基于Landsat8影像时间序列NDVI的作物种植结构提取

为提高内蒙古平原灌区作物种植结构遥感监测精度和效率,提出一种基于时序NDVI曲线的作物种植结构提取方法。以内蒙古土默特右旗平原区为研究区域,以2015年覆盖作物生育期的多时相Landsat影像为数据源,根据不同地物其NDVI值范围不同,将研究区地表分为植被覆盖地表,无植被覆盖地表和水体3类。在植被覆盖区域内,根据林地和荒草地时序NDVI曲线特征,提取林地和荒草地,其余区域即为农田。根据小麦、玉米、葵花和西葫芦的时间序列NDVI曲线特征差异构建分类决策树模型,在农田区域内提取上述作物的空间种植分布信息。研究区各类地物及作物遥感提取面积与实际统计面积接近,土地利用分类总体精度达到85.71%,作物分类总体精度达到82.69%。研究结果表明该方法提取作物种植信息的精度较高,能够实现区域作物种植信息的高效准确监测。     

Detection of geothermal anomalies using Landsat 8 TIRS data in Tulu Moye geothermal prospect,Main Ethiopian Rift

Despite the high geothermal potential of the Main Ethiopian Rift (MER), risks associated with the industry and the difficulty of identifying possible targets using ground surveys alone continue to impede the development of geothermal power diligence in Ethiopia. In this paper, we investigate the geothermal potential of the Tulu Moye prospect area in the MER using Landsat 8, which is an important and cost-effective method of detecting geothermal anomalies. Data with a path/row of 168/054 were obtained from the Landsat 8 Operational Land Imager (OLI) and Thermal Infrared (TIR) sensors for October 17, 2014. Based on radiometric calibration, atmospheric correction (with the 6S model) and an NDVI-based threshold method for calculating land surface emissivity, a split-window algorithm was applied to retrieve the land surface temperature (LST) of the study area. Results show LST values ranging from 292.2 to 315.8 K, with the highest values found in barren lands. A comparison of LST between the Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat 8 shows a maximum difference of 1.47 K. Anomalous areas were also discovered, where LST was about 3-9 K higher than the background area. We identified seven of these as areas of high geothermal activity in the Tulu Moye prospective geothermal area. Auxiliary data and overlay analysis tools eliminated any non-geothermal influences. The research reveals that the distribution of highy prospective geothermal areas is consistent with the development and distribution of faults in the study area. Magmatism is the thermal source and faults provide conduits for the heat to flow from earth’s interior to the surface, facilitating the presence of geothermal anomalies. Finally, TIR remote sensing methods prove to be a robust and cost-effective technique for detecting LST anomalies in the geologically active area of MER. Moreover, combining TIR remote sensing with knowledge of the structural geology of the area and geothermal mechanisms is an efficient approach to detecting geothermal areas.     

基于混合像元分解理论的路域植被等量水厚度遥感反演

为削弱混合像元对植被参数反演的影响,提出了基于混合像元分解理论反演路域植被等量水厚度的方法。利用PRO4SAIL模型正演获得的高光谱窄波段数据,模拟Landsat 8遥感影像宽波段植被冠层光谱数据,并进行等量水厚度的敏感植被指数的筛选;对覆盖研究区域的Landsat 8遥感影像进行线性混合像元分解,获取更加精确的植被冠层光谱反射率;同时,利用支持向量机构建等量水厚度估测模型,实现对路域植被等量水厚度的遥感反演。研究结果表明,利用混合像元分解后得到的植被冠层光谱参与模型反演得到的路域植被等量水厚度更加符合实际情况,为遥感影像反演植被参数提供了有效数据。     

基于Landsat 8影像的黄河口悬浮物质量浓度遥感反演

悬浮物质量浓度是黄河口海域重要的水质和水环境监测参数之一,直接影响着水面以下光场的分布,进而影响水体的初级生产力和水域生态环境。本文基于2011年6—7月和11—12月共计89组现场实测悬浮物质量浓度和光谱数据,分析了黄河口及其附近海域不同悬浮物质量浓度的水体光谱特征,尝试利用多种波段组合建立悬浮物质量浓度遥感反演算法。结果表明865 nm波段与波段比655 nm/560 nm组合形式算法反演结果最优,算法相关系数R²为0.95,平均相对误差为25.65%。将算法应用于2014—2016年共7景Landsat 8 OLI遥感影像,分析了不同年份黄河口悬浮物质量浓度的时空分布特征,黄河口海域悬浮物质量浓度分布总体呈现近岸高,离岸低的特点,不同时期悬浮物质量浓度量值上有显著变化。     

1980s以来巢湖藻华物候时空变化遥感分析

蓝藻水华暴发时间变化一定程度上表征了藻华物候特征,研究藻华物候变化可为湖泊水环境健康问题治理和缓解水生生态系统环境退化提供科学依据。以往巢湖蓝藻水华遥感监测主要基于2000年以来的MODIS卫星数据,限制了对巢湖蓝藻水华暴发时空变化过程的理解。本文利用Landsat扩展时间序列,联合MODIS数据,基于浮游藻类指数和阈值分割技术提取巢湖蓝藻水华,在评估二者藻华提取结果一致性的基础上,获取并分析了巢湖1987-2020年蓝藻水华暴发物候的规律及影响因子。结果表明：(1) 2000年前,巢湖蓝藻水华暴发规模较小,2000年后面积显著上升,大面积蓝藻水华出现频繁,2011年达到最高峰(608.4 km²);(2)1987-2020年间,巢湖蓝藻水华暴发可以分为3个阶段：(1)1987-2004年,巢湖蓝藻水华年暴发开始时间显著提前,暴发持续时间显著增加;(2)2005-2010年,藻华年暴发开始时间显著延迟,但暴发持续时间变化不显著;(3)2011-2020年,巢湖藻华暴发开始、结束和持续时间呈现年际波动,年暴发开始时间、结束时间和持续时间有所提前,但不显著;(3)巢湖...     

Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces

Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R² > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R² = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.     

1987 - 2018年祁连山冰川变化遥感监测及影响因子分析

祁连山冰川融水是维系我国西北地区生态平衡的重要因素。为评估祁连山冰川在全球气候变暖背景下的状态, 利用Landsat-TM、 ETM+、 OLI等遥感影像, 基于波段比值阈值法提取1987 - 2018年共计7期冰川边界进行时序变化分析。结果显示： 近31年来祁连山冰川面积从2 080.39 km²退缩到1 442.09 km², 年均退缩率达0.99%, 相比1956 - 1990年间的退缩率（0.58%）大幅增加; 近31年来冰川物质平衡线高度稳步上升; 冰川主要分布在海拔4 700 ~ 5 100 m之间, 冰川退缩随海拔降低而增加; 约93%的冰川的面积小于2.0 km², 小于0.1 km²的冰川的总数和总面积呈增加态势; 0.5 ~ 1.0 km²的冰川退缩最快, 年均退缩率达1.53%, 而大于10.0 km²的冰川退缩最慢, 年均退缩率为0.59%; 祁连山冰川退缩主要由夏季均温升高引起, 且最近十年间冰川呈现出加速退缩的态势。     

Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data

The goal of this study was to evaluate whether harmonic regression coefficients derived using all available cloud-free observations in a given Landsat pixel for a three-year period can be used to estimate tree canopy cover (TCC), and whether models developed using harmonic regression coefficients as predictor variables are better than models developed using median composite predictor variables, the previous operational standard for the National Land Cover Database (NLCD). The two study areas in the conterminous USA were as follows: West (Oregon), bounded by Landsat Worldwide Reference System 2 (WRS-2) paths/rows 43/30, 44/30, and 45/30; and South (Georgia/South Carolina), bounded by WRS-2 paths/rows 16/37, 17/37, and 18/37. Plot-specific tree canopy cover (the response variable) was collected by experienced interpreters using a dot grid overlaid on 1 m spatial resolution National Agricultural Imagery Program (NAIP) images at two different times per region, circa 2010 and circa 2014. Random forest model comparisons (using 500 independent model runs for each comparison) revealed the following (1) harmonic regression coefficients (one harmonic) are better predictors for every time/region of TCC than median composite focal means and standard deviations (across times/regions, mean increase in pseudo R² of 6.7% and mean decrease in RMSE of 1.7% TCC) and (2) harmonic regression coefficients (one harmonic, from NDVI, SWIR1, and SWIR2), when added to the full suite of median composite and terrain variables used for the NLCD 2011 product, improve the quality of TCC models for every time/region (mean increase in pseudo R² of 3.6% and mean decrease in RMSE of 1.0% TCC). The harmonic regression NDVI constant was always one of the top four most important predictors across times/regions, and is more correlated with TCC than the NDVI median composite focal mean. Eigen analysis revealed that there is little to no additional information in the full suite of predictor variables (47 bands) when compared to the harmonic regression coefficients alone (using NDVI, SWIR1, and SWIR2; 9 bands), a finding echoed by both model fit statistics and the resulting maps. We conclude that harmonic regression coefficients derived from Landsat (or, by extension, other comparable earth resource satellite data) can be used to map TCC, either alone or in combination with other TCC-related variables.     

A satellite data driven approach to monitoring and reporting fire disturbance and recovery across boreal and temperate forests

The regular and consistent measurements provided by Earth observation satellites can support the monitoring and reporting of forest indicators. Although substantial scientific literature espouses the capabilities of satellites in this area, the techniques are under-utilised in national reporting, where there is a preference for aggregating ad hoc data. In this paper, we posit that satellite information, while perhaps of low accuracy at single time steps or across small areas, can produce trends and patterns which are, in fact, more meaningful at regional and national scales. This is primarily due to data consistency over time and space. To investigate this, we use MODIS and Landsat data to explore trends associated with fire disturbance and recovery across boreal and temperate forests worldwide. Our results found that 181 million ha (9 %) of the study area (2 billion ha of forests) was burned between 2001 and 2018, as detected by MODIS satellites. World Wildlife Fund biomes were used for a detailed analysis across several countries. A significant increasing trend in area burned was observed in Mediterranean forests in Chile (8.9 % yr⁻¹), while a significant decreasing trend was found in temperate mixed forests in China (-2.2 % yr⁻¹). To explore trends and patterns in fire severity and forest recovery, we used Google Earth Engine to efficiently sample thousands of Landsat images from 1991 onwards. Fire severity, as measured by the change in the normalized burn ratio (NBR), was found to be generally stable over time; however, a slight increasing trend was observed in the Russian taiga. Our analysis of spectral recovery following wildfire indicated that it was largely dependent on location, with some biomes (particularly in the USA) showing signs that spectral recovery rates have shortened over time. This study demonstrates how satellite data and cloud-computing can be harnessed to establish baselines and reveal trends and patterns, and improve monitoring and reporting of forest indicators at national and global scales.     

Woody vegetation cover,height and biomass at 25-m resolution across Australia derived from multiple site,airborne and satellite observations

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha^-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.