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The trend of land use changes and the level ‎of ‎ecological risk in the Hara Protected Area

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Document Type : Original article

Abstract

Background and purpose
Monitoring the trend of long-term Land Use (LU) changes in Protected Areas (PAs) and ecological risk assessment is one of the appropriate management methods for prevention of habitat destruction and threats in these areas. The Hara Protected Area (PA) is known as the largest natural habitat in the Persian Gulf and in terms of diversity in habitat form, there are three types of deltaic, island and coastal types with dense, low and thin covers. This area is also rich in flora and fauna and pristine natural landscapes. Therefore, it is necessary to control the process of changes caused by various human activities development and also to assess the ecological risk level caused by these activities. In this regard, the main research questions are: 1) What is the trend of LU changes in the studied area? 2) What is the ecological risk in this area? 3) Considering land use changes trend and ecological risk in the area, what is the status of the landscape pattern indicators?
 
Materials and Methods
In this study, from the topographic map of the studied area (on a scale of 1:25000) and satellite images of Landsat TM in 1989 and 1999, Landsat ETM+ in 2009 and OLI_TIRS in 2021 to evaluate LU changes and habitat integrity was used. In order to classify the images, the random forest algorithm was used and the accuracy of the classification was also evaluated using the overall accuracy obtained from the confusion matrix.
To obtain the ecological risk index, distance sampling method was used in time scale. The studied area was divided into ecological risk assessment units with an area of 10 * 10 km. Then, based on the number of lost (damaged) landscape indicators in each year, ecological risk level was calculated and the obtained results were assigned to evaluation areas as the central pixel. In addition to measuring ecological risk level, from the landscape ‎Separation index (Si) to show the degree of dispersion of patches, from the landscape Fragmentation index (Fi) to check the integrity of the habitat, the Fractal Dimension index (FDi) ‎and Vulnerability (Vi) were also used to measure the geometric order and complexity of the ‎patches. ‎
 
Findings and discussion
Human activities development has caused significant changes in the structure and ‎function of the natural ecosystems of the land. Therefore, it is necessary to continuously ‎monitor and understand the spatial-temporal dynamics of LU changes and the resulting ‎ecological risk. Among the LU classes in the area, tidal zones ‎have the most increasing trend, and water areas show a decreasing trend. The results of the ‎ecological risk assessment also indicate that due to population growth, economic development, ‎and human activities, especially tourism, ecological risk level has increased in very high ‎and high levels classes. Fi, Si, Di, FDi, and Vi indices evaluation, also indicated that most ‎changes are related to mangrove forests and the distance between small forest patches has ‎increased.
 
Conclusion
According to the results, the mangrove forests of the Hara PA have experienced many changes in terms of size and quality over time, and despite their high importance, their distribution and expansion are decreasing due to human activities. In line with these changes, the landscape pattern of the Hara PA has also been affected and its quality has decreased. Accordingly, continuous control and monitoring of human activities to prevent the increase of ecological risk and the reduction of habitat quality in the PAs is essential, and according to the protection approaches, any ‎indiscriminate and unplanned exploitation is prohibited in these areas. ‎
 

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References

Aber, J.S., Marzolff, I. and Ries, J.B., 2010. Image Processing and Analysis (Chapter 11). Small-Format Aerial Photography Principles, Techniques and Geoscience Applications: 159-181. https://doi.org/10.1016/B978-0-444-53260-2.10011-0.
Ait El Haj, F., Ouadif, L. and Akhssas, A., 2023. Simulating and predicting future land-use/land cover trends using CA Markov and LCM models. Case Studies in Chemical and Environmental Engineering: 7, 100342. https://doi.org/10.1016/j.cscee.2023.100342.
Akar, O. and Gungor, O., 2012. Classification of Multispectral Images Using Random Forest Algorithm, Journal of Geodesy and Geoinformation: 1(2), 105-112. https://doi.org/10.9733/jgg.241212.1.
Ansari, A. and Golabi, M.H., 2019. Using Ecosystem Service Modeler (ESM) for Ecological Quality, rarity, and Risk Assessment of the wild goat habitat, in the Haftad-Gholleh protected area, International Soil and Water Conservation Research: 346-353. https://doi.org/10.1016/j.iswcr.2019.08.004
Batar, A.K., Watanabe, T. and Kumar, A., 2017. Assessment of land-use/land-cover change and forest fragmentation in the Garhwal Himalayan Region of India. Environments: 4, 34. https://doi.org/10.3390/environments4020034.
Borrini-Feyerabend, G., Pimbert, M., Farvar, M.T., Kothari, A. and Renard, Y., 2004. Sharing Power: Learning by doing in co-management of natural resources throughout the world, Cenesta, Tehran: IIED and IUCN CEESP/CMWG.
Boston, T., Van Dijk, A., Larraondo, P.R. and Thackway, R., 2022. Comparing CNNs and Random Forests for Landsat Image Segmentation Trained on a Large Proxy Land Cover Dataset. Remote Sens: 14, 3396. https://doi.org/10.3390/rs14143396.
Breiman, L., 2001. Random Forest, Machine Learning: 45(1), 5-32. http://dx.doi.org/10.1023/A:1010933404324.
Chapman, P.M., 2016. Ecological Risk and Weight of Evidence Assessments. Marine Ecotoxicology, http://dx.doi.org/ 10.1016/B978-0-12-803371-5.00009-6.
Chavez, P.S., 1996. Image-based atmospheric Corrections-Revisited and improved. Photogram. Eng. Remote Sens: 62, 1025-1036. 0099-1112/96/6209-1025$3.00/0
Chen, J., Dong, B., Li, H., Zhang, Sh., Peng, L., Fang, L., Zhang, Ch. and Li, Sh., 2020. Study on landscape ecological risk assessment of Hooded Crane breeding and overwintering habitat. Environmental Research: 187, 109649. https://doi.org/10.1016/j.envres.2020.109649.
Cuenca, P., Arriagada, R. and Echeverría, C., 2016. How much deforestation do protected areas avoid in tropical Andean landscapes? Environ. Sci. Policy: 56, 56-66. https://doi.org/10.1016/j.envsci.2015.10.014.
Dai, L., Wang, Y., Lewis, B.J., Xu, D., Zhou, L., Gu, X. and Jiang, L., 2011. The trend of land-use sustainability around the Changbai Mountain Biosphere Reserve in Northeastern China: 1977–2007. Int. J. Sustain. Dev. World Ecol: 19, 369-377. https://doi.org/10.1080/13504509.2012.675599.
Dewa, R.P. and Danoedoro, P., 2017. The effect of image radiometric correction on the accuracy of vegetation canopy density estimate using several Landsat-8 OLI’s vegetation indices: A case study of Wonosari area, Indonesia. IOP Conf. Series: Earth and Environmental Science: 54, 012046. https://doi.org/10.1088/1755-1315/54/1/012046.
Du, Y., Zhang, Y., Ling, F., Wang, Q., Li, W. and Li, X., 2016. Water bodiesʼ mapping from Sentinel-2 imagery with Modified Normalized Difference Water Index at 10-m spatial resolution produced by sharpening the SWIR band. Remote Sens: 8, 354-373. https://doi.org/10.3390/rs8040354.
Elhag, M., Gitas, I., Othman, A., Bahrawi, J. and Gikas, P., 2019. Assessment of water quality parameters using temporal remote sensing spectral reflectance in arid environments. Saudi Arabia. Water: 11, 556. https://doi.org/10.3390/w11030556.
Fang, X.D., Hou, X.Y., Li, X.W., Hou, W., Nakaoka, M. and Yu, X.B., 2018. Ecological connectivity
Fitrian, E.B. and Boro, W.G., 2022. Analysis of Land Use Land Cover Change in Protected Areas Against Spatial Planning in East Luwu. Earth and Environmental Science: 1097, 012061. https://doi.org/10.1088/1755-1315/1097/1/012061.
Gong, J., Yang, J.X. and Tang, W.W., 2015. Spatially explicit landscape-level ecological risks induced by land use and land cover change in a national ecologically representative region in China. International Journal of Environmental Research and Public Health: 12(11), 14192-14215. https://doi.org/10.3390/ijerph121114192
Guo, H., Cai, Y., Li, B., Tang, Y., Qi, Z., Huang, Y. and Yang, Zh., 2022. An integrated modeling approach for ecological risks assessment under multiple scenarios in Guangzhou, China. Ecological Indicators: 142, 109270. https://doi.org/10.1016/j.ecolind.2022.109270.
Huang, B., Hu, X.P., Fuglstad, G.A., Zhou, X., Zhao, W.W. and Cherubini, F., 2020. Predominant regional biophysical cooling from recent land cover changes in Europe. Nat. Commun: 11, 1-13. https://hdl.handle.net/11250/3012242.
Jamal Faruque, Md., Vekerdy, Z., Yeasir Hasan, Md., Islam, K.Z., Young, B., Tofayal Ahmed, M., Monir, M.D., Shovon, Sh.M., Kakon, J.F. and Kundu, P., 2022. Monitoring of land use and land cover changes by using remote sensing and GIS techniques at human-induced mangrove forests areas in Bangladesh. Remote Sensing Applications: Society and Environment: 25, 100699. https://doi.org/10.1016/j.rsase.2022.100699.
Jin, X., Jin, Y. and Mao, X., 2019. Ecological risk assessment of cities on the Tibetan Plateau based on land use/land cover changes—Case study of Delingha City. Ecol. Indic: 101, 185-191. https://doi.org/ 10.1016/j.ecolind.2018.12.050
Jones, D.A., Hansen, A.J., Bly, K., Doherty, K., Verschuyl, J.P., Paugh, J.I., Carle, R. and Story, S.J., 2009. Monitoring land use and cover around parks: A conceptual approach. Remote Sensing of Environment Journal: 113, 1346-1356. https://doi.org/10.1016/j.rse.2008.08.018.
Kamusoko, C. and Jonah, J., 2015. Simulating Urban Growth Using a Random Forest-Cellular Automata (RF-CA) Model, International Journal of Geo-Information: 4 (2), 447-470.
https://doi.org/10.3390/ijgi4020447.
Juan, C., Jiménez-Munoz, J., Sobrino, A., Mattar, C. and Franch, B., 2010. Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products. Remote Sensing of Environment: 114, 2195-2210. https://doi.org/10.1016/j.rse.2010.04.022.
Kleeman, J., Baysal, G., Bulley, H.N.N. and Furst, C., 2017. Assessing driving forces of land use and land cover change by a mixed-method approach in North-Eastern Ghana, West Africa. J. Environ. Manag: 196, 411-442. https://doi.org/10.1016/j.jenvman.2017.01.053.
Lacaux, J.P., Tourre, Y.M., Vignolles, C., Ndione, J.A. and Lafaye, M., 2007. Classification of ponds from high-spatial resolution remote sensing: application to Rift Valley Fever epidemics in Senegal. Remote Sens. Environ: 106, 66-74. https://doi.org/10.1016/j.rse.2006.07.012.
Leberger, R., Rosa, I.M., Guerra, C.A., Wolf, F. and Pereira, H.M., 2019. Global patterns of forest loss across IUCN categories of protected areas. Biol. Conserv: 241, 108299. https://doi.org/10.1016/j.biocon.2019.108299.
Leirpoll, M.E., Naess, J.S., Cavalett, O., Dorber, M., Hu, X. and Cherubini, F., 2021. Optimal combination of bioenergy and solar photovoltaic for renewable energy production on abandoned cropland. Renew. Energ: 168, 45-56. https://doi.org/10.1016/j.renene.2020.11.159.
Li, L., Feng, R. and Xi, J., 2021. Ecological Risk Assessment and Protection Zone Identification for Linear Cultural Heritage: A Case Study of the Ming Great Wall. Int. J. Environ. Res. Public Health: 18 (21), 11605. https://doi.org/10.3390/ijerph182111605.
Li, Sh., He, W., Wang, L., Zhang, Z., Chen, X., Lei, T., Wang, Sh. and Wang, Sh., 2023. Optimization of landscape pattern in China Luojiang Xiaoxi basin based on landscape ecological risk assessment, Ecological Indicators: 146, 109887. https://doi.org/10.1016/j.ecolind.2023.109887.
Li, W., Wei, W. and Boni, L., 2018. Geometric Correction Algorithm for Hyperspectral Images based on GPS, International Journal of Science and Research (IJSR), ISSN: 2319-7064. https://doi.org/10.21275/SR20331142109.
Li, Z., Jiang, W., Wang, W., Chen, Z., Ling, Z. and Lv, J., 2020. Ecological risk assessment of the wetlands in Beijing-Tianjin-Hebei urban agglomeration. Ecol. Indic: 117, 106677. https://doi.org/ 10.1016/j.ecolind.2020.106677.
Lin, Y.Y., Hu, X.S., Zheng, X.X., Hou, X.Y., Zhang, Z.X. and Zhou, X.N., 2019. Spatial variations in the relationships between road network and landscape ecological risks in the highest forest coverage region of China. Ecological Indicators: 96, 392-403. https://doi.org/10.1016/j.ecolind.2018.09.016.
Ling, Y., Zhang, Z., Zhao, X., Liu, B., Wang, X. and Zuo, L., 2014. Spatio-temporal process of unused land resources in China and its ecological effects, In 2013 IEEE International Geoscience and Remote Sensing Symposium-IGARSS, 2665-2668. https://doi.org/10.1002/ldr.4794.
Mafi-Gholami, D. and Jafari, A., 2022. Monitoring changes in the integrity of mangroves in the mangrove biosphere reserve in the face of changes in rainfall and the occurrence of drought. Journal of Marine Science and Technology. https://doi.org/10.22113/jmst.2022.223331.2359.
Mengist, W., Soromessa, T. and Feyisa, G.L., 2023. Forest fragmentation in a forest Biosphere Reserve: Implications for the sustainability of natural habitats and forest management policy in Ethiopia. Resources, Environment and Sustainability: 8, 100058. https://doi.org/10.1016/j.resenv.2022.100058.
Mukul, S.A., 2007. Biodiversity conservation strategies in Bangladesh: The state of protected areas, Tigerpaper: 34, 28-32.
Nunes, J.R., Loures, L., Lopez-Pineiro, A., Loures, A. and Vaz, E., 2016. Using GIS towards the characterization and soil mapping of the caia irrigation perimeter. Sustainability: 8, 368. https://doi.org/10.3390/su8040368.
Resende, F.M., Cimon-Morin, J., Poulin, M., Meyer, L., Joner, D.C. and Loyola, R., 2021. The Importance of Protected Areas and Indigenous Lands in Securing Ecosystem Services and Biodiversity in the Cerrado. Ecosyst. Serv: 49, 101282. https://doi.org/10.1016/j.ecoser.2021.101282.
Sobhani, P., Esmaeilzadeh, H., Barghjelveh, S., Sadeghi, S.M.M. and Marcu, M.V., 2021a. Habitat Integrity in Protected Areas Threatened by LULC Changes and Fragmentation: A Case Study in Tehran Province, Iran. Land: 11, 6. https://doi.org/10.3390/land11010006. Sobhani, P., Esmaeilzadeh, H. and Mostafavi, H., 2021b. Simulation and impact assessment of future land use and land cover changes in two protected areas in Tehran, Iran. Sustainable Cities and Society: 75, 103296. https://doi.org/10.1016/j.scs.2021.103296.
Sobhani, P. and Danehkar, A., 2023. Spatial-temporal changes in mangrove Forests for Analyzing habitat Integrity: A case of Hara Biosphere Reserve, Iran, Environmental and Sustainability Indicators: 20, 100293. https://doi.org/10.1016/j.indic.2023.100293.
Shimu, S., Aktar, M., Afjal, M., Nitu, A., Uddin, M. and Al Mamun, M., 2019. NDVI -based change detection in Sundarban Mangrove Forest using remote sensing data. In Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh: 1-5. https://doi.org/10.1109/EICT48899.2019.9068819.
Tuffour-Mills, D., Antwi-Agyei, Ph. and Addo-Fordjour, P., 2020. Trends and drivers of land cover changes in a tropical urban forest in Ghana, Trees, Forests and People, 2, 100040. https://doi.org/10.1016/j.tfp.2020.100040.
Wang, E., Song, J.P. and Xu, T., 2011. From “spatial bond” to “spatial mismatch”: An assessment of changing jobs-housing relationship in Beijing, Habitat International: 35, 398-409. https://doi.org/10.1016/j.habitatint.2010.11.008
Wang, G., Yu, Q., Yang, D., Zhang, Q.B., Yue, D.P. and Liu, J.H., 2020. Hierarchical ecological network structure based on complex network analysis. Trans. Chin. Soc. Agric. Mach: 50, 258-266. https://doi.org/ 10.6041/j.issn.1000-1298.2019.07.028
Wang, Y.Q., 2009. Remote sensing of land-cover changes and landscape context of the national parks: A case study of the Northeast Temperate Network, Remote Sensing of Environment Journal: 113, 1453-1461. https://doi.org/10.1016/j.rse.2008.09.017.
Wang, Y., Bonynge, G., Nugranad, J., Traber, M., Ngusaru, A., Tobey, J., Hale, L., Bowen, R. and Makota, V., 2003. Remote sensing of mangrove changes along the Tanzania coast, Marine Geodesy: 26, 35-48.
https://doi.org/10.1080/01490410306708.
Wolf, I.D., Sobhani, P. and Esmaeilzadeh, H., 2023. Assessing Changes in Land Use/Land Cover and Ecological Risk to Conserve Protected Areas in Urban–Rural Contexts. Land: 12, 231. https://doi.org/ 10.3390/land12010231.
Xia, Q., Qin, C.Z., Li, H., Huang, C., Su, F.Z. and Jia, M.M., 2020. Evaluation of submerged mangrove recognition index using multi-tidal remote sensing data. Ecol. Indic: 113, 106196. https://doi.org/10.1016/j.ecolind.2020.106196.
Xie, H., Wang, P. and Huang, H., 2013. Ecological Risk Assessment of Land Use Change in the Poyang Lake Eco-economic Zone, China. Int. J. Environ. Res. Public Health: 10, 328-346. https://doi.org/10.3390/ijerph10010328.
Yu, L., Tian, Y. and Wu, W., 2019. A Dark Target Detection Method Based on the Adjacency Effect: A Case Study on Crack Detection. Sensors (Basel): 19(12), 2829. https://doi.org/10.3390/s19122829. PMID: 31242615; PMCID: PMC6630261.
Zeng, H. and Liu, G.J., 1999. Analysis of regional ER based on landscape structure, China Environ. Sci: 19, 454-457. https://doi.org/10.3321/j.issn.1000-6923.%1999.05.017.
Zhang, F., Yu, S., Jiang, A. and Wang, D., 2018. ER assessment due to land use/cover changes (LULC) in Jinghe county, Xinjiang, China from 1989 to 2014 based on landscape patterns and spatial statistics. Environ. Earth Sci: 77, 491. https://doi.org/10.1007/s12665-018-7676-z
Zhang, M., Zhang, C., Al Kafy, A.A. and Tan, S., 2022. Simulating the Relationship between Land Use/Cover Change and Urban Thermal Environment Using Machine Learning Algorithms in Wuhan City, China. Land: 11, 14. https://doi.org/10.3390/land11010014.
Zhang, W., Chang, W.J., Zhu, Z.C. and Hui, Z., 2020. Landscape ER assessment of Chinese coastal cities based on land use change. Appl. Geogr: 117, 102174. https://doi.org/10.1016/j.apgeog.2020.102174.
Zomer, R.J., Ustin, S.L. and Carpenter, C.C., 2001. Land cover change along tropical and subtropical riparian corridors within the Makalu Barun National Park and Conservation Area, Nepal. Mt. Res. Dev: 21, 175-183. https://doi.org/10.1659/0276-4741(2001)021[0175: LCCATA]2.0.CO;2.
Zurmure, N., Sawant, S., Shindikar, M. and Lele, N., 2021. Mapping the spatio-temporal changes in mangrove vegetation along Thane Creek, India, In Proceedings of the 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium: 7557-7560. https://igarss2021.com/view_paper.
 
Sustainable Development of Geographical Environment
Volume 5, Issue 9
January 2024
Pages 1-19
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