Forest fragmentation and human population varies logarithmically along elevation gradient in Hindu Kush Himalaya - utility of geospatial tools and free data set

Hindu Kush Himalaya(HKH) is the largest and the most diverse mountain region in the world that provides ecosystem services to one fifth of the total world population. The forests are fragmented to different degrees due to expansion and intensification of human land use. However, the quantitative relationship between fragmentation and demography has not been established before for HKH vis-à-vis along elevation gradient. We used the globally available tree canopy cover data derived from Landsat-TM satellite to find out the decadal forest cover change over 2000 to 2010 and their corresponding fragmentation levels. Using SRTMderived DEM, we observed high forest cover loss up to2400 m that highly corroborated with the population distribution pattern as derived from satellite observation. In general, forest cover loss was found to be higher in south-eastern part of HKH. Forest fragmentation obtained using ‘area-weighted mean radius of gyration' as indicator, was found to be very high up to 2400 m that also corroborated with high human population for the year 2000 and 2010. We observed logarithmic decrease in fragmentation change(area-weighted mean radius of gyration value),forest cover loss and population growth during 2000-2010 along the elevation gradient with very high R~2 values(i.e., 0.889, 0.895, 0.944 respectively). Our finding on the pattern of forest fragmentation and human population across the elevation gradient in HKH region will have policy level implication for different nations and would help in characterizing hotspots of change. Availability of free satellite derived data products on forest cover and DEM, griddata on demography, and utility of geospatial tools helped in quick evaluation of the forest fragmentation vis-a-vis human impact pattern along the elevation gradient in HKH.     

High resolution land cover datasets integration and application based on Landsat and Globcover data from 1975 to 2010 in Siberia

Land cover is recognized as one of the fundamental terrestrial datasets required in land system change and other ecosystem related researches across the globe. The regional differentiation and spatial-temporal variation of land cover has significant impact on regional natural environment and socio-economic sustainable development. Under this context, we reconstructed the history land cover data in Siberia to provide a comparable datasets to the land cover datasets in China and abroad. In this paper, the European Space Agency(ESA) Global Land Cover Map(GlobCover), Landsat Thematic Mapper(TM), Enhanced Thematic Mapper(ETM), Multispectral Scanner(MSS) images, Google Earth images and other additional data were used to produce the land cover datasets in 1975 and 2010 in Siberia. Data evaluation show that the total user′s accuracy of land cover data in 2010 was 86.96%, which was higher than ESA GlobCover data in Siberia. The analysis on the land cover changes found that there were no big land cover changes in Siberia from 1975 to 2010 with only a few conversions between different natural forest types. The mainly changes are the conversion from deciduous needleleaf forest to deciduous broadleaf forest, deciduous needleleaf forest to mixed forest, savannas to deciduous needleleaf forest etc., indicating that the dominant driving factor of land cover changes in Siberia was natural element rather than human activities at some extent, which was very different from China. However, our purpose was not just to produce the land cover datasets at two time period or explore the driving factors of land cover changes in Siberia, we also paid attention on the significance and application of the datasets in various fields such as global climate change, geopolitics, cross-border cooperation and so on.     

Remote sensing classification of western Sierra Leone using landsat TM and ETM +

The study examines the changes of land cover/use resources for the period under investigation.An unsupervised vegetation classification is being performed that provides five distinctive classes and thus assesses these changes in five broad land cover classes-high/moist forests,forest regrowth,mixed savanna,bare land/ grass and water.The remote sensing images used in this work are both images of TM and ETM+in different time periods(1986 to 2001)to determine land cover/use changes.A fairly accuracy report is recorded after performing the unsupervised classification,which shows vegetation has been depleted for over the years.Changes created are mostly human and to a lesser extent environment.Human activities are mainly encroachment thus altering the landscape through activities such as population growth,agriculture,settlements,etc.and environment due to some perceive climatic changes.This vegetation classification highlights the importance to acquire and publish information about the country’s partial vegetation cover and vegetation change including vegetation maps and other basic vegetation influencing factors,leading to an understanding of its evolution for a period.     

近20年黄土高原土地利用/覆被变化特征分析

本文根据黄土高原地区20世纪80年代末、2000年、2008年3期土地利用/覆被空间数据集,计算2个时段（20世纪80年代末-2000年,2000-2008年）土地利用/覆被转类方向及其幅度、土地利用/覆被转类指数、土地利用/覆被状况指数及其变化率,分析黄土高原地区自20世纪80年代末以来土地利用/覆被时空变化特征以及宏观生态状况的变化趋势。结果显示：黄土高原地区近20年来平均土地利用/覆被状况指数为24.07,其中土石山区生态系统综合功能最好,其次为河谷平原区,最差的为农灌区。20世纪80年代末-2000年,黄土高原地区主要土地利用/覆被转类是森林和草地转为耕地,生态级别由高级向低级转移,2000-2008年主要土地利用/覆被转类是耕地转为林地和草地,低覆盖草地转为中高覆盖草地,生态级别由低级向高级转移。近20年来黄土高原地区地覆被状况指数变化以及土地利用/覆被转类指数表明,该区域的宏观生态状况总体上经历了转差（20世纪80年代末-2000年土地利用/覆被转类指数为-1.08）,后转好（2000-2008年土地利用/覆被转类指数为2.66）2个过程。这一变化过程前期受区域气候变化以及人口增长共同驱动,后期则叠加了生态工程的影响。     

Land use and land cover change within the Koshi River Basin of the central Himalayas since 1990

Land change is a cause and consequence of global environmental change.Land use and land cover have changed considerably due to increasing human activities and climate change,which has become the core issue of major international research projects.This study interprets land use and land cover status and the changes within the Koshi River Basin(KRB)using Landsat remote sensing(RS)image data,and employs logistic regression model to analyze the influence of natural and socioeconomic driving forces on major land cover changes.The results showed that the areas of built-up land,bare land and forest in KRB increased from 1990 to 2015,including the largest increases in forest and the highest growth rate in construction land.Areas of glacier,grassland,sparse vegetation,shrub land,cropland,and wetland all decreased over the study period.From the perspective of driving analysis,the role of human activities in land use and land cover change is significant than climate factors.Cropland expansion is the reclamation of cropland by farmers,mainly from early deforestation.However,labor force separation,geological disasters and drought are the main factors of cropland shrinkage.The increase of forest area in India and Nepal was attributed to the government’s forest protection policies,such as Nepal’s community forestry has achieved remarkable results.The expansion and contraction of grassland were both dominated by climatic factors.The probability of grassland expansion increases with temperature and precipitation,while the probability of grassland contraction decreases with temperature and precipitation.     

基于小流域抽样单元的中国FROM-GLC30数据精度评价

土地覆盖数据是全球环境变化相关研究和应用的重要数据基础,在诸多领域中被广泛运用.FROM-GLC30 2017数据是最新的全球高分辨率(30m)公开土地覆盖数据集之一.土地覆盖数据集的精度是其在不同领域应用中的重要问题.本研究旨在探讨FROM-GLC30 2017数据集精度在全国范围内的空间分布,并比较不同土地覆盖类型...     

Shrinking greenery: Land use and land cover changes in Kurram Agency,Kohi Safid Mountains of north-western Pakistan

Deforestation and other Land Use and Land Cover (LULC) changes, driven by variety of physical and anthropogenic factors, have altered the mountainous environment. Mountains around the world including northern and north western belts of Pakistan are highly sensitive to deforestation and other LULC changes, which have profound impacts on various sectors of bio-physical and socio-economic systems. Assessment of LULC changes has high significance for protection, conservation and monitoring mountainous environment. The present study is an attempt to assess the landscape changes with particular reference to forest cover depletion in Kurram Agency located in the north western mountain belt of Pakistan. For detailed comparative analysis the study area has been divided into three sections, which coincide with the present administrative divisions of the Agency, i.e., Upper, Lower and Central Kurram. Temporal span of this study covers four decades. In this study, land use map of 1970 and land sat satellite imageries of 1987, 2000 and 2014 were used as spatial data sets. The images were processed and classified into six LULC classes through geospatial packages and change detection maps were prepared for each division and time period. Findings of the study reveal two trends in the four major LULC categories. Forest and rangeland have shrunk, on average, by 15% and 7.5% respectively while, bare soil and rocks outcrops have expanded by 89% and agriculture land by 7.2% in Kurram agency. The water bodies and snow cover have minor fluctuation in its land area. Major causes of shrinking greenery is attributed to high influx of Afghan refugees and high energy demand of growing population. However, with outflow of the refugees from Kurram agency the general trend in forest cover has reverted and deforestation rate has slowed down.     

Spatial assessment of forest cover and land-use changes in the Hindu-Kush mountain ranges of northern Pakistan

Anthropogenic activities and natural processes are continuously altering the mountainous environment through deforestation, forest degradation and other land-use changes. It is highly important to assess, monitor and forecast forest cover and other land-use changes for the protection and conservation of mountainous environment. The present study deals with the assessment of forest cover and other land-use changes in the mountain ranges of Dir Kohistan in northern Pakistan, using high resolution multi-temporal SPOT-5 satellite images. The SPOT-5 satellite images of years 2004, 2007, 2010 and 2013 were acquired and classified into land-cover units. In addition, forest cover and land-use change detection map was developed using the classified maps of 2004 and 2013. The classified maps were verified through random field samples and Google Earth imagery (Quick birds and SPOT-5). The results showed that during the period 2004 to 2013 the area of forest land decreased by 6.4%, however, area of range land and agriculture land have increased by 22.1% and 2.9%, respectively. Similarly, barren land increased by 1.1%, whereas, area of snow cover/glacier is significantly decreased by 21.3%. The findings from the study will be useful for forestry and landscape planning and can be utilized by the local, provincial and national forest departments; and REDD+ policy makers in Pakistan.     

山西省土地覆盖时空变化特征及其驱动因子分析

以遥感与GIS为技术支撑,对山西省20世纪80年代到2005年的土地覆盖进行了动态监测,并分析了土地覆盖的变化特征及其驱动因子;监测表明,山西省土地覆盖时空变化特征:(1)变化数量:城镇建设用地和农村聚落面积增加显著,草地有所增加;旱地和水浇地减少明显,森林和河湖滩地略有减少。(2)变化速度:城镇建设用地增速迅猛,沼泽和农村聚落面积增速较快;河湖滩地减少速度最快,其次,是旱地和水浇地减少速度较快。(3)空间转换:以农田和聚落之间的转换为主,另外城镇建设用地的增加包括其扩展融入的农村聚落等;典型草地的增加主要来自旱地;灌丛减少主要是转变成典型草地,其次是转变成灌丛草地;河湖滩地减少主要是变为内陆水体,再是转变成水浇地。驱动力分析表明,区域自然因素相对稳定,对土地覆盖变化的影响较小,人口、经济、农业结构调整、政策和交通等是山西省土地覆盖变化的主要驱动力。山西省土地覆盖变化的总体特征与区域经济发展、产业布局及其工业化、城市化演变的特征基本一致。     

Multi-temporal forest cover dynamics in Kashmir Himalayan region for assessing deforestation and forest degradation in the context of REDD+ policy

The role of forests is being actively considered under the agenda of REDD+ (Reducing Emissions from Deforestation and Forest Degradation plus) aimed at reducing emissions related to changes in forest cover and forest quality. Forests in general have undergone negative changes in the past in the form of deforestation and degradation, while in some countries positive changes are reported in the form of conservation, sustainable management of forests and enhancement of carbon stock. The present study in the Kashmir Himalayan forests is an effort to assess historical forest cover changes that took place from 1980 to 2009 and to predict the same for 2030 on the basis of past trend using geospatial modeling approach. Landsat data (Multispectral Scanner (MSS), Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+)) was used for the years 1980, 1990 and (2001, 2009) respectively and change detection analysis between the dates was performed. The maps generated were validated through ground truthing. The study area (3375.62 km²) from 1980-2009 has uffered deforestation and forest degradation of about 126 km² and 139.02 km² respectively which can be claimed under negative options of REDD+, while as the area that experienced no change (1514 km²) can be claimed under conservation. A small area (23.31 km2) observed as positive change can be claimed under positive options. The projected estimates of forest cover for 2030 showed increased deforestation and forest degradation on the basis of trend analysis using Cellular Automata (CA) Markov modeling. Despite the fact that country as a whole has registered a net positive change in the past few decades, but there are regions like Kashmir region of western Himalaya which have constantly undergoing deforestation as well as degradation in the past few decades.     

中国1:25万土地覆盖遥感制图精度评价——以鄱阳湖地区为例

 全球及区域尺度的土地覆盖数据是陆地表层过程研究的重要基础。土地覆盖遥感制图是全球变化和区域可持续发展研究重要的支撑数据,制图精度评价对于数据生产者和数据用户都具有非常重要的意义。2011年在鄱阳湖地区的野外考察共采集包括定点验证、GPS以及解译标志3种类型的土地覆盖样点321个,本文利用剔除了时间差异影响后的287个土地覆盖样点,将样点的实际土地覆盖类型与遥感制图中相应位置的土地覆盖类型进行对比,并利用分层次评估法,即分别在土地覆盖一级类和二级类两个尺度上,采用正确得1分,错误得0分的计分方法,对2005年中国1:25万土地覆盖遥感制图在鄱阳湖地区的精度进行实地验证。结果表明:(1)在土地覆盖一级类型尺度上总体的制图精度为61.67%。其中,湿地/水体的制图精度为100%,农田的制图精度为98.4%,森林的制图精度为80.0%,聚落和草地的精度均低于20%。(2)在二级类型尺度上总体的制图精度为44.25%。其中,2个草地和3个森林及1个农田的二级类型的分类精度为0,旱地、城镇建设用地和农村聚落的分类精度也很低,分别为21.1%、29.0%和1.7%。实地调查发现,2005年左右的全国1:25万土地覆盖遥感制图基本上反映了鄱阳湖地区的土地覆盖状况。但是,对于一些具有过渡性质的土地覆盖类型,如森林和草地等,仅依靠遥感技术准确识别区分仍有一定的难度。     

Land cover change in different altitudes of Guizhou-Guangxi karst mountain area,China: patterns and drivers

Topography, especially altitude, will influence the way, process and characteristics of land cover changes in mountainous area, simultaneously, the vertical difference of land cover changes will affect soil quality and regional ecological environment. Therefore, the gradient relationship analysis between land cover changes and altitude is very important for regional sustainability. This study investigated land cover dynamics based on land cover data from a typical mountainous area in the Guizhou-Guangxi karst mountain area, China, in 2000 and 2010, then explored the relationship between altitude and land cover change and analyzed different drivers of land cover change at different altitudes. Our findings are as follows. 1) From 2000 to 2010, the total area of land cover transition was 7167.04 km~2 or 2.8% of the region. The increasing area of build-up land(926.23 km~2) was larger than that of forest(859.38 km~2), suggesting that the urban construction speed was higher than that of reforestation. 2) Intensity of land cover transition in northwestern Guizhou-Guangxi karst mountain area was much larger than that of southeast part and their transition trend was also significantly different, which was consistent with regional population and economy. 3) Human activity was the most dramatic at altitudes between 0–500 m. For 500–1000 m, grassland mainly converted to forest and build-up land. Area of land cover transition was the greatest between 1000–1500 m, while above 1500 m, the transition of grassland was the most obvious. 4) The drivers of land cover change varied. Land cover change was positively correlated with gross domestic product and population density but was inversely related to relief amplitude. There were correlations between land cover change and distance to roads and rivers, and their correlations varied with altitude. By revealing patterns and causes of land cover changes in different altitudes, we hope to understand the vertical dependence of land cover changes, so as to improve land productivity and protect land ecological environment scientifically.     

Object-based classification of cloudy coastal areas using medium-resolution optical and SAR images for vulnerability assessment of marine disaster

Efficient and accurate access to coastal land cover information is of great significance for marine disaster prevention and mitigation. Although the popular and common sensors of land resource satellites provide free and valuable images to map the land cover, coastal areas often encounter significant cloud cover, especially in tropical areas, which makes the classification in those areas non-ideal. To solve this problem, we proposed a framework of combining medium-resolution optical images and synthetic aperture radar(SAR) data with the recently popular object-based image analysis(OBIA) method and used the Landsat Operational Land Imager(OLI) and Phased Array type L-band Synthetic Aperture Radar(PALSAR) images acquired in Singapore in 2017 as a case study. We designed experiments to confirm two critical factors of this framework: one is the segmentation scale that determines the average object size, and the other is the classification feature. Accuracy assessments of the land cover indicated that the optimal segmentation scale was between 40 and 80, and the features of the combination of OLI and SAR resulted in higher accuracy than any individual features, especially in areas with cloud cover. Based on the land cover generated by this framework, we assessed the vulnerability of the marine disasters of Singapore in 2008 and 2017 and found that the high-vulnerability areas mainly located in the southeast and increased by 118.97 km2 over the past decade. To clarify the disaster response plan for different geographical environments, we classified risk based on altitude and distance from shore. The newly increased high-vulnerability regions within 4 km offshore and below 30 m above sea level are at high risk; these regions may need to focus on strengthening disaster prevention construction. This study serves as a typical example of using remote sensing techniques for the vulnerability assessment of marine disasters, especially those in cloudy coastal areas.     

城市地表覆盖组分及植被多样性与热环境关联性分析——以贵阳市为例

本文以贵阳市为研究对象,研究了地表覆盖组分及植被多样性对地表温度的影响。首先基于Landsat-8 OLI多时相影像数据在GEE平台上实现了研究区域地表覆盖精细分类;然后结合不同季节8天合成的MODIS温度产品数据,利用时空统计分析、相关分析等方法分析了研究区不同地表覆盖类型地表温度时空分布特征,地表温度与不同地表覆盖组分、地表覆盖多样性和植被覆盖多样性的相关性。结果表明：贵阳市建成区主要分布有常绿阔叶林、常绿针叶林等植被,常绿阔叶林在不同季节对地表温度的降温效应明显,而不透水面对地表温度具有明显的增温效应,尤其以夏季最为显著;地表覆盖多样性与地表温度之间具有较强相关性,其中植被覆盖多样性较植被覆盖率对地表温度的影响更为显著,而不透水面的增加会明显降低植被多样性的影响。因此,要发挥城市绿地对城市温度和热岛效应的调节作用,建议可以适当增加常绿阔叶林的绿化面积,同时在空间上要提升植被多样性水平,能够较大程度改善城市热环境。     

京津风沙源区生态环境时空变化的遥感监测分析

本文利用由遥感影像解译获得的土地利用数据和MODIS NDVI产品,采用空间转移矩阵、最大值合成法、均值法和一元线性回归模拟等方法,分析了2000年以来京津风沙源区生态环境变化的时空特征.结果表明:(1)2000-2005年京津风沙源区耕地开垦、林草地减少的趋势有所减缓,但区域气候变化趋于干旱,水域面积明显减少,草地退...     

近20年来福州市土地利用时空变化的遥感分析

以1986、1996和2006年三期Landsat TM影像为基本数据源,采用最大似然法分类,并以同期高分辨率的SPOT5影像进行分类后处理,最后分类后变化检测法监测了福州市1986~2006年的土地利用变化及其转移模式。结果表明,福州市近20年间土地利用发生了较大的变化,城乡建筑用地处于快速增长阶段,而耕地数量却在不断下降。经济发展、人口增长、政府决策等因素是福州市土地利用发生变化的主要驱动力。     

Integrated assessment of extreme climate and landuse change impact on sediment yield in a mountainous transboundary watershed of India and Pakistan

Assessment of climate and land use changes impact including extreme events on the sediment yield is vital for water and power stressed countries. Mangla Reservoir is the second-largest reservoir in Pakistan, and its capacity is being reduced due to rapid sedimentation and will be threatened under climate and land use changes. This paper discusses the consequences of climate and land use change on sediment yield at Mangla Dam using General Circulation Models(GCMs), Land Change Modeler(LCM), Soil and Water Assessment Tool(SWAT) model after calibration and validation.Results show that over the historical period temperature is observed to increase by 0.10 o C/decade and forest cover is observed to reduce to the level of only 16% in 2007. Nevertheless, owing to the forest conservation policy, the forest cover raised back to 27% in 2012. Anticipated land use maps by using LCM of 2025, 2050 and 2100 showed that the forest cover will be 33%, 39.2%, and, 53.7%, respectively. All seven GCMs projected the increase in temperature and five GCMs projected an increase in precipitation,however, two GCMs projected a decrease in precipitation. Owing to climate change, land use change and combined impact of climate and land use change on annual sediment yield(2011-2100) may vary from-42.9% to 39.4%, 0% to-27.3% and,-73%to 39.4%, respectively. Under climate change scenarios projected sediment yield is mainly linked with extreme events and is expected to increase with the increase in extreme events. Under land use change scenarios projected sediment yield is mainly linked with the forest cover and is expected to decrease with the increase in forest cover. The results of this study are beneficial for planners, watershed managers and policymakers to mitigate the impacts of climate and land use changes to enhance reservoir life by reducing the sediment yield.     

Socio-economic drivers of deforestation in Roghani Valley,Hindu-Raj Mountains,Northern Pakistan

Deforestation and associated ecological disturbances are the issues of global concern.Researchers have investigated a number of driving forces which accelerate the rate of deforestation at local and regional levels.These include poverty,population growth,market demand and prices,political instability,agricultural expansion and changes in property right and ownership regimes.This paper seeks to explore the impacts of population growth,changing tenure system and other socioeconomic factors on the forest cover of Roghani Valley,located in Hindu Raj Mountains,Northern Pakistan.The present study is mainly based on information collected through participatory observation,selfadministered interviews and questionnaire survey.Geographical Information System（GIS） database is also used for mapping and quantification.The results reveal that in the past three to four decades the study area has been subjected to severe deforestation and about half of the forest area has been converted into barren land.Thus,the area under natural forests decreased from 2099 to 1444 hectares in four decades.This large-scale deforestation is attributed to both proximate and under lying causes particularly traditional land tenure system and demographic development.Consequently,forest resources have been degraded and a number of plant species have disappeared from the forests of the study area while several others are in the process of disappearance.     

基于地学知识的大尺度土地利用/土地覆盖精细化分类方法研究

人类活动对生态环境具有显著影响,大尺度土地利用/覆盖变化（Land Use/Cover Change,LUCC）作为人类活动最直接的表征,能够很好地反映这一过程,因此进行精确而迅速的大尺度土地利用/覆盖分类与提取方法研究尤为关键。全球覆盖产品GlobCover（2005/2006）数据已经具有良好的空间精度和数据准确度,但仍然存在一些分类误差。为提高地表覆被分类精度,本文以GlobCover（2005/2006）的巴西数据为例,以2005年Landsat TM/ETM影像为主要信息源,结合相应地学知识与辅助数据,利用人机交互逐栅格修改方法得到2005年土地利用数据产品。结果表明：通过对GlobCover数据和本次成果数据进行精度评价与对比分析,GlobCover数据巴西地区的总体精度为67.17%,Kappa系数为0.58,改进后产品总体精度为93.39%,Kappa系数为0.91。此外,改进后数据显示巴西常绿阔叶林面积最大,面积比例达45.67%;农地/自然植被镶嵌面积次之,比例为19.19%;封闭灌丛面积最小,比例为12.34%。农地/自然植被镶嵌和灌丛与草地2种地类的修改比例最大,其中混合像元地类比例减少3.54%,灌丛与草地比例增加3.81%。综上,改进方法可以有效地提高土地利用/覆盖分类的效率和精度,为后续大尺度LUCC产品的制作和以LUCC产品为基础的相关研究提供参考。     

基于最邻近时空距离的土地变化过程时空模式分析

土地利用/土地覆盖受人类活动和城市的快速扩张的影响发生了巨大的变化,这种变化对生态环境和地表景观的影响极大。土地利用/土地覆盖的变化过程既受自然、经济等多种因素的影响,也是人类活动规律和自然因素制约的外部表征。因此研究土地利用/土地覆盖的变化过程具有重要的意义。土地利用/覆盖变化的监测和分析,在传统的方法中侧重于分别对各个时空快照上土地利用结构的整体差异的研究,割裂了不同快照间土地利用单元在演化过程上的有机联系。本文以序列土地利用数据构成的土地变化过程为核心研究对象,在土地变化过程的最邻近时空距离度量的基础上,开展基于蒙特卡洛随机模拟的土地变化过程的时空聚集性度量,量化分析土地利用变化过程时空聚集模式的显著性。使用淮南市市辖区2008-2017年土地利用数据,选取典型的时空演化类型(任意2年间从"耕地"演变为"草地")进行实证研究,结果表明此类土地变化过程在过去10年呈现出时空聚集模式,但统计上并不显著。本文的研究有利于把握土地利用单元在时空上的演变过程,探查土地利用变化过程中潜在的时空演化模式。