时空推理模型的烟草长势图谱分析与监测

光谱曲线的差异是由多种因素所致的,不同地物光谱反射曲线是地物对电磁波反射或发射差异的集中体现。由此可根据不同地物对入射电磁波反射形成的不同光谱曲线来区分不同的地物类型。据此,我们将地物光谱的时空特性变化作为研究的重点,运用地学图谱理论,结合超图时空概念模型,对地学现象时空特征及其规律的概念性表达,并使用面向对象技术结合影响烟草长势的时空因子,构建了一个实用的时空推理模型,并应用于烟草的长势监测。     

绿洲农田作物的显热和潜热输送

本文根据波文比－能量平衡法，利用观测资料计算了荒漠－绿洲交界处绿洲农田作物（棉花、水稻）的显热和潜热，并进行了分析和比较。     

Spatial distribution and morphologic characteristics of gullies in the Black Soil Region of Northeast China: Hebei watershed

Gully erosion in the Black Soil Region of China has posed a threat to food security. This study aimed to determine the spatial distribution and morphologic characteristics of gullies in the region and their topographic thresholds. A 28 km² watershed was surveyed and 117 gullies measured. The results showed that: (1) Gullies were distributed equally on both hillslope and valley floor positions, with a total gully density of .66 km/km². (2) The mean depth, width, and cross-sectional area of gullies were .74 m, 2.39 m, and 2.43 m², respectively. These characteristics varied among gullies according to their topographic positions and slope gradients. Individual gully volume (V) was well predicted from gully length (L) by V = 2.08L^0.96 (r² = .66). Total gully volume (V) of each sub-watershed was predicted from mean slope gradient (S) and drainage area (A) as V = 275800S ? 8600A (r² = .73). (3) Gully erosion was more serious in steeper sub-watersheds and steeper hillslope positions. Gullies were wider in regions with relatively larger drainage areas, except for those developed in the main valley. The topographic threshold for gully initiation was S = .10A^?0.34, which indicated gully erosion was dominated by surface runoff. (4) Human activities, such as road construction, played a significant role in gully erosion.     

Images difference of ASAR data for rice crop mapping in Fuzhou,China

This paper introduces ENVISAT ASAR data application on rice field mapping in the Fuzhou area, using multi-temporal ASAR dual polarization data acquired in 2005. The procedure for ASAR data processing here includes data calibration, image registration, speckle reduction and conversion of data format from amplitude to dB for backscatter. The backscatter of rice increases with the rice growing stages, which was much different from other land covers. Based on image difference techniques, 6 schemes were designed with ASAR different temporal and polarization data for rice field mapping. Difference images between images in the early period of rice crop and growing or ripening period, are more suitable for rice extraction than those difference images between different polarizations in the same date. The most accurate result of late rice extraction was achieved based on the difference of HH polarization data acquired in October and August. Therefore, for rice field mapping, the temporal information is more important than polarization information. The data during the early growing season of rice is very important for high accuracy rice mapping.     

典型岩溶断陷盆地农作物生态需水研究——以蒙自地区为例

从合理调控生态用水角度出发,探讨典型岩溶断陷盆地区农作物生态需水问题。根据蒙自断陷盆地的岩溶地貌特征,分别选取位于盆地、坡面和高原面的大洼子、朵古、牛耳坡3个观测点,利用Penman-Monteith公式、作物系数（FAO推荐）及同期有效降雨量,估算3个观测点2018年的参考蒸散量、生态需水量以及不同作物生长所需的人工灌溉水量。结果表明:（1）大洼子、朵古及牛耳坡的参考蒸散量分别为1 346.10 mm、1 200.00 mm、1 064.30 mm,远大于同期降水量,均呈现出蒸发旺盛的特点,加大了作物对于水分的需求;同时三者的参考蒸散量表现出较为明显的时空差异,使得3个观测点的农业种植条件存在差异;（2）大洼子种植的水稻、小麦、花生、油菜、大豆、马铃薯、葡萄等作物的生态需水定额均远大于大洼子同期的有效降水,在大洼子种植的作物均需要大量人工浇灌才能正常生长,而农作物的种植与其种植条件匹配度不高;（3）朵古及牛耳坡种植的玉米、万寿菊的生态需水定额与同期有效降水之间的差值较小甚至完全满足,表明玉米、万寿菊在高原山区的种植是与当地种植条件相匹配的;而种植在朵古及牛耳坡的苹果、烤烟的生态需水定额与同期有效降水之间的差值较大,表明在高原山区大量种植苹果及烤烟对于人工浇灌要求较高;（4）研究区大部分农作物在生长发育阶段内所需的水分主要依靠人工灌溉,与本地降水分布规律匹配度不高,区内农业结构与种植模式有待调整。在岩溶断陷盆地内要种植耗水较少,对热量要求较高的作物,山区则需要发展具有生态保护和经济效益的作物,但种植的重点区域仍是盆地区。      

Crop type mapping using LiDAR,Sentinel-2 and aerial imagery with machine learning algorithms

LiDAR data are becoming increasingly available, which has opened up many new applications. One such application is crop type mapping. Accurate crop type maps are critical for monitoring water use, estimating harvests and in precision agriculture. The traditional approach to obtaining maps of cultivated fields is by manually digitizing the fields from satellite or aerial imagery and then assigning crop type labels to each field - often informed by data collected during ground and aerial surveys. However, manual digitizing and labeling is time-consuming, expensive and subject to human error. Automated remote sensing methods is a cost-effective alternative, with machine learning gaining popularity for classifying crop types. This study evaluated the use of LiDAR data, Sentinel-2 imagery, aerial imagery and machine learning for differentiating five crop types in an intensively cultivated area. Different combinations of the three datasets were evaluated along with ten machine learning. The classification results were interpreted by comparing overall accuracies, kappa, standard deviation and f-score. It was found that LiDAR data successfully differentiated between different crop types, with XGBoost providing the highest overall accuracy of 87.8％. Furthermore, the crop type maps produced using the LiDAR data were in general agreement with those obtained by using Sentinel-2 data, with LiDAR obtaining a mean overall accuracy of 84.3％ and Sentinel-2 a mean overall accuracy of 83.6％. However, the combination of all three datasets proved to be the most effective at differentiating between the crop types, with RF providing the highest overall accuracy of 94.4％. These findings provide a foundation for selecting the appropriate combination of remotely sensed data sources and machine learning algorithms for operational crop type mapping.     

基于地统计学空间插值法的作物单产估算

针对第三次全国农业普查农产量抽样调查的样本调查方法改革后,现行的传统农作物单产抽样调查估算方法无法有效利用调查样本所包含的空间差异性来估算抽样设计中子总体的作物单产等问题,提出了一种基于地统计学的空间插值估算方法,对调查队取得的样本数据进行深度挖掘。以河南省辉县为研究区,以冬小麦单产为研究对象,进行实验和结果分析。结果表明,利用地统计学克里格插值法取得的村级冬小麦平均单产估算精度在90%以上的村达到研究区村总量的91%,且其中83%的村估算精度优于95%;估算精度在90%以下的所有村的冬小麦种植总面积仅占全县的2.26%,对全县产量的影响微乎其微。基于地统计学的空间插值法很好地分析和利用了样本属性中的冬小麦单产信息表现出的空间相关性和异质性,不仅能较高精度地估算出现行的传统农作物单产推算方法无法给出的抽样设计中子总体(全县各村)的单产信息,而且能较好地给出总体(全县)的单产信息。利用该方法得到的全县冬小麦平均单产估算精度达到97.75%,高于现行的传统农作物单产抽样调查估算精度,估算效果良好,方法可行性高,相对传统方法还可起到费省效宏的作用。     

基于Landsat-8数据对甘肃省金塔县主要农作物分类

近年来,随着经济社会发展和人类活动加剧,水资源短缺、生态环境恶化、自然灾害频发等问题对于农作物的生长具有较大影响,因此准确地提取农作物显得尤为重要.本文基于2016年Landsat-8 OLI全生育期的时序遥感影像数据,融合金塔县农作物的物候特征和地表纹理特性,提取主要农作物小麦、玉米、棉花和甜菜的时间序列NDVI曲线...