Combining Moderate and High Resolution of Satellite Images for Characterizing Suitable Habitat for Vegetation and Wildlife

https://doi.org/10.22146/jtbb.77710

Sheriza Mohd Razali(1*), Zaiton Samdin(2), Marryanna Lion(3)

(1) Institute of Tropical Forestry and Forest Products, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor
(2) Institute of Tropical Forestry and Forest Products, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor; School of Business and Economics, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor
(3) Forest Research Institute Malaysia, 52109 Kepong, Selangor
(*) Corresponding Author

Abstract


Combining different resolution of remote sensing satellites becomes a unique approach for vegetation and wildlife habitat assessment study. Remote sensing technology can reach land and water on the Earth's surface, and it can interpret signals from spectral responses. When these techniques are combined with Geographical Information Systems (GIS), land can be monitored in a variety of ways. Meanwhile, changes in land use led to changes in vegetation on the ground, with natural vegetation being removed from natural forests, leaving a degraded forest. This issue was not investigated for assessing habitat suitability for important plantations such as Eucalyptus plantation. Therefore, the study employed remote sensing and Geographical Information System (GIS) to model suitability of habitat to live and to survive in the Eucalyptus plantation. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from a mathematical equation can demonstrate intensity of greenness of green vegetation in particular area and time, and availability of soil moisture, respectively, is very suitable to model the greenness of the area. WorldView-2 satellite image was pre-proceed, proceed, and classified to produce land use indicator in Sabah Softwoods Berhad plantation majoring Eucalyptus spp. tree planted in Tawau, Sabah. Sentinel and Landsat 8 image were used for vegetation and water stress indicator were downloaded from Land Viewer application. Net Primary Productivity (NPP) at monthly scale was also calculated and ranked the productivity for the suitability mapping. Climatic condition based on monthly precipitation and seasonality derived from ASEAN Specialized Meteorological Centre (ASMC) was employed for ranking its suitability value. In this study, natural forest and oil palm plantation is tested to developed suitability map for vegetation and wildlife habitat to live with. All indicators were ranked 10 to 40 presenting benefit and usefulness of the indicator to vegetation and wildlife in the study area. Then, final classification was made from accumulation of those indicators into 0 to 200 (Not suitable to Highly suitable). The results showed 59.9% of the area classified as moderately suitable, 36.9% highly suitable, 3.2% least suitable and no area was classified as not suitable. This type of study assisted forest managers and policymakers for better managing of their forests for better life of trees and wildlife under their management. The methodology adapted in the study is ecologically sounded and economically viable to be modified and complied in Sustainable Forest Management (SFM) in Malaysia and other tropical forest regions.

 


Keywords


High resolution satellite image; wildlife habitat; NDVI

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References

Abbas, S. et al., 2020. Approaches of Satellite Remote Sensing for the Assessment of Above-Ground Biomass across Tropical Forests : Pan-tropical to National Scales. Remote Sens., 12(20), 3351. doi: 10.3390/rs12203351

Bhuiyan, C., Singh, R.P., & Kogan, F.N., 2006. Monitoring Drought Dynamics in the Aravalli Region (India) Using Different Indices Based on Ground and Remote Sensing Data. International Journal of Applied Earth Observation and Geoinformation, 8(4), pp.289–302. doi: 10.1016/j.jag.2006.03.002.

Bowyer, P. & Danson, F.M., 2004. Sensitivity of Spectral Reflectance to Variation in Live Fuel Moisture Content at Leaf and Canopy Level. Remote Sensing of Environment 92(3), pp.297–308. doi: 10.1016/j.rse.2004.05.020.

Braswell, B.H. et al., 2003. A Multivariable Approach for Mapping Sub-Pixel Land Cover Distributions Using MISR and MODIS: Application in the Brazilian Amazon Region. Remote Sensing of Environment, 87(2–3), pp.243–256. doi: 10.1016/j.rse.2003.06.002.

Caccamo, G. et al., 2011. Assessing the Sensitivity of MODIS to Monitor Drought in High Biomass Ecosystems. Remote Sensing of Environment, 115(10), pp.2626–39. doi: 10.1016/j.rse.2011.05.018.

Caccamo, G. et al., 2015. Using MODIS Data to Analyse Post-Fire Vegetation Recovery in Australian Eucalypt Forests. Journal of Spatial Science, 60(2), pp.341–52. doi: 10.1080/14498596.2015.974227.

Caturegli, L. et al., 2020. Effects of Water Stress on Spectral Reflectance of Bermudagrass. Scientific Reports, 10, 15055. doi: 10.1038/s41598-020-72006-6.

Chandrasekar, K. et al., 2010. Land Surface Water Index (LSWI) Response to Rainfall and NDVI Using the MODIS Vegetation Index Product. International Journal of Remote Sensing, 31(15), pp.3987–4005. doi: 10.1080/01431160802575653.

Chen, P. et al., 2021. Vegetation dynamic assessment by ndvi and field observations for sustainability of China’s Wulagai river basin. International Journal of Environmental Research and Public Health, 18(5), pp.1–20. doi: 10.3390/ijerph18052528.

Cheng, Y.-B. et al., 2006. Estimating vegetation water content with hyperspectral data for different canopy scenarios: Relationships between AVIRIS and MODIS indexes. Remote Sensing of Environment, 105(4), pp.354–366. doi: 10.1016/j.rse.2006.07.005.

Coops, N. et al., 2010. Estimation of Light-Use Efficiency of Terrestrial Ecosystems from Space: A Status Report. BioScience, 60(10), pp.788–797. doi: 10.1525/bio.2010.60.10.5.

Darmawan, Y. & Sofan, P., 2012. Comparison of the Vegetation Indices to Detect the Tropical Rain Forest Changes Using Breaks for Additive Seasonal and Trend (Bfast) Model. International Journal of Remote Sensing and Earth Sciences (IJReSES), 9(1), pp.21-34. doi: 10.30536/j.ijreses.2012.v9.a1823.

Dash, J.P. et al., 2017. Assessing very high resolution UAV imagery for monitoring forest health during a simulated disease outbreak. ISPRS Journal of Photogrammetry and Remote Sensing, 131, pp.1–14. doi: 10.1016/j.isprsjprs.2017.07.007.

Gao, Bo-cai., 1996. NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sensing of Environment, 58, pp.257–66.

Gardner, T.A. et al., 2007. The value of primary, secondary, and plantation forests for a neotropical herpetofauna. Conservation Biology, 21(3), pp.775–787. doi: 10.1111/j.1523-1739.2007.00659.x.

Hansen, M.C. et al., 2017. Temporal Forest Change Detection and Forest Health Assessment using Remote Sensing. IOP Conf. Ser.: Earth Environ. Sci., 19, 012017. doi: 10.1088/1755-1315/19/1/012017.

IC-CFS, 2021, 'Introducing the IC-CFS', in Improving Connectivity in The Centra Forest Spine (CFS), viewed 4 January 2022, from https://www.ic-centralforestspine.com.my

Malhi, Y. et al., 2022. Logged tropical forests have amplified and diverse ecosystem energetics. Nature, 612, . doi: 10.1038/s41586-022-05523-1.

Markos, F. et al., 2018. Solar Radiation Resources Under Climate Change Scenarios-A Case Study in Kota Kinabalu, Sabah, Malaysia. Transactions on Science and Technology, 5(1), pp.12–24.

MPOCC., 2022, ‘Co-Existing with Elephants - The Sabah Sofywoods Berhad Experience’, in Malaysia Palm Oil Certification Council, from https://www.mpocc.org.my/collection-stories-from-the-field/co-existing-with-elephants.

Nathan, R., 2016. Human Wildlife Conflict and Evolving Mitigation Methods: Sabah Softwoods Berhad’s Experience. Malaysia Palm Oil Council (MPOC) 1–2.

Ng, C.K.C. et al., 2019. Precipitation Trend and Heterogeneity of Sabah, North Borneo. Sepilok Bulletin, 28, pp.19–43.

Nunes, E.L. et al., 2012. Monitoring carbon assimilation in South America’s tropical forests: Model specification and application to the Amazonian droughts of 2005 and 2010. Remote Sensing of Environment, 117, pp.449–463. doi: 10.1016/j.rse.2011.10.022.

O’Neil, S. et al., 2020. Wildfire and the Ecological Niche: Diminishing Habitat Suitability for an Indicator Species within Semi-Arid Ecosystems. Global Change Biology, 26(11), pp.6296–6312. doi: 10.1111/gcb.15300.

Potter, C. et al., 2013. Forest production predicted from satellite image analysis for the Southeast Asia region. Carbon balance and management, 8, 9. doi: 10.1186/1750-0680-8-9.

Penuelas, J. et al., 1997. Estimation of Plant Water Concentration by the Reflectance Water Index WI (R900/R970). International Journal of Remote Sensing, 18(13), 2869–2875. doi: 10.1080/014311697217396.

Pujiono, E. et al., 2013. RGB-NDVI Color Composites for Monitoring the Change in Mangrove Area at the Maubesi Nature Reserve, Indonesia. Forest Science and Technology, 9(4),171–179. doi: 10.1080/21580103.2013.842327.

Razali, S.M. et al., 2015. Monitoring Vegetation Drought Using MODIS Remote Sensing Indices for Natural Forest and Plantation Areas. Journal of Spatial Science, 61(1), pp.157-172. doi: 10.1080/14498596.2015.1084247.

Razali, M.R., Nuruddin, A.A. & Lion, M., 2019. Mangrove Vegetation Health Assessment Based on Remote Sensing Indices for Tanjung Piai, Malay Peninsular. Journal of Landscape Ecology, 12(2), pp.1–16. doi: 10.2478/jlecol-2019-0008.

Razali, S. M. & Lion, M., 2021. Eucalyptus Forest Plantation Assessment of Vegetation Health Using Satellite Remote Sensing Techniques. IOP Conference Series: Earth and Environmental Science, 918, 012041. doi: 10.1088/1755-1315/918/1/012041.

Razali, S.M. et al., 2022. Monitoring green biomass utilizing remote sensing techniques for agriculture and forest areas in East Malaysia. IOP Conference Series: Earth and Environmental Science, 1064, 012004. doi: 10.1088/1755-1315/1064/1/012004.

Rouse, J. W. et al., 1973. Monitoring Vegetation Systems in the Great Plains with ERTS. Third Earth Resources Technology Satellite (ERTS) Symposium, 1, pp.309–317. doi: citeulike-article-id:12009708.

Russ, E.R. et al., 2022. Habitat Classification Predictions on an Undeveloped Barrier Island Using a GIS-Based Landscape Modeling Approach. Remote Sensing, 14(6). doi: 10.3390/rs14061377

Sukarno, K. et al., 2017. Comparison of Power Output between Fixed and Perpendicular Solar Photovoltaic PV Panel in Tropical Climate Region. Advanced Science Letters, 23(2), pp.1259–1263. doi: 10.1166/asl.2017.8379.

Toomey, M. & Vierling, L.A., 2005. Multispectral remote sensing of landscape level foliar moisture : techniques and applications for forest ecosystem monitoring. Canadian Journal of Forest Research, 1097, pp.1087–1097. doi: 10.1139/X05-043.

Trishchenko, A.P., 2009. Effects of spectral response function on surface reflectance and NDVI measured with moderate resolution satellite sensors: Extension to AVHRR NOAA-17, 18 and METOP-A. Remote Sensing of Environment, 113(2), pp.335–341. doi: 10.1016/j.rse.2008.10.002.

Vallan, D., 2002. Effects of anthropogenic environmental changes on amphibian diversity in the rain forests of eastern Madagascar. Journal of Tropical Ecology, 18(5), pp.725–742. doi: 10.1017/S026646740200247X.

Vidhya, R. et al., 2014. Improved Classification of Mangroves Health Status Using Hyperspectral Remote Sensing Data. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL–8, pp.667–670. doi: 10.5194/isprsarchives-XL-8-667-2014.

Xiao, X. et al., 2004. Satellite-Based Modeling of Gross Primary Production in an Evergreen Needleleaf Forest. Remote Sensing of Environment, 89, pp.519–534. doi: 10.1016/j.rse.2003.11.008.



DOI: https://doi.org/10.22146/jtbb.77710

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