Measuring and Predicting Urban Expansion in the Angkor Region of Cambodia

Abstract

Recent increases in urbanization and tourism threaten the viability of UNESCO world heritage sites across the globe. The Angkor world heritage site located in southern Cambodia is now facing such a challenge. Over the past two decades, Angkor has seen over 300,000% growth in international tourist arrivals, which has led to uncontrolled development of the nearby city of Siem Reap. This study uses remote sensing and GIS to comprehend the process of urban expansion during the past 14 years, and has applied the CA-Markov model to predict future urban expansion. This paper analyzes the urban pressure on the Angkor site at different scales. The results reveal that the urban area of Siem Reap city increased from 28.23 km2 in 2004 to 73.56 km2 in 2017, an increase of 160%. Urban growth mainly represented a transit-oriented pattern of expansion, and it was also observed that land surfaces, such as arable land, forests, and grasslands, were transformed into urban residential land. The total constructed land area in the core and buffer zones increased by 12.99 km2 from 2004 to 2017, and 72% of the total increase was in the buffer zone. It is predicted that the built-up area in Siem Reap is expected to cover 135.09 km2 by 2025 and 159.14 km2 by 2030. The number of monuments that are most likely be affected by urban expansion is expected to increase from 9 in 2017 to 14 in 2025 and 17 in 2030. The urban area in Siem Reap has increased dramatically over the past decade and monuments continue to be decimated by urban expansion. This paper urges closer attention and urgent actions to minimize the urban pressure on the Angkor site in the future.

Research progress and knowledge system of world heritage tourism: a bibliometric analysis

“…The main focus is on techniques and methods for WH resource survey and assessment (n = 32), management and conservation enhancement (n = 57), presentation and education (n = 14), visitor flow forecasting (n = 3), visitor safety management (n = 6), and itinerary design (n = 12). Thanks to the continuous progress of technology, a series of new technologies and methods such as 3D technology [65], geoinformation technology, and remote sensing(RS) [66,67], augmented reality(AR) [68], and mixed reality (MR) [69] have facilitated the management and conservation of WH tourism resources.…”

Forecasting Spatio-Temporal Dynamics on the Land Surface Using Earth Observation Data—A Review

“…Section 3.3.3). Since MC modeling as such is not spatially explicit [40,41], these methods are in most cases integrated with CA models that also consider the state of the neighboring pixels when calculating the transition probabilities and allocate state changes according to a local suitability map [40][41][42][43][44][56][57][58][66][67][68][69][70][71]158]. To improve the accuracy of these models, the transition probabilities and suitability (i.e., probability whether and where LULC change will occur) are frequently expressed as functions of multiple explanatory variables such as topography, socio-economic metrics and distance functions.…”

“…The most important applications in EO-based forecasting of the anthroposphere are LULC (54%) and crop yield (40%). In most LULC simulations, forecasts focus on urban sprawl or LULC change in an urban environment, simulating more general LULC maps of urban centers [40][41][42][43][44][45][46][47][48][49][50][51][52][53][54][55] or binary urban/non-urban masks [56][57][58][59][60][61][62][63][64]. Musa et al [36] reviewed urban modeling studies and showed that modeling approaches based on CA are most popular in the scientific literature due to their flexibility and ability for spatially explicit simulation.…”

Spatiotemporal Evolution and Simulation Prediction of Ecosystem Service Function in the Western Sichuan Plateau Based on Land Use Changes

“…As early studies on ecosystem service function are mostly focused on the present and the past, there are fewer simulation predictions for the future (Liu et al, 2021), which provided insufficient support for ecological environmental protection and management. The artificial neural network (Li and Yeh, 2005;Lei et al, 2012), CLUE-S (Zhang et al, 2014;Tian and Ren, 2012), SLEUTH (Nigussie and Altunkaynak, 2016;Nigussie and Altunkaynak, 2019), CA-Markov (Liu et al, 2019;Wang et al, 2020;Matlhodi et al, 2021) and other prediction models is applied in the study of land use evolution, especially CA-Markov model that predicts the change trend of land use in the future based on land use transfer matrix and land use data.…”