Airborne high‐resolution images for grape classification: changes in correlation between technological and late maturity in a Sangiovese vineyard in Central Italy

Background and Aims: The goal of this study was to investigate the relationships between NDVI values at different phenological stages and measurements of grape parameters at two different harvest dates. Methods and Results: The research was done on a Sangiovese vineyard in Central Italy. Over four seasons, airborne NDVI measurements acquired between June and August were related to grape parameters (yield per vine, pH, °Brix, anthocyanins and polyphenols) at technological harvest (H1) and two weeks later (H2). Correlations were higher at H1 and decreased at H2 with a different rate depending on the parameter. °Brix and pH correlations showed a moderate rate of variation between H1 and H2; bigger differences and a different inter‐annual dynamic were observed in anthocyanins and polyphenols between H1 and H2. Conclusions: The ability of NDVI to discriminate different grape classes was confirmed, but its efficacy substantially varies depending on the harvest date. These results suggest the existence, within the same vineyard, of different grape populations having specific timing and shape of ripening curve; as a consequence, distinct vigour zones of the vineyard show a different evolution of the content of grape parameters between the two harvests thus influencing the degree of correlation between grape quality and NDVI measurements. Significance of the Study: This is the first study in which harvest date has been considered for its influence on the predictive skill of RS. It therefore highlights not only the importance of spatial variation within the single vineyard, but also the importance of ripening dynamics.     

在GIS支持下利用MODIS数据监测多种作物和果树种植面积

MODIS数据以其时间分辨率高、监测范围大、可免费接收、获取方便及时等优势,已经成为土地利用研究的重要信息源。以河北省38°N带为研究区,选取不同时相的MODIS数据,分别计算出NDVI,根据植被的生长过程中叶面积的变化规律,观察其NDVI的变化,建立分类规则,确定出主要植被的种植区域。在GIS软件的支持下,利用分类精度为91%的TM数据分类结果从提取区域的面积和形状两个方面对MODIS数据的分类结果进行像元尺度上的精度分析。     

仪征地区农田深层土壤湿度遥感反演初探

利用MODIS合成产品数据MOD11A2和MOD13A2获取的陆地表面温度(Ts)和归一化植被指数(NDVI)构建Ts/NDVI特征空间,依据该特征空间计算温度植被干旱指数(TVDI),进而反演了仪征地区不同季节的40 cm土壤相对湿度。使用野外同步实测数据进行验证,结果显示,总体平均相对误差为11.83%,2004年11月误差最小,为4.30%。遥感反演的仪征地区土壤湿度分布图表明该地区存在两个土壤湿度高值区,分别位于仪征南部的长江冲积平原和西北部的谷底平原地带,并且土壤平均相对湿度越大,其高值区与低值区之间的差异越小。     

内蒙古植被稳定性及其影响因素分析

植被是生态系统的主要组成部分,在调节全球气候变化和生态平衡方面有重要作用,是气候变化的指示器.因此,研究植被弹性和抗性及影响因素对区域植被稳定性现状和生态可持续发展具有重要意义.基于2000—2019年MOD13A2 NDVI数据、气象数据和植被类型数据,通过自回归模型、趋势分析及地理空间分析方法,对内蒙古植被稳定性及...     

恭城县岩溶石漠化环境变化定量遥感研究

恭城县喀斯特地貌占全县土地面积60％以上,岩溶石漠化分布广泛,制约了该县经济发展。采用1991、2000和2006年时间序列的卫星遥感影像数据,对该县岩溶石漠化环境变化开展了植被指数NDVI、监督分类和重心轨迹等遥感定量分析。采用4种图像监督分类方法,提取了6种主要的生境信息。1991、2000和2006年NDVI分别为0．176417、0．06884和0．336478,岩溶石漠化面积分别为160．8、234．1和118．0km^2,均说明了恭城县的岩溶生态环境恢复正在朝好的方向发展。研究表明：运用最小距离分类在本试验区对识别6类生境分区比较可行;岩溶石漠化灾害在总体上具有向南东方向偏移的趋势,年均偏移速度约为49m。     

HJ-1星多光谱影像火烧迹地信息提取研究——以百色市为例

为识别火烧迹地等地类,以广西百色市为研究区,采用HJ-1星多光谱影像数据近红外波段光谱值、林火发生前后两时相各自NDVI值以及NDVI变化值,基于先验知识和统计分析构建决策树分类模型,通过与传统最大似然分类提取结果的比较分析,表明基于多特征的决策树模型能够有效地对HJ-1星多光谱遥感数据进行火烧迹地等地类提取,在研究区并具有良好的推广性。     

废弃矿区环境恢复状况遥感监测研究

废弃矿区环境恢复监测对合理开采矿区具有重要的指导意义.以双鸭山废弃矿区为例,基于该区域2000年和2009年的MODIS数据,利用NDVI对两期的植被覆盖度变化进行了统计分析,总结了这两期的植被覆盖在时间和空间上的变化特征.采用分类方法,对比分析废弃矿区土地利用在时间序列上的变化情况.结果表明,双鸭山市废弃矿区植被覆盖度在时间特征上有了一定的提高.为废弃矿区的环境调查与生态恢复模式的研究提供了技术支撑.     

城市异质植被的覆盖度估算模型比较研究

植被覆盖度是城市生态环境评价的一个重要指标。针对亚热带城市异质植被覆盖特征,选择像元尺度的植被指数(NDVI)转换模型、亚像元尺度的植被—土壤两端元模型(V-S Model)和植被—高—低反射率三端元模型(V-H-L Model)在TM影像上估算植被覆盖度,并结合野外实地调查对比验证3种模型的估算精度及其适用性。结果表明模型尺度和背景亮度对植被覆盖度估算有着不同程度的影响。NDVI转换模型整体高估覆盖度为27%,V-S模型和V-H-L模型整体低估覆盖度分别为23%和5%;验证结果证明:NDVI转换模型对高密度(60%)植被的估算结果最好,低估4%;V-H-L模型对中密度(40%~60%)和低密度(40%)植被的估算结果最优,仅低估2%,并受背景亮度的影响最小。因此,NDVI转换模型适用于高密度植被覆盖度的估算,亚像元尺度下的V-S模型和V-H-L模型适用于低、中密度植被覆盖度的估算,并以V-H-L模型估算较为准确。     

Analysis of monotonic greening and browning trends from global NDVI time-series

Remotely sensed vegetation indices are widely used to detect greening and browning trends; especially the global coverage of time-series normalized difference vegetation index (NDVI) data which are available from 1981. Seasonality and serial auto-correlation in the data have previously been dealt with by integrating the data to annual values; as an alternative to reducing the temporal resolution, we apply harmonic analyses and non-parametric trend tests to the GIMMS NDVI dataset (1981-2006). Using the complete dataset, greening and browning trends were analyzed using a linear model corrected for seasonality by subtracting the seasonal component, and a seasonal non-parametric model. In a third approach, phenological shift and variation in length of growing season were accounted for by analyzing the time-series using vegetation development stages rather than calendar days. Results differed substantially between the models, even though the input data were the same. Prominent regional greening trends identified by several other studies were confirmed but the models were inconsistent in areas with weak trends. The linear model using data corrected for seasonality showed similar trend slopes to those described in previous work using linear models on yearly mean values. The non-parametric models demonstrated the significant influence of variations in phenology; accounting for these variations should yield more robust trend analyses and better understanding of vegetation trends.     

基于CASA-WOFOST耦合模型的大豆单产遥感估算研究

中国是一个农业大国,在田块甚至是亚田块尺度上进行快速、准确的作物产量估算,不仅可以对农民田间管理进行指导,对于农田生态系统对全球变化的响应评价、制定科学合理的粮食政策、对外粮食贸易和国家粮食安全都具有重要意义。目前主流的估产模型主要有经验统计模型、光能利用率模型、作物生长模型等,每一类模型在各自研究领域相对完整,但是都形成了固定的局限性,为了研究利用遥感技术在小区域范围内田块尺度的作物估产,选取黑龙江省双山农场为研究区,以大豆为研究对象,基于CASA-WOFOST耦合估产模式,利用覆盖作物生长季的时间序列HJ-1A/B遥感影像数据构建高时间分辨率归一化植被指数(Normalized Difference Vegetation Index, NDVI),实现逐日连续监测,分别利用CASA模型和CASA-WOFOST耦合模型对作物进行单产模拟,结果表明：耦合得到的新模型能够具有光能利用率模型较高的运行速度,同时还能发挥作物生长模型模型的机理优势,克服CASA模型在小区域田块尺度上应用的局限性。大豆单产模拟线性回归判定系数(R²)由0.668 53上升到0.844 72,均方根误差(RMSE) 由51.41 kg/hm²下降到29.52 kg/hm²,说明耦合后的模型可以综合考虑光能利用与作物生长生态生理全过程,从而提高作物估产的精度、可靠性和稳定性,为区域田块尺度作物估产提供理论支持,更好地服务于精准农业发展。