 and 2000-2010.The changing results in individual periods were then linked to climatic variables to investigate the climatic drivers of these changes.Results showed that the NEC rice planting area expanded quickly and increased by nearly 4.5 times during 1980-2010.The concentration of newly planted rice areas in NEC constantly moved northward and the changes were strongly dependent on latitude.This confirmed that climate change,increases in temperature in particular,greatly influenced the shift in the rice planting area.The shift in the north limit of the NEC rice planting area generally followed a 1°C isoline migration pattern,but with an obvious time-lag effect.These findings can help policy makers and crop producers take proper adaptation measures even when exposed to the global warming situation in NEC.
XIA TianWU Wen-binZHOU Qing-boYU Qiang-yiPeter H VerburgYANG PengLU Zhong-junTANG Hua-jun
Although climate change impacts and agricultural adaptations have been studied extensively,how smallholder farmers perceive climate change and adapt their agricultural activities is poorly understood.Survey-based data(presents farmers’personal perceptions and adaptations to climate change)associated with external biophysical-socioeconomic data(presents real-world climate change)were used to develop a farmer-centered framework to explore climate change impacts and agricultural adaptations at a local level.A case study at Bin County(1980s-2010s),Northeast China,suggested that increased annual average temperature(0.6°C per decade)and decreased annual precipitation(46 mm per decade,both from meteorological datasets)were correctly perceived by 76 and 66.9%,respectively,of farmers from the survey,and that a longer growing season was confirmed by 70%of them.These reasonably correct perceptions enabled local farmers to make appropriate adaptations to cope with climate change:Longer season alternative varieties were found for maize and rice,which led to a significant yield increase for both crops.The longer season also affected crop choice:More farmers selected maize instead of soybean,as implicated from survey results by a large increase in the maize growing area.Comparing warming-related factors,we found that precipitation and agricultural disasters were the least likely causes for farmers’agricultural decisions.As a result,crop and variety selection,rather than disaster prevention and infrastructure improvement,was the most common ways for farmers to adapt to the notable warming trend in the study region.
农作物遥感识别是地理学和生态学研究的前沿和热点,多源数据在农作遥感识别中日益发挥重要作用。笔者从多源数据融合的角度,归纳了2000年后多源数据在农作物遥感识别中应用的总体概况,系统梳理并提炼了当前多源数据融合的主要融合技术和融合模式。围绕与多源数据融合和农作物遥感识别相关的关键词,在Google学术、ISI Web of Knowledge和中国知网中对2000—2014年间国内外发表的论文进行检索,并统计不同传感器的使用频率及结合方式。研究表明,以提高空间分辨率为目标的多源数据融合和以提高时间分辨率为目标的多源数据融合技术是当前的两种主要方式,可以在一定程度上实现时空尺度的扩展。前者的融合技术包括图像融合、正态模糊分布神经网络模型、成分替换、半经验数据模型融合及多分辨率小波分解等,可以提升遥感数据的空间分解力和清晰度,较好弱化混合像元产生的影响,但农作物光谱信息有一定程度的丢失或扭曲,农作物空间分布局部细节信息与纹理特征依然会缺失;后者的融合技术形式灵活多样,可分为同源数据联合扩展时序的时空优化技术和异源数据联合扩展时序的时空优化技术,其可以有效排除短时间段内农作物生育期交叉,但易受不同遥感数据源间光谱反射率或植被指数转换模型及光谱波段设置差异的影响。在融合模式方面,根据数据类型分为光学数据的融合、光学数据与微波数据的融合以及遥感与非遥感数据的融合,以实现卫星资源优势互补为宗旨,充分挖掘不同类型农作物在遥感数据上呈现的光谱、时间和空间特征差异信息。同样,农作物遥感识别研究中的多源遥感数据融合也存在诸多挑战,在未来一段时间内,完善不同传感器之间的合作、更深层次挖掘融合信息以及多尺度长时间序列的中高分辨率农作物空间分布数据
