Kırgızistan’da ülke çapında uzaktan algılama tabanlı arazi kullanım sınıflandırmasının saha doğrulaması

Year 2024, , 206 - 223, 15.12.2024

Çağlar Başsüllü , Pablo Martín-ortega

https://doi.org/10.17568/ogmoad.1533789

Cited By: 1

Abstract

Arazi kullanımı, arazi kullanım değişikliği ve ormancılık (AKAKDO) eğilimlerini gözlemlemek ve izlemek için uzaktan algılama yaygın olarak kullanılmaktadır. Ücretsiz bir uzaktan algılama yazılımı olan Collect Earth, Kırgızistan’da 2015 ve 2019 yıllarındaki tarihi ve mevcut AKAKDO eğilimlerini değerlendirmek için kullanılmıştır. Ancak, yeterli zamansal ve mekansal çözünürlüğe sahip uydu görüntüleri mevcut değilse, kullanıcıların arazi örtüsünü sınıflandırması ve arazi kullanımındaki değişiklikleri belirlemesi oldukça zordur. Yüksek/çok yüksek mekansal ve zamansal çözünürlüklü uydu görüntülerinin eksikliği (%7,2) ve düşük mekansal ve zamansal çözünürlüklü uydu görüntülerinin (%7,8) varlığı, zorunlu saha doğrulamasının birincil nedeni olmuştur. 2019 yılında, arazide seçilen örnek sahalar ziyaret edilerek bir saha çalışması yürütülmüştür. Toplamda 941 örnek saha ziyaret edilmiş ve arazi doğrulama çalışması sırasında 119 yanlış sınıflandırılmış örnek saha tespit edilmiştir. Bu nedenle, bu makale Kırgızistan’daki AKAKDO değerlendirmesinin güncellenmiş bir versiyonunu sunmaktadır. Veritabanı güncellemesi, Kırgızistan›daki 1073 örnek alanın yeniden sınıflandırılmasıyla sonuçlanmıştır. Saha doğrulama sonuçları, Kırgızistan’da ormanlık alanların 2019’da toplam arazinin 1,81 milyon hektarını (%9) kapladığını %5,33’lük bir belirsizlik ile göstermiştir. Halbuki, bu alan, uzaktan algılama çalışmasına göre 1,36 milyon ha olarak hesaplanmıştır.

Keywords

Collect Earth, Saha doğrulaması, Arazi kullanımı, Uzaktan algılama, Kırgızistan

Thanks

Saygılarımla

Field validation of country-wide remote sensing based-land use classification in Kyrgyzstan

Abstract

Observing and monitoring land use, land-use change and forestry (LULUCF) trends has extensively been used remote sensing. Collect Earth, a free remote sensing tool, was used in Kyrgyzstan to assess the historical and present LULUCF trends in 2015 and 2019. However, it is quite difficult for users to classify land cover and determine changes in land use if no satellite images with sufficient temporal and spatial resolution are available. The unavailability of high/very high spatial and temporal resolution satellite images (7.2%) or the availability of low spatial and temporal resolution satellite images (7.8%) was the primary reason for mandatory field verification. A fieldwork was conducted to validate the remote sensing assessment in 2019. In total, 941 sample plots were visited, and 119 misclassified sample plots were detected during the field validation work. Hence, this article reports an updated version of LULUCF assessment in Kyrgyzstan. The database update resulted in the re-classification of 1073 sample plots in Kyrgyzstan. The results of the field validation showed that forestlands occupied 1.81 million ha (9%) of the total land in 2019, with a 5.33% uncertainty in Kyrgyzstan. However, it was 1.36 million ha based on the remote sensing study.

Keywords

Collect Earth, Field validation, Land use, Remote sensing, Kyrgyzstan

References

• Achard, F., Stibig, H.J., Eva, H.D., Lindquist, E.J., Bouvet, A., Arino, O., et al., 2010. Estimating tropical deforestation from Earth observation data. Carbon Management. 1:2, 271-287. doi: 10.4155/cmt.10.30
• Ardo, J., 1992. Volume quantification of coniferous forest compartments using spectral radiance recorded by Landsat Thematic Mapper. International Journal of Remote Sensing. 13(9): 17791786
• Arsanjani, J.J., 2011. Dynamic Land Use / Cover Change Modelling: Geosimulation and Agent-Based Modelling.: University of Vienna; Vienna
• Baccini, A., Friedl, M.A., Woodcock, C.E., Zhu, Z., 2007. Scaling field data to calibrate and validate moderate spatial resolution remote sensing models. Photogrammetric Engineering & Remote Sensing 73(8): 945-954
• Bassullu, C., and Martín-Ortega, P., 2023. Using Open Foris Collect Earth in Kyrgyzstan to support greenhouse gas inventory in the land use, land use change, and forestry sector. Environmental Monitoring and Assessment. 195. Doi.org/10.1007/s10661-023-11591-1
• Bastin, J.-F., Berrahmouni, N., Grainger, A., Maniatis, D., Mollicone, D., Moore, R., Patriarca, C., Picard, N., Sparrow, B., Abraham, E.M., Aloui, K., Atesoglu, A., Attore, F., Bassüllü, Ç., Bey, A., Garzuglia, M., García-Montero, L.G., Groot, N., Guerin, G., Laestadius, L., Lowe, A.J., Mamane, B., Marchi, G., Patterson, P., Rezende, M., Ricci, S., Salcedo, I., Diaz, A.S.-P., Stolle, F., Surappaeva, V., Castro, R., 2017. The extent of forest in dryland biomes. Science. 356(6338): 635-638
• Bey, A., Diaz, A.S., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., et al., 2016. Collect Earth: land use and land cover assessment through augmented visual interpretation. Remote Sensing. 8(10). Doi.org/10.3390/rs8100807
• Chyngojoev, А., Surappaeva, В., Altrell, D., 2011. Integrated Assessment of Natural Resources of Kyrgyzstan 2008-2010. State Agency for Environmental Protection and Forestry. Kyrgyz Republic, Bishkek
• Cohen, W.B., Spies, T.A., 1992. Estimating structural attributes of Douglas-fir/western hemlock forest stands from Landsat and SPOT imagery. Remote Sensing of Environment. 41(1): 1-17
• Cohen, W., Maiersperger, T., Spies, T., Oetter, D., 2001. Modeling forest cover attributes as continuous variables in a regional context with Thematic Mapper data. International Journal of Remote Sensing. 22(12): 2279-2310
• Cohen, W., Maiersperger, T., Gower, S., Turner, D., 2003. An improved strategy for regression of biophysical variables and Landsat ETM data. Remote Sensing of Environment. 84(4): 561-571
• Crowther, T.W., Glick, H.B., Covey, K.R, Bettigole, C, Maynard, D.S., Thomas, S.M., et al., 2015. Mapping tree density at a global scale. Nature. 525, 201–205. doi:10.1038/nature14967
• Danson, F.M., and Curran, P.J., 1993. Factors affecting the remotely sensed response of coniferous forest plantations. Remote Sensing of Environment. 43(1): 55-65
• De Simone, L., Navarro, D., Gennari, P., Pekkarinen, A., de Lamo, J., 2021. Using standardized time series land cover maps to monitor the SDG indicator “Mountain Green Cover Index” and assess its sensitivity to vegetation dynamics. ISPRS International Journal of Geo-Information. 10(7): 427. doi.org/10.3390/ijgi10070427
• Franklin, J., 1986. Thematic mapper analysis of coniferous forest structure and composition. International Journal of Remote Sensing. 7(10): 1287-1301
• García-Montero, L.G., Pascual, C., Martín-Fernández, S., Sanchez-Paus Díaz, A., Patriarca, C., Martín-Ortega, P., Mollicone, D., 2021a. Medium- (MR) and Very-High-Resolution (VHR) image integration through Collect Earth for monitoring forests and land-use changes: Global Forest Survey (GFS) in the temperate FAO Ecozone in Europe (2000-2015), Remote Sensing. 13(21): 4344. doi.org/10.3390/rs13214344
• García-Montero, L.G., Pascual, C., Sanchez-Paus Díaz, A., Martín-Fernández, S., Martín-Ortega, P., García-Robredo, F., et al., 2021b. Land use sustainability monitoring: “Trees outside forests” in temperate FAO-Ecozone (oceanic, continental, and Mediterranean) in Europe (2000-2015). Sustainability. 13(18): 10175.
• Gemmell, F.M., 1995. Effects of forest cover, terrain, and scale on timber volume estimation with Thematic Mapper data in a Rocky Mountain site. Remote Sensing of Environment. 51(2): 291-305
• GoK., 2022. Kyrgyzstan Brief Statistical Handbook. National Statistical Committee of the Kyrgyz Republic. stat.kg/media/publicationarchive/672efdec-dda1-400c-96b4-f0508d24d220.pdf [Accessed on April 5, 2024] Hansen, M.C., Potapov, P.V., Moore, R., Hancher, M., Turubanova, S.A., Tyukavina, A., et al., 2013. High-resolution Global Maps of 21st-century forest cover change. Science. 342(6160): 850-853. glad.earthengine.app/view/global-forest-change (Accessed on April 5, 2024)
• Haub, C., Kleinewillinghöfer, L., García, V., Di Gregorio, A., 2015. Protocol for Land Cover Validation, SIGMA– Stimulating Innovation for Global Monitoring of Agriculture.. eftas.de/upload/15356999-SIGMA-D33-2-Protocol-for-land-cover-validation-v2.0-2015-06-22vprint.pdf (Accessed on 20 Aug 2024)
• Hua, A.K., 2017. Land use land cover changes in detection of water quality: A study based on remote sensing and multivariate statistics. Journal of Environmental and Public Health. 2017(1): 1-12. doi.org/10.1155/2017/7515130 IPCC, 2006. The Intergovernmental Panel on Climate Change. IPCC Guidelines for National Greenhouse Gas Inventories. ipcc.ch/report/2006-ipcc-guidelines-for-national-greenhouse-gas-inventories/ [Accessed on March 8, 2024]
• Isaev E., Kulikov, M., Shibkov, E., Sidle, R.C., 2022. Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan. Journal of Applied Remote Sensing. 17(2). 022204-1
• Jia, T., Li, Y., Shi, W., Zhu, L., 2019. Deriving a forest cover map in Kyrgyzstan using a hybrid fusion strategy. Remote Sensing. 11(19): 2325. doi:10.3390/rs11192325
• Khadka, A., Dhungana, M., Khanal, S., Kharal, D.K., 2020. Forest and other land cover assessment in Nepal using Collect Earth. Banko Janakari. 30(1): 3‒11. doi.org/10.3126/banko.v30i1.29176
• Klein, I., Gessner, U., Kuenzer, C., 2012. Regional land cover mapping and change detection in Central Asia using MODIS time-series. Applied Geography. 35(1-2): 219-234. dx.doi.org/10.1016/j.apgeog.2012.06.016
• Klein, T., Nilsson, M., Persson, A., Hakansson, B., 2017. From open data to open analysis—new opportunities for environmental applications? Environments. 4, 32
• Lambin, E., 2006. Land Cover Assessment and Monitoring. Encyclopedia of Analytical Chemistry: : Applications, Theory and Instrumentation. doi.org/10.1002/9780470027318.a2311
• Liping, C., Yujun, S., Saeed, S., 2018. Monitoring and predicting land use and land cover Changes using remote sensing and GIS techniques-A case study of a hilly area, Jiangle, China. PLoS One 13(7): e0200493. doi.org/10.1371/journal.pone.0200493
• Lister, A., Lister, T., Weber, T., 2019. Semiautomated sample-based forest degradation monitoring with photointerpretation of high-resolution imagery. Forests. 10, 896. doi:10.3390/f10100896
• Martín-Ortega, P., Picard, N., García-Montero, L.G., del Río, S., Penas, A., Marchetti, M. et al., 2018. Importance of Mediterranean forests. In: State of Mediterranean Forests, 2018, 1st ed., Food and Agriculture Organization of the United Nations: Rome, Plan Bleu: Marseille, p. 31-50 (openknowledge.fao.org/items/25b72969-96f1-4af8-885b-40e2a07995a1)
• Martínez, S., Mollicone, D., 2012. From land cover to land use: A methodology to assess land use from remote sensing data. Remote Sensing. 4(4): 1024-1045. doi: 10.3390/rs4041024
• McConnell, W.J., 2015. Land Change: The Merger of Land Cover and Land use Dynamics A2—Wright, James D. International Encyclopedia of the Social & Behavioral Sciences (Second Edition). Oxford: Elsevier; 2015. p. 220–3.
• Milne, B.T., and Cohen, W.B., 1999. Multiscale assessment of binary and continuous landcover variables for MODIS validation, mapping, and modeling applications. Remote Sensing of Environment. 70(1): 82-98
• Mishra, V.N., Rai, P.K., Kumar, P., Prasad, R., 2016. Evaluation of land use/land cover classification accuracy using multi-resolution remote sensing images. Forum Geografic. XV(1): 45-53. doi.org/10.5775/fg.2016.137.i
• Nazarkulov, K., Koshoev, M., Toktomametova, J., Sakyev, D., 2021. Geohazards inventory in Central Asia using the Geohazard Mapping Module of the FAO Collect Earth and Earth Map Tools. International Journal of Geoinformatics. 17(1): 93-98. Doi: 10.52939/ijg.v17i1.1719
• Olokeogun, O.S., Iyiola, K., Iyiola, O.F., 2014. Application of remote sensing and GIS in land use/land cover mapping and change detection in Shasha Forest Reserve, Nigeria. ISPRS-Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8(8): 613-616.
• Pervez, W., Uddin, V., Khan, S.A., Khan, J.A., 2016. Satellite-based land use mapping: comparative analysis of Landsat-8, Advanced Land Imager, and big data Hyperion imagery. J. Appl. Remote Sens. 10(2). doi.org/10.1117/1.jrs.10.026004
• Piroton, V., Schlögel, R., Barbier, C., Havenith, H.B., 2020. Monitoring the recent activity of landslides in the Mailuu-Suu Valley (Kyrgyzstan) using radar and optical remote sensing techniques. Geosciences. 10(5): 164. doi:10.3390/geosciences10050164
• Potapov, P., Turubanova, S., Hansen, M.C., 2011. Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia. Remote Sensing of Environment. 115, 548–561
• Pradhan, B., Lee, S., Mansor, S., Buchroithner, M., Jamaluddin, N., Khujaimah, Z., 2008. Utilization of optical remote sensing data and geographic information system tools for regional landslide hazard analysis by using binomial logistic regression model. Journal of Applied Remote Sensing. 2(1). 023542. doi.org/10.1117/1.3026536
• Puhr, C.B., Donoghue, D.N.M., 2000. Remote sensing of upland conifer plantations using Landsat TM data: A case study from Galloway, South-West Scotland. International Journal of Remote Sensing. 21(4): 633-646
• Rai, P.K., Vishwakarma, C.A., Thakur, S., Kamal, V., Mukherjee, S., 2016. Changing Land Trajectories: A Case Study from India using a remote sensing based approach. European Journal of Geography. 7(2): 63-73
• Reis, S., 2008. Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey. Sensors. 8(10): 6188-202. doi.org/10.3390/s8106188. PMID: 27873865
• Romero-Sanchez, M.E., Ponce-Hernandez, R., 2017. Assessing and monitoring forest degradation in a deciduous tropical forest in Mexico via remote sensing indicators. Forests. 8, 302. doi:10.3390/f8090302
• Schepaschenko, D., See, L., Lesiv, M., McCallum, I., Fritz, S., Salk, C., et al., 2015. Development of a global hybrid forest mask through the synergy of remote sensing, crowdsourcing and FAO statistics. Remote Sensing of Environment. 162: 208-220
• Schepaschenko, D., See, L., Lesiv, M., Bastin, J.F., Mollicone, D., Tsendbazar, N.E., et al., 2019. Recent advances in forest observation with visual interpretation of very high‑resolution imagery. Surveys in Geophysics. 40: 839–862. doi.org/10.1007/s10712-019-09533-z
• Scheuber, M., 1999. First National Forest Inventory of the Kyrgyz Republic and Leshoz Management Inventory. Singh, S.K., Laari, P.B., Mustak, S., Srivastava, P.K., Szabo´, S., 2017. Modeling of land use land cover change using earth observation datasets of Tons River Basin, Madhya Pradesh, India. Geocarto Int. 33(11): 1-34
• Srivastava, P.K., Singh, S.K., Gupta, M., Thakur, J.K., Mukherjee, S., 2013. Modeling impact of land use change trajectories on groundwater quality using remote sensing and GIS. Environmental Engineering and Management Journal. 12(12): 2343-55
• Tian, Y., Woodcock, C.E., Wang, Y., Privette, J.L., Shabanov, N.V., Zhou, L., Zhang, Y., Buermann, W., Dong, J., Veikkanen, B., Hame, T., Andersson, K., Ozdogan, M., Knyazikhin, Y., Myneni, R.B., 2002. Multiscale analysis and validation of the MODIS LAI product I. Uncertainty assessment. Remote Sensing of Environment. 83(3): 414-430
• Turner, W., Rondinini, C., Pettorelli, N., Mora, B., Leidner, A.K., Szantoi, Z., et al., 2015. Free and open-access satellite data are key to biodiversity conservation. Biological Conservation. 182, 173–176
• UN, 2011. Map of Kyrgyzstan. https://www.un.org/geospatial/content/kyrgyzstan.
• Wulder, M., 1998. Optical remote sensing techniques for the assessment of forest inventory and biophysical parameters. Progress in Physical Geography: Earth and Environment. 22(4): 449-476
• Wulder, M.A., Coops, N.C., 2014. Make Earth observations open access. Nature. 513, 30–31