1998-2013年新疆艾比湖湖面时空动态变化及其驱动机制

采用1998年9月,2002年9月,2007年9月,2011年9月以及2013年9月多期Landsat数据,利用归一化水体指数模型(NDWI)和修正归一化水体指数模型(MNDWI)提取新疆艾比湖水域面积,研究近年来艾比湖湖面的动态变化。以最大似然分类结果作为标准,验证了用NDWI和MNDWI模型提取面积的精度,得出NDWI模型所提取的湖泊面积更符合实际情况,湖泊总面积从1998年的519.26km²减少到2013年的422.73km²,缩小了18.59%,表明目前艾比湖正在退化,从而促使生态环境受到影响。对5期影像中的艾比湖湖面进行了边界的提取和叠加,利用湖泊面积动态模型研究艾比湖湖面积的动态变化,在此基础上分析了影响艾比湖湖面积变化的驱动机制,近年来随着温度的逐渐升高,降水量呈下降的趋势,加上大量的蒸发作用、径流量变化及沙尘日数等综合作用的结果,导致了艾比湖面积的缩小。多年来艾比湖流域内随着人口数量的增加、耕地面积的不断扩张、牲畜的大量增长,导致需水量逐渐增大,因此也是导致湖面面积减少的主要原因之一。开展艾比湖湖面时空动态变化及其驱动机制研究,对于干旱区湖泊来说具有重要的理论和实际意义。     

2000—2010年武汉市中心城区湖泊景观变化

武汉雅称"百湖之市",湖泊是武汉市的重要名片。以2000、2005和2010年三期Landsat TM数据为数据源,采用归一化差异水体指数(NDWI)提取湖泊水体信息,建立了各个时期武汉市中心城区湖泊矢量图层,计算了湖泊的面积萎缩率、斑块分维数和破碎度等景观指数,对2000—2010年武汉市中心城区湖泊变化特征进行了分析。研究表明,武汉市各个湖泊均有不同程度的萎缩,湖泊面积萎缩率大小和其所隶属的环线及政策因素有较大的关系;各湖泊斑块分维数在1-1.3之间,并越来越接近于1,表明在人类活动持续影响下,其形状变得越来越规则;同时斑块数目增加,湖泊在面积萎缩的同时,也变得越来越破碎,研究显示主要是道路修建所致导致的湖泊分割,湖泊被分割后,自净能力下降,会导致水体污染而最终被填埋。总之,10年武汉市湖泊景观环境朝着不良方向发展,应该制定更严格的政策进行水域管理。     

Disrupted montane forest recovery hinders biodiversity conservation in the tropical Andes

Aim

Andean montane forests are biodiversity hotspots and large carbon stores and they provide numerous ecosystem services. Following land abandonment after centuries of forest clearing for agriculture in the Andes, there is an opportunity for forest recovery. Field-based studies show that forests do not always recover. However, large-scale and long-term knowledge of recovery dynamics of Andean forests remains scarce. This paper analyses tropical montane forest recovery trajectories over a 15-year time frame at the landscape and tropical Andean scale to inform restoration planning.

Methods

We first detect “potential recovery” as areas that have experienced a forest transition between 2000 and 2005. Then, we use Landsat time series analysis of the normalized difference water index (NDWI) to classify four “realized recovery” trajectories (“ongoing”, “arrested”, “disrupted” and “no recovery”) based on a sequential pattern of 5-yearly Z-score anomalies for 2005–2020. We compare these results against an analysis of change in tree cover to validate against other datasets.

Results

Across the tropical Andes, we detected a potential recovery area of 274 km² over the period. Despite increases in tree cover, most areas of the Andes remained in early successional states (10–25% tree cover), and NDWI levelled out after 5–10 years. Of all potential forest recovery areas, 22% showed “ongoing recovery”, 61% showed either “disrupted” or “arrested recovery”, and 17% showed “no recovery”. Our method captured forest recovery dynamics in a Peruvian arrested succession context and in landscape-scale tree-planting efforts in Ecuador.

Main conclusions

Forest recovery across the Andes is mostly disrupted, arrested or unsuccessful, with consequences for biodiversity recovery and provision of ecosystem services. Low-recovery areas identified in this study might be good candidates for active restoration interventions in this UN Decade on Restoration. Future studies could determine restoration strategies and priorities and suggest management strategies at a local planning scale across key regions in the biodiversity hotspot.     

Bud-burst modelling in Siberia and its impact on quantifying the carbon budget

Vegetation phenology is affected by climate change and in turn feeds back on climate by affecting the annual carbon uptake by vegetation. To quantify the impact of phenology on terrestrial carbon fluxes, we calibrate a bud‐burst model and embed it in the Sheffield dynamic global vegetation model (SDGVM) in order to perform carbon budget calculations. Bud‐burst dates derived from the VEGETATION sensor onboard the SPOT‐4 satellite are used to calibrate a range of bud‐burst models. This dataset has been recently developed using a new methodology based on the normalized difference water index, which is able to distinguish snowmelt from the onset of vegetation activity after winter. After calibration, a simple spring warming model was found to perform as well as more complex models accounting for a chilling requirement, and hence it was used for the carbon flux calculations. The root mean square difference (RMSD) between the calibrated model and the VEGETATION dataset was 6.5 days, and was 6.9 days between the calibrated model and independent ground observations of bud‐burst available at nine locations over Siberia. The effects of bud‐burst model uncertainties on the carbon budget were evaluated using the SDGVM. The 6.5 days RMSD in the bud‐burst date (a 6% variation in the growing season length), treated as a random noise, translates into about 41 g cm^?2 yr^?1 in net primary production (NPP), which corresponds to 8% of the mean NPP. This is a moderate impact and suggests the calibrated model is accurate enough for carbon budget calculations. In addition to random differences between the calibrated model and VEGETATION data, systematic errors between the calibrated bud‐burst model and true ground behaviour may occur, because of bias in the temperature dataset or because the bud‐burst detected by VEGETATION is because of some other phenological indicator. A systematic error of 1 day in bud‐burst translates into a 10 g cm^?2 yr^?1 error in NPP (about 2%). Based on the limited available ground data, any systematic error because of the use of VEGETATION data should not lead to significant errors in the calculated carbon flux. In contrast, widely used methods based on the normalized difference vegetation index from the advanced very high resolution radiometer satellite are likely to confuse snowmelt and vegetation greening, leading to errors of up to 15 days in bud‐burst date, with consequent large errors in carbon flux calculations.