Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery

Projo Danoedoro, Diwyacitta Dirda Gupita

Abstract


Forest cover density (FCD) transformation is an 8-bit Landsat imagery-based method for vegetation mapping, which uses a set of indices comprising vegetation, soil, shadow, and thermal components. With the advent of 16-bit Landsat-8 imagery, radiometric correction and pan-sharpening methods could be applied to generate new datasets with different spectral and spatial characteristics. This study combined several methods of pan-sharpening and FCD transformations for mapping vegetation density in Salatiga and Ambarawa region, Indonesia, based on Landsat-8 dataset. The imagery was treated differently to constitute five new datasets, i.e., original multispectral imagery (30 m), radiometrically corrected multispectral imagery (30 m), and three pan-sharpening datasets generated using Gram-Schmidt (GS), Principal Component Analysis (PCA), and Hyper-spherical Color Space (HCS) methods (15 m). Each dataset was then processed using FCD transformation as a basis for vegetation density and structural composition mapping. Field observation and vegetation density measurement using high-spatial-resolution imagery was used as a reference for accuracy assessment. This study found that the pan-sharpening methods produced new datasets with various correlation coefficients with their corresponding original bands, affecting the accuracy of spectral modeling in FCD. Moreover, the generated FCD models were found less accurate as compared to that of the original one. However, the accuracy could be increased by rescaling the original DNs and regrouping the original classes into simpler categorization. Besides the problem of data characteristics, all FCD models were also found inaccurate compared to previous studies due to the landscape complexity of the study area.

Keywords


Image processing; radiometric correction; pan-sharpening; vegetation mapping; forest cover density.

Full Text:

PDF

References


J. G. Zaehringer Llopis, J.C., Latthachack, P. Tun Tun Thein, T.T., and Heinimann, A, “A novel participatory and remote-sensing-based approach to mapping annual land use change on forest frontiers in Laos, Myanmar, and Madagascar,†J. Land Use Sci., vol. 13, no. 1–2, pp. 16–31, 2018, doi: 10.1080/1747423X.2018.1447033.

J. Deka, O.P. Tripathi, and M. L. Khan, “Implementation of Forest Canopy Density Model to Monitor Tropical Deforestation,†J. Indian Soc. Remote Sens., vol. 41, no. 2, pp. 469–475, 2013.

P. Danoedoro, “Multidimensional Land-use Information for Local Planning and Land Resources Assessment in Indonesia: Classification Scheme for Information Extraction from High-Spatial Resolution Imagery,†Indones. J. Geogr., vol. 51, no. 2, pp. 131–146, 2019, doi: 10.22146/ijg.32781.

Y. Xi et al., “Mapping Tree Species Composition Using OHS-1 Hyperspectral Data and Deep Learning Algorithms in Changbai Mountains, Northeast China,†Forests, vol. 10, no. 818, pp. 2–17, 2019, doi: 10.3390/f10090818.

P. Danoedoro et al., “Developing interpretation methods for detailed categorization-based land-cover/land-use mapping at 1:50,000 scale in Indonesia,†in Sixth International Symposium on LAPAN-IPB Satellite, 2019, vol. 11372.

M. Y. McPartland et al., “Characterizing Boreal Peatland Plant Composition and Species Diversity with Hyperspectral Remote Sensing,†Remote Sens., vol. 11, pp. 1685–1716, 2019, doi: 10.3390/rs11141685.

K. C. Hennessy and M. Lewis, “Hyperspectral Classification of Plants: A Review of Waveband Selection Generalisability,†Remote Sens., vol. 12, no. 113, pp. 2–27, 2019, doi: 10.3390/rs12010113.

W. Zhao, T. Mu, and D. Li, “Classification of hyperspectral images based on two-channel convolutional neural network combined with support vector machine algorithm,†J. Applied. Remote Sensing, vol. 14, no. 2, pp. 2–17, 2020.

P. Danoedoro, “Multisource Classification for Land-Use Mapping Based on Spectral, Textural, and Terrain Information Using Landsat Thematic Mapper Imagery A Case Study of Semarang-Ungaran Area,†Indones. J. Geogr., vol. 35, no. 2, pp. 81–106, 2003.

E. F. Berra and R. Gaulton, “Remote sensing of temperate and boreal forest phenology: A review of progress, challenges and opportunities in the intercomparison of in-situ and satellite phenological metrics,†For. Ecol. Manage., vol. 480, pp. 1–17, 2020, doi: 10.106/j.foreco.2020.118663.

J. Xue and B. Su, “Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications,†J. Sensors, vol. 2017, pp. 1–17, 2017, doi: 10.1155/2017/1353691.

R.P. Dewa and P. Danoedoro, “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,†in the 4th LISAT Conference, 2017, vol. 54, no. 1, doi: 10.1088/1755-1315/54/1/012046.

A. Rikimaru, P.S. Roy, and S. Miyatake, “Tropical forest cover density mapping,†Trop. Ecol., vol. 43, no. 1, pp. 39–47, 2002.

H. van Gils, I. S. Zonneveld, W. van Wijngaarden, A. Kannegieter, and H. Huizing, Land Ecology and Land-use Survey. Enschede: International Institute for Aerospace Survey and Earth Sciences (ITC), 1990.

S. Pladsrichua, R. Suwanwerakamtorn, and N. Pannucharoenwong, “Estimating Vegetation Canopy Density in the Lower Chi Basin, Northeast,Thailand Using Landsat Data,†Int. J. Appl. Eng. Res., vol. 13, no. 6, pp. 3215–3219, 2018.

B. Bera, S. Saha, and S. Bhattacharjee, “Estimation of Forest Canopy Cover and Forest Fragmentation Mapping Using Landsat Satellite Data of Silabati River Basin (India),†KN - J. Cartogr. Geogr. Inf., vol. 70, no. 4, pp. 181–197, 2020, doi: 10.1007/s42489-020-00060-1.

A. Abdollahnejad, D. Panagiotidis, and P. Surovy, “ Forest canopy density assessment using different approaches – Review,†J. For. Sci., vol. 63, no. 3, pp. 106–115, 2020, doi: 10.17221/110/2016-JFS.

S. Himayah, Hartono, and P. Danoedoro, “Pemanfaatan Citra Landsat 8 Multitemporal dan Model Forest Canopy Density (FCD) untuk Analisis Perubahan Kerapatan Kanopi Hutan di Kawasan Fakultas Geografi Universitas Gadjah Mada Gunung Kelud, Jawa Timur,†Maj. Geogr. Indones., vol. 51, no. 1, pp. 65–72, 2017, doi: 10.22146/mgi.24236.

M. Ismail, Hartono., and P. Danoedoro, “The Application of Forest Cover Density (FCD) Model for Structural Composition of Vegetation Changes in Part of Lore Lindu National Park, Central Sulawesi Province,†in 5th Geoinformation Science Symposium, 2017, vol. 98, no. 012056.

R. Ahmed, “Temporal Assessment of Forest Canopy Density Around Hojai-Diphu Railway Line in Assam, India,†Int. J. Sci. Technol. Res., vol. 9, no. 03, pp. 3040–3043, 2020.

J. G. Liu and J. Mason, Image Processing and GIS for Remote Sensing: Techniques and Applications,. London: Wiley-Blackwell, 2016.

H. Sunuprapto, P. Danoedoro, and S. Ritohardoyo, “Evaluation of pan-sharpening method: applied to artisanal gold mining monitoring in Gunung Pani Forest area,†in 2nd International Symposium on LAPAN-IPB Satellite for Food Security and Environmental Monitoring, LISAT-FSEM, 2016, vol. 33, pp. 230–238.

J. R. Eastman, TerrSet Geospatial Monitoring and Modeling System - Manual. Morcester, MA: Clark Labs, 2020.

Q. K. Rahaman, Q. . Hassan, and M. . Ahmed, “Pan-Sharpening of Landsat-8 Images and Its Application in Calculating Vegetation Greenness and Canopy Water Contents,†ISPRS Int. J. Geo-Information, vol. 6, no. 168, pp. 1–16, 2019, doi: 10.3390/ijgi6060168.

X. Meng, H. Shen, H. Li, L. Zhang, and R. Fu, “Review of the pansharpening methods for remote sensing images based on the idea of meta-analysis: Practical discussion and challenges,†Inf. Fusion, vol. 46, pp. 102–113, 2019, doi: 10.1016/j.inffus.2018.05.006.

P. Danoedoro, T. Ardiansyah, and Adiwijoyo, “Perbandingan Hasil Klasifikasi Per-Piksel dan Klasifikasi Berbasis Objek menggunakan Citra ALOS Pan-Sharpened: Studi Kasus Daerah Pinggiran Kota Yogyakarta,†2018.

P. Danoedoro and A. Zukhrufiyati, “Integrating Spectral Indices and Geostatistics based on Landsat-8 Imagery for Surface Clay Content Mapping in Gunung Kidul Area, Yogyakarta, Indonesia,†2015.

P. S. Chavez, “Image-Based Atmospheric Corrections Revisited and Improved,†Photogramm. Eng. Remote Sens., vol. 62, no. 9, pp. 1025–1036, 1996.

E. G. Jones, S. Wong, A. Milton, J. Sclauzero, H. Whittenbury, and M. D. and McDonnell, “The Impact of Pan-Sharpening and Spectral Resolution on Vineyard Segmentation through Machine Learning,†Remote Sens., vol. 12, no. 6, pp. 1–24, 2020, doi: 10.3390/rs12060934.

A. Rasul, H. Balzter, G. R. F. Ibrahim, H. M. Hameed, J. Wheeler, and P. M. Najmaddin, “Applying Built-Up and Bare-Soil Indices from Landsat 8 to Cities in Dry Climates,†Land, vol. 7, no. 81, pp. 1–13, 2018, doi: 10.3390/land7030081.

Y. Xu, L. Wang, K. W. Ross, C. Liu, and K. Berry, “Standardized Soil Moisture Index for Drought Monitoring Based on Soil Moisture Active Passive Observations and 36 Years of North American Land Data Assimilation System Data: A Case Study in the Southeast United States,†Remote Sens., vol. 10, no. 301, pp. 1–13, 2018, doi: 10.3390/rs10020301.

Y. Wang et al., “Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery,†Remote Sens. Environ., vol. 221, pp. 474–488, 2019, doi: 10.1016/j.rse.2018.11.028.

V. Vani, K.P. Kumar, and M. V Ravibabu, “Temperature and vegetation indices-based surface soil moisture estimation: a remote sensing data approach,†in International Conference on Remote Sensing for Disaster Management, 2019, pp. 281–289.

ITTO/JOFCA, FCD-Mapper Ver.2 User Guide, Semi-Expert Remote Sensing System for Canopy Density Mapping. ITTO/JOFCA, 2003.

I.N. Ananda, A. F. Umela, N. Ratnasari, D. A. Putri, Y. S. Wulandari, and P. Danoedoro, “Development of land-cover spatial database using satellite imagery: lesson learned from southern part of Sumatera,†in Sixth Geoinformation Science Symposium, 2019, vol. 11311, doi: 10.1117/12.2548890.

J. Deka, O. P. Tripathi, M. L. Khan, and V. K. Srivastava, “Study on land-use and land-cover change dynamics in Eastern Arunachal Pradesh, N.E. India using remote sensing and GIS,†Trop. Ecol., vol. 60, pp. 199–208, 2019, doi: 10.1007/s42965-019-00022-3.

S. Godinho et al., “Assessment of environment, land management, and spatial variables on recent changes in Montado land cover in southern Portugal,†Agroforest Syst., no. 90, pp. 177–192, 2016, doi: 10.1007/s10457-014-9757-7.

C. Li, M. Li, and Y. Li, “Improving estimation of forest aboveground biomass using Landsat 8 imagery by incorporating forest crown density as a dummy variable,†Can. J. For. Res., vol. 50, no. 4, pp. 390–398, 2020, doi: 10.1139/cjfr-2019-0216.

R. M. Sukarna, “Aplikasi Model Forest Canopy Density Citra Landsat 7 ETM untuk Menentukan Indeks Luas Tajuk (Crown Area Index) Dan Kerapatan Tegakan (Stand Density) Hutan Rawa Gambut Di DAS Sebangau Provinsi Kalimantan Tengah,†Maj. Geogr. Indones., vol. 22, no. 1, pp. 1–21, 2008.

C. Li, Y. Li, and M. Li, “Improving forest aboveground biomass (AGB) estimation by incorporating crown density and using Landsat 8 OLI images of a subtropical forest in Western Hunan in Central China,†Forests, vol. 10, no. 104, pp. 1–14, 2019.

A. P. P. Hartoyo, L. B. Prasetyo, I. Z. Siregar, Supriyanto, I. Theilade, and U. J. Siregar, “Carbon Stock Assessment Using Forest Canopy Density Mapper In Agroforestry Land In Berau, East Kalimantan, Indonesia,†Biodiversitas, vol. 20, no. 9, pp. 2661–2676, 2019, doi: 10.13057/biodiv/d200931.

S. Dittmann, E. Thiessen, and E. and Hartung, “Applicability of Different Non-invansive Methods for Tree Mass Estimation: A Review,†For. Ecol. Manage., no. 398, pp. 2018–2215, 2017, doi: 10.1016/j.foreco.2017.05.013.

N.Y. Krakauer, T. Lakhankar, and J. D. Anadón, “Mapping and Attributing Normalized Difference Vegetation Index Trends for Nepal,†Remote Sens., vol. 9, no. 986, pp. 1–15, 2017, doi: 10.3390/rs9100986.

T. Dawson, J.S.O. Sandoval, V. Sagan, and and T. Crawford, “A Spatial Analysis of the Relationship between Vegetation and Poverty,†Int. J. Geo-information, vol. 7, no. 83, pp. 1–26, 2018, doi: 10.3390/ijgi7030083.

A.A. Tesfaye and B. G. Awoke, “Evaluation of the saturation property of vegetation indices derived from sentinel-2 in mixed crop-forest ecosystem,†Spat. Inf. Res., vol. 29, no. 1, pp. 109–121, 2021, doi: 10.1007/s41324-020-00339-5.

F. Carneiro M, C. E. A. Furlani, C. Zerbato, P. C. de Menezes, L. A. da Silva Gírio, and M. F. de Oliveira, “Comparison between vegetation indices for detecting spatial and temporal variabilities in soybean crop using canopy sensors,†Precis. Agric., 2019.

Q. Ma, Y. Su, L. Luo, L. Li, M. Kelly, and Q. Guo, “Evaluating the uncertainty of Landsat-derived vegetation indices in quantifying forest fuel treatments using bi-temporal LiDAR data,†Ecol. Indic., vol. 95, pp. 298–310, 2018, doi: 10.1016/j.ecolind.2018.07.050.




DOI: http://dx.doi.org/10.18517/ijaseit.12.3.12514

Refbacks

  • There are currently no refbacks.


Published by INSIGHT - Indonesian Society for Knowledge and Human Development