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Philippe De Maeyer
Sino-Belgian Joint Laboratory for Geo-Information, 9000 Ghent, Belgium

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Journal article
Published: 13 August 2021 in Remote Sensing
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The spatial calculation of vector data is crucial for geochemical analysis in geological big data. However, large volumes of geochemical data make for inefficient management. Therefore, this study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG) to solve the problem of low efficiency of electronic form management. The SSMG method consists of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study achieved two different types of high-throughput and high-efficiency methods for geochemical data storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in which to test the proposed methods. Using geochemical data from shapefile as a data source, several experiments were performed to improve geochemical data storage efficiency and achieve efficient retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage using different architectures, so as to achieve management of geochemical data organization in an efficient way, through time consumed and data compression ratio (DCR), in order to better support geological big data. The purpose of this study was to find ways to build a storage method that can improve the speed of geochemical data insertion and retrieval by using excellent big data technology to help us efficiently solve problem of geochemical data preprocessing and provide support for geochemical analysis.

ACS Style

Yinyi Cheng; Kefa Zhou; Jinlin Wang; Philippe De Maeyer; Tim Van de Voorde; Jining Yan; Shichao Cui. A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods. Remote Sensing 2021, 13, 3208 .

AMA Style

Yinyi Cheng, Kefa Zhou, Jinlin Wang, Philippe De Maeyer, Tim Van de Voorde, Jining Yan, Shichao Cui. A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods. Remote Sensing. 2021; 13 (16):3208.

Chicago/Turabian Style

Yinyi Cheng; Kefa Zhou; Jinlin Wang; Philippe De Maeyer; Tim Van de Voorde; Jining Yan; Shichao Cui. 2021. "A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods." Remote Sensing 13, no. 16: 3208.

Journal article
Published: 06 July 2021 in ISPRS International Journal of Geo-Information
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More and more digital 3D city models might evolve into spatiotemporal instruments with time as the 4th dimension. For digitizing the current situation, 3D scanning and photography are suitable tools. The spatial future could be integrated using 3D drawings by public space designers and architects. The digital spatial reconstruction of lost historical environments is more complex, expensive and rarely done. Three-dimensional co-creative digital drawing with citizens’ collaboration could be a solution. In 2016, the City of Ghent (Belgium) launched the “3D city game Ghent” project with time as one of the topics, focusing on the reconstruction of disappeared environments. Ghent inhabitants modelled in open-source 3D software and added animated 3D gamification and Transmedia Storytelling, resulting in a 4D web environment and VR/AR/XR applications. This study analyses this low-cost interdisciplinary 3D co-creative process and offers a framework to enable other cities and municipalities to realise a parallel virtual universe (an animated digital twin bringing the past to life). The result of this co-creation is the start of an “Animated Spatial Time Machine” (AniSTMa), a term that was, to the best of our knowledge, never used before. This research ultimately introduces a conceptual 4D space–time diagram with a relation between the current physical situation and a growing number of 3D animated models over time.

ACS Style

Mario Matthys; Laure De Cock; John Vermaut; Nico Van de Weghe; Philippe De Maeyer. An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling. ISPRS International Journal of Geo-Information 2021, 10, 460 .

AMA Style

Mario Matthys, Laure De Cock, John Vermaut, Nico Van de Weghe, Philippe De Maeyer. An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling. ISPRS International Journal of Geo-Information. 2021; 10 (7):460.

Chicago/Turabian Style

Mario Matthys; Laure De Cock; John Vermaut; Nico Van de Weghe; Philippe De Maeyer. 2021. "An “Animated Spatial Time Machine” in Co-Creation: Reconstructing History Using Gamification Integrated into 3D City Modelling, 4D Web and Transmedia Storytelling." ISPRS International Journal of Geo-Information 10, no. 7: 460.

Journal article
Published: 25 May 2021 in Journal of Geophysical Research: Atmospheres
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Mountain snow is a fundamental freshwater supply in the arid regions. Climate warming alters the timing of snowmelt and shortens the snow cover duration, which greatly influences the regional climate and water management. However, a reliable estimation of snow mass in the Tianshan Mountains (TS) is still unclear due to the scarcity of extensive continuous surface observations and a complex spatial heterogeneity. Therefore, a long-time snow simulation from 1982 to 2018 was performed in WRF/Noah-MP to quantify snow mass in the TS, forced by ERA5 reanalysis data and real-time updated leaf area index and green vegetation fraction. Meanwhile, March snow mass (close to the annual peak snow mass), snow cover fraction (SCF), and their associated trends were investigated in the TS. The results indicated a good accuracy of the simulated snow water equivalent (root mean square error [RMSE]: 7.82 mm/day) with a slight overestimation (2.84 mm/day). Compared with ERA5 data set, the RMSE and mean bias of the daily snow depth from WRF/Noah-MP downscaling were significantly reduced by 95.74% and 93.02%, respectively. The climatological March snow mass measured 97.85 (±16.60) Gt in the TS and exhibited a negligible tendency during the study period. The total precipitation during the cold season controlled the variations of March snow mass. The increased precipitation in the high-altitude regions contributed to an extensive snow mass, which could offset the loss in the TS lowland. In contrast, rapidly rising air temperature caused a significant reduction of March SCF, particularly in the Southern TS.

ACS Style

Tao Yang; Qian Li; Xi Chen; Rafiq Hamdi; Philippe De Maeyer; Lanhai Li. Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Tao Yang, Qian Li, Xi Chen, Rafiq Hamdi, Philippe De Maeyer, Lanhai Li. Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia. Journal of Geophysical Research: Atmospheres. 2021; 126 (10):1.

Chicago/Turabian Style

Tao Yang; Qian Li; Xi Chen; Rafiq Hamdi; Philippe De Maeyer; Lanhai Li. 2021. "Variation of Snow Mass in a Regional Climate Model Downscaling Simulation Covering the Tianshan Mountains, Central Asia." Journal of Geophysical Research: Atmospheres 126, no. 10: 1.

Journal article
Published: 21 May 2021 in Water
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Water users in the Amudarya River Basin in Uzbekistan are suffering severe water use competition and uneven water allocation, which seriously threatens ecosystems, as shown, for example, in the well-known Aral Sea catastrophe. This study explores the optimized water allocation schemes in the study area at the provincial level under different incoming flow levels, based on the current water distribution quotas among riparian nations, which are usually ignored in related research. The optimization model of the inexact two-stage stochastic programming method is used, which is characterized by probability distributions and interval values. Results show that (1) water allocation is redistributed among five different sectors. Livestock, industrial, and municipality have the highest water allocation priority, and water competition mainly exists in the other two sectors of irrigation and ecology; (2) water allocation is redistributed among six different provinces, and allocated water only in Bukhara and Khorezm can satisfy the upper bound of water demand; (3) the ecological sector can receive a guaranteed water allocation of 8.237–12.354 km3; (4) under high incoming flow level, compared with the actual water distribution, the total allocated water of four sectors (except for ecology) is reduced by 3.706 km3 and total economic benefits are increased by USD 3.885B.

ACS Style

Min Wang; Xi Chen; Ayetiguli Sidike; Liangzhong Cao; Philippe DeMaeyer; Alishir Kurban. Optimal Allocation of Surface Water Resources at the Provincial Level in the Uzbekistan Region of the Amudarya River Basin. Water 2021, 13, 1446 .

AMA Style

Min Wang, Xi Chen, Ayetiguli Sidike, Liangzhong Cao, Philippe DeMaeyer, Alishir Kurban. Optimal Allocation of Surface Water Resources at the Provincial Level in the Uzbekistan Region of the Amudarya River Basin. Water. 2021; 13 (11):1446.

Chicago/Turabian Style

Min Wang; Xi Chen; Ayetiguli Sidike; Liangzhong Cao; Philippe DeMaeyer; Alishir Kurban. 2021. "Optimal Allocation of Surface Water Resources at the Provincial Level in the Uzbekistan Region of the Amudarya River Basin." Water 13, no. 11: 1446.

Journal article
Published: 28 April 2021 in Journal of Cleaner Production
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Rapid cropland reformation is occurring in the cold region of China (hereafter referred to as Cold China), affecting national crop structure production. In addition, different agricultural systems, including state and private farms, exist in Cold China. To date, the different effects of cropland reformation on grain production in state and private farms are lacking. Focusing on this issue and using synergistic methodology, results revealed that the transformation from upland crops to paddy field was principal land change across Cold China from 1990 to 2015. This transformation increased grain production by 434.0 × 104 t, accounting for over 14.0% of the total grain production increase in Cold China (i.e., from 748.0 × 104 t in 1990–3785.1 × 104 t in 2015) in the study period, showing positive feedback on grain security. Between two agricultural systems, more intensive transformation area (10993.3 km2 vs. 4673.5 km2) and a larger contribution to grain production increase (11.1% vs. 3.2%) occurred on state compared with private farms. Crop structure also evolved differently in the two agricultural systems. Dominant crop changed from soybean (1990–2000) to rice paddy (2000–2015) on state farms but from soybean (1990–2005) to corn (2005–2015) on private farms, indicating state farms focused on human dietary supply and private farms mainly served industrial needs. This study showed cropland reformation in response to global food trade increased grain production in Cold China. State farms were more efficient in such reformation; more market-oriented policies should be designed to encourage the reformation on private farms. This study provided a new reference for other regions/countries’ investigation on cropland and food structural security in different agricultural systems.

ACS Style

Tao Pan; Chi Zhang; Wenhui Kuang; Geping Luo; Guoming Du; Philippe DeMaeyer; Zherui Yin. A large-scale shift of cropland structure profoundly affects grain production in the cold region of China. Journal of Cleaner Production 2021, 307, 127300 .

AMA Style

Tao Pan, Chi Zhang, Wenhui Kuang, Geping Luo, Guoming Du, Philippe DeMaeyer, Zherui Yin. A large-scale shift of cropland structure profoundly affects grain production in the cold region of China. Journal of Cleaner Production. 2021; 307 ():127300.

Chicago/Turabian Style

Tao Pan; Chi Zhang; Wenhui Kuang; Geping Luo; Guoming Du; Philippe DeMaeyer; Zherui Yin. 2021. "A large-scale shift of cropland structure profoundly affects grain production in the cold region of China." Journal of Cleaner Production 307, no. : 127300.

Journal article
Published: 09 April 2021 in Remote Sensing
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Estimating the fractional coverage of the photosynthetic vegetation (f PV) and non-photosynthetic vegetation (f NPV) is essential for assessing the growth conditions of vegetation growth in arid areas and for monitoring environmental changes and desertification. The aim of this study was to estimate the f PV, f NPV and the fractional coverage of the bare soil (f BS) in the lower reaches of Tarim River quantitatively. The study acquired field data during September 2020 for obtaining the f PV, f NPV and f BS. Firstly, six photosynthetic vegetation indices (PVIs) and six non-photosynthetic vegetation indices (NPVIs) were calculated from Sentinel-2A image data. The PVIs include normalized difference vegetation index (NDVI), ratio vegetation index (RVI), soil adjusted vegetation index (SAVI), modified soil adjusted vegetation index (MSAVI), reduced simple ratio index (RSR) and global environment monitoring index (GEMI). Meanwhile, normalized difference index (NDI), normalized difference tillage index (NDTI), normalized difference senescent vegetation index (NDSVI), soil tillage index (STI), shortwave infrared ratio (SWIR32) and dead fuel index (DFI) constitutes the NPVIs. We then established linear regression model of different PVIs and f PV, and NPVIs and f NPV, respectively. Finally, we applied the GEMI-DFI model to analyze the spatial and seasonal variation of f PV and f NPV in the study area in 2020. The results showed that the GEMI and f PV revealed the best correlation coefficient (R 2) of 0.59, while DFI and f NPV had the best correlation of R 2 = 0.45. The accuracy of f PV, f NPV and f BS based on the determined PVIs and NPVIs as calculated by GEMI-DFI model are 0.69, 0.58 and 0.43, respectively. The f PV and f NPV are consistent with the vegetation phonological development characteristics in the study area. The study concluded that the application of the GEMI-DFI model in the f PV and f NPV estimation was sufficiently significant for monitoring the spatial and seasonal variation of vegetation and its ecological functions in arid areas.

ACS Style

Zengkun Guo; Alishir Kurban; Abdimijit Ablekim; Shupu Wu; Tim Van de Voorde; Hossein Azadi; Philippe Maeyer; Edovia Dufatanye Umwali. Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data. Remote Sensing 2021, 13, 1458 .

AMA Style

Zengkun Guo, Alishir Kurban, Abdimijit Ablekim, Shupu Wu, Tim Van de Voorde, Hossein Azadi, Philippe Maeyer, Edovia Dufatanye Umwali. Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data. Remote Sensing. 2021; 13 (8):1458.

Chicago/Turabian Style

Zengkun Guo; Alishir Kurban; Abdimijit Ablekim; Shupu Wu; Tim Van de Voorde; Hossein Azadi; Philippe Maeyer; Edovia Dufatanye Umwali. 2021. "Estimation of Photosynthetic and Non-Photosynthetic Vegetation Coverage in the Lower Reaches of Tarim River Based on Sentinel-2A Data." Remote Sensing 13, no. 8: 1458.

Journal article
Published: 22 March 2021 in Journal of Geophysical Research: Atmospheres
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In the Aral Sea region, significant land use/cover change (LUCC) occurred in the past 50 years, especially the shrinking of Aral Sea due to unreasonable usage of water resources under intensified agricultural activities. However, to date, regional climatic feedbacks on fine‐scale exerted by such LUCC in Central Asia have not been studied clearly. In this study, ALARO‐SURFEX regional climate model was used to perform climate simulations under different underlying surface scenarios with 4 km spatial resolution to explore the impacts of historical LUCC on summer climate during 1980–2015. Our results show that compared to default land surface conditions, the modified ones improved the model's ability in simulating temperature, precipitation, and surface energy fluxes. During the period 1980–2015, LUCC accelerated the warming trend, reduced the summer precipitation and altered allocation of surface energy fluxes. Exposed dry bottom of Aral Sea has undergone the most conspicuous warming, which caused increase of the 2 m maximum temperature, average temperature, and diurnal temperature range by 2.56 ± 0.88°C, 1.04 ± 0.53°C, and 3.42 ± 1.10°C, respectively, while minimum temperature decrease by 1.14 ± 0.56°C. The summer precipitation (mainly convective precipitation) decreased by about 2.33 mm overlay the exposed dry bottom of Aral Sea and approximately 400 km “buffer” region in its eastern side. Additionally, the energy balance changed as follows: −47.9, 50.19, −78.67, and −23.72 W m−2 for net radiation, sensible heat, latent heat, and soil heat, respectively. Quantified contribution of LUCC on regional climate provides useful information for developing mitigation and adaption strategies under the global warming threat.

ACS Style

Huili He; Rafiq Hamdi; Peng Cai; Geping Luo; Friday Uchenna Ochege; Miao Zhang; Piet Termonia; Philippe De Maeyer; Chaofan Li. Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region. Journal of Geophysical Research: Atmospheres 2021, 126, 1 .

AMA Style

Huili He, Rafiq Hamdi, Peng Cai, Geping Luo, Friday Uchenna Ochege, Miao Zhang, Piet Termonia, Philippe De Maeyer, Chaofan Li. Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region. Journal of Geophysical Research: Atmospheres. 2021; 126 (6):1.

Chicago/Turabian Style

Huili He; Rafiq Hamdi; Peng Cai; Geping Luo; Friday Uchenna Ochege; Miao Zhang; Piet Termonia; Philippe De Maeyer; Chaofan Li. 2021. "Impacts of Historical Land Use/Cover Change (1980–2015) on Summer Climate in the Aral Sea Region." Journal of Geophysical Research: Atmospheres 126, no. 6: 1.

Model evaluation paper
Published: 09 March 2021 in Geoscientific Model Development
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To allow for climate impact studies on human and natural systems, high-resolution climate information is needed. Over some parts of the world plenty of regional climate simulations have been carried out, while in other regions hardly any high-resolution climate information is available. The CORDEX Central Asia domain is one of these regions, and this article describes the evaluation for two regional climate models (RCMs), REMO and ALARO-0, that were run for the first time at a horizontal resolution of 0.22∘ (25 km) over this region. The output of the ERA-Interim-driven RCMs is compared with different observational datasets over the 1980–2017 period. REMO scores better for temperature, whereas the ALARO-0 model prevails for precipitation. Studying specific subregions provides deeper insight into the strengths and weaknesses of both RCMs over the CAS-CORDEX domain. For example, ALARO-0 has difficulties in simulating the temperature over the northern part of the domain, particularly when snow cover is present, while REMO poorly simulates the annual cycle of precipitation over the Tibetan Plateau. The evaluation of minimum and maximum temperature demonstrates that both models underestimate the daily temperature range. This study aims to evaluate whether REMO and ALARO-0 provide reliable climate information over the CAS-CORDEX domain for impact modeling and environmental assessment applications. Depending on the evaluated season and variable, it is demonstrated that the produced climate data can be used in several subregions, e.g., temperature and precipitation over western Central Asia in autumn. At the same time, a bias adjustment is required for regions where significant biases have been identified.

ACS Style

Sara Top; Lola Kotova; Lesley De Cruz; Svetlana Aniskevich; Leonid Bobylev; Rozemien De Troch; Natalia Gnatiuk; Anne Gobin; Rafiq Hamdi; Arne Kriegsmann; Armelle Reca Remedio; Abdulla Sakalli; Hans Van De Vyver; Bert Van Schaeybroeck; Viesturs Zandersons; Philippe De Maeyer; Piet Termonia; Steven Caluwaerts. Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain. Geoscientific Model Development 2021, 14, 1267 -1293.

AMA Style

Sara Top, Lola Kotova, Lesley De Cruz, Svetlana Aniskevich, Leonid Bobylev, Rozemien De Troch, Natalia Gnatiuk, Anne Gobin, Rafiq Hamdi, Arne Kriegsmann, Armelle Reca Remedio, Abdulla Sakalli, Hans Van De Vyver, Bert Van Schaeybroeck, Viesturs Zandersons, Philippe De Maeyer, Piet Termonia, Steven Caluwaerts. Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain. Geoscientific Model Development. 2021; 14 (3):1267-1293.

Chicago/Turabian Style

Sara Top; Lola Kotova; Lesley De Cruz; Svetlana Aniskevich; Leonid Bobylev; Rozemien De Troch; Natalia Gnatiuk; Anne Gobin; Rafiq Hamdi; Arne Kriegsmann; Armelle Reca Remedio; Abdulla Sakalli; Hans Van De Vyver; Bert Van Schaeybroeck; Viesturs Zandersons; Philippe De Maeyer; Piet Termonia; Steven Caluwaerts. 2021. "Evaluation of regional climate models ALARO-0 and REMO2015 at 0.22° resolution over the CORDEX Central Asia domain." Geoscientific Model Development 14, no. 3: 1267-1293.

Journal article
Published: 24 February 2021 in Hydrology and Earth System Sciences
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The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN) and focus on the basin-scale water–energy–food–ecology (WEFE) nexus. We applied it to the Syr Darya River basin (SDB) and the Amu Darya River basin (ADB), of which poor water management caused the Aral Sea disaster. The causality of the nexus was effectively compared and universality of this framework was discussed. In terms of changes in the nexus, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexus after the disintegration. The water–energy contradiction of the SDB is more severe than that of the ADB, partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of the SDB more controversial. Due to this, the water–food–ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology, and this effect of the SDB is more significant. The construction of reservoirs on the Panj River of the upstream ADB should be cautious to avoid an intense water–energy conflict such as the SDB's. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation.

ACS Style

Haiyang Shi; Geping Luo; Hongwei Zheng; Chunbo Chen; Olaf Hellwich; Jie Bai; Tie Liu; Shuang Liu; Jie Xue; Peng Cai; Huili He; Friday Uchenna Ochege; Tim Van de Voorde; Philippe de Maeyer. A novel causal structure-based framework for comparing a basin-wide water–energy–food–ecology nexus applied to the data-limited Amu Darya and Syr Darya river basins. Hydrology and Earth System Sciences 2021, 25, 901 -925.

AMA Style

Haiyang Shi, Geping Luo, Hongwei Zheng, Chunbo Chen, Olaf Hellwich, Jie Bai, Tie Liu, Shuang Liu, Jie Xue, Peng Cai, Huili He, Friday Uchenna Ochege, Tim Van de Voorde, Philippe de Maeyer. A novel causal structure-based framework for comparing a basin-wide water–energy–food–ecology nexus applied to the data-limited Amu Darya and Syr Darya river basins. Hydrology and Earth System Sciences. 2021; 25 (2):901-925.

Chicago/Turabian Style

Haiyang Shi; Geping Luo; Hongwei Zheng; Chunbo Chen; Olaf Hellwich; Jie Bai; Tie Liu; Shuang Liu; Jie Xue; Peng Cai; Huili He; Friday Uchenna Ochege; Tim Van de Voorde; Philippe de Maeyer. 2021. "A novel causal structure-based framework for comparing a basin-wide water–energy–food–ecology nexus applied to the data-limited Amu Darya and Syr Darya river basins." Hydrology and Earth System Sciences 25, no. 2: 901-925.

Research article
Published: 14 February 2021 in Spatial Cognition & Computation
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As indoor wayfinding can be very challenging, adapted systems, which adapt the route instruction type, are being developed to facilitate more supportive indoor route guidance. In this study, such a system has been developed based on the results of an online survey. This adapted system was compared with a non-adapted system by use of eye tracking, position tracking, an orientation test and a questionnaire. The results revealed that using symbols instead of photos reduced the imposed cognitive load, while using 3D-simulations instead of photos improved the environmental awareness. This resulted in less wayfinding errors with the adapted system, compared to the non-adapted system. Therefore, the present study provides additional evidence on the benefits of adapted systems for indoor route guidance.

ACS Style

Laure De Cock; Nico Van de Weghe; Kristien Ooms; Nina Vanhaeren; Matteo Ridolfi; Eli De Poorter; Philippe De Maeyer. Taking a closer look at indoor route guidance; usability study to compare an adapted and non-adapted mobile prototype. Spatial Cognition & Computation 2021, 1 -23.

AMA Style

Laure De Cock, Nico Van de Weghe, Kristien Ooms, Nina Vanhaeren, Matteo Ridolfi, Eli De Poorter, Philippe De Maeyer. Taking a closer look at indoor route guidance; usability study to compare an adapted and non-adapted mobile prototype. Spatial Cognition & Computation. 2021; ():1-23.

Chicago/Turabian Style

Laure De Cock; Nico Van de Weghe; Kristien Ooms; Nina Vanhaeren; Matteo Ridolfi; Eli De Poorter; Philippe De Maeyer. 2021. "Taking a closer look at indoor route guidance; usability study to compare an adapted and non-adapted mobile prototype." Spatial Cognition & Computation , no. : 1-23.

Journal article
Published: 17 January 2021 in Atmosphere
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The accelerated global warming and heterogeneous change in precipitation have been resulting in climate system shifts, which plays a key role in the stability of ecosystem and social economic development. Central Asia is account 80% of the temperate desert, characterized by fragile ecosystem; however, it has experienced the fastest warming in recent decades and projected warming in future. The Köppen-Geiger climate classification is a useful tool to assess the potential impacts of climate change on regional ecosystem. The spatial shift and temporal evolution of each climatic zone based on Köppen-Geiger climate classification are analyzed in historical and future period under different scenarios (RCP2.6, RCP4.5 and RCP8.5), high risk regions that might experience more frequent climatic zone shifts are delimited in this study, which could provide the useful information for developing mitigate strategies in coping with the warming threat. The hotter and dryer subtypes of arid climatic zone and warmer subtypes of temperate climatic zone expanded their coverage in Central Asia, corresponding to the tundra climatic, cooler subtype of arid and temperate climatic zone contracted. Based on a method defining the climate-sensitivity, high risk regions are mainly distributed in northern Kazakhstan and Tianshan Mountains region.

ACS Style

Huili He; Geping Luo; Peng Cai; Rafiq Hamdi; Piet Termonia; Philippe De Maeyer; Alishir Kurban; Jianjun Li. Assessment of Climate Change in Central Asia from 1980 to 2100 Using the Köppen-Geiger Climate Classification. Atmosphere 2021, 12, 123 .

AMA Style

Huili He, Geping Luo, Peng Cai, Rafiq Hamdi, Piet Termonia, Philippe De Maeyer, Alishir Kurban, Jianjun Li. Assessment of Climate Change in Central Asia from 1980 to 2100 Using the Köppen-Geiger Climate Classification. Atmosphere. 2021; 12 (1):123.

Chicago/Turabian Style

Huili He; Geping Luo; Peng Cai; Rafiq Hamdi; Piet Termonia; Philippe De Maeyer; Alishir Kurban; Jianjun Li. 2021. "Assessment of Climate Change in Central Asia from 1980 to 2100 Using the Köppen-Geiger Climate Classification." Atmosphere 12, no. 1: 123.

Journal article
Published: 11 January 2021 in Remote Sensing
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Hydrological modeling has always been a challenge in the data-scarce watershed, especially in the areas with complex terrain conditions like the inland river basin in Central Asia. Taking Bosten Lake Basin in Northwest China as an example, the accuracy and the hydrological applicability of satellite-based precipitation datasets were evaluated. The gauge-adjusted version of six widely used datasets was adopted; namely, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (CDR), Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), Global Precipitation Measurement Ground Validation National Oceanic and Atmospheric Administration Climate Prediction Center (NOAA CPC) Morphing Technique (CMORPH), Integrated Multi-Satellite Retrievals for GPM (GPM), Global Satellite Mapping of Precipitation (GSMaP), the Tropical Rainfall Measuring Mission (TRMM) and Multi-satellite Precipitation Analysis (TMPA). Seven evaluation indexes were used to compare the station data and satellite datasets, the soil and water assessment tool (SWAT) model, and four indexes were used to evaluate the hydrological performance. The main results were as follows: (1) The GPM and CDR were the best datasets for the daily scale and monthly scale rainfall accuracy evaluations, respectively. (2) The performance of CDR and GPM was more stable than others at different locations in a watershed, and all datasets tended to perform better in the humid regions. (3) All datasets tended to perform better in the summer of a year, while the CDR and CHIRPS performed well in winter compare to other datasets. (4) The raw data of CDR and CMORPH performed better than others in monthly runoff simulations, especially CDR. (5) Integrating the hydrological performance of the uncorrected and corrected data, all datasets have the potential to provide valuable input data in hydrological modeling. This study is expected to provide a reference for the hydrological and meteorological application of satellite precipitation datasets in Central Asia or even the whole temperate zone.

ACS Style

Jiabin Peng; Tie Liu; Yue Huang; Yunan Ling; Zhengyang Li; Anming Bao; Xi Chen; Alishir Kurban; Philippe De Maeyer. Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sensing 2021, 13, 221 .

AMA Style

Jiabin Peng, Tie Liu, Yue Huang, Yunan Ling, Zhengyang Li, Anming Bao, Xi Chen, Alishir Kurban, Philippe De Maeyer. Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia. Remote Sensing. 2021; 13 (2):221.

Chicago/Turabian Style

Jiabin Peng; Tie Liu; Yue Huang; Yunan Ling; Zhengyang Li; Anming Bao; Xi Chen; Alishir Kurban; Philippe De Maeyer. 2021. "Satellite-based Precipitation Datasets Evaluation Using Gauge Observation and Hydrological Modeling in a Typical Arid Land Watershed of Central Asia." Remote Sensing 13, no. 2: 221.

Journal article
Published: 06 January 2021 in Atmospheric Research
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Precipitation outputs from 30 Global Circulation Models (GCMs) of the Coupled Model Inter-comparison Project Phase 6 (CMIP6) were evaluated from 1951 to 2014 over six climate zones in arid Central Asia (ACA) using the Climate Research Unit TS 4.04 (CRU) precipitation datasets as reference. An evaluation framework was constructed taking into account metrics of annual precipitation patterns, annual cycle precipitation statistics, categorical validation and long-term precipitation trend. The performance of GCMs vary from region to region. Compared to CRU, the 30 selected GCMs present considerable wet bias in the Southern Xinjiang (SX) and Hexi Corridor (HC) regions which can be larger than 100%. The simulated precipitation from most GCMs shows a consistent annual cycle shape and closer with that of observations in the western ACA, but fails to capture precipitation peak in NK and NX regions. Compared to NK, CD, TM and NX, all models and the full model ensemble have limited ability in reproducing the Probability Density Function (PDF) of observations over SX and HC. Most GCMs show limited competence to reproduce the long-term precipitation trend with high wet bias over the Hexi Corridor. Based on the comprehensive performance ranking, it is discovered that CESM2, CESM2-FV2, CESM2-WACCM and CESM2-WACCM-FV2 from NCAR, ACCESS-CM2 from CSIRO and CanESM5 from Canadian Centre for Climate Modeling demonstrate better performances across ACA. The ensemble of 30 selected GCMs has limited ability to accurately simulate precipitation according to the above four types of metrics. The results of this paper may provide scientific guidance to CMIP6 end-users when selecting the most suitable GCMs for their specific applications over ACA.

ACS Style

Hao Guo; Anming Bao; Tao Chen; Guoxiong Zheng; Yunqian Wang; Liangliang Jiang; Philippe De Maeyer. Assessment of CMIP6 in simulating precipitation over arid Central Asia. Atmospheric Research 2021, 252, 105451 .

AMA Style

Hao Guo, Anming Bao, Tao Chen, Guoxiong Zheng, Yunqian Wang, Liangliang Jiang, Philippe De Maeyer. Assessment of CMIP6 in simulating precipitation over arid Central Asia. Atmospheric Research. 2021; 252 ():105451.

Chicago/Turabian Style

Hao Guo; Anming Bao; Tao Chen; Guoxiong Zheng; Yunqian Wang; Liangliang Jiang; Philippe De Maeyer. 2021. "Assessment of CMIP6 in simulating precipitation over arid Central Asia." Atmospheric Research 252, no. : 105451.

Journal article
Published: 12 November 2020 in Remote Sensing
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As two competitive pathways of surface energy partitioning, latent (LE) and sensible (H) heat fluxes are expected to be strongly influenced by climate change and wide spread of global greening in recent several decades. Quantifying the surface energy fluxes (i.e., LE and H) variations and controlling factors is still a challenge because of the discrepancy in existing models, parameterizations, as well as driven datasets. In this study, we assessed the ability of random forest (RF, a machine learning method) and further predicted the global surface energy fluxes (i.e., LE and H) by combining FLUXNET observations, climate reanalysis and satellite-based leaf area index (LAI). The results show that the surface energy fluxes variations can be highly explained by the established RF models. The coefficient of determination (R2) ranges from 0.66 to 0.89 for the LE, and from 0.53 to 0.90 for the H across 10 plant functional types (PFTs), respectively. Meanwhile, the root mean square error (RMSE) ranges from 12.20 W/m2 to 21.94 W/m2 for the LE and from 12.05 W/m2 to 22.34 W/m2 for the H at a monthly scale, respectively. The important influencing factors in building RF models are divergent with respect to LE and H, but the solar radiation is common to both LE and H and to all 10 PFTs in this study. We also found a contrasting trend of LE and H: a positive trend in LE and a negative trend in H during 1982–2016 and these contrasting trends are dominated by the elevated CO2 concentration level. Our study suggested an important role of the CO2 concentration in determining surface energy partitioning which is needed to be considered in future studies.

ACS Style

Xiuliang Yuan; Friday Uchenna Ochege; Philippe De Maeyer; Alishir Kurban. Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning. Remote Sensing 2020, 12, 3712 .

AMA Style

Xiuliang Yuan, Friday Uchenna Ochege, Philippe De Maeyer, Alishir Kurban. Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning. Remote Sensing. 2020; 12 (22):3712.

Chicago/Turabian Style

Xiuliang Yuan; Friday Uchenna Ochege; Philippe De Maeyer; Alishir Kurban. 2020. "Partitioning Global Surface Energy and Their Controlling Factors Based on Machine Learning." Remote Sensing 12, no. 22: 3712.

Journal article
Published: 15 October 2020 in Water
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In the past few decades, the shrinkage of the Aral Sea is one of the biggest ecological catastrophes caused by human activity. To quantify the joint impact of both human activities and climate change on groundwater, the spatiotemporal groundwater dynamic characteristics in the Amu Darya Delta of the Aral Sea from 1999 to 2017 were analyzed, using the groundwater level, climate conditions, remote sensing data, and irrigation information. Statistics analysis was adopted to analyze the trend of groundwater variation, including intensity, periodicity, spatial structure, while the Pearson correlation analysis and principal component analysis (PCA) were used to quantify the impact of climate change and human activities on the variabilities of the groundwater level. Results reveal that the local groundwater dynamic has varied considerably. From 1999 to 2002, the groundwater level dropped from −189 cm to −350 cm. Until 2017, the groundwater level rose back to −211 cm with fluctuation. Seasonally, the fluctuation period of groundwater level and irrigation water was similar, both were about 18 months. Spatially, the groundwater level kept stable within the irrigation area and bare land but fluctuated drastically around the irrigation area. The Pearson correlation analysis reveals that the dynamic of the groundwater level is closely related to irrigation activity within the irrigation area (Nukus: −0.583), while for the place adjacent to the Aral Sea, the groundwater level is closely related to the Large Aral Sea water level (Muynak: 0.355). The results of PCA showed that the cumulative contribution rate of the first three components exceeds 85%. The study reveals that human activities have a great impact on groundwater, effective management, and the development of water resources in arid areas is an essential prerequisite for ecological protection.

ACS Style

Xiaohui Pan; Weishi Wang; Tie Liu; Yue Huang; Philippe De Maeyer; Chenyu Guo; Yunan Ling; Shamshodbek Akmalov. Quantitative Detection and Attribution of Groundwater Level Variations in the Amu Darya Delta. Water 2020, 12, 2869 .

AMA Style

Xiaohui Pan, Weishi Wang, Tie Liu, Yue Huang, Philippe De Maeyer, Chenyu Guo, Yunan Ling, Shamshodbek Akmalov. Quantitative Detection and Attribution of Groundwater Level Variations in the Amu Darya Delta. Water. 2020; 12 (10):2869.

Chicago/Turabian Style

Xiaohui Pan; Weishi Wang; Tie Liu; Yue Huang; Philippe De Maeyer; Chenyu Guo; Yunan Ling; Shamshodbek Akmalov. 2020. "Quantitative Detection and Attribution of Groundwater Level Variations in the Amu Darya Delta." Water 12, no. 10: 2869.

Preprint content
Published: 09 October 2020
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The previous comparative studies on watersheds were mostly based on the comparison of dispersive characteristics, which lacked systemicity and causality. We proposed a causal structure-based framework for basin comparison based on the Bayesian network (BN), and focus on the basin-scale water-energy-food-ecology (WEFE) nexuses. We applied it to the Syr Darya river basin (SDB) and the Amu Darya river basin (ADB) that caused the Aral Sea disaster. The causality of the nexuses was effectively compared and universality of this framework was discussed. In terms of changes of the nexuses, the sensitive factor for the water supplied to the Aral Sea changed from the agricultural development during the Soviet Union period to the disputes in the WEFE nexuses after the disintegration. The water-energy contradiction of SDB is more severe than that of ADB partly due to the higher upstream reservoir interception capacity. It further made management of the winter surplus water downstream of SDB more controversial. Due to this, the water-food-ecology conflict between downstream countries may escalate and turn into a long-term chronic problem. Reducing water inflow to depressions and improving the planting structure prove beneficial to the Aral Sea ecology and this effect of SDB is more significant. The construction of reservoirs on the Panj river of the upstream ADB should be cautious to avoid an intense water-energy conflict as SDB. It is also necessary to promote the water-saving drip irrigation and to strengthen the cooperation.

ACS Style

Haiyang Shi; Geping Luo; Hongwei Zheng; Chunbo Chen; Jie Bai; Tie Liu; Shuang Liu; Jie Xue; Peng Cai; Huili He; Friday Uchenna Ochege; Tim van de Voorde; Philippe de Maeyer. A novel causal structure-based framework for comparing basin-wide water-energy-food-ecology nexuses applied to the data-limited Amu Darya and Syr Darya river basins. 2020, 2020, 1 -36.

AMA Style

Haiyang Shi, Geping Luo, Hongwei Zheng, Chunbo Chen, Jie Bai, Tie Liu, Shuang Liu, Jie Xue, Peng Cai, Huili He, Friday Uchenna Ochege, Tim van de Voorde, Philippe de Maeyer. A novel causal structure-based framework for comparing basin-wide water-energy-food-ecology nexuses applied to the data-limited Amu Darya and Syr Darya river basins. . 2020; 2020 ():1-36.

Chicago/Turabian Style

Haiyang Shi; Geping Luo; Hongwei Zheng; Chunbo Chen; Jie Bai; Tie Liu; Shuang Liu; Jie Xue; Peng Cai; Huili He; Friday Uchenna Ochege; Tim van de Voorde; Philippe de Maeyer. 2020. "A novel causal structure-based framework for comparing basin-wide water-energy-food-ecology nexuses applied to the data-limited Amu Darya and Syr Darya river basins." 2020, no. : 1-36.

Articles
Published: 25 September 2020 in Cartography and Geographic Information Science
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In recent years, there has been a rising awareness about the relevance of spatial ability and integrating spatial information into educational curricula. It is now considered essential to problem-solving and understanding a variety of natural and cultural phenomena. To analyze people’s global-scale cognitive map and which factors influence it, a short playful test was developed that allowed participants to estimate the real land area of certain countries, regions, and continents. These estimates are analyzed to define how accurately the size of countries and continents is perceived. The results show that country of residence does not significantly affect people’s spatial knowledge. However, gender, migration background, and familiarity with maps have a significant impact.

ACS Style

Lieselot Lapon; Kristien Ooms; Bart De Wit; Nina Vanhaeren; Philippe De Maeyer. People’s global-scale cognitive map versus their personal characteristics: a worldwide study. Cartography and Geographic Information Science 2020, 47, 550 -564.

AMA Style

Lieselot Lapon, Kristien Ooms, Bart De Wit, Nina Vanhaeren, Philippe De Maeyer. People’s global-scale cognitive map versus their personal characteristics: a worldwide study. Cartography and Geographic Information Science. 2020; 47 (6):550-564.

Chicago/Turabian Style

Lieselot Lapon; Kristien Ooms; Bart De Wit; Nina Vanhaeren; Philippe De Maeyer. 2020. "People’s global-scale cognitive map versus their personal characteristics: a worldwide study." Cartography and Geographic Information Science 47, no. 6: 550-564.

Research article
Published: 12 September 2020 in Journal of Hydrology
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The snowpack evolution has a significant impact on the water cycle and energy exchange at the watershed and regional scales, especially in the mountainous area with complex topography and land surface properties. An accurate description of the vegetation parameters for the regional climate model (RCM) coupled with the land surface model (LSM) is necessary to achieve a more accurate simulation of the mountainous snow process. However, the default vegetation options could not update real-time in the RCM LSMs, causing large uncertainties in the snow mass estimation. Thus, this study investigated the effect of the key vegetation parameters on the snow simulation in the Tianshan Mountains (TS) through real-time updates with remotely sensed leaf area index (LAI), green vegetation fraction (FVEG) and land cover (LC) products in the Weather Research and Forecasting (WRF) model coupled with the Noah LSM with Multiparameterization Options (Noah-MP). The results demonstrated that more realistic vegetation parameters could improve the performance of snow simulation in the WRF/Noah-MP, especially in the forest regions. The underestimated vegetation parameters of the integrated remote sensing products caused an increased surface albedo and less snow interception, particularly in the snow ablation period, and less vegetation density could also reduce the net longwave radiation emitted from the canopy at the surface, causing a lower near-surface temperature and less snowmelt. Additionally, less snow interception and melted snow contributed to a larger snow water equivalent on the ground, such as in the Western TS and the high-altitude regions of the Ili Valley. The updating vegetation parameters' approach will help to provide information so as to accurately model the snow resources in the mountainous areas.

ACS Style

Tao Yang; Qian Li; Xi Chen; Rafiq Hamdi; Philippe De Maeyer; Alishir Kurban; Lanhai Li. Improving snow simulation with more realistic vegetation parameters in a regional climate model in the Tianshan Mountains, Central Asia. Journal of Hydrology 2020, 590, 125525 .

AMA Style

Tao Yang, Qian Li, Xi Chen, Rafiq Hamdi, Philippe De Maeyer, Alishir Kurban, Lanhai Li. Improving snow simulation with more realistic vegetation parameters in a regional climate model in the Tianshan Mountains, Central Asia. Journal of Hydrology. 2020; 590 ():125525.

Chicago/Turabian Style

Tao Yang; Qian Li; Xi Chen; Rafiq Hamdi; Philippe De Maeyer; Alishir Kurban; Lanhai Li. 2020. "Improving snow simulation with more realistic vegetation parameters in a regional climate model in the Tianshan Mountains, Central Asia." Journal of Hydrology 590, no. : 125525.

Article
Published: 01 September 2020 in Water History
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In this article, we will present an overview of possible research methods to handle historical sources, in the specific case of karez landscapes. A karez system is an underground water collection system, prevalent in the Turpan basin of China. Sources and the associated methodology have become more important today, because of contemporary issues such as modernisation, urbanisation and agricultural expansion. These problems make it harder to read the landscape, which is why we have to start extracting our data from maps, reports, photographs, and satellite imagery. We will give a short overview of sources, each with an explanation of their processing method. Despite certain cautions that should be taken into account, these methods clearly complement the current state of knowledge on the Turpan karez. As this paper is part of a special issue, Water History in the time of COVID-19, it has undergone modified peer review.

ACS Style

Sophie Barbaix; Alishir Kurban; Philippe De Maeyer; Xi Chen; Jean Bourgeois. The use of historical sources in a multi-layered methodology for karez research in Turpan, China. Water History 2020, 12, 281 -297.

AMA Style

Sophie Barbaix, Alishir Kurban, Philippe De Maeyer, Xi Chen, Jean Bourgeois. The use of historical sources in a multi-layered methodology for karez research in Turpan, China. Water History. 2020; 12 (3):281-297.

Chicago/Turabian Style

Sophie Barbaix; Alishir Kurban; Philippe De Maeyer; Xi Chen; Jean Bourgeois. 2020. "The use of historical sources in a multi-layered methodology for karez research in Turpan, China." Water History 12, no. 3: 281-297.

Journal article
Published: 24 August 2020 in The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Agriculture is one of the most critical sectors of the Mongolian economy. In Mongolia, land degradation is increasing in the cropland region, especially in a cultivated area. The country has challenges to identify new croplands with sufficient capacity for cultivation, especially for local decision-makers. GIS applications tremendously help science in making land assessments. This study was carried out in Bornuur soum, Mongolia. The goal of this study to estimate that best suitable area for supporting crop production in Bornuur soum, using a GIS-based multi-criteria analysis (MCA) and remote sensing. GIS-based multi-criteria analysis (MCA) has been widely used in land suitability analyses in many countries. In this research, the GIS-based spatial MCA among the Analytical Hierarchy Process (AHP) method has employed. The approach was enhanced for each criterion which as soil, topography and vegetation. The opinions of agronomist experts and a literature review helped in identifying criteria (soil data, topography, water and vegetation data) that are necessary to determine areas suitable for crops. The detailed cropland suitability maps indicate that 46.12 % is highly suitable for cropland, 34.68 % is moderate suitable, 13.64 % is marginal suitable and 5.56 % is not suitable. The MCA and AHP tools play an essential role in the multi-criteria analysis. Therefore, the results of these methods allow us to estimate an appropriate area for cultivation in Bornuur soum, Tuv province. The crop suitability method implies significant decisions on different levels and the result will be used for cropland management plan to make a decision. It is an integral role in agricultural management and land evaluation. Future research should further develop this method by including socio-economic (potential citizens for agriculture, current crop growth, water resource, etc.) and environmental variables (rainfall, vegetation types, permafrost distribution, etc.) to obtain specific results. However, it could be also be applied for a single crop type (mainly barley, wheat and potato) in Mongolia.

ACS Style

E. Natsagdorj; T. Renchin; P. De Maeyer; R. Goossens; T. Van de Voorde; B. Darkhijav. A GIS-BASED MULTI-CRITERIA ANALYSIS ON CROPLAND SUITABILITY IN BORNUUR SOUM, MONGOLIA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 2020, XLIII-B4-2, 149 -156.

AMA Style

E. Natsagdorj, T. Renchin, P. De Maeyer, R. Goossens, T. Van de Voorde, B. Darkhijav. A GIS-BASED MULTI-CRITERIA ANALYSIS ON CROPLAND SUITABILITY IN BORNUUR SOUM, MONGOLIA. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. 2020; XLIII-B4-2 ():149-156.

Chicago/Turabian Style

E. Natsagdorj; T. Renchin; P. De Maeyer; R. Goossens; T. Van de Voorde; B. Darkhijav. 2020. "A GIS-BASED MULTI-CRITERIA ANALYSIS ON CROPLAND SUITABILITY IN BORNUUR SOUM, MONGOLIA." The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B4-2, no. : 149-156.