Updating land cover automatically based on change detection using satellite images: case study of national forests in Southern California

Observing dynamic change patterns and higher-order complexities from remotely sensed images is warranted, but the main challenges include image inconsistency, plant phenological differences, weather variations, and difficulties of incorporating natural conditions into automatic image processing. In this study, we proposed a new algorithm and demonstrated it by producing 2002–2008 and 2010 land-cover maps in heterogeneous Southern California based on an existing 2009 land-cover map. The new algorithm improves the baseline land-cover map quality by discarding potential bad land-cover pixels and dividing each land-cover type into several subclasses. Time series Landsat images were used to detect changed and unchanged areas between baseline year and target year t. Subsequently, for each individual year t, each pixel that was identified as unchanged inherited the baseline classification. Otherwise, each pixel in the changed areas was classified by a similar surrogate majority classifier. The demonstration results in Southern California showed that the land-cover temporal pattern captured the observed successional stages of the ecosystem very well. The accuracy assessment had an overall classification accuracies ranging from 81% to 86% and overall kappa coefficients ranging from 0.79 to 0.83.     

基于ZY-102C星数据的遥感地质解译——以塔吉克斯坦帕米尔地区为例

为了对境外地质矿产勘查开发提供遥感信息支持,在综合前人研究成果及野外验证基础上,对塔吉克斯坦帕米尔地区的地质和构造特征进行深入分析和研究;根据不同地质体的特征影像,对照"资源一号"(ZY-1)02C星遥感图像中所显示的地貌景观、色调及纹形等,建立了塔吉克斯坦南部地区主要沉积岩、变质岩和侵入岩以及断裂构造、褶皱构造和环形构造等的遥感解译标志;通过对ZY-1 02C星的全色/多光谱相机(P/MS)和全色高分辨率相机(HR)数据的遥感地质解译效果对比,认为P/MS数据中地质体的色调差异更为明显,易于建立区域构造格架、开展岩石大类解译;而HR数据所反映地质体的微地貌、纹理及细节更为清晰,易于进一步识别各类岩性及小型构造,尤其对碎屑岩的粒度、层状构造类型、第四系、矿业活动以及特殊成分的岩石等具有较好的反映。     

Developing a semi-analytical algorithm to estimate particulate organic carbon (POC) levels in inland eutrophic turbid water based on MERIS images: A case study of Lake Taihu

Particulate organic carbon (POC) plays an important role in the carbon cycle in water due to its biological pump process. In the open ocean, algorithms can accurately estimate the surface POC concentration. However, no suitable POC-estimation algorithm based on MERIS bands is available for inland turbid eutrophic water. A total of 228 field samples were collected from Lake Taihu in different seasons between 2013 and 2015. At each site, the optical parameters and water quality were analyzed. Using in situ data, it was found that POC-estimation algorithms developed for the open ocean and coastal waters using remote sensing reflectance were not suitable for inland turbid eutrophic water. The organic suspended matter (OSM) concentration was found to be the best indicator of the POC concentration, and POC has an exponential relationship with the OSM concentration. Through an analysis of the POC concentration and optical parameters, it was found that the absorption peak of total suspended matter (TSM) at 665 nm was the optimum parameter to estimate POC. As a result, MERIS band 7, MERIS band 10 and MERIS band 12 were used to derive the absorption coefficient of TSM at 665 nm, and then, a semi-analytical algorithm was used to estimate the POC concentration for inland turbid eutrophic water. An accuracy assessment showed that the developed semi-analytical algorithm could be successfully applied with a MAPE of 31.82% and RMSE of 2.68 mg/L. The developed algorithm was successfully applied to a MERIS image, and two full-resolution MERIS images, acquired on August 13, 2010, and December 7, 2010, were used to map the POC spatial distribution in Lake Taihu in summer and winter.