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Remote Sensing And Image Interpretation

Several machine-learning algorithms have been proposed for remote sensing image classification during the past two decades. Among these machine learning algorithms, Random Forest (RF) and Support Vector Machines (SVM) have drawn attention to image classification in several remote sensing applications. This article reviews RF and SVM concepts relevant to remote sensing image classification and applies a meta-analysis of 251 peer-reviewed journal papers. A database with more than 40 quantitative and qualitative fields was constructed from these reviewed papers. The meta-analysis mainly focuses on 1) the analysis regarding the general characteristics of the studies, such as geographical distribution, frequency of the papers considering time, journals, application domains, and remote sensing software packages used in the case studies, and 2) a comparative analysis regarding the performances of RF and SVM classification against various parameters, such as data type, RS applications, spatial resolution, and the number of extracted features in the feature engineering step. The challenges, recommendations, and potential directions for future research are also discussed in detail. Moreover, a summary of the results is provided to aid researchers to customize their efforts in order to achieve the most accurate results based on their thematic applications.

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Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

Assessing ‘‘dangerous climate change’’: Required reduction of carbon emissions to protect young people, future generations and nature /  James Hansen et al. PLoS ONE, vol. 8, n° 12, pp. 1-26. http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0081648 Authors's abstract : We assess climate impacts of global warming using ongoing observations and paleoclimate data. We use Earth’s measured energy imbalance, paleoclimate data, and simple representations of the global carbon cycle...

Assessing ‘‘dangerous climate change’’: Required reduction of carbon emissions to protect young people, future generations and nature

Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.

Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

Detecting the dynamics of vegetation disturbance and recovery in surface mining area via Landsat imagery and LandTrendr algorithm.

Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands.

One objective of the present study was to evaluate the performance of support vector machine (SVM)-based image classification technique with the maximum likelihood classification (MLC) technique for a rapidly changing landscape of an open-cast mine. The other objective was to assess the change in land use pattern due to coal mining from 2006 to 2016. Assessing the change in land use pattern accurately is important for the development and monitoring of coalfields in conjunction with sustainable development. For the present study, Landsat 5 Thematic Mapper (TM) data of 2006 and Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) data of 2016 of a part of Jharia Coalfield, Dhanbad, India, were used. The SVM classification technique provided greater overall classification accuracy when compared to the MLC technique in classifying heterogeneous landscape with limited training dataset. SVM exceeded MLC in handling a difficult challenge of classifying features having near similar reflectance on the mean signature plot, an improvement of over 11 % was observed in classification of built-up area, and an improvement of 24 % was observed in classification of surface water using SVM; similarly, the SVM technique improved the overall land use classification accuracy by almost 6 and 3 % for Landsat 5 and Landsat 8 images, respectively. Results indicated that land degradation increased significantly from 2006 to 2016 in the study area. This study will help in quantifying the changes and can also serve as a basis for further decision support system studies aiding a variety of purposes such as planning and management of mines and environmental impact assessment.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas

Groundwater vulnerability assessment in degraded coal mining areas using the AHP–Modified DRASTIC model

Reduction of spatial distribution of risk factors for transportation of contaminants released by coal mining activities.

Limited measurements of soil erosion, sediment transport and deposition due to coal mining activities hamper the planning for soil and water management in large coalfields. Management of erosion in these areas requires the identification of vulnerable zones. In this study, a modified Universal Soil Loss Equation model coupled with Analytic Hierarchy Process has been used to develop an Erosion Potential Index (EPI) to identify probable zones of soil erosion in the Upper Damodar Basin (UDB). The Damodar river valley has been subjected to severe environmental contamination over the years due to large scale mining and industrial activities. The results revealed that north-western and the central part of the UDB lied in the high EPI zone. Revised Universal Soil Loss Equation (RUSLE) model was used to estimate the soil loss for the UDB. The results of the RUSLE model revealed that the soil loss in the study area varied from a minimum of 0 to a maximum of 91.34 t ha−1 yr−1 in some areas. The spatial distribution of soil loss in the UDB was classified into 5 categories (Very Slight, Slight, Moderate, Severe and Extremely Severe). From results it was observed that about 959.9 km2 or 22% of the total study area lied in the severe soil loss zone i.e., soil losses in these areas ranged between 20 and 40 t ha−1 yr−1. Most of these areas were located in the vicinity of coal mining areas. Although, the lack of available data may impair the prediction accuracy of the proposed model at such a large watershed scale. However, the results of the proposed approach indicate usefulness in identifying erosion hotspots in large distributed watersheds with limited data.

Shivesh Kishore Karan

Papers

Assessment of the capability of remote sensing and GIS techniques for monitoring reclamation success in coal mine degraded lands.

Accuracy of land use change detection using support vector machine and maximum likelihood techniques for open-cast coal mining areas

Groundwater vulnerability assessment in degraded coal mining areas using the AHP–Modified DRASTIC model

Reduction of spatial distribution of risk factors for transportation of contaminants released by coal mining activities.

Identification of spatially distributed hotspots for soil loss and erosion potential in mining areas of Upper Damodar Basin – India