A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

Aguilar, Fernando J.; Nemmaoui, Abderrahim; Aguilar, Manuel A.; Chourak, Mimoun; Zarhloule, Yassine; Lorca, Andrés Miguel García

doi:10.3390/f7010023

Cited by 21 publications

(13 citation statements)

References 61 publications

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“…The thresholds of non-forest classes from the forest class were determined by matching their values within the derived object features. The objects that had maximum difference values less than 0.52 were classified as water, residential areas, and grasslands; objects that had green vegetation index (GVI) [92] values that were negative and standard deviations derived from the gray-level co-occurrence matrix (GLCM) less than 21 were classified as dry-farming; the objects that had enhanced vegetation index2 [93] values higher than 0.98 and entropy derived from the GLCM less than 7 were classified as irrigated-farming; and the remaining unclassified objects were classified as forest [82]. The accuracy of the classifications was validated using the provided ground truth samples and confusion matrix [94], as shown in Table A1.…”

Section: Conditioning and Triggering Factorsmentioning

confidence: 99%

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Shirvani

2020

Remote Sensing

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Despite recent progress in landslide susceptibility mapping, a holistic method is still needed to integrate and customize influential factors with the focus on forest regions. This study was accomplished to test the performance of geographic object-based random forest in modeling the susceptibility of protected and non-protected forests to landslides in northeast Iran. Moreover, it investigated the influential conditioning and triggering factors that control the susceptibility of these two forest areas to landslides. After surveying the landslide events, segment objects were generated from the Landsat 8 multispectral images and digital elevation model (DEM) data. The features of conditioning factors were derived from the DEM and available thematic layers. Natural triggering factors were derived from the historical events of rainfall, floods, and earthquake. The object-based image analysis was used for deriving anthropogenic-induced forest loss and fragmentation. The layers of logging and mining were obtained from available historical data. Landslide samples were extracted from field observations, satellite images, and available database. A single database was generated including all conditioning and triggering object features, and landslide samples for modeling the susceptibility of two forest areas to landslides using the random forest algorithm. The optimal performance of random forest was obtained after building 500 trees with the area under the receiver operating characteristics (AUROC) values of 86.3 and 81.8% for the protected and non-protected forests, respectively. The top influential factors were the topographic and hydrologic features for mapping landslide susceptibility in the protected forest. However, the scores were loaded evenly among the topographic, hydrologic, natural, and anthropogenic triggers in the non-protected forest. The topographic features obtained about 60% of the importance values with the domination of the topographic ruggedness index and slope in the protected forest. Although the importance of topographic features was reduced to 36% in the non-protected forest, anthropogenic and natural triggering factors remarkably gained 33.4% of the importance values in this area. This study confirms that some anthropogenic activities such as forest fragmentation and logging significantly intensified the susceptibility of the non-protected forest to landslides.

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Section: Conditioning and Triggering Factorsmentioning

confidence: 99%

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Shirvani

2020

Remote Sensing

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“…Other aspects have focused on exploring proposals to evaluate the accuracy and precision of photogrammetric mappings, such as through the variation of the number of control points [18] or the use of sub-pixel technology [19], also presenting the development of automatic procedures that take advantage of satellite images to draw building plans [20]. Both this technology and other surveying procedures, such as laser and LIDAR (Laser Imaging Detection and Ranging) scanners, have brought enormous advances in heritage conservation.…”

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confidence: 99%

Architectural Graphics Research: Topics and Trends through Cluster and Map Network Analyses

López-Chao

Lorenzo

2020

Symmetry

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Graphic representation is a fundamental language in architecture and engineering because it facilitates the communication of any type of information. Therefore, professionals and students need continuous and updated training, with scientific references being the best source of knowledge. However, accessing the latest findings is a complex process for people in the professional world or without an extensive research background since there are no specific filters in the databases, such as architectural graphics. This manuscript aims to define the research topics and trends in architectural graphics as a point of reference for novel professors and new researchers in graphics or drawing. A database on the Scopus-indexed scientific production of the professors of architectural graphics from public architecture schools in Spain has been developed. Furthermore, cluster and map network analyses have been performed using VOSviewer with different levels of co-occurrence to define what this group of academics investigates and how the issues are related. The results evidenced a structure in four categories: the philosophy and theory of architectural graphics, the theory of geometry in architectural heritage, the application of digital graphics in architecture education and urban design management. Research gaps are mentioned and a base framework for the future of research in architectural graphics is proposed.

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“…Monitoring the trends to which ecosystem is changing by the use of indicators such as land cover and socio-demographic information will be essential in order to make proper planning for sustainable ecosystem management. Nowadays, the advancement of geospatial technology such as RS and geographic information system (GIS) present the best efficient tool for analyzing land cover trends as demonstrated by many researchers [5,6,7,8]. Geospatial technologies in addition to conventional inventories can facilitate quantitative evaluation and provide a baseline for monitoring the extent, impacts and trend of LCC.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Land Cover Change and Vegetation Dynamics Using Remote Sensing and DPSIR Framework in Kebbi State, Nigeria

Aliero¹,

M²,

M³

et al. 2017

Preprint

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Assessment of the trends of land cover and vegetation dynamics (VD) using remote sensing (RS) and indicators such as anthropogenic activities and the socio-demographic information is essential in order to make proper planning for sustainable management. This paper attempts to evaluate land cover change (LCC) and VD in Kebbi State, Nigeria using historical Landsat data from 1986-2016 by means of remote sensing. The Driver-Pressure-State-Impact-Response (DPSIR) framework was later employed using both primary and secondary data for a better understanding of the drivers, the state of the environmental condition, the causes as well as the impact of the change. The images were classified into five thematic land cover classes as Dense Vegetation, shrubs/built area, farmland, bare/grassland and water body by means of Maximum likelihood supervised classification technique in accordance with Anderson classification scheme level 1, with acceptable accuracy. Pre-classification and post-classification change detection (CD) methodologies were executed using Normalized difference vegetation index (NDVI) and Image differencing respectively. The study illustrates a steady decline in dense vegetation and shrubs/build areas while farmland and bare/grassland increases, however, water bodies remain unchanged. The DPSIR pin-point that the major drivers of change in the study area have been the pressing need for farming land as the population grows and socioeconomic demands including fuelwood consumption and endemic poverty. Expansion of Farming land, fuelwood consumption and the need for construction materials are identified as the main key elements exerting pressure for the change. The state of the condition indicates a steady decline in dense vegetation and shrubs areas while farmland and bare/grassland are increasing significantly. The impacts include land degradation, the decline in the provision ecosystem goods and services, biodiversity loss through loss of habitats. The study, however, noted that many international and national policies in response to land degradation are channelled toward land restoration and remediating of the environment, through afforestation programs and improving the livelihood of the rural people through providing alternative income sources since they depend heavily on land for sustenance. However, the state governments, communities and individual commonly organized annual tree planting campaign with the main purpose of environmental protection.

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A Quantitative Assessment of Forest Cover Change in the Moulouya River Watershed (Morocco) by the Integration of a Subpixel-Based and Object-Based Analysis of Landsat Data

Cited by 21 publications

References 61 publications

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

A Holistic Analysis for Landslide Susceptibility Mapping Applying Geographic Object-Based Random Forest: A Comparison between Protected and Non-Protected Forests

Architectural Graphics Research: Topics and Trends through Cluster and Map Network Analyses

Evaluation of Land Cover Change and Vegetation Dynamics Using Remote Sensing and DPSIR Framework in Kebbi State, Nigeria

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