北方农牧交错带东段土地水蚀荒漠化监测——以扎鲁特旗为例

以20世纪80年代末和2000年TM影像作为信息源,经合成、增强、几何纠正、镶嵌和人机交互判读并结合野外调查提取土地水蚀荒漠化信息,建立土地水蚀荒漠化空间数据库,结果表明:研究区水蚀荒漠化率为10.64%,以轻度侵蚀为主。水蚀荒漠化空间分异明显,在北部中山区形成三个集中分布区。研究期内土地水蚀荒漠化动态为整体扩展,局部逆转,程度加强;中度、强烈和严重水蚀面积增加,轻度的减少;发生逆转的主要为轻度水蚀土地。人类不合理的经济和社会活动与脆弱的自然条件耦合是研究区土地水蚀荒漠化发生并扩展的全部原因。因此要从控制人口增长,调整不合理的土地利用方式和强度,加强生态环境管理入手,高度重视农牧交错带东段退化土地的综合防治,为区域可持续发展提供良好的生态环境。     

Accuracy assessment of NLCD 2011 percent impervious cover for selected USA metropolitan areas

The emergence of high-resolution land cover data has created the opportunity to assess the accuracy of impervious cover (IC) provided by the National Land Cover Database (NLCD). We assessed the accuracy of the 900 m² NLCD2011 %IC for 18 metropolitan areas throughout the conterminous United States using reference data from 1 m² land cover data developed as part of the United States Environmental Protection Agency’s EnviroAtlas project. Agreement was assessed from two perspectives: 1) sensitivity to the size of the assessment unit used for the comparison, and 2) utility of NLCD %IC to serve as a proxy for high-resolution IC. The former perspective was considered because statistical relationships can be sensitive to assessment unit size and shape, and the latter perspective was considered because high resolution (reference) %IC data are not available nationwide. The utility of NLCD %IC as a proxy for the high resolution data was assessed for seven lattice (square) cell sizes ranging from 1 ha to 200 ha using four EnviroAtlas IC indicators: 1) %IC per 100 ha (1 km²); 2) %IC by Census block group; 3) %IC within a 15 m (radius) of the riparian zone, and; 4) %IC within a 50 m (radius) of the riparian zone. Agreement was quantified as per assessment unit deviation (NLCD %IC – reference %IC) and summarized as Mean Absolute Deviation (MAD) and Mean Deviation (MD) both within and across the 18 metropolitan areas. Ordinary least squares (OLS) regression (y = reference %IC and x = NLCD %IC) was also used to evaluate the quality of the NLCD %IC data. MAD was ≤ 5% for six of the seven lattice cell sizes. MAD was also ≤ 5% for Census block groups > 100 ha and for both riparian units. These results suggest that uncertainty attributable to the measurement of %IC was no greater than the uncertainty related to the effect of IC on aquatic resources that have been derived from studies of aquatic condition (e.g., benthic fauna) over a range of %IC. Overall, agreement was variable from one metropolitan area to the next. Agreement improved as assessment unit size increased and declined as the level of urbanization (NLCD %IC) increased. NLCD %IC tended to underestimate reference %IC overall, but NLCD %IC was sometimes greater than reference %IC in urbanized settings.     

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.     

Integration of remote sensing,county-level census,and machine learning for century-long regional cropland distribution data reconstruction

The Lower Mississippi Alluvial Valley (LMAV) was home to about ten million hectare bottomland hardwood (BLH) forests in the Southern U.S. It experienced over 80 % area loss of the BLH forests in the past centuries and large-scale afforestation in recent decades. Due to the lack of a high-resolution cropland dataset, impacts of land use change (LUC) on the LMAV ecosystem services have not been fully understood. In this study, we developed a novel framework by integrating the machine learning algorithm, county-level agricultural census, and satellite-based cropland products to reconstruct the LMAV cropland distribution during 1850–2018 at a 30-m resolution. Results showed that the LMAV cropland area increased from 0.78 × 10⁴ km² in 1850 to 6.64 × 10⁴ km² in 1980 and then decreased to 6.16 × 10⁴ km² in 2018. Cropland expansion rate was the largest in the 1960s (749 km² yr⁻¹) but decreased rapidly thereafter, whereas cropland abandonment rate increased substantially in recent decades with the largest rate of 514 km² yr⁻¹ in the 2010s. Our dataset has three notable features: (1) the depiction of fine spatial details, (2) the integration of the county-level census, and (3) the inclusion of a machine-learning algorithm trained by satellite-based land cover product. Most importantly, our dataset well captured the continuous increasing trend in cropland area from 1930–1960, which was misrepresented by other cropland datasets reconstructed from the state-level census. Our dataset would be important to accurately evaluate the impacts of historical deforestation and recent afforestation efforts on regional ecosystem services, attribute the observed hydrological changes to anthropogenic and natural driving factors, and investigate how the socioeconomic factors control regional LUC pattern. Our framework and dataset are crucial to developing managerial and policy strategies for conserving natural resources and enhancing ecosystem services in the LMAV.     

基于3S的荣成湾沿岸土地覆被变化及驱动力研究

运用3S技术对最近40年来荣成湾沿岸土地覆被动态变化及驱动力进行研究.结果表明,该区海岸湿地土地覆被总面积和陆域土地覆被总面积变化不大,但其各自类型结构和分布均发生了显著调整,而且二者变化在时间上不同步.从面积上讲,前者以1981年为界,渴湖和沙地先减少后波动增加,滩涂和沼泽地先增加后波动减少;养殖池从无到有并迅速增加...     

粒度特征揭示的中全新世以来毛乌素沙地演化过程

通过对毛乌素沙地现代固定沙丘、半固定沙丘、半流动沙丘和流动沙丘的粒度特征分析,发现研究区沙地变化敏感粒度是>245 μm的沉积物粒度组分,尝试性地将其用于地质时期沙地演化研究。结合区域典型风成砂-古土壤剖面的沉积相特征,探讨了中晚全新世以来毛乌素沙地的时间演化过程及其特征。结果表明：中全新世以来毛乌素沙地存在多次活化扩张和固定萎缩事件,除7.5 ka BP前为流动沙地占优势以外,其余阶段多在半流动沙地、半固定沙地和固定沙地之间频繁转换,即使在全新世适宜期古土壤大规模发育期间仍存在局地短时间的沙地活化事件。     

基于STIRPAT模型的内蒙古沙漠化地区环境压力分析

实现经济与生态环境的协调发展,已经成为当前治理沙漠化的巨大挑战。如何定量分析人类活动和自然条件对环境的影响,是当前环境经济学界研究的热点问题。以内蒙古自治区71个沙漠化县为研究对象,以1986-2010年为研究区间做面板空间计量实证分析。选取了人口、富裕度、技术水平、风速和降水作为环境基础指标构建STIRPAT模型,从环境压力内在驱动角度定量分析不同的自然因素和人为因素对沙漠化地区环境的综合影响。结果表明:在观测数据范围内,富裕度、技术水平、风速和降水是内蒙古自治区环境压力的主要驱动因素;富裕度每增加1%,环境压力显著增加0.096%;技术水平每变化1%,将引起环境压力相应发生0.084%的变化;风速和降水每变动1%分别使得沙漠地区环境压力显著地增加0.640%和减少0.216%。     

基于时间序列遥感数据的嵯岗国家沙化土地封禁保护成效评估

2013年中国启动实施了内蒙古自治区新巴尔虎左旗嵯岗国家沙化土地封禁保护区试点。本研究利用2001-2017年生长季NDVI数据分析了嵯岗封禁保护区及周边区域植被长势时空演变特征,分析了多年的生长季降水量和历年旱情,之后利用植被降水利用效率和NDVI残差趋势分析对保护成效进行了评估。结果表明:(1)封禁项目实施之前,封禁区内外植被长势变化趋势基本一致,而在项目实施后的2016、2017年,封禁区内NDVI距平明显高于封禁区外;(2)降水为影响该区域植被长势的主导气候因素,在封禁实施后的2015-2017年该区域连续3 a干旱,极大地限制了区域植被生长;(3)封禁区内植被降水利用效率和NDVI残差均呈现明显的增加趋势,而封禁区外变化不明显,说明封禁提升了植被的自我修复能力;(4)嵯岗封禁保护区由3个地块组成,其中嵯岗林场封禁效果比牧场八队和牧场十一队明显。国家沙化土地封禁保护措施有效促进了植被自我修复,提高了沙漠生态系统应对气候变化的能力。     

放牧对浑善达克沙地丘间低地植被群落及土壤的影响

为探究放牧对丘间低地植被群落及土壤的影响,分析了浑善达克沙地丘间低地在重度和轻度放牧下植被的地上生物量、物种重要值、物种丰富度、Shannon-Wiener多样性指数、Pielou均匀度指数、Simpson生态优势度指数及0~10 cm土壤中黏粒、粉粒、砂粒、有机碳和全氮含量。结果表明：（1）植被地上生物量、土壤黏粒、粉粒、砂粒、土壤有机碳和全氮含量在不同放牧程度下存在极显著差异（P<0.01）,物种丰富度、Shannon-Wiener多样性指数和Pielou均匀度指数在不同放牧程度下差异不显著（P>0.05）,Simpson生态优势度指数在不同放牧程度下差异显著（P<0.05）。（2）放牧区植被以禾本科为主,禾本科植被地上生物量占整个植被群落的54.88%~57.76%;重度放牧区植被地上生物量为63.59 g·m^-2,比轻度放牧区低26.61%。（3）羊草（Leymus chinensis）和狗尾草（Setaria viridis）是放牧区的优势种,其重要值依次为14.16%（重度放牧区）和19.10%（轻度放牧区）、13.40%（重度放牧区）和15.42%（轻度放牧区）,重度放牧下雾滨藜（Bassia dasyphylla）、克氏针茅（Stipa krylovii）和猪毛蒿（Artemisia scoparia）的重要值较显著高于轻度放牧区,而羊草则低于轻度放牧区。（4）重度放牧加剧了土壤沙漠化,使砂粒含量增加,草地生产力下降,土壤有机碳和全氮含量降低。     

基于专利的石漠化治理技术分析

石漠化是发生面积仅次于沙漠化的荒漠化类型,已成为中国西南喀斯特山区可持续发展的主要障碍,其治理与恢复是复杂的系统工程。从治理技术层面,以1 159组简单专利族为数据基础,揭示国内石漠化治理技术类别分布和主要领域。在对专利进行简要计量分析以提供概貌认识的基础上,通过阅读专利摘要内容,采用自下而上的聚类方法将当前专利划分为石漠化判断、治理、产业经济3个大类,监测与评价、科研仪器、生物治理、工程治理、化学治理、综合治理、综合产业、农业和工业9个中类,更细分至雨水搜集净化等42个小类。根据专利数量认定的四大主要技术领域为：工程治理（8.63%）、水分利用（12.51%）、栽培种植（27.52%）、植物资源加工（9.67%）,占总量的58.33%,专利领域分布相对集中。对主要技术领域采用内容分析法,提取工程治理具体措施、集雨工具、植物、加工制品等的信息并对其进行了详细阐述,指出目前专利技术中的问题：石漠化地区特有的植物资源众多,但目前加工层次偏低,应在拓展加工类型、延长加工产业链条、发掘植物药用和日用价值方面加大研发力度;防止土壤漏失技术、设施农业技术和生命基因技术亟待发展。分析可为全面认识国内石漠化治理技术提供专利视角的参照。