Spatial Monitoring of Desertification Extent in Western Iraq using Landsat Images and GIS

Ajaj, Qayssar Mahmood; Pradhan, Biswajeet; Noori, Abbas Mohammed; Jebur, Mustafa Neamah

doi:10.1002/ldr.2775

Cited by 53 publications

(24 citation statements)

References 34 publications

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“…In China, sandy land dominates the changes occurring to land use/land use cover in arid and semiarid regions, which account for 18.12% of China's land area [39,40]. Climate change directly affects NPP variations [41,42], which in turn affect the extent of desertification in northern China. Therefore, to predict and evaluate the impact of future global C cycles on human social development and formulate scientific climate policies, investigations of the total and spatial changes of NPP in sandy areas under future climatic changes are of great importance.…”

Section: Introductionmentioning

confidence: 99%

Projection of Net Primary Productivity under Global Warming Scenarios of 1.5 °C and 2.0 °C in Northern China Sandy Areas

Huo

Zhao

et al. 2020

Atmosphere

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Empirical evidence suggests that variations in climate affect the net primary productivity (NPP) across sandy areas over time. However, little is known about the relative impacts of climate change on NPP with global warming of 1.5 and 2.0 °C (GW_1.5 °C_2.0 °C) relative to pre-industrial levels. Here, we used a new set of climate simulations from four Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP 2b) datasets, modified the Carnegie-Ames-Stanford approach (CASA) model and assessed the spatio-temporal variation in NPP in sandy areas of northern China (SAONC). Compared with the reference period (RP, 1986–2005), the NPP variation under four emission scenarios showed clear rising trends and increased most significantly under RCP8.5 with an annual average increase of 2.34 g C/m2. The estimated annual NPP under global warming of 1.5 °C (GW_1.5 °C) increased by 14.17, 10.72, 8.57, and 26.68% in different emission scenarios, and under global warming of 2.0 °C (GW_2.0 °C) it increased by 20.87, 24.01, 29.31, and 39.94%, respectively. In terms of seasonal change, the NPP value under the four emission scenarios changed most significantly in the summer relative to RP, exhibiting a growth of 16.48%. Temperature changes (p > 0.614) had a greater impact on NPP growth than precipitation (p > 0.017), but solar radiation showed a certain negative impact in the middle- and low-latitude regions. NPP showed an increasing trend that changed from the southeast to the central and western regions at GW_1.5 to GW_2.0 °C. NPP was consistent with the spatial change in climate factors and had a promoting role in high latitudes in SAONC, but it was characterized by a certain inhibitory effect at middle and low latitudes in SAONC. The uncertainty of NPP under the four models ranged from 16.29 to 26.52%. Our findings suggest that the impact of GW_1.5 °C is relatively high compared with the current conditions, whereas GW_2.0 °C implies significantly lower projected NPP growth in all areas.

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Section: Introductionmentioning

confidence: 99%

Projection of Net Primary Productivity under Global Warming Scenarios of 1.5 °C and 2.0 °C in Northern China Sandy Areas

Huo

Zhao

et al. 2020

Atmosphere

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“…During this period numerous both supervised and unsupervised classification algorithms have been developed to derive standard land cover maps. Classification of image can be defined as grouping image pixels or objects into selected classes for producing a thematic map (Ajaj et al, 2017). Continuous supervision of retrieved locations offers valuable data on the change in vegetation cover (Shareef et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Change Detection and Gis-Based Fuzzy Ahp to Evaluate the Degradation and Reclamation Land of Tikrit City, Iraq

Shareef

Ameen

Ajaj

2021

Geodesy and Cartography

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LULC factors in Tikrit city (Iraq) and the neighboring municipalities are studied among 1989, 2002 and 2015 using various techniques of remote sensing, geographical information system (GIS), and fuzzy analytical hierarchy process (FAHP). Satellite imagery with GIS helped to assess the standard LULC changes in the long term period. FAHP permitted estimating the importance of various LULC by determination of the suitable weight for used factors and then producing the evaluating models. Using different techniques, two models were created (1) to estimate the degradation of the land (2) is generated to determine the reclamation of the area. The finding reveals that the a overall accuracy of 97.0939%, 98.9199% and 99.5817% or 1989, 2002 and 2015 respectively. The outcomes also revealed that urban, vegetation, and water features area are developed in the long term (1989–2015) about 4.35%, 4.28%, and 1.49%, respectively, while barren area is reduced about 5.57%.The degradation map index showed that the lands strongly debased are these converted from vegetation to barren, followed by moderate to high these changed from water areas to urban, while moderate degradation is noticed of urban transformed to barren soil. Contrary, the reclamation map index illustrated that the lands are powerfully transformed from barren to the vegetation and followed by those converted from barren to the water, while barren transformed to the urban is marked as moderate reclamation. The transformation from urban to vegetation or water was classified as the low and deficient class to evaluate the area. The study is also revealed that the integration of remote sensing and GIS produces a successful method for LULC monitoring and managing the environment.

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“…However, in the face of different surface information diversity and global needs, multi-dimensional remote-sensing applications [3][4][5] are complex. In particular, many applications, such as earthquakes [6], flood monitoring [7], and monitoring of other disasters [8][9][10], require real-time or near real-time processing capabilities. Therefore, effective processing of disaster-monitoring applications put forward requirements for increased computing power.…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing Based on Computational Characteristics for Disaster Monitoring

Zou

2020

Applied Sciences

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Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.

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Spatial Monitoring of Desertification Extent in Western Iraq using Landsat Images and GIS

Cited by 53 publications

References 34 publications

Projection of Net Primary Productivity under Global Warming Scenarios of 1.5 °C and 2.0 °C in Northern China Sandy Areas

Projection of Net Primary Productivity under Global Warming Scenarios of 1.5 °C and 2.0 °C in Northern China Sandy Areas

Change Detection and Gis-Based Fuzzy Ahp to Evaluate the Degradation and Reclamation Land of Tikrit City, Iraq

Cloud Computing Based on Computational Characteristics for Disaster Monitoring

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