Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

Tarantino, Eufemia; Novelli, Antonio; Aquilino, Mariella; Figorito, Benedetto; Fratino, Umberto

doi:10.4018/ijaeis.2015100105

Cited by 23 publications

(10 citation statements)

References 49 publications

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“…the opportunity to achieve long-term time series through the use of Landsat data, in orbit since 1972, renouncing the higher spatial resolution provided by the newest generation satellites that would not allow historical evaluations of similar time periods [27,28]; 2. the possibility to provide national agencies and local authorities with a low-cost dataset.…”

Section: Study Areamentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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Abstract:The negative impacts of land take on natural components and economic resources affect planning choices and territorial policies. The importance of land take monitoring, in Italy, has been only recently considered, but despite this awareness, in the great part of the country, effective monitoring and containment measures have not been started, yet. This research proposes a methodology to map and monitor land use changes. To this end, a time series from 1985-2010, based on the multi-temporal Landsat data Thematic Mapper (TM), has been analyzed in the Vulture Alto-Bradano area, a mountain zone of the Basilicata region (Southern Italy). Results confirm a double potentiality of using these data: on the one hand, the use of multi-temporal Landsat data allows going very back in time, producing accurate datasets that provide a phenomenon trend over time; on the other hand, these data can be considered a first experience of open data in the field of spatial information. The proposed methodology provides agencies, local authorities and practitioners with a valuable tool to implement monitoring actions. This represents the first step to pursue territorial governance methods based on sustainability, limiting the land take.

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Section: Study Areamentioning

confidence: 99%

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Palma

Amato

Nolè

et al. 2016

IJGI

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“…The LULC information can be obtained from the multiband raster imageries through the process of image interpretation and classification (Li et al 2014). Image classification (supervised or unsupervised) is intended for an automatic categorisation of pixels with a common reflectance range into specific LULC class (Lillesand and Kiefer 1994;Chica-Olmo and Abarca-Hernandez 2000;Tarantino et al 2015). Supervised classification is a user guided approach that involves selection of training sites as reference for the categorization (Campbell 1996;Lillesand and Kiefer 1994;Jensen 2007).…”

Section: Land Use and Land Cover Classificationmentioning

confidence: 99%

Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley

2019

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Land use and land cover (LULC) change has been one of the most immense and perceptible transformations of the earth's surface. Evaluating LULC change at varied spatial scales is imperative in wide range of perspectives such as environmental conservation, resource management, land use planning, and sustainable development. This work aims to examine the land use and land cover changes in the Kashmir valley between the time periods from 1992-2001-2015 using a set of compatible moderate resolution Landsat satellite imageries. Supervised approach with maximum likelihood classifier was adopted for the classification and generation of LULC maps for the selected time periods. Results reveal that there have been substantial changes in the land use and cover during the chosen time periods. In general, three land use and land cover change patterns were observed in the study area: (1) consistent increase of the area under marshy, built-up, barren, plantation, and shrubs; (2) continuous decrease in agriculture and water; (3) decrease (1992-2001) and increase (2001-2015) in forest and pasture classes. In terms of the area under each LULC category, most significant changes have been observed in agriculture (-), plantation (?), builtup (?), and water (-); however, with reference to percent change within each class, the maximum variability was recorded in built-up (198.45%), plantation (87.98%), pasture (-71%), water (-48%) and agriculture (-28.85%). The massive land transformation is largely driven by anthropogenic actions and has been mostly adverse in nature, giving rise to multiple environmental issues in the ecologically sensitive Kashmir valley.

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“…The maximum-likelihood classifier was adopted from a parametric classification algorithm [27][28][29][30] and divided into four classes: urban, vegetation, forest and waterbodies ( Table 2). The classes that were involved in the selection of the training sites were used as a reference in the user-guided approach [17,[31][32][33][34]. For each of the predetermined change detection types, training samples were selected by delimiting the polygons in the study area.…”

Section: Data Acquisition and Sourcesmentioning

confidence: 99%

“…Monitoring and analysis for change detection is the most adopted application of the satellite data [13][14][15]. Among them, Landsat satellite datasets have been used for change detection analysis [11,14,16,17]. Change detection analysis can enhance the land-use planning within a framework of laws and policies to guide forest zone allocation.…”

Section: Introductionmentioning

confidence: 99%

Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

Nurda

Noguchi

Ahamed

2020

Forests

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The objective of this research was to detect changes in forest areas and, subsequently, the potential forest area that can be extended in the South Sumatra province of Indonesia, according to the Indonesian forest resilience classification zones. At first, multispectral satellite remote sensing datasets from Landsat 7 ETM+ and Landsat 8 OLI were classified into four classes, namely urban, vegetation, forest and waterbody to develop Land Use/Land Cover (LULC) maps for the year 2003 and 2018. Secondly, criteria, namely distance from rivers, distance from roads, elevation, LULC and settlements were selected and the reclassified maps were produced from each of the criteria for the land suitability analysis for forest extension. Thirdly, the Analytical Hierarchy Process (AHP) was incorporated to add expert opinions to prioritize the criteria referring to potential areas for forest extension. In the change detection analysis, Tourism Recreation Forest (TRF), Convertible Protection Forest (CPF) and Permanent Production Forest (PPF) forest zones had a decrease of 20%, 13% and 40% in area, respectively, in the forest class from 2003 to 2018. The Limited Production Forest (LPF) zone had large changes and decreased by 72% according to the LULC map. In the AHP method, the influential criteria had higher weights and ranked as settlements, elevation, distance from roads and distance from rivers. CPF, PPF and LPF have an opportunity for extension in the highly suitable classification (30%) and moderately suitable classification (41%) areas, to increase coverage of production forests. Wildlife Reserve Forests (WRFs) have potential for expansion in the highly suitable classification (30%) and moderately suitable classification (52%) areas, to keep biodiversity and ecosystems for wildlife resources. Nature Reserve Forests (NRFs) have an opportunity for extension in the highly suitable classification (39%) and moderately suitable classification (48%) areas, to keep the forests for nature and biodiversity. In case of TRF, there is limited scope to propose a further extension and is required to be managed with collaboration between the government and the community.

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Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

Cited by 23 publications

References 49 publications

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

A SMAP Supervised Classification of Landsat Images for Urban Sprawl Evaluation

Using Landsat satellite data for assessing the land use and land cover change in Kashmir valley

Change Detection and Land Suitability Analysis for Extension of Potential Forest Areas in Indonesia Using Satellite Remote Sensing and GIS

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