厦门市绿色植被降温服务功能核算及其时空动态特征

为了准确核算厦门市绿色植被降温服务功能,收集厦门市2010年和2015年18个气象站点数据,采用30 m空间分辨率Landsat卫星数据和250 m空间分辨率、16 d合成的MODIS植被指数产品,在已有基于能量平衡估算模型的基础上,通过考虑植被覆盖及降温服务时长,构建了绿色植被降温服务功能核算的改进模型,并对厦门市2010—2015年绿色植被降温服务功能时空动态特征进行分析.结果表明:①改进模型能够较为准确且合理地描述绿色植被降温服务功能的时空变化特征.②厦门市北部山区由于高植被覆盖度降温服务功能高于南部城市建成区,而城市建成区中的城市绿地也具有明显的降温作用.③2010—2015年各区降温服务功能实物量整体呈增加趋势.其中,同安区降温服务功能实物量变化量最多,为166.12×106 kW·h;湖里区变化量最少,为9.72×106 kW·h;其余各区变化量都在40×106~75×106 kW·h范围内.④森林在降温服务中贡献最大,达60%以上.相比2010年,2015年降温服务功能实物量除了灌木林地变化率为-4.29%外,其余绿色植被类型均呈增长趋势,如森林和农田的增长率为11.97%、14.23%,草地和城市绿地的增长率为87.45%、92.11%.研究显示,厦门市2010—2015年的绿色植被降温服务功能总体呈明显增强趋势,其中城市绿地的降温服务功能增强尤为明显.      

Water quality monitoring in a slightly-polluted inland water body through remote sensing — Case study of the Guanting Reservoir in Beijing, China

This study focused on the water quality of the Guanting Reservoir, a possible auxiliary drinking water source for Beijing. Through a remote sensing (RS) approach and using Landsat 5 Thematic Mapper (TM) data, water quality retrieval models were established and analyzed for eight common water quality variables, including algae content, turbidity, and concentrations of chemical oxygen demand, total nitrogen, ammonia nitrogen, nitrate nitrogen, total phosphorus, and dissolved phosphorus. The results show that there exists a statistically significant correlation between each water quality variable and remote sensing data in a slightly-polluted inland water body with fairly weak spectral radiation. With an appropriate method of sampling pixel digital numbers and multiple regression algorithms, retrieval of the algae content, turbidity, and nitrate nitrogen concentration was achieved within 10% mean relative error, concentrations of total nitrogen and dissolved phosphorus within 20%, and concentrations of ammonia nitrogen and total phosphorus within 30%. On the other hand, no effective retrieval method for chemical oxygen demand was found. These accuracies were acceptable for the practical application of routine monitoring and early warning on water quality safety with the support of precise traditional monitoring. The results show that performing the most traditional routine monitoring of water quality by RS in relatively clean inland water bodies is possible and effective.     

Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

High-conservation-value forests (HCVFs) are critically important for biodiversity and ecosystem service provisioning, but they face many threats. Where systematic HCVF inventories are missing, such as in parts of Eastern Europe, these forests remain largely unacknowledged and therefore often unprotected. We devised a novel, transferable approach for detecting HCVFs based on integrating historical spy satellite images, contemporary remote sensing data (Landsat), and information on current potential anthropogenic pressures (e.g., road infrastructure, population density, demand for fire wood, terrain). We applied the method to the Romanian Carpathians, for which we mapped forest continuity (1955–2019), canopy structural complexity, and anthropogenic pressures. We identified 738,000 ha of HCVF. More than half of this area was identified as susceptible to current anthropogenic pressures and lacked formal protection. By providing a framework for broad-scale HCVF monitoring, our approach facilitates integration of HCVF into forest conservation and management. This is urgently needed to achieve the goals of the European Union's Biodiversity Strategy to maintain valuable forest ecosystems.     

遥感数据时间分辨率对土地覆盖变化监测的影响

由于土地覆盖通常存在年际变化和季节变化,区分人为活动引起的变化与这种自然变化需要两期数据时间一致。论文利用黑龙江省1975、1990、2000、和2005代表年Landsat数据,分析了数据时间差在16 d MODIS-NDVI时间序列的误差。研究发现,Landsat数据4个代表年的年际差分别为1975±4 a,1990±4.5 a,2000±1.5 a,2005±1.5 a,季节差为47 d。Landsat季节差对MODIS-NDVI的影响是:当季节差为16~48 d时,东北地区耕地与草地以生长高峰期为参照的MODIS-NDVI变化最高可达0.4以上,林地MODIS-NDVI变化0.1~0.2;当时间差<16 d时,如果仍以生长高峰期为绝对参照,东北大部分地区误差<0.1,只有少数耕地误差0.1~0.2。如果以两个时间为相对参照,则<16 d的误差也可能导致较大的MODIS-NDVI误差,其大小有明显的季节性。在生长高峰期7月12日至8月28日,8 d±8 d的时间差导致的NDVI差大于0.1的像元仅2.93%,是变化检测最佳时期;但在除此之外的6月10日至9月29日间,8 d±8 d时间差导致NDVI差大于0.1的像元比例超过11.42%,选择Landsat必须避开这一敏感期。结论:利用高时间分辨率MODIS-NDVI可在3个方面提高Landsat变化检测效果:①辅助选择最佳时间;②当无法选择最佳时间时,评估时间差可能引起的误差和方向;③直接使用生物物理参数作为变化检测的指标之一。     

盐城海滨湿地植被地上生物量遥感估算研究

以盐城湿地自然保护区核心区的ETM+图像数据和同期野外实测的31 个样方地上生物量干重、湿重数据为数据源,分析了15 个遥感信息变量与湿地植被地上生物量干重、湿重的相关关系,并选择在0.01 水平上显著相关的8 个遥感变量建立一元线性回归模型、一元曲线回归模型以及多元逐步回归模型,并对比得出最优模型,进而计算出整个研究区的地上生物量。研究得出：①与研究区湿地植被地上生物量干重和湿重相关性最大的都是ETM+4 波段,干重的相关系数为0.833,湿重的相关系数为0.796;②研究区植被地上生物量干重和湿重的遥感估算模型都是一元三次函数模型,且干重模型的拟合精度要优于湿重模型;③得到研究区地上生物量干重总重量为2.28×10⁸ kg,湿重总重量为6.10×10⁸ kg。     

A comparison of two models with Landsat data for estimating above ground grassland biomass in Inner Mongolia,China

Two models, artificial neural network (ANN) and multiple linear regression (MLR), were developed to estimate typical grassland aboveground dry biomass in Xilingol River Basin, Inner Mongolia, China. The normalized difference vegetation index (NDVI) and topographic variables (elevation, aspect, and slope) were combined with atmospherically corrected reflectance from the Landsat ETM+ reflective bands as the candidate input variables for building both models. Seven variables (NDVI, aspect, and bands 1, 3, 4, 5 and 7) were selected by the ANN model (implemented in Statistica 6.0 neural network module), while six (elevation, NDVI, and bands 1, 3, 5 and 7) were picked to fit the MLR function after a stepwise analysis was executed between the candidate input variables and the above ground dry biomass. Both models achieved reasonable results with RMSEs ranging from 39.88% to 50.08%. The ANN model provided a more accurate estimation (RMSE_r = 39.88% for the training set, and RMSE_r = 42.36% for the testing set) than MLR (RMSE_r = 49.51% for the training, and RMSE_r = 53.20% for the testing). The final above ground dry biomass maps of the research area were produced based on the ANN and MLR models, generating the estimated mean values of 121 and 147 g/m², respectively.     

Identifying the possibilities and pitfalls of conducting IUCN Red List assessments from remotely sensed habitat information based on insights from poorly known Cuban mammals

The International Union for Conservation of Nature's Red List of Threatened Species (RLS) is the key global tool for objective, repeatable assessment of species’ extinction risk status, and plays an essential role in tracking biodiversity loss and guiding conservation action. Satellite remote sensing (SRS) data sets on global ecosystem distributions and functioning show exciting potential for informing range-based RLS assessment, but their incorporation has been restricted by low temporal resolution and coverage of data sets, lack of incorporation of degradation-driven habitat loss, and noninclusion of assumptions related to identification of changing habitat distributions for taxa with varying habitat dependency and ecologies. For poorly known mangrove-associated Cuban hutias (Mesocapromys spp.), we tested the impact of possible assumptions regarding these issues on range-based RLS assessment outcomes. Specifically, we used annual (1985–2018) Landsat data and land-cover classification and habitat degradation analyses across different internal time series slices to simulate range-based RLS assessments for our case study taxa to explore potential assessment uncertainty arising from temporal SRS data set coverage, incorporating proxies of (change in) habitat quality, and assumptions on spatial scaling of habitat extent for RLS parameter generation. We found extensive variation in simulated species-specific range-based RLS assessments, and this variation was mostly associated with the time series over which parameters were estimated. However, results of some species-specific assessments differed by up to 3 categories (near threatened to critically endangered) within the same time series, due to the effects of incorporating habitat quality and the spatial scaling used in RLS parameter estimation. Our results showed that a one-size-fits-all approach to incorporating SRS information in RLS assessment is inappropriate, and we urge caution in conducting range-based assessments with SRS for species for which habitat dependence on specific ecosystem types is incompletely understood. We propose novel revisions to parameter spatial scaling guidelines to improve integration of existing time series data on ecosystem change into the RLS assessment process.     

基于热红外遥感的广西平果县石漠化初步研究

中国西南喀斯特地区石漠化问题十分严重。为更好地利用卫星遥感监测和评价石漠化现状,提出了一种基于卫星热红外遥感的石漠化等级划分的新方法。以广西平果县为研究区,利用2014年Landsat 8热红外遥感影像分别提取夏季和冬季的亮温值并计算亮温差,经过高分影像建立典型样本区并分析亮温差,确定不同石漠化等级的亮温差阈值,最后对全县石漠化进行等级划分。结果显示,平果县2014年重度石漠化面积占喀斯特区域面积的比例为3.87%,中度石漠化占9.19%,轻度石漠化占22.26%,潜在石漠化占57.46%,非石漠化占7.22%。研究表明,通过确定适当的夏冬季亮温差阈值可以有效地对石漠化程度进行分级划分,该新方法与已有方法相比,具有物理意义明确、方法简单有效、结果客观等优点,具有推广应用价值。     

基于多源遥感影像融合的武汉市土地利用分类方法研究

准确高效的获取土地利用信息,对于合理利用和开发土地资源具有十分重要的意义。在快速城镇化地区,土地利用活动频繁且密集,土地利用格局演变十分剧烈,增加了城市土地利用精准分类的不确定性;且受环境气候和云雨天气影响增加了有效光学影像获取的难度。为提高城市土地分类精度,该文选取武汉市中心城区为研究案例,以Sentinel-1A和Landsat8 OLI影像为数据源,采用Gram-Schmidt变换方法进行影像融合,选取最大似然、支持向量机、CART决策树、BP神经网络等4种分类方法对融合的影像进行分类,提取了研究区土地利用信息,并对其进行分析。进一步,通过与光学影像的分类结果对比,探究了Sentinel-1A和Landsat8 OLI融合影像在土地利用信息提取方面是否具有优势。研究结果表明:(1)对比其他3种方法,CART决策树分类方法对于融合后的影像分类精度最高,总体分类精度和Kappa系数分别达到88.55%和0.841 4;(2)与光学影像相比,Sentinel-1A和Landsat8 OLI融合影像可以更有效地获取高精度城市土地利用信息;(3)基于多源遥感影像融合的CART决策树分类方法是获取研究区高精度土地利用信息的一种行之有效的技术手段。研究成果可为快速城镇化区域的土地利用分类提供参考。     

基于多源同步数据的闽江下游悬浮物定量遥感

利用2006-09-18的同步Landsat TM数据、水面实测光谱数据和现场水样数据,研究了闽江下游的悬浮物,在这3种同步数据的基础上建立了分别基于实测光谱与影像光谱的悬浮物遥感预测模型.结果表明,实测光谱数据在690nm波长处,归一化光谱反射率与悬浮物浓度达到最大正相关,由690nm和530nm二处的反射率构成的比值预测模型与实测悬浮物浓度的拟合精度最高,最佳拟合模型可表达为SS=116.2(R690/R530)-33.4.TM影像各波段中以(TM2 TM3)2波段组合与实测悬浮物浓度的相关性最佳.由其所建立的影像光谱预测模型与实测悬浮物浓度的拟合精度最高.最佳拟合模型可表达为SS=3793.7(RTM3 RTM2)2-16.5.模型的精度评价表明,实测光谱模型的预测能力要强于影像光谱模型.但二者差异不大.在缺乏地面实测光谱数据时.基于影像光谱的遥感定量模型仍不失为一种预测悬浮物浓度的有效方法,其对闽江下游悬浮物浓度的反演结果能较准确地反映出该区域悬浮物浓度分布的空间差异,具有较高的实用性.