冬小麦不同株型品种光谱响应及株型识别方法研究

以直立和平展2种株型的冬小麦品种为材料，研究了它们的光谱响应以及田间植被覆盖度的差异，探讨了利用冠层光谱反射率、光谱特征参量NDVI及植被覆盖度识别小麦株型的方法。结果表明，（1）小麦不同株型品种在近红外波段（700～1300 nm）光谱反射率有明显差异，生育前期平展型品种高于直立型品种，并以拔节期的差异为最显著，随着生育进程差异逐渐变小。拔节期是进行株型识别的最佳时期，并且此期冠层的敏感波段680 nm和760～900 nm的反射率在2种株型品种之间差异明显。（2）小麦冠层叶面积指数（LAI）与归一化差异植被指数NDVI(680,890)呈正相关，并且不同生育阶段其相关程度有差异，这是利用NDVI和植被覆盖度(COV)识别不同株型的基础。（3）相同COV条件下，直立型品种的NDVI高于平展型品种的NDVI，并且随着COV的增加，差异逐渐变小，二者的变化关系体现了直立型品种株型紧凑和平展型品种株型披散的特点，利用NDVI和COV的关系可以对株型进行识别，以小麦拔节期为最佳识别阶段，此期2种株型品种的NDVI具有显著差异（P＜0.05）。     

基于遥感技术的气象因素变化影响水稻单产定量研究

以黑龙江省五常市为研究区域，应用在水稻移栽期至成熟期选择的系列MODIS影像反演NDVI，通过NDVI间接反演LAI，并逐日拟合水稻全生育期的LAI，结合SIMRIW模型，计算研究区域2006年低温影响下的水稻单产。经演算，水稻单产值为5411．74kg／hm^2，是实际值的84％。研究结果表明应用高时间分辨率MODIS图像能够准确反演并拟合逐日LAI，可以为冷害影响下水稻单产的研究提供参考。     

MODIS数据的积雪密度遥感监测分析

基于中分辨率成像光谱仪(MOD IS)影像数据,对选取的样本点的各个波段的反射率进行运算,结合该点的积雪密度建立各积雪密度ρ与各波段的积雪反射率计算值的回归模型。结果表明,实测数据与理论值的绝对误差的平均值为0.035 243,平均相对误差为15.30%,模型精度较高。此模型的建立,对实时进行积雪信息分析处理,及时、准确地提供雪情报告,合理利用水资源,支持社会经济的发展以及防灾减灾都具有重要意义。     

Characterizing stand-replacing disturbance in western Alberta grizzly bear habitat, using a satellite-derived high temporal and spatial resolution change sequence

Timely and accurate mapping of anthropogenic and natural disturbance patterns can be used to better understand the nature of wildlife habitats, distributions and movements. One common approach to map forest disturbance is by using high spatial resolution satellite imagery, such as Landsat 5 Thematic Mapper (TM) or Landsat 7 Enhanced Thematic Mapper plus (ETM+) imagery acquired at a 30 m spatial resolution. However, the low revisit times of these sensors acts to limit the capability to accurately determine dates for a sequence of disturbance events, especially in regions where cloud contamination is a frequent occurrence. As wildlife habitat use can vary significantly seasonally, annual patterns of disturbance are often insufficient in assessing relationships between disturbance and foraging behaviour or movement patterns.The Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH) allows the generation of high-spatial (30 m) and -temporal (weekly or bi-weekly) resolution disturbance sequences using fusion of Landsat TM or ETM+ and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. The STAARCH algorithm is applied here to generate a disturbance sequence representing stand-replacing events (disturbances over 1 ha in area) for the period 2001-2008, over almost 6 million ha of grizzly bear habitat along the eastern slopes of the Rocky Mountains in Alberta. The STAARCH algorithm incorporates pairs of Landsat images to detect the spatial extent of disturbances; information from the bi-weekly MODIS composites is used in this study to assign a date of disturbance (DoD) to each detected disturbed area. Dates of estimated disturbances with areas over 5 ha are validated by comparison with a yearly Landsat-based change sequence, with producer's accuracies ranging between 15 and 85% (average overall accuracy 62%, kappa statistic of 0.54) depending on the size of the disturbance event. The spatial and temporal patterns of disturbances within the entire region and in smaller subsets, representative of the size of a grizzly bear annual home range, are then explored. Disturbance levels are shown to increase later in the growing season, with most disturbances occurring in late August and September. Individual events are generally small in area (<10 ha) except in the case of wildfires, with, on average, 0.4% of the total area disturbed each year. The application of STAARCH provides unique high temporal and spatial resolution disturbance information over an extensive area, with significant potential for improving understanding of wildlife habitat use.     

Enhancing the Simplified Surface Energy Balance (SSEB) approach for estimating landscape ET: Validation with the METRIC model

Evapotranspiration (ET) can be derived from satellite data using surface energy balance principles. METRIC (Mapping EvapoTranspiration at high Resolution with Internalized Calibration) is one of the most widely used models available in the literature to estimate ET from satellite imagery. The Simplified Surface Energy Balance (SSEB) model is much easier and less expensive to implement. The main purpose of this research was to present an enhanced version of the Simplified Surface Energy Balance (SSEB) model and to evaluate its performance using the established METRIC model. In this study, SSEB and METRIC ET fractions were compared using 7 Landsat images acquired for south central Idaho during the 2003 growing season. The enhanced SSEB model compared well with the METRIC model output exhibiting an r² improvement from 0.83 to 0.90 in less complex topography (elevation less than 2000 m) and with an improvement of r² from 0.27 to 0.38 in more complex (mountain) areas with elevation greater than 2000 m. Independent evaluation showed that both models exhibited higher variation in complex topographic regions, although more with SSEB than with METRIC. The higher ET fraction variation in the complex mountainous regions highlighted the difficulty of capturing the radiation and heat transfer physics on steep slopes having variable aspect with the simple index model, and the need to conduct more research. However, the temporal consistency of the results suggests that the SSEB model can be used on a wide range of elevation (more successfully up 2000 m) to detect anomalies in space and time for water resources management and monitoring such as for drought early warning systems in data scarce regions. SSEB has a potential for operational agro-hydrologic applications to estimate ET with inputs of surface temperature, NDVI, DEM and reference ET.     

Multi-time scale analysis of sugarcane within-field variability: improved crop diagnosis using satellite time series?

Within-field spatial variability is related to multiple factors that can be time-independent or time-dependent. In this study, our working hypothesis is that a multi-time scale analysis of the dynamics of spatial patterns can help establish a diagnosis of crop condition. To test this hypothesis, we analyzed the within-field variability of a sugarcane crop at seasonal and annual time scales, and tried to link this variability to environmental (climate, topography, and soil depth) and cropping (harvest date) factors. The analysis was based on a sugarcane field vegetation index (NDVI) time series of fifteen SPOT images acquired in the French West Indies (Guadeloupe) in 2002 and 2003, and on an original classification method that enabled us to focus on crop spatial variability independently of crop growth stages. We showed that at the seasonal scale, the within-field growth pattern depended on the phenological stage of the crop and on cropping operations. At the annual scale, NDVI maps revealed a stable pattern for the two consecutive years at peak vegetation, despite very different rainfall amounts, but with inverse NDVI values. This inversion is linked with the topography and consequently to the plant water status. We conclude that (1) it is necessary to know the crop growing cycle to correctly interpret the spatial pattern, (2) single-date images may be insufficient for the diagnosis of crop condition or for prediction, and (3) the pattern of vigour occurrence within fields can help diagnose growth anomalies.

Ground-Sensor Soil Reflectance as Related to Soil Properties and Crop Response in a Cotton Field

Bare soil reflectance from airborne imagery or laboratory spectrometers has been used to infer soil properties such as soil texture, organic matter, water content, salinity and crop residue cover. However, the relation of soil properties to reflectance data often varies with soil type and conditions and surface reflectance may not be representative of the conditions in the root zone. The objectives of this study were to assess the soil reflectance data obtained by ground-based sensors and to model soil properties in the root zone as a function of surface soil reflectance and plant response. Ground-based sensors were used to simultaneously monitor soil and canopy reflectance in the visible and near-infrared (VNIR) along six rows and in two growth stages in a 7 ha cotton field. The reflectance data were compared to soil properties, leaf nutrients and biomass measured at 33 sampling positions along the rows. Brightness values of the blue and green bands of soil reflectance were better correlated to soil water content, particulate organic matter and extractable potassium and phosphorus, while those in the red and NIR bands were correlated to soil carbonate content, total nitrogen, electrical conductivity and foliar nutrients. The correlation of red soil reflectance with canopy reflectance was significant and indicated an indirect inverse relationship between soil fertility and plant stress. The integration of surface soil reflectance and plant response variables in a multiple regression model did not substantially improve the prediction of soil properties in the root zone. However, crop nutrient status explained a significant portion of the spatial variability of soil properties related to nitrification processes when soil reflectance did not. The implication of these findings to agricultural management is discussed.     

Investigation on pistachio distribution in the mountain regions of northeast Iran by ALOS

Iran supports five different vegetation zones. One of those is the Irano-Touranian zone that is located in the northeast of Iran. This vegetation zone includes arid and semi-arid lands, and its area is about 3.5 million hm². It supports growth of pistachio (Pistacia vera), a deciduous-broadleaved species, which is one of the ecologically and economically most important native species. In this study, we analyzed three images acquired by ALOS satellite, including 10m resolution multispectral band (AVNIR-2), 2.5 m resolution “Backward” PRISM image, and 2.5 m resolution “Nadir” PRISM image, based on a provided rational polynomial coefficient (RPC). Using the “Backward” and “Nadir” images, a 2.5 m resolution digital elevation model (DEM) was produced. Four methods with AVNIR-2 and PRISM data were used to produce pan-sharpening images and conduct an object-based feature extraction process. Normalized Difference Vegetation Index (NDVI) was used to determine the maximum distribution of pistachio in related elevation. The accuracy of the DEM was tested on 28 ground control points in the pair image as tie points, with the value of parallax error of 0.9027 m. The created elevation map indicated that pistachio trees grow up at 650m above sea level (a.s.l.). The result from NDVI in the related elevation showed the maximum density of pistachio at 800m a.s.l. In addition, the result of feature extraction in the forest showed the area of each target element calculated. The results of this research will improve decision-making and lead to sustainable management in general.     

北京永定河流域森林植被覆盖研究

利用1978～2009年间共6期TM遥感数据,采用归一化植被指数（NDVI）结合二值化分析方法确定植被覆盖度的阈值,对北京市永定河流域32年来的森林植被覆盖变化情况进行研究,揭示永定河流域森林植被覆盖变化的驱动力因子与作用机制．结果表明,永定河流域上游植被覆盖度较高,中下游覆盖度较低,整个研究时期内植被覆盖度呈现波动变化,1978～1987年间,植被覆盖度急剧下降,而1987～1995年间植被覆盖度略有提高,但之后又迅速下降,2000年植被覆盖度处于历年最低值,2000～2004年间植被覆盖度呈上升趋势,2004—2009年间植被覆盖度趋于平稳．驱动力分析表明,引起植被覆盖变化的主要驱动因素是人为因素,包括人类破坏和保护2方面,其次是气候因素,包括降水和温度,其中降水占主导地位．     

基于MODIS数据的云检测及其在新疆的应用

基于遥感手段,以云的可见光和红外波段特性为依据,借鉴国内外多种云检测方法,利用MODIS 1,6,26通道资料,以指数法和光谱阈值相结合的多光谱云检测算法,对新疆山区2009年的MODIS资料进行云检测处理。检测结果的统计分析得出,2009年全疆平均云覆盖日数为82 d,其中山区平均云覆盖日数101 d,平原地区平均云...