Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.     

Update and spatial extension of strategic forest inventories using time series remote sensing and modeling

Up-to-date forest inventory information relating the characteristics of managed and natural forests is fundamental to sustainable forest management and required to inform conservation of biodiversity and assess climate change impacts and mitigation opportunities. Strategic forest inventories are difficult to compile over large areas and are often quickly outdated or spatially incomplete as a function of their long production cycle. As a consequence, automated approaches supported by remotely sensed data are increasingly sought to provide exhaustive spatial coverage for a set of core attributes in a timely fashion. The objective of this study was to demonstrate the integration of current remotely-sensed data products and pre-existing jurisdictional inventory data to map four forest attributes of interest (stand age, dominant species, site index, and stem density) for a 55 Mha study region in British Columbia, Canada. First, via image segmentation, spectrally homogenous objects were derived from Landsat surface-reflectance pixel composites. Second, a suite of Landsat-based predictors (e.g., spectral indices, disturbance history, and forest structure) and ancillary variables (e.g., geographic, topographic, and climatic) were derived for these units and used to develop predictive models of target attributes. For the often difficult classification of dominant species, two modelling approaches were compared: (a) a global Random Forests model calibrated with training samples collected over the entire study area, and (b) an ensemble of local models, each calibrated with spatially constrained local samples. Accuracy assessment based upon independent validation samples revealed that the ensemble of local models was more accurate and efficient for species classification, achieving an overall accuracy of 72% for the species which dominate 80% of the forested areas in the province. Results indicated that site index had the highest agreement between predicted and reference (R² = 0.74, %RMSE = 23.1%), followed by stand age (R² = 0.62, %RMSE = 35.6%), and stem density (R² = 0.33, %RMSE = 65.2%). Inventory attributes mapped at the image-derived unit level captured much finer details than traditional polygon-based inventory, yet can be readily reassembled into these larger units for strategic forest planning purposes. Based upon this work, we conclude that in a multi-source forest monitoring program, spatially localized and detailed characterizations enabled by time series of Landsat observations in conjunction with ancillary data can be used to support strategic inventory activities over large areas.     

Satellite image analysis using crowdsourcing data for collaborative mapping: current and opportunities

ABSTRACT

Researchers are continually finding new applications of satellite images because of the growing number of high-resolution images with wide spatial coverage. However, the cost of these images is sometimes high, and their temporal resolution is relatively coarse. Crowdsourcing is an increasingly common source of data that takes advantage of local stakeholder knowledge and that provides a higher frequency of data. The complementarity of these two data sources suggests there is great potential for mutually beneficial integration. Unfortunately, there are still important gaps in crowdsourced satellite image analysis by means of crowdsourcing in areas such as land cover classification and emergency management. In this paper, we summarize recent efforts, and discuss the challenges and prospects of satellite image analysis for geospatial applications using crowdsourcing. Crowdsourcing can be used to improve satellite image analysis and satellite images can be used to organize crowdsourced efforts for collaborative mapping.     

南海北部底层沉积物捕获器中海雪和生物粪粒组成及来源探究

Sediment traps are an important tool for studying the source, composition and sedimentation processes of sinking particulate matter in the ocean. An in situ observational mooring(TJ-A-1) is located in the northern South China Sea(20.05°N, 117.42°E) at a water depth of 2 100 m and equipped with two sediment traps deployed at 500 m and 1 950 m. Samples were collected at 18-day intervals, and 20 samples were obtained at both depths from May 2014 to May 2015. Large amounts of fecal matter and marine snow were collected in the lower trap. The fluxes of marine snow and fecal pellets exhibited a fluctuating decrease between May 2014 and early August 2014 and then stabilized at a relatively low level. Scanning electron microscopy observations revealed that the main components of the marine snow and fecal pellets were diatoms, coccolithophores, radiolarians, and other debris, all of which are planktons mostly produced in photic zone. Used in conjunction with the particle collection range estimates from the lower trap and data on ocean surface chlorophyll, these marine snow and fecal pellets were related to the lateral transport of deep water and not vertical migrations from overlying water column. Moreover, the source area might be southwest of Taiwan.     

面向遥感大数据的地学知识图谱构想

由于地球表面的时空异质性与复杂性,传统从遥感影像具有的信息特征出发,构建智能解译算法解决遥感地学认知的思路在应对面向全球的海量遥感大数据分析时,其精度和地学实用性已触及瓶颈.为此,本文从地学知识为核心的角度出发,结合当前知识图谱理论的发展,提出一种新的面向遥感大数据分析的地学思维构想——地学知识图谱.本构想将地学知识的...     

1961—2017年新疆积雪期时空变化特征及其与气象因子的关系

利用新疆89个地面站逐日积雪深度观测资料,研究探讨了1961—2017年新疆区域积雪期、积雪初日、积雪终日的时空变化规律,分析了北疆和天山山区积雪期的年代际和周期变化特征及其与气温、降水的关系.结果表明:新疆各地积雪期、积雪初日和终日存在明显的差异,积雪期以天山为界北多南少;从空间分布看,天山山区和新疆北部阿勒泰、塔城...     

四川盆地干旱灾害统计特征

利用四川盆地1980—2009年17市103个县(市)实测逐日降水资料,按照四川省气象局制定的四川盆地的干旱地方标准DB51/T581—2007,对四川盆地近30年干旱灾害进行统计分析。结果表明:四川盆地夏旱出现的频率最高,夏旱的高发区集中在盆地西北部的成都、德阳和绵阳,伏旱的发生频率最低,强度最强。春、夏、伏旱的空间分布高发区依次从盆地西北部向东南转移。干旱发生的频率整体呈增长趋势,且严重干旱发生频次增长明显,与20世纪80年代相比90年代增幅达到110.3%,21世纪00年代在90年代的基础上又递增20.0%,21世纪00年代发生的严重干旱频次为80年代的2.5倍。     

2001-2015年中国植被覆盖人为影响的时空格局

基于MODIS-NDVI和气温、降水数据,使用基于变异系数的人为影响模型定量计算了2001-2015年中国植被覆盖人为影响,辅以趋势分析、Hurst指数等方法探讨了中国植被覆盖人为影响的时空变化特征及未来演变趋势。研究发现：① 2001-2015年,中国植被覆盖人为影响由南向北空间分异愈发明显,年均值为-0.0102,植被覆盖在人类活动影响下轻微减少,负影响面积占51.59%,略大于正影响面积。② 中国植被覆盖人为影响年际变化特征明显,整体呈负影响波动减少趋势,降速为0.5%/10a,其中正影响、负影响均呈增大趋势,正影响增速（0.3%/10a）远大于负影响（0.02%/10a）。③ 2001-2015年间,中国植被覆盖人为正影响重心向东北方向移动,负影响重心向西南方向移动,东北部植被覆盖在人为影响下不断改善,西南部人类活动对植被破坏程度不断增大。④ 中国植被覆盖人为影响主要呈负影响减少和正影响增大趋势,面积占比分别为28.14%和25.21%,生态环境趋于改善。⑤ Hurst指数分析表明,中国植被覆盖人为影响未来演变趋势的反向特征强于正向特征,主要呈人为负影响先减少后增大趋势,面积占比15.59%。     

1992-2015年中亚五国土地覆盖与蒸散发变化

1991年苏联解体,中亚五国独立使得土地覆盖与蒸散发格局发生深刻变化。以中亚五国为研究区,采用欧空局气候变化项目（CCI）土地覆盖和全球陆地数据同化系统（GLDAS）蒸散发数据,分析1992-2015年土地覆盖与蒸散发时空变化特征,进一步研究耕地蒸散耗水特征。结果表明：① 中亚五国土地覆盖变化具有阶段性特征,耕地扩张引起土地覆盖格局变化。1992-2003年耕地快速增加（1.1万km ²/a）,林地和草地大幅减少。2003-2015年耕地增速趋缓（0.3万km ²/a）,林地和草地有一定恢复,裸地和水体持续减少,城镇用地持续增长。耕地共增加12.3万km ²,林地和草地分别减少4.0万km ²和2.3万km ²,且集中于哈萨克斯坦中北部。裸地减少3.5万km ²,集中于哈萨克斯坦西南部,水体减少3.1万km ²,集中在咸海湖泊。乌兹别克斯坦耕地减少、裸地增加,吉尔吉斯斯坦、塔吉克斯坦和土库曼斯坦土地覆盖变化幅度较小;② 中亚五国蒸散发变化与土地覆盖格局基本一致。蒸散发总体呈增加态势（6 mm/a）,1992-2003年快速增加（11.3 mm/a）,2003-2015年缓慢上升（2.4 mm/a）。中亚五国年蒸散发达到276.8 mm,东南部的吉尔吉斯斯坦（347.3 mm）和塔吉克斯坦（302.9 mm）最高,中北部的哈萨克斯坦（297.9 mm）次之,西南部的乌兹别克斯坦（211.0 mm）和土库曼斯坦（150.0 mm）最低;③ 中亚五国蒸散耗水结构受耕地面积大小的影响。中亚五国耕地蒸散耗水的贡献由24.7%增至27.9%,土库曼斯坦耕地蒸散耗水仅占本国的11%,其他国家均超过25%。草地、林地和裸地的蒸散耗水贡献降低,但哈萨克斯坦、吉尔吉斯斯坦和塔吉克斯坦仍以草地和林地蒸散耗水为主（≥ 50%）,土库曼斯坦（61.3%）和乌兹别克斯坦（46.4%）的裸地蒸散耗水占绝对优势。本文明确了中亚五国土地覆盖连续动态变化过程,细化各国土地覆盖与蒸散发特征及差异,增强对土地覆盖与蒸散发现状的认识,可为水土资源管理和生态环境保护提供数据参考。