Using historical spy satellite photographs and recent remote sensing data to identify high-conservation-value forests

High-conservation-value forests (HCVFs) are critically important for biodiversity and ecosystem service provisioning, but they face many threats. Where systematic HCVF inventories are missing, such as in parts of Eastern Europe, these forests remain largely unacknowledged and therefore often unprotected. We devised a novel, transferable approach for detecting HCVFs based on integrating historical spy satellite images, contemporary remote sensing data (Landsat), and information on current potential anthropogenic pressures (e.g., road infrastructure, population density, demand for fire wood, terrain). We applied the method to the Romanian Carpathians, for which we mapped forest continuity (1955–2019), canopy structural complexity, and anthropogenic pressures. We identified 738,000 ha of HCVF. More than half of this area was identified as susceptible to current anthropogenic pressures and lacked formal protection. By providing a framework for broad-scale HCVF monitoring, our approach facilitates integration of HCVF into forest conservation and management. This is urgently needed to achieve the goals of the European Union's Biodiversity Strategy to maintain valuable forest ecosystems.     

Molecular modeling in environmental science∗

Quantum mechanic computer models are often used in chemistry to predict properties of molecules and to simulate reaction pathways. Such models calculate bond lengths, bond angles, dihedral angles, the energy of the molecular‐orbitals (MO's), the dipole moment, the ionisation potential, and the heat of formation. These results lead to detailed information on electronic structures like bond orders, electronic charges and levels of frontier orbitals (HOMO, LUMO).

It is possible to calculate properties of ground state molecules as well as ionic and radicalic structures, or reaction intermediates and other compounds with very short lifetimes.

From the exact knowledge of electronic structure, frontier orbitals, and reaction intermediates it is possible to predict stability of chemicals in the environment but also of fictive chemicals, which are not yet synthesized. Some authors have shown that there are correlations between electronic structure and toxicity.

The most interesting models for environmental chemistry are semiempirical models, such as MINDO, MNDO or MOPAC. Theses programs are able to handle molecules with 30 and more heavy atoms (all elements without hydrogen), and it is possible to install them on main frame computers (CPU‐time several minutes to hours) and on personal computers, with coprocessors (CPU‐time several hours to a few days).

Normally the molecule is read in from a Z‐matrix (a matrix of polar coordinates of atoms connected in the molecule) and with symmetry data. Furthermore, programs are available which create a Z‐matrix from the molecular structure plotted on the screen with a mouse.     

利用多信息源提高半干旱地区TM影像的森林类型制图精度:以北京西部山区为例

以地处半干旱地区的北京西部山区为例,利用研究区森林类型的季相特征、已有的少部分林相图、Google Earth免费影像数据等信息选择不同坡向的相同森林类型做训练样本,通过加入其他辅助数据(海拔和坡向数据),来提高Landsat TM影像的森林类型分类精度,同时对比了基于像元和面向对象方法提取森林类型的效果。结果表明:1)就半干旱山区的森林类型划分来说,TM影像的TM4、TM5、TM4-TM2及辅助数据DEM和坡向可作为TM影像森林类型划分的最佳数据源。2)单独加入海拔信息,阔叶林的提取精度提高23%,针叶林和混交林的分类精度只提高了4%~5%;单独加入坡向信息,阔叶林的提取精度只提高21%,但是针叶林和混交林的分类精度则分别提高了13%、18%,显著优于单独加入海拔信息的效果。同时加入海拔信息和坡向信息,至少可以准确区分出约70%以上的针叶林、阔叶林和混交林。3)就本研究区而言,坡向比海拔更有效地辅助提高森林分类精度。4)就混淆矩阵数据而言,面向对象的分类方法比基于像元分类结果总体精度低3%,Kappa系数低4%,但面向对象的分类结果更加符合研究区实际情况。该研究对中分辨率影像应用于半干旱山区森林类型划分具有一定的借鉴意义。     

Visual interpretation of satellite imagery for monitoring floods in Bangladesh

This article presents the methods and results of visual interpretation of NOAA AVHRR imagery for mapping flood areas in Bangladesh. Color composites of channels 1 and 2 for 18 August 1987 and 10, 15, and 24 September 1988 were interpreted visually for delineating flood boundaries. On such imagery flood areas appear in dark tones and are separated from land (light tones) owing to the absorption of near infrared by water and its reflectance by land and non-waterbodies. Visual interpretation was aided by the use of ground information, such as physiographic and river maps, previous flood maps, newspaper reports, and other published and unpublished documents on the 1987 and 1988 floods. Interpreted flood areas on selected images ranged from 31% to 43% of the total area of Bangladesh. Visual interpretation overestimated flood areas by 5%–10%, compared to the digitally analyzed data. The main advantage of visual analysis lies in the cost effectiveness of AVHRR photographic products, which make them more accessible than the digital image analysis of computer-compatible tapes.     

Tracking land cover change along the western edge of the U.S. Corn Belt from 1984 through 2016 using satellite sensor data: observed trends and contributing factors

The Cropland Data Layers (CDL) are high-resolution geo-referenced data products made available by the U.S. Department of Agriculture. However, the CDL lacks in its ability to be employed as a tool to identify the impact of the gradually evolving drivers of land use change, e.g., climate change, due to its limited historical depth. We implement a robust, phenology-based satellite image classification algorithm to identify historical cropland allocation in eastern South Dakota and North Dakota predating the initial CDL by 13 and 22 years, respectively. Five major land covers, i.e., corn, soybeans, wheat, alfalfa and grass (including native grass, hay and pasture) are identified using archived Landsat-5 surface reflectance data, while achieving CDL-like accuracy. The long-term rate of grassland loss during 1985–2011 is found to be significantly lower (26,781 hectares or 1.5% annually) relative to the near-term rate of grassland loss during 2006-’11 (84,545 hectares or 5.2% annually). We find similar discrepancy in regional corn expansion rates. Our value-added raster data provide opportunities for improved identification of land use drivers, whereas relying solely on the CDL’s restricted historical extent may lead to biased land use change estimates and misguide policy.     

USE OF HEMISPHERIC IMAGERY FOR ESTIMATING STREAM SOLAR EXPOSURE1

ABSTRACT: Solar exposure profoundly affects stream processes and species composition. Despite this, prominent stream monitoring protocols focus on canopy closure (obstruction of the sky as a whole) rather than on measures of solar exposure or shading. We identify a candidate set of solar exposure metrics that can be derived from hemispheric images. These metrics enable a more mechanistic evaluation of solar exposure than can be achieved with canopy closure metrics. Data collected from 31 stream reaches in eastern Oregon enable us to quantify and compare metrics of solar exposure from hemispheric images and a metric of canopy closure with a concave densiometer. Repeatability of hemispheric metrics is generally as good as or better than the densiometer closure metric, and variation in the analysis of hemispheric images attributable to differences between analysts is negligibly small. Metrics from the hemispheric images and the densiometer are typically strongly correlated, at the scale of an individual observation and for 150 m stream reaches, but not always in a linear fashion. We quantify the character of the uncertainty in the relationship between the densiometer and the hemispheric metrics. Hemispheric imagery produces repeatable metrics representing an important ecological attribute; thus those researching the effects of solar exposure on stream ecosystems should consider the use of hemispheric imagery.     

乌梁素海湖滨带盐碱土碱化参数与特征分析

为了研究乌梁素海湖滨带,不同植被的土壤盐碱特征,测定了乌梁素海湖滨带盐碱土的碱化度(ESP)、钠吸附比(SAR)、总碱度、pH值和含盐量等指标,分析了该类型的土壤的简化特征及其垂直分布态势.结果表明,乌梁素海湖滨带土壤可溶件例子组成以Na+、SO2-4-和Cl-为主,各土层中Na+占阳离子总量比例平均多在63.6%以上...     

Classifying environmentally significant urban land uses with satellite imagery

We investigated Bayesian networks to classify urban land use from satellite imagery. Landsat Enhanced Thematic Mapper Plus (ETM(+)) images were used for the classification in two study areas: (1) Marina del Rey and its vicinity in the Santa Monica Bay Watershed, CA and (2) drainage basins adjacent to the Sweetwater Reservoir in San Diego, CA. Bayesian networks provided 80-95% classification accuracy for urban land use using four different classification systems. The classifications were robust with small training data sets with normal and reduced radiometric resolution. The networks needed only 5% of the total data (i.e., 1500 pixels) for sample size and only 5- or 6-bit information for accurate classification. The network explicitly showed the relationship among variables from its structure and was also capable of utilizing information from non-spectral data. The classification can be used to provide timely and inexpensive land use information over large areas for environmental purposes such as estimating stormwater pollutant loads.     

Image Themes and Frames in US Print News Stories about Climate Change

Research on frames in climate change (CC) news coverage has advanced substantially over the past decade, but the emerging understanding of the framing role of visual imagery that often accompanies news texts remains fragmented. We report on a set of image frames identified through content analysis of 350 images associated with 200 news articles from 11 US newspaper and magazine sources from 1969 through late 2009. We reliably identified and quantified the occurrence of 118 image themes. We then hierarchically clustered the themes based on their co-occurrence in images to identify an integrated framework of 42 image frames. We highlight frames associated with particular types of images (e.g., photographs and maps) or geographic regions. From among the full set of frames, we identify 15 that commonly appear in US CC news imagery and discuss the ways in which image frames make salient (or render invisible) particular categories of people, geographic regions, aspects of science, and spheres of activity.