Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan,Indonesia

Fire disturbance in many tropical forests, including peat swamps, has become more frequent and extensive in recent decades. These fires compromise a variety of ecosystem services, among which mitigating global climate change through carbon storage is particularly important for peat swamps. Indonesia holds the largest amount of tropical peat carbon globally, and mean annual CO₂ emissions from decomposition of deforested and drained peatlands and associated fires in Southeast Asia have been estimated at ∼2000 Mt y-1. A key component to understanding and therefore managing fire in the region is identifying the land use/land cover classes associated with fire ignitions. We assess the oft-asserted claim that escaped fires from oil palm concessions and smallholder farms near settlements are the primary sources of fire in a peat-swamp forest area in Central Kalimantan, Indonesia, equivalent to around a third of Kalimantan's total peat area. We use the MODIS Active Fire product from 2000 to 2010 to evaluate the fire origin and spread on the land use/land cover classes of legal, industrial oil palm concessions (the only type of legal concession in the study area), non-forest, and forest, as well as in relation to settlement proximity. We find that most fires (68–71%) originate in non-forest, compared to oil palm concessions (17%–19%), and relatively few (6–9%) are within 5 km of settlements. Moreover, most fires started within oil palm concessions and in close proximity to settlements stay within those boundaries (90% and 88%, respectively), and fires that do escape constitute only a small proportion of all fires on the landscape (2% and 1%, respectively). Similarly, a small proportion of fire detections in forest originate from oil palm concessions (2%) and within close proximity to settlements (2%). However, fire ignition density in oil palm (0.055 ignitions km⁻²) is comparable to that in non-forest (0.060 km-2 ignitions km-²), which is approximately ten times that in forest (0.006 ignitions km⁻²). Ignition density within 5 km of settlements is the highest at 0.125 ignitions km⁻². Furthermore, increased anthropogenic activity in close proximity to oil palm concessions and settlements produces a detectable pattern of fire activity. The number of ignitions decreases exponentially with distance from concessions; the number of ignitions initially increases with distance from settlements, and, around from 7.2 km, then decreases with distance from settlements. These results refute the claim that most fires originate in oil palm concessions, and that fires escaping from oil palm concessions and settlements constitute a major proportion of fires in this study region. However, there is a potential for these land use types to contribute substantially to the fire landscape if their area expands. Effective fire management in this area should therefore target not just oil palm concessions, but also non-forested, degraded areas where ignitions and fires escaping into forest are most likely to occur.     

利用多光谱卫星遥感和深度学习方法进行青藏高原积雪判识

青藏高原积雪对全球气候变化十分重要,针对已有积雪遥感判识方法中普遍采用的可见光与红外光谱数据易受复杂地形与高海拔影响,导致青藏高原地区积雪判识精度较低的问题,提出了一种基于多光谱遥感与地理信息数据特征级融合的积雪遥感判识方法:以风云三号卫星可见光与红外多光谱遥感资料与多要素地理信息作为数据源,由地面实测雪深数据与现有积雪产品交叉筛选出样本标签,构建并训练基于层叠去噪自编码器(SDAE)的特征融合与分类网络,从而有效辨识青藏高原遥感图像中的云、积雪以及无雪地表。经地面实测雪深数据验证,该方法分类精度显著高于使用相同数据源的FY-3A/MULSS积雪产品,略高于国际主流积雪产品MOD10A1与MYD10A1,并且年均云覆盖率最低。试验结果表明该方法可有效地减少云层对积雪判识的干扰,提升分类精度。     

地表覆盖遥感产品更新完善的研究动向

近年来,多尺度地表覆盖遥感产品的不断涌现,为环境变化研究、地球系统模拟、地理国(世)情监测和可持续发展规划等提供了重要科学数据。为更好地满足广大用户日益增长的应用需求,应对地表覆盖遥感产品进行持续更新完善,保持其时效性、增强时序性、丰富多样性。针对大面积地表覆盖遥感产品更新完善所面临的主要问题,介绍和评述了国内外有关研究动向,包括影像与众源信息相结合的更新、数据类型细化与完善、地表覆盖真实性验证,并作了简要展望。     

利用端元光谱内插的青海植被覆盖度估算研究

针对利用像元二分模型估算植被覆盖度的精度不高的问题,该文基于OSAVI,提出了选定模型参数(OSAVIs和OSAVIv)的方法,并将该方法应用于青海省植被覆盖度估算。该方法通过高分辨率影像在研究区内选取纯裸地和纯植被样点,并将纯裸地样点的OSAVI作为纯裸地样点像元的OSAVIs,将纯植被样点的OSAVI作为纯植被样点像元的OSAVIv,利用样点像元的OSAVIs和OSAVIv值,通过普通克里金内插法,求得研究区每个像元对应的OSAVIs和OSAVIv。经精度验证结果表明:此方法较常规的参数选取方法,RMSE由0.170降至0.156,MAE由0.137降至0.124。经进一步分析表明,此方法对边缘验证点和非边缘验证点的估算精度都有所提高,由于配准误差和周围地表漫反射的影响,边缘验证点的估算精度低于对非边缘验证点的估算精度。     

中国版大洋洲地表覆盖产品研制

针对全球变化研究对大洋洲地表覆盖产品的需求,该文以2000年和2010年的Landsat卫星影像为数据源,提出了对大洋洲影像按照月份分组并进行样本采集与规则训练的方法,采用GLC树分类器进行自动分类,经过分类后处理和数据集成,完成了2000年和2010年两期、30m分辨率的大洋洲地表覆盖产品研制工作。利用高分辨率影像、实地采集照片等进行室内精度评定,该大洋洲地表覆盖产品的精度达到90%以上。     

Quantification of LST and CO2 levels using Landsat-8 thermal bands on urban environment

Land surface temperature (LST) is an important aspect in global to regional change studies, for control of climate change and balancing of high temperature. Urbanization is one of the influencing factors increasing land surface and atmospheric temperature, by the emission of greenhouse gases (e.g. CO₂, NO and methane). In the present study, LST was derived from Landsat-8 of multitemporal data sets to analyse the spatial structure of the urban thermal environment in relation to the urban surface characteristics and land use–land cover (LULC). LST is influenced by the greenhouse gases i.e. CO₂ plays an important role in increasing the earth’s surface temperature. In order to provide the evidence of influence of CO₂ on LST, the relationship between LST, air temperature and CO₂ was analysed. Landsat-8 satellite has two thermal bands, 10 and 11. These bands were used to accurately to calculate the temperature over the study area. Results showed that the strength of correlation between ground monitoring data and satellite data was high. Based on correlation values of each month April (R² = 0.994), May (R² = 0.297) and June (R² = 0.934), observed results show that band 10 was significantly correlating with air temperature. Relationship between LST and CO₂ levels were obtained from linear regression analysis. band 11 was correlating significantly with CO₂ values in each of the months April (R² = 0.217), May (R² = 0.914) and June, (R² = 0.934), because band 11 is closer to the 15-micron band of CO₂. From the results, it was observed that band 10 can be used for calculating air temperature and band 11 can be used for estimation of greenhouse gases.     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

Forest fragmentation in Massachusetts,USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation

Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address these objectives: 1) characterize the spatial pattern of forest fragmentation in Massachusetts towns using Morphological Spatial Pattern Analysis (MSPA); and (2) identify regional trends using archetypal towns in relation to town history, geography and socioeconomic characteristics. Six fragmentation indicators were calculated using MSPA for each town to represent patterns and processes of fragmentation. We then used these indicators and the proportion of forested land to group towns across Massachusetts with similar patterns of fragmentation. Six representative towns typify different types of forest fragmentation, and illustrate the commonalities and differences between different fragmentation types. The objective selection of representative towns suggests that they might be used as the target of future studies, both in retrospective studies that seek to explain current patterns and in analyses that predict future fragmentation trends.     

基于随机森林模型的云南元阳梯田地形因子分析

为探究地形因子对梯田空间分布的影响,以云南元阳梯田坝达流域为研究区,运用GeoEye-1遥感卫星数据(2010年,1 m分辨率)和数字高程模型(20 m分辨率),提取梯田空间分布信息以及海拔高程、地形坡度、地形坡向、地面曲率、高程变异系数、地形起伏度、地面粗糙度、地表切割度、到水系距离9个地形因子。采用随机森林(Random Forest)方法建模,结合ROC曲线和AUC值对模型进行精度评价,根据随机置换残差均方减少量和因子作用曲线,探讨地形因子对梯田分布的影响规律。研究结果表明:该流域内梯田总面积1 158395 hm2,模型AUC值为0.947,海拔高度因子的随机置换残差均方减少量为38814,到水系距离因子为199.77,地面坡向因子为80.26,三者占总值的73.45%。由此可见,元阳梯田的空间分布主要受海拔高度、到水系距离、地面坡向3类地形因子的影响,其因子曲线表明梯田分布与地形因子间呈非线性关系。     

珠江流域降水集中度时空变化特征及成因分析

基于珠江流域内43个常规气象站点1960~2012年的逐日降水资料,计算了流域内各站点长期降水集中度(LCI)和逐年降水集中度(ACI);采用Mann-Kendall趋势检验法和Sen’s坡度检验法检测ACI时间上的变化趋势;同时采用反距离权重插值法(IDW)对LCI的区域特征和ACI的变化趋势进行空间插值以分析降水集中度的时空分布规律;采用随机森林(RF)算法对降水集中度的影响因子进行重要度分析。结果表明:(1)珠江流域逐年降水集中度ACI的年际变化不明显,东南部呈上升趋势,西北部呈下降趋势;(2)珠江流域西北部长期降水集中度LCI值偏小,即降水分布较平均;东南部长期降水集中度LCI值偏大,即降水较集中,表明该地区降水极值情况发生的几率相对较高,该空间分布趋势可能是受距离海洋的远近及海拔的影响;(3)众多气候影响因子中,东亚夏季季风(EASMI)对珠江流域的降水集中度影响最明显。