scholarly journals Assessing Riyadh’s Urban Change Utilizing High-Resolution Imagery

Land ◽  
2019 ◽  
Vol 8 (12) ◽  
pp. 193
Author(s):  
Ali Alghamdi ◽  
Anthony R. Cummings
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Accuracy Assessment ◽  
Middle Eastern ◽  
Object Oriented ◽  
Training Data ◽  
High Resolution Imagery ◽  
Significant Research ◽  
Desert Areas ◽  
Spot 5

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

scholarly journals Regional High-Resolution Benthic Habitat Data from Planet Dove Imagery for Conservation Decision-Making and Marine Planning

2021 ◽  
Vol 13 (21) ◽  
pp. 4215
Author(s):  
Steven R. Schill ◽  
Valerie Pietsch McNulty ◽  
F. Joseph Pollock ◽  
Fritjof Lüthje ◽  
Jiwei Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Marine Protected Area ◽  
Accuracy Assessment ◽  
Marine Conservation ◽  
Benthic Habitat ◽  
Storm Damage ◽  
Ecological Data ◽  
Management Actions ◽  
Marine Habitats ◽  
High Resolution Imagery

High-resolution benthic habitat data fill an important knowledge gap for many areas of the world and are essential for strategic marine conservation planning and implementing effective resource management. Many countries lack the resources and capacity to create these products, which has hindered the development of accurate ecological baselines for assessing protection needs for coastal and marine habitats and monitoring change to guide adaptive management actions. The PlanetScope (PS) Dove Classic SmallSat constellation delivers high-resolution imagery (4 m) and near-daily global coverage that facilitates the compilation of a cloud-free and optimal water column image composite of the Caribbean’s nearshore environment. These data were used to develop a first-of-its-kind regional thirteen-class benthic habitat map to 30 m water depth using an object-based image analysis (OBIA) approach. A total of 203,676 km2 of shallow benthic habitat across the Insular Caribbean was mapped, representing 5% coral reef, 43% seagrass, 15% hardbottom, and 37% other habitats. Results from a combined major class accuracy assessment yielded an overall accuracy of 80% with a standard error of less than 1% yielding a confidence interval of 78%–82%. Of the total area mapped, 15% of these habitats (31,311.7 km2) are within a marine protected or managed area. This information provides a baseline of ecological data for developing and executing more strategic conservation actions, including implementing more effective marine spatial plans, prioritizing and improving marine protected area design, monitoring condition and change for post-storm damage assessments, and providing more accurate habitat data for ecosystem service models.

scholarly journals Clasificación de imágenes Landsat-8 en la Demarcación Hidrográfica del Segura

2019 ◽  
pp. 33
Author(s):  
M. I. Rodríguez-Valero ◽  
F. Alonso-Sarria
Keyword(s):  
Maximum Likelihood ◽  
High Resolution ◽  
Random Forest ◽  
Cross Validation ◽  
Landsat 8 ◽  
Land Uses ◽  
High Resolution Imagery ◽  
The One

<p>This work presents a cartography of land uses in the Segura Hydrographic Demarcation obtained by classifying 2017 Landsat 8 images. The classification was carried out using two classifiers: Maximum Likelihood (ML) and Random Forest (RF). Training areas were obtained from historical high resolution imagery until 2016. Prior to classification, a cross validation analysis of the training areas was carried out to determine which of them may have undergone a change of use between 2016 and 2017. The results obtained with ML and RF, both with the original set of training areas and with the one obtained eliminating the problem, are compared to determine the best option. In the case of ML, the results improve after eliminating the changing training areas, from 77.7% to 81.4%; however, with RF this improvement is not so important, going from 84.1% to 85.1%. Therefore, it can be concluded that, with both methods, the classification is more exact when the modified training areas are used and, although the results obtained are quite acceptable for both ML and RF, the latter performs a more accurate classification.</p>

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