基于GEE的山东省近30年农业大棚时空动态变化研究

针对精确获取大尺度空间范围内农业大棚的分布情况并进行长时间的序列动态监测存在数据量大、计算效率低、精度不高等问题,利用Google Earth Engine(GEE)云平台能够实现快速存取、实时处理海量卫星数据,基于多时相Landsat影像进行农业大棚时序光谱特征和纹理特征的自动提取,采用随机森林算法实现山东省农业大棚的遥感分类,从而生成了山东省近30年农业大棚的空间分布和时空动态变化图。结果表明,本文分类流程具有较高的分类精度,其平均总体精度达到91.63%,Kappa系数均值为0.8642。经分析,山东省农业大棚从1990年的6.67 km^2增加到2018年的9919.40 km^2,增长速度为354.03 km^2/a。     

Prediction of pine mistletoe infection using remote sensing imaging: A comparison of the artificial neural network model and logistic regression model

In this study, the prediction of pine mistletoe distribution in Scots pine ecosystems was explored using remote sensing variables to compare the multilayer perceptron (MLP) artificial neural network (ANN) and logistic regression (LR) model performances. For this purpose, 109 sample plots were distinguished in pure Scots pine forests (natural) in the Eastern Black Sea Region of Turkey. Distinguishing mistletoe-infected stands (69) and uninfected stands (40) was performed with field observations. The variables acquired from Landsat 8 (Level 1) images were used as independent variables for independent-sample t-test, MLP ANN and LR models. Remote sensing variables indicated that mistletoe-infected stands were in drier areas with a lower vegetation-leaf area index. Based on the performance results of both models, the sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV) and accuracy of the MLP ANN model were superior to those of the LR model. The prediction percentages (SEN, SPE, PPV and NPV) of mistletoe-infected stands were better than the prediction percentages of uninfected stands. The prediction accuracies of LR and MLP ANN models were 74.3% and 89.6%, respectively. However, all remote sensing variables were included in the prediction equation of the MLP ANN model, while the thermal infrared 1 (TIRS1) variable was included in the LR model. In the MLP ANN model, the TIRS1 variable also had the highest normalized importance (100%). The area under the curve (AUC) value for identifying the mistletoe-infected stands of Scots pine forests used by the MLP ANN model (0.892 ± 0.034) was higher than in the LR model (0.838 ± 0.039), explaining the more accurate predictions obtained from the MLP ANN model. The MLP ANN model showed much better performance than the LR model. The results of this study are expected to make important contributions to the identification of potential mistletoe-infected areas.     

基于Landsat 8的东莞市热岛效应研究

以2014年广东省东莞市Landsat 8卫星的热红外波段数据建立模型,反演东莞市地表温度,研究东莞市热岛效应分布情况,从优势度指数、分维数指数和形状指数来分析热力场景观格局,根据热岛效应的不同影响因素分析东莞市热岛现象的成因。结果表明：Landsat 8卫星影像反演2014年东莞市地表温度是可行的;东莞市热力景观以中热岛为主,热岛效应不明显,分布呈西北偏高,中部地区和东南区域偏低;热力景观斑块较复杂、热力场呈条带状零星分布;受绿化、水体影响区域的热岛强度较小,而受人为热源、道路、城市下垫面和建筑等因素影响的区域,热岛强度较大。     

Differences in the forest landscape structure along the Finnish–Russian border in southern Karelia

Abstract

This study describes forest landscape fragmentation and connectivity along the Finnish–Russian border near the Karelian Isthmus. The landscape pattern was analysed using classification data based on Landsat ETM+ and Landsat TM images in combination with systematic surveys in Finland (Finnish National Forest Inventory) (n=546) and the authors’ own fieldwork data in Russia (n=101). On the Finnish side the forest patches are significantly smaller than on the Russian side. In addition, the Finnish forests landscape is more scattered and distances between patches of the same forest type are longer. The Russian side is more dominated by broadleaved and mixed forest stands. The disparities are due to differences in forestry policy and traditions of forest practices. The growing conditions of the areas are similar. The habitat fragmentation and habitat connectivity are important issues because the Karelian Isthmus is one of three main corridors and migrating routes connecting large Russian boreal taiga forests and their fauna and flora with Finnish isolated boreal forests.     

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.     

Estimating aboveground biomass of Pinus densata-dominated forests using Landsat time series and permanent sample plot data

Southwest China is one of three major forest regions in China and plays an important role in carbon sequestration.Accurate estimations of changes in aboveground biomass are critical for understanding forest carbon cycling and promoting climate change mitigation.Southwest China is characterized by complex topographic features and forest canopy structures,complicating methods for mapping aboveground biomass and its dynamics.The integration of continuous Landsat images and national forest inventory data provides an alternative approach to develop a long-term monitoring program of forest aboveground biomass dynamics.This study explores the development of a methodological framework using historical national forest inventory plot data and Landsat TM timeseries images.This method was formulated by comparing two parametric methods:Linear Regression for Multiple Independent Variables(MLR),and Partial Least Square Regression(PLSR);and two nonparametric methods:Random Forest(RF)and Gradient Boost Regression Tree(GBRT)based on the state of forest aboveground biomass and change models.The methodological framework mapped Pinus densata aboveground biomass and its changes over time in Shangri-la,Yunnan,China.Landsat images and national forest inventory data were acquired for 1987,1992,1997,2002 and 2007.The results show that:(1)correlation and homogeneity texture measures were able to characterize forest canopy structures,aboveground biomass and its dynamics;(2)GBRT and RF predicted Pinus densata aboveground biomass and its changes better than PLSR and MLR;(3)GBRT was the most reliable approach in the estimation of aboveground biomass and its changes;and,(4)the aboveground biomass change models showed a promising improvement of prediction accuracy.This study indicates that the combination of GBRT state and change models developed using temporal Landsat and national forest inventory data provides the potential for developing a methodological framework for the long-term mapping and monitoring program of forest aboveground biomass and its changes in Southwest China.     

Estimation of fire severity by use of Landsat TM images and its relevance to vegetation and topography in the 2000 Samcheok forest fire

The severity of the 2000 Samcheok forest fire was classified by using Landsat TM images, and the effects of vegetation structures and topographic conditions on fire severity were analyzed. The estimated normalized difference vegetation index differences between the pre and post-fire Landsat TM images were used as the criteria in determining the levels of fire severity–low, moderate, and extreme. According to the results from fire severity estimation, of the 10,600 ha forest stands, 28% was severely damaged by crown fires, 38% was moderately damaged, and the remaining 34% was damaged slightly by surface fires. The overall accuracy of the fire severity classification was 83% (Kappa coefficient = 0.76). The results of χ ²-tests showed that fire severity differed significantly with the vegetation and topographic conditions as follows. The coniferous stands, compared with the mixed and broad-leaved, were more vulnerable to fire damage; the higher the slope of fire sites, the greater the fire damage; the south was the most vulnerable aspect; fire severity of coniferous forest stands increased with increasing elevation. However, in the study area it was found that fire severity of broad-leaved forest stands were negatively related to the elevation of the corresponding fire sites and affected more by vegetation conditions rather than by topographic conditions.     

基于TM影像的森林地表温度遥感反演

选取海南三亚市的一个典型区域为研究对象,以Landsat TM影像数据进行林地地表温度遥感反演,分析不同森林类型的地表温度差异,以及地表温度与NDVI之间的关系。结果表明:研究区不同森林类型的地表温度有着显著差异;有林地平均地表温度比无林地低0.7℃;天然林平均地表温度比人工林低2.2℃,天然林在缓解城市热岛效应比人工林更具优势;人工林按优势树种平均地表温度由高到低的顺序为芒果龙眼槟榔橡胶桉树;地表温度和NDVI之间存在高度负相关,相关系数为-0.76。     

Evaluating Modified Atmospheric Correction Methods for Landsat Imagery: Image‐Based and Model‐Based Calibration Methods

Abstract

To increase the accuracy of remotely sensed data for agricultural forecasting, pixel values must be corrected for atmospheric effects and converted to spectral reflectance. The objective of this research was to compare two atmospheric correction methods of Landsat imagery under a range of atmospheric conditions. Ground‐based dark‐object subtraction (GDOS) is an image‐based calibration method that used in situ ground data that the dark‐object subtraction (DOS) method did not use, whereas atmospheric calibration (AC) is a model‐based calibration method that required a standard atmospheric profile refined with the use of in situ atmospheric data. GDOS and AC methods improved the reflectance values and had relationships with measured bands, which were approximately 1 to 1 in all bands. However, the GDOS generally had lower root‐mean‐square errors (RMSE) than AC. Data from this study suggest that at the present time the GDOS method may be more accurate than the AC method.     

基于多时相遥感的露天铁矿及其周边植被动态变化特征分析

以新疆某铁矿及其周边区域作为研究对象,运用2007—2016年多时相TM/ETM+、OLI影像,分析铁矿及其周边区域植被覆盖度指数(fractional vegetation cover, FVC)及植被生长状况指数(vegetation condition index, VCI),评价研究区植被覆盖度、植被生长状况时空变化特征及铁矿开采对周边环境的影响,为矿区环境治理提供决策支持。结果表明,FVC及VCI指数能够较好地反映出研究区植被覆盖度及植被生长状况等级不高且铁矿区域几乎无植被生长的实际状况,为研究区生态风险防范提供理论支持。2007—2016年,研究区整体植被覆盖度及植被生长状况呈波动上升趋势,较低植被覆盖度等级和植被生长状况较差等级同时向较好等级发展。FVC及VCI较高等级主要分布在南坡及西南坡。但铁矿面积逐年增大,铁矿开采对其所在区域及周边区域造成了严重的植被退化和生态破坏,极易导致水土流失及山体滑坡等灾害的发生,应及时采取防控措施。