Third Earth Resources Technology Satellite Symposium. Volume 3: Discipline summary reports

Presentations at the conference covered the following disciplines: (1) agriculture, forestry, and range resources; (2) land use and mapping; (3) mineral resources, geological structure, and landform surveys; (4) water resources; (5) marine resources; (6) environment surveys; and (7) interpretation techniques

Comparison of hyperspectral and multi-spectral imagery to building a spectral library and land cover classification performanc

The main aim of this research work is to compare k-nearest neighbor algorithm (KNN) supervised classification with migrating means clustering unsupervised classification (MMC) method on the performance of hyperspectral and multispectral data for spectral land cover classes and develop their spectral library in Samara, Russia. Accuracy assessment of the derived thematic maps was based on the analysis of the classification confusion matrix statistics computed for each classified map, using for consistency the same set of validation points. We were analyzed and compared Earth Observing-1 (EO-1) Hyperion hyperspectral data to Landsat 8 Operational Land Imager (OLI) and Advance Land Imager (ALI) multispectral data. Hyperspectral imagers, currently available on airborne platforms, provide increased spectral resolution over existing space based sensors that can document detailed information on the distribution of land cover classes, sometimes species level. Results indicate that KNN (95, 94, 88 overall accuracy and .91, .89, .85 kappa coefficient for Hyp, ALI, OLI respectively) shows better results than unsupervised classification (93, 90, 84 overall accuracy and .89, .87, .81 kappa coefficient for Hyp, ALI, OLI respectively). Development of spectral library for land cover classes is a key component needed to facilitate advance analytical techniques to monitor land cover changes. Different land cover classes in Samara were sampled to create a common spectral library for mapping landscape from remotely sensed data. The development of these libraries provides a physical basis for interpretation that is less subject to conditions of specific data sets, to facilitate a global approach to the application of hyperspectral imagers to mapping landscape. In addition, it is demonstrated that the hyperspectral satellite image provides more accurate classification results than those extracted from the multispectral satellite image. The higher classification accuracy by KNN supervised was attributed principally to the ability of this classifier to identify optimal separating classes with low generalization error, thus producing the best possible classes’ separation.This work was partially supported by the Ministry of education and science of the Russian Federation; by the Russian Foundation for Basic Research grants (# 16-41-630761; # 16-29-11698, # 17-01-00972)

Resource assessment of deciduous forests in Bangladesh

This research makes a new assessment of both the physical and social dimensions of deciduous forest resources located in the central part of Bangladesh. Satellite remote sensing data and techniques are used to detect spatial and temporal forest change, to measure forest biophysical variables and to appraise their potential for developing model predictions based on a field survey conducted in 2003. Post classification assessment and regression analysis were the main methods in remote sensing data analysis. The study focused on a part of deciduous forest (64 sq km) located in Madhupur thana for fine-scale forest assessment. Remote sensing results suggest that only 16 percent forest left in the study area compared to 3826 hectares in 1962. The forest biophysical variables show strong association with spectral information of satellite data. For instance, an R-squared of 0.79 for predicted variable (for tree height) was achieved while regressing with field data, indicating that remote sensing methods can be efficiently used even in the tropical forests where heterogeneity is common. The second part of the thesis focuses on the underlying social factors/drivers that impacted on the forest, ranging from social dynamics such as land tenancy disputes,historical legacies and local corruption to policy failure by employing the theoretical framework of political ecology. Political ecological analysis in this research helped to evaluate the role and inter-relations of power, the ideological dilemmas and methodological disputes (i.e. the way forest problems are perceived) over forest resources in the study area. Field survey and observation was also found useful in gathering information about social variables by interviewing local inhabitants, forest officials, NGO activists, and politicians. The research employs methodologies from both science (i.e. remote sensing) and social science (i.e. political ecology) and the findings suggest that these two strands can work together for the better management (including resource assessment, monitoring and progress evaluation) of resources in Bangladesh