Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images

Makhamreh, Zeyad

doi:10.1007/s12665-018-7534-z

Cited by 9 publications

(6 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In summary, different spectral indices have different advantages, disadvantages, and applications, but a drought index applicable to different vegetation cover types is still worth exploring because the vegetation cover in the study area may change over time [47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Wang

et al. 2022

Sustainability

View full text Add to dashboard Cite

Soil moisture plays an important role in hydrology, climate, agriculture, and ecology, and remote sensing is one of the most important tools for estimating the soil moisture over large areas. Soil moisture, which is calculated by remote sensing inversion, is affected by the uneven distribution of vegetation and therefore the results cannot accurately reflect the spatial distribution of the soil moisture in the study area. This study analyzes the soil moisture of different vegetation covers in the Wushen Banner of Inner Mongolia, recorded in 2016, and using Landsat and MODIS images fused with multispectral bands. Firstly, we compared and analyzed the ability of the visible optical and short-wave infrared drought index (VSDI), the normalized differential infrared index (NDII), and the short-wave infrared water stress index (SIWSI) in monitoring the soil moisture in different vegetation cover soils. Secondly, we used the stepwise multiple regression analysis method in order to correlate the multispectral fusion bands with the field-measured soil water content and established a soil moisture inversion model based on the multispectral fusion bands. As the results show, there was a strong correlation between the established model and the measured soil water content of the different vegetation cover soils: in the bare soil, R2 was 0.86; in the partially vegetated cover soil, R2 was 0.84; and in the highly vegetated cover soil, R2 was 0.87. This shows that the established model could better reflect the actual condition of the surface soil moisture in the different vegetation covers.

show abstract

Section: Discussionmentioning

confidence: 99%

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Wang

et al. 2022

Sustainability

View full text Add to dashboard Cite

show abstract

“…However, time-series approaches require a high imagery frequency (Behling et al 2016;Kennedy et al 2010), imposing difficulties for long-term annual landslide mapping. While multi-seasonal Landsat images have been used for long-term land-cover mapping and change detection (Grȃdinaru et al 2017;Makhamreh 2018;Prishchepov et al 2012), their utility for constructing historical landslide maps are underexplored.…”

Section: Introductionmentioning

confidence: 99%

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Chen

Prishchepov

Fensholt

et al. 2019

Remote Sensing of Environment

View full text Add to dashboard Cite

Monitoring long-term landslide activity is of importance for risk assessment and land management. Daytime airborne drones or very high-resolution optical satellites are often used to create landslide maps. However, such imagery comes at a high cost, making long-term risk analysis cost-prohibitive. Despite the widespread use of open-access 30m Landsat imagery, their utility for landslide detection is often limited due to low classification accuracy. One of the major challenges is to separate landslides from other anthropogenic disturbances. Here, we produce landslide maps retrospectively from 1998 to 2017 for landslide-prone and highly populated Taiwan (35,874 km 2). To improve classification accuracy of landslides, we integrate nighttime light imagery from the Defense Meteorological Satellite Program (DMSP) and the Visible Infrared Imaging Radiometer Suite (VIIRS), with multi-seasonal daytime optical Landsat time-series, and digital elevation data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). We employed a non-parametric machine-learning classifier, random forest, to classify the satellite imagery. The classifier was trained with data from three years (2005, 2010, and 2015), and was validated with an independent reference sample from twelve years. Our results demonstrated that combining nighttime light data and multi-seasonal imagery significantly improved the classification (p<0.001), compared to conventional methods based on single-season optical imagery. The results confirmed that the developed classification model enabled mapping of landslides across Taiwan over a long period with annual overall accuracy varying between 96% and 97%, user's and producer's accuracies between 73% and 86%. Spatiotemporal analysis of the landslide inventories from 1998 to 2017 revealed different temporal patterns of landslide activities, showing those areas where landslides were persistent and other areas where landslides tended to reoccur after vegetation regrowth. In sum, we provide a robust method to detect long-term landslide activities based on freely available satellite imagery, which can be applied elsewhere. Our mapping effort of landslide spatiotemporal patterns is expected to be of high importance in developing effective landslide remediation strategies.

show abstract

“…The northern to southern highlands of Jordan, known for being densely populated, are pivotal for the nation's agriculture and biodiversity. These areas, as reported by Makhamreh [41], benefit from a comparatively moderate climate, especially when contrasted with the warmer Jordan Rift Valley to the west and the desert regions to the east and south [42].…”

Section: Figure 1 Location Map Of the Study Areamentioning

confidence: 99%

Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

Al-Omari,

Shatnawi,

Shbeeb

et al. 2024

Civ Eng J

View full text Add to dashboard Cite

In this paper, a comprehensive flood hazard map for the vicinity of King Talal Dam in Jordan, utilizing advanced remote sensing (RS) and GIS methodologies, is developed. Key geographical and environmental factors, encompassing terrain slope, elevation, aspect, proximity to water streams, drainage density, and land use/land cover, are integrated to highlight areas with increased flood risk. This study, by employing a novel theoretical approach, harnesses the synergistic capabilities of RS and GIS to collect and analyze geospatial data. The Analytic Hierarchy Process (AHP) is applied to assign weights to various flood-conditioning factors, quantifying their relative importance in flood risk assessment. Through the weighted sum overlay technique, the aforementioned factors are integrated to categorize flood risk levels from very low to very high. This study successfully maps flood hazards, identifying areas near main water channels, ravines, and lower-elevation areas prone to flooding. This research provides a robust framework for flood risk assessment, contributing valuable knowledge to the fields of environmental management and disaster mitigation. It underscores the importance of continuous monitoring and updating of flood hazard maps to accommodate changing land use, climate, and hydrological conditions. The innovative application offers crucial insights for urban planners and policymakers, emphasizing the need for proactive strategies in flood-prone areas and serving as a model for similar geographical regions. Doi: 10.28991/CEJ-2024-010-05-05 Full Text: PDF

show abstract

Derivation of vegetation density and land-use type pattern in mountain regions of Jordan using multi-seasonal SPOT images

Cited by 9 publications

References 43 publications

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Surface Soil Moisture Inversion and Distribution Based on Spatio-Temporal Fusion of MODIS and Landsat

Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017

Utilizing Remote Sensing and GIS Techniques for Flood Hazard Mapping and Risk Assessment

Contact Info

Product

Resources

About