A land cover map of South America

A digital land cover map of South America has been produced using remotely sensed satellite data acquired between 1995 and the year 2000. The mapping scale is defined by the 1 km spatial resolution of the map grid‐cell. In order to realize the product, different sources of satellite data were used, each source providing either a particular parameter of land cover characteristic required by the legend, or mapping a particular land cover class. The map legend is designed both to fit requirements for regional climate modelling and for studies on land cover change. The legend is also compatible with a wider, global, land cover mapping exercise, which seeks to characterize the world's land surface for the year 2000. As a first step, the humid forest domain has been validated using a sample of high‐resolution satellite images. The map demonstrates both the major incursions of agriculture into the remaining forest domains and the extensive areas of agriculture, which now dominate South America's grasslands.     

Reliability of map accuracy assessments: A reply to Roff et al. (2016)

Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) provide a discussion of the criteria expected for the best approach to validation of mapping programs and uses Hunter (Ecological Management & Restoration 17 , 2016, 40) to highlight issues involved. While we support the general principles outlined, we note that the review does not apply the same standards to Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011), the original document critiqued by Hunter (Ecological Management & Restoration 17 , 2016, 40). The Hunter (Ecological Management & Restoration 17 , 2016, 40) validation was based on a larger sample size, greater sampling within mapping units and greater representation of landscapes than Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011). Survey and validation sites being placed along public roads and lands are common to both the general Office of Environment and Heritage (OEH) and Hunter (Ecological Management & Restoration 17 , 2016, 40) validation methodologies. Thus, the criticisms of Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) of the Hunter (Ecological Management & Restoration 17 , 2016, 40) approach apply equally, if not more, to Sivertsen et al. (Greater Hunter Native Vegetation Mapping Geodatabase Guide (Version 4.0). Office of Environment and Heritage, Department of the Premier and Cabinet, Sydney, Australia, 2011). We outline in the article how the Roff et al. (Ecological Management and Restoration, 17 , 2016, 000) critique was selective and in some cases incorrect in its analysis of issues presented in Hunter (Ecological Management & Restoration 17 , 2016, 40) and did not apply the same criteria to their own work. We conclude by discussing future directions for validating and mapping vegetation communities.     

Probabilistic key for identifying vegetation types in the field: A new method and Android application

Quick identification of vegetation types in the field, based on species composition but not requiring time‐consuming plot sampling, is often needed for vegetation mapping, conservation assessment, teaching and other applications of vegetation classification. Here, we propose a new method that identifies the probability of belonging to the units of an established vegetation classification for vegetation stands encountered in the field. The method is based on calculating the probability that a few species observed in the field would co‐occur in a priori defined vegetation types, using the existing information on species occurrence frequency in these types. The method has been implemented in a freely available Android application called Probabilistic Vegetation Key, which makes it possible to employ it in the field using smartphones or tablets, even in the absence of internet access.     

The impact of land use and land cover change on biodiversity within and adjacent to Kibasira Swamp in Kilombero Valley,Tanzania

Wetlands are crucial ecosystems with multiple values and functions to a range of different stakeholders. The future of wetlands depends both on the legacy of the past and how they are currently used. Using 48 vegetation survey plots (0.08 ha) combined with Landsat 5 and 7 TM imagery, we assessed the influence of long‐term (1990–2011) land use and land cover change on the biodiversity of the Kibasira Swamp. Information on perceptions of adjacent communities on historical changes and drivers for the changes were also collected. Results showed an increase in the area covered by open water by 1% and forest by 4% between 1990 and 1998 whilst Cyperus papyrus L and cultivated land area decreased by 8% and 3%, respectively on the same period. Between 1998 and 2011, there was a decrease in areas covered by water by 35% and forest by 9% whereas C. papyrus L increased by 40% and cultivated land increased by 8%. These changes have affected the biodiversity of the swamp and adjacent to it as numbers of mammals have declined. However, the Swamp still provides extensive habitat for plants and bird species despite the ongoing human pressure. Interventions may be necessary to maintain biodiversity in Kibasira Swamp to ensure sustainable ecosystem services.     

2000—2015年西南地区土地利用与植被覆盖的时空变化

西南地区是我国重要的生态资源区和生态脆弱区,在国家“绿水青山”战略发展中具有重要地位。本研究基于1 km空间分辨率的土地利用数据集,结合土地利用转移矩阵,定量分析2000—2015年间西南地区土地利用变化特征及其驱动力。并基于MODIS遥感植被指数,利用像元二分模型计算西南地区植被覆盖度,分析归一化植被指数(NDVI)和植被覆盖度的变化规律。结果表明: 研究期间,西南地区的主要地类是林地、农田和草地。建设用地面积增加5874 km²,增长率为55.8%;农田面积减少最多,下降6211 km²,其次是草地,减少2099 km²。2000—2015年间,西南地区建设用地的转入面积最多,主要由农田(贡献率68.2%)、林地(贡献率19.2%)和草地(贡献率13.1%)转化而来,转化的区域多靠近城区。农田的转出面积和转出率分别为7079 km²和2.2%,占所有转出类型面积的46.0%。林地多由草地(贡献率61.8%)转化而来,转化区域多分布在贵州中南部和云南西部等地。全区NDVI和植被覆盖度均呈显著增加趋势,说明研究区整体呈变绿趋势。其中,自然植被和农田的NDVI均显著增长,建设用地扩张地区的NDVI下降,说明自然植被和农田主导了该地区植被变化。通过残差分析发现,气候变化和人类活动对研究区变绿趋势的贡献显著。     

黄土高原不同植被覆被类型NDVI对气候变化的响应

植被与气候是目前研究生态与环境的重要内容。为探究黄土高原地区植被与气候因子之间的响应机制,利用线性趋势分析、Pearson相关分析、多元线性回归模型以及通径分析的方法,对黄土高原2000—2015年全区和不同植被覆被类型区内NDVI与气候因子的变化趋势以及相互作用关系进行分析。植被覆被分类数据和植被指数数据分别来源于ESA CCI-LC(The European Space Agency Climate Change Initiative Land Cover)以及MODND1T/NDVI(Normalized Difference Vegetation Index)。结果表明:(1) 2000—2015年黄土高原全区植被年NDVI_(max)显著增加的区域占总面积的74.25%,不同植被覆被类型年NDVI_(max)分别为常绿阔叶林常绿针叶林落叶阔叶林落叶针叶林镶嵌草地农田镶嵌林地草地灌木,并且都呈显著增加趋势,其中常绿阔叶林和农田增加幅度最大,为0.012/a。(2)黄土高原全区NDVI与气温、日照、降水和相对湿度等气候因子之间没有显著相关性,但在不同植被覆被类型区,气候因子对NDVI存在显著作用,且不同植被覆被类型差异明显。(3)在全区和不同植被覆被类型区NDVI仅对降水的响应比较一致,气温无论在整个区域尺度还是不同植被覆被类型区对植被的影响均不显著。(4)常绿阔叶林、落叶阔叶林、常绿针叶林及镶嵌林地等以乔木为主的植被覆被类型受年均相对湿度和年总日照时数的显著负效应驱动,草地、镶嵌草地等以草本为主的植被覆被类型则受到年总降水量的显著正效应影响。这说明对植被类型进行区分,更有利于揭示气候对植被的作用机制。     

Reliability of map accuracy assessments: A comment on Hunter et al. (2016)

Map validation data that are ambiguously allocated to map units and collected via poorly designed sampling methods are not statistically reliable and will misrepresent map quality. A recent paper published in Ecological Management and Restoration (Ecological Management & Restoration, 17, 2016 and 40) reported that a map in south‐eastern Australia provided little or no predictive accuracy based on new field data, but the validation suffered from the aforementioned pitfalls. In this comment, we outline the basic guidelines for a robust, reliable and transparent accuracy assessment of thematic maps, pointing out where (Ecological Management & Restoration, 17, 2016 and 40) fails to meets these guidelines.     

Creating spatially continuous maps of past land cover from point estimates: A new statistical approach applied to pollen data

Reliable estimates of past land cover are critical for assessing potential effects of anthropogenic land-cover changes on past earth surface-climate feedbacks and landscape complexity. Fossil pollen records from lakes and bogs have provided important information on past natural and human-induced vegetation cover. However, those records provide only point estimates of past land cover, and not the spatially continuous maps at regional and sub-continental scales needed for climate modelling.We propose a set of statistical models that create spatially continuous maps of past land cover by combining two data sets: 1) pollen-based point estimates of past land cover (from the REVEALS model) and 2) spatially continuous estimates of past land cover, obtained by combining simulated potential vegetation (from LPJ-GUESS) with an anthropogenic land-cover change scenario (KK10). The proposed models rely on statistical methodology for compositional data and use Gaussian Markov Random Fields to model spatial dependencies in the data.Land-cover reconstructions are presented for three time windows in Europe: 0.05, 0.2, and 6 ka years before present (BP). The models are evaluated through cross-validation, deviance information criteria and by comparing the reconstruction of the 0.05 ka time window to the present-day land-cover data compiled by the European Forest Institute (EFI). For 0.05 ka, the proposed models provide reconstructions that are closer to the EFI data than either the REVEALS- or LPJ-GUESS/KK10-based estimates; thus the statistical combination of the two estimates improves the reconstruction. The reconstruction by the proposed models for 0.2 ka is also good. For 6 ka, however, the large differences between the REVEALS- and LPJ-GUESS/KK10-based estimates reduce the reliability of the proposed models. Possible reasons for the increased differences between REVEALS and LPJ-GUESS/KK10 for older time periods and further improvement of the proposed models are discussed.     

A continent under stress: interactions, feedbacks and risks associated with impact of modified land cover on Australia's climate

Global climate change is the major and most urgent global environmental issue. Australia is already experiencing climate change as evidenced by higher temperatures and more frequent and severe droughts. These impacts are compounded by increasing land use pressures on natural resources and native ecosystems. This paper provides a synthesis of the interactions, feedbacks and risks of natural climate variability, climate change and land use/land cover change (LUCC) impacting on the Australian continent and how they vary regionally. We review evidence of climate change and underlying processes resulting from interactions between global warming caused by increased concentration of atmospheric greenhouse gases and modification of the land surface. The consequences of ignoring the effect of LUCC on current and future droughts in Australia could have catastrophic consequences for the nation's environment, economy and communities. We highlight the need for more integrated, long-term and adaptive policies and regional natural resource management strategies that restore the beneficial feedbacks between native vegetation cover and local-regional climate, to help ameliorate the impact of global warming.     

Comparison of phenology trends by land cover class: a case study in the Great Basin, USA

Direct impacts of human land use and indirect impacts of anthropogenic climate change may alter land cover and associated ecosystem function, affecting ecological goods and services. Considerable work has been done to identify long‐term global trends in vegetation greenness, which is associated with primary productivity, using remote sensing. Trend analysis of satellite observations is subject to error, and ecosystem change can be confused with interannual variability. However, the relative trends of land cover classes may hold clues about differential ecosystem response to environmental forcing. Our aim was to identify phenological variability and 10‐year trends for the major land cover classes in the Great Basin. This case study involved two steps: a regional, phenology‐based land cover classification and an identification of phenological variability and 10‐year trends stratified by land cover class. The analysis used a 10‐year time series of Advanced Very High Resolution Radiometer satellite data to assess regional scale land cover variability and identify change. The phenology‐based regional classification was more detailed and accurate than national or global products. Phenological variability over the 10‐year period was high, with substantial shifts in timing of start of season of up to 9 weeks. The mean long‐term trends of montane land cover classes were significantly different from valley land cover classes due to a poor response of montane shrubland and pinyon‐juniper woodland to the early 1990s drought. The differential response during the 1990s suggests that valley ecosystems may be more resilient and montane ecosystems more susceptible to prolonged drought. This type of regional‐scale land cover analysis is necessary to characterize current patterns of land cover phenology, distinguish between anthropogenically driven land cover change and interannual variability, and identify ecosystems potentially susceptible to regional and global change.     

A vegetation-based method to map climatic variation in the arctic–boreal transition area of Finnmark, north-easternmost Norway

Aim To develop a new method for bioclimate mapping where the vegetation layer is the main source of climate information. Location The study area includes four subareas, all situated on the Varangerhalvøya peninsula in Finnmark, north‐easternmost Norway (70–71° N). The four subareas were chosen to represent most of the climatic, topographic, geomorphologic and botanic diversity along the arctic–boreal gradient in the area. The four meteorological stations in the area show a climatic gradient with mean July temperature ranging from 10.1 to 12.3 °C. Methods The new vegetation‐based method is based on the fact that most plant species and plant communities both in the Arctic and adjacent areas have a distribution pattern limited by temperature to some extent. The vegetation is mapped using Landsat TM data and a contextual correction process in a geographic information system. The mapped vegetation units are defined as temperature indicators based on their total distribution patterns and the temperature indicator value of their high frequency and dominant species. The indicator value and degree of cover of all thermophilous vegetation units, within each 500 × 500 m study unit, are combined in a Vegetation‐based Index of Thermophily, VI_tm. This new vegetation‐based method is based on the same basic idea as a recently published floristic‐based method for calculating a Floristic‐based Index of Thermophily, FI_tm. The VI_tm values are tested by comparison with the FI_tm values, and temperature data collected in the field during two growing seasons, and the differences are interpreted ecologically. Results Twenty‐one of the mapped vegetation units were defined as thermophilous and categorized in five groups of temperature indicators. The VI_tm values showed a strong positive linear relationship with the temperatures measured during the years 2001 and 2002, with r² values of 0.79 and 0.85, respectively. The VI_tm values show a high linear relationship (r² = 0.76) with the 71 study units where the FI_tm values were calculated. As interpreted from the relationship with temperature measurements and FI_tm values, the vegetation‐based method seems to work at a broad range of ecological conditions, with very dry, acidic sites being the most important exception. The VI_tm values are related to growing degree‐days of a normal year, and the four subareas are mapped, showing a diversity of 13 bioclimatic classes. The birch forest line is estimated to occur at about 980 °C‐days. The results show climatic gradients with temperatures increasing from the cold coast towards the interior, from wind‐exposed convex hills towards wind‐protected valleys, and from mountain plateaux towards south‐facing lowlands. The north‐easternmost study site at the coast is positioned within the arctic shrub tundra zone. Main conclusions The vegetation‐based method shows a strong positive correlation both with measured temperatures and the floristic‐based method within a broad range of different ecological conditions. The vegetation‐based method has the potential for bioclimatic mapping of large areas in a cost‐effective way. The floristic‐based method has higher accuracy and is more flexible than the vegetation‐based method, and the two methods seem to complement each other.     

黔中喀斯特山地城市土地利用/覆被变化及其生态效应评价——以贵阳市花溪区为例

快速城市化发展对脆弱喀斯特山地城市生态环境造成严重威胁,系统监测评价城市土地利用/覆被格局变化及其生态效应,协调生态保护与城市发展的关系是新时期喀斯特山地城市生态文明示范城市建设的重要命题。以贵阳市花溪区为对象,以2013年和2018年Landsat ETM/OLI遥感影像为主要数据源,运用遥感和GIS技术,采用遥感生态指数（Remote Sensing Ecological Index,RSEI）模型,在系统分析研究区土地利用/覆被类型和生态环境质量时空动态变化的基础上,剖析土地利用/覆被变化的生态效应。结果表明：（1）2013-2018年花溪区土地利用/覆被格局发生明显变化,形成以林地、建设用地和耕地3种类型占优的格局态势,以耕地的大量减少（减少约15353.37 hm²）且90%转为建设用地或林地、灌木地为主要特征,并伴有局部林地退化（约2683.80 hm²）的现象;（2）5年间,花溪区生态环境质量呈下降趋势,RSEI从2013年的0.622下降到2018年的0.499,下降约20%,反映植被覆盖度和不透水建设用地的绿度指标和干度指标对花溪区生态环境质量的贡献最大;（3）土地利用/覆被与生态环境质量的分布和变化在空间上基本吻合;林地面积或林地与灌木地面积的增减对生态环境质量的变化具有显著影响,林地或林地与灌木地面积增加10%,可使生态质量好转面积增加约15%-20%,或减少生态质量恶化面积约4%;而林地的退化面积增加10%,可导致生态质量恶化面积增加约14%。研究可为喀斯特山地城市国土空间格局优化、城市生态环境改善、生态文明城市建设提供科学依据。     

不同土地覆被格局情景下多种生态系统服务的响应与权衡——以雅砻江二滩水利枢纽为例

在二滩水库集水区,按照当地生态政策发展以坡度为指标构建了10种未来土地覆被格局情景,研究生态系统减轻水库泥沙淤积、减轻水库面源污染、产水发电服务及价值对未来覆被格局的响应程度,并兼顾相关产业收益的变化,权衡各种情景格局的服务效益,优选利益相关方福祉提升幅度最大的情景格局。结果表明:1)现有土地覆被格局对减轻水库泥沙淤积功效的发挥不合理,而以退耕还林政策指导区域土地覆被格局变化,对集水区减轻河道和水库泥沙淤积具有较好功效。10种情景下,保沙价值的高低关系同入库泥沙量正好相反,遵循"随地表林地和草地面积比例的增加,保沙价值增加"的规律。随产沙总量的增加,高产沙强度像元呈现从水库周边逐渐向中上游蔓延的趋势。2)退耕还林政策单纯以坡度作为指标,指导土地覆被的转化,对集水区减轻水环境磷素非点源污染功效较差。除了全为未利用地覆被的情景10,余下9类情景模拟的入库磷素量及过滤磷素经济价值的高低关系完全一致,呈农田草地林地的规律。随入库磷素总量的增加,高强度磷素输出像元从雅砻江流域源区和大河湾区,逐渐蔓延扩散至集水区整个水网。3)随着集水区地表林地覆盖面积比例的减少,集水区生态系统总发电净利润值增加,且高净利润像元呈现从冕宁和喜德县的东部逐渐向整个集水区东南部乃至整个下游蔓延的趋势。4)从减轻对环境的负效应以及提升利益相关方福祉的角度,以坡度6°上下划分林地和耕地的情景5和以坡度15°和6°为阈值划分林草农的情景3为除极端的情景外,综合指标提升最高的两种情景。     

Spatial genetic structure and landscape connectivity in black bears: Investigating the significance of using different land cover datasets and classifications in landscape genetics analyses

Landscape genetic analyses allow detection of fine‐scale spatial genetic structure (SGS) and quantification of effects of landscape features on gene flow and connectivity. Typically, analyses require generation of resistance surfaces. These surfaces characteristically take the form of a grid with cells that are coded to represent the degree to which landscape or environmental features promote or inhibit animal movement. How accurately resistance surfaces predict association between the landscape and movement is determined in large part by (a) the landscape features used, (b) the resistance values assigned to features, and (c) how accurately resistance surfaces represent landscape permeability. Our objective was to evaluate the performance of resistance surfaces generated using two publicly available land cover datasets that varied in how accurately they represent the actual landscape. We genotyped 365 individuals from a large black bear population (Ursus americanus) in the Northern Lower Peninsula (NLP) of Michigan, USA at 12 microsatellite loci, and evaluated the relationship between gene flow and landscape features using two different land cover datasets. We investigated the relative importance of land cover classification and accuracy on landscape resistance model performance. We detected local spatial genetic structure in Michigan''s NLP black bears and found roads and land cover were significantly correlated with genetic distance. We observed similarities in model performance when different land cover datasets were used despite 21% dissimilarity in classification between the two land cover datasets. However, we did find the performance of land cover models to predict genetic distance was dependent on the way the land cover was defined. Models in which land cover was finely defined (i.e., eight land cover classes) outperformed models where land cover was defined more coarsely (i.e., habitat/non‐habitat or forest/non‐forest). Our results show that landscape genetic researchers should carefully consider how land cover classification changes inference in landscape genetic studies.     

Long‐term terrestrial carbon dynamics in the Midwestern United States during 1850–2015: Roles of land use and cover change and agricultural management

To meet the increasing food and biofuel demand, the Midwestern United States has become one of the most intensively human‐disturbed hotspots, characterized by widespread cropland expansion and various management practices. However, the role of human activities in the carbon (C) cycling across managed landscape remains far from certain. In this study, based on state‐ and national census, field experiments, and model simulation, we comprehensively examined long‐term carbon storage change in response to land use and cover change (LUCC) and agricultural management in the Midwest from 1850 to 2015. We also quantified estimation uncertainties related to key parameter values. Model estimation showed LUCC led to a reduction of 1.35 Pg (with a range of 1.3–1.4 Pg) in vegetation C pool of the Midwest, yet agricultural management barely affected vegetation C change. In comparison, LUCC reduced SOC by 4.5 Pg (3.1 to 6.2 Pg), while agricultural management practices increased SOC stock by 0.9 Pg. Moreover, we found 45% of the study area was characterized by continuously decreasing SOC caused by LUCC, and SOC in 13% and 31% of the area was fully and partially recovered, respectively, since 1850. Agricultural management was estimated to increase the area of full recovery and partial recovery by 8.5% and 1.1%. Our results imply that LUCC plays an essential role in regional C balance, and more importantly, sustainable land management can be beneficial for strengthening C sequestration of the agroecosystems in the Midwestern US, which may serve as an important contributor to C sinks in the US.     

基于区域城市化LUCC的人类生态过程定量研究——以上海市区典型城市化样带为例

借鉴流域研究的范例,根据上海市1987年、1995年和2003年3期Landsat5 TM遥感影像记录的土地利用／覆被数据和有关的社会经济统计数据,运用Rs和GIS技术在上海市中心西南城市边缘建立典型城市化样带。采用生态足迹法分析该样带内人类的生态供给与需求;提出一城市化区域人类生态过程的新研究方法,得出城市化过程中区域的人类生态质量指数与波动指数,并根据模型计算出的指数值,对样带区域总体及其中城乡区域在城市化中的人类生态状况进行了动态分析,以尝试对区域城市化中人类生态过程进行定量化研究。