Landsat TM

Landsat TM data was utilised for coastal geomorphic mapping around the Gulf of Khambhat. Various landforms like shoreline, estuaries, mudflats, islands, mangroves, relict aluvium, cliffs, dunes, flood plains, paleochannel, paleomeanders, oxbow lake, etc. were delineated. The estuaries were divided into salt-wedge estuary, fully mixed estuary and partially mixed estuary based on amount of freshwater discharge and nature of tidal currents.

El desarrollo de una propuesta de clasificación de Cobetura
de la tierra buscó hacer un proceso de aprendizaje y
evaluación crítica de los distintos avances regionales en
la temática. (Perú, Colombia, Ecuador, Bolivia) En base a
ese desarrollo, se procedió a generar una propuesta que
permita desarrollar procesos de mapeo a escala nacional,
según los estándares regionales definidos por el Comité
Técnico Subregional del Proyecto. Con lo desarrollado, se
implementarón un conjunto de pruebas en la región central
de Perú y en secciones previamente consensuadas en las
fronteras con Ecuador y con Bolivia.

Cita Sugerida: Arnillas, C.A; Barrena, V; Llactayo, W; Ortíz, H; Regal, F; Rubín de Celis, E; Vásquez, P; Drenkhan, F; Llerena, C.E; Espino,
P; García, V; Gonzáles-Zúñiga, S; Gushiken, E; López, D. y Vásquez, R. 2012. Informe Final del Proyecto: Análisis de las
Dinámicas de Cambio de Cobertura de la Tierra en la Comunidad Andina. Componente Nacional Perú – Primera Etapa.
Centro de Datos para la Conservación (CDC-UNALM). Laboratorio de Teledetección (LTAUNALM). Dirección General
de Ordenamiento Territorial - Ministerio del Ambiente (DGOT - MINAM). Fundación para el Desarrollo Agrario (FDA).
Lima - Perú. pp. 56.

Being able to quantify land cover changes due to mining and reclamation at a
watershed scale is of critical importance in managing and assessing their potential
impacts to the Earth system. In this study, a remote sensing-based methodology is
proposed for quantifying the impact of surface mining activity and reclamation
from a watershed to local scale. The method is based on a Support Vector
Machines (SVMs) classifier combined with multi-temporal change detection of
Landsat TM imagery. The performance of the technique was evaluated at selected
open mining sites located in the island of Milos in Greece. Assessment of the
mining impact in the studied areas was based on the confusion matrix statistics,
supported by co-orbital QuickBird-2 very high spatial resolution imagery. Overall
classification accuracy of the thematic land cover maps produced was reported
over 90%. Our analysis showed expansion of mining activity throughout the
whole 23-year study period, while the transition of mining areas to soil and
vegetation was evident in varying rates. Our results evidenced the ability of the
method under investigation in deriving highly and accurate land cover change
maps, able to identify the mining areas as well as those in which excavation was
replaced by natural vegetation. All in all, the proposed technique showed
considerable promise towards the support of a sustainable environmental
development and prudent resource management.

This study is placed in an area between Guayaquil and Chanduy. The original image used in this work is an extraction of a TM Landsat scene from 21 February 1990. Principally, among others, we used the following principal digital methodology: colour composite produced from the 3, 5, 4 TM Landsat channels; principal component analysis of the 3, 5, 4 TM

Information on burnt area is of critical importance in many applications as for example in assessing the disturbance of natural ecosystems due to a fire or in proving important information to policy makers on the land cover changes for establishing restoration policies of fire-affected regions. Such information is commonly obtained through remote sensing image thematic classification and a wide range of classifiers have been suggested for this purpose. The objective of the present study has been to investigate the use of Support Vector Machines (SVMs) classifier combined with multispectral Landsat TM image for obtaining burnt area mapping. As a case study a typical Mediterranean landscape in Greece was used, in which occurred one of the most devastating fires during the summer of 2007. Accuracy assessment was based on the classification overall statistical accuracy results and also on comparisons of the derived burnt area estimates versus validated estimates from the Risk-EOS Burnt Scar Mapping service. Results from the implementation of the SVM using diverse kernel functions showed an average overall classification accuracy of 95.87% and a mean kappa coefficient of 0.948, with the burnt area class always clearly separable from all the other classes used in the classification scheme. Total burnt area estimate computed from the SVM was also in close agreement with that from Risk-EOS (mean difference of less than 1%). Analysis also indicated that, at least for the studied here fire, the inclusion of the two middle infrared spectral bands TM5 and TM7 of TM sensor as well as the selection of the kernel function in SVM implementation have a negligible effect in both the overall classification performance and in the delineation of total burnt area. Overall, results exemplified the appropriateness of the spatial and spectral resolution of the Landsat TM imagery combined with the SVM in obtaining rapid and cost-effective post-fire analysis. This is of considerable scientific and practical value, given the present open access to the archived and new observations from this satellite radiometer globally.