Mapping the spatial distribution of the yellowwood tree (Podocarpus henkelii) in the Weza-Ngele forest using the newly launched Sentinel-2 multispectral imager data

References

• Adam, E., Mutanga, O., Odindi, J., & Abdel-Rahman, E. M. (2014). Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: Evaluating the performance of random forest and support vector machines classifiers. International Journal of Remote Sensing, 35(10), 3440–3458.
   Web of Science ®Google Scholar
• Adão, T., Hruška, J., Pádua, L., Bessa, J., Peres, E., Morais, R., & Sousa, J. J. (2017). Hyperspectral imaging: A review on UAV-based sensors, data processing and applications for agriculture and forestry. Remote Sensing, 9(11), 1110.
   Web of Science ®Google Scholar
• Adelabu, S., & Dube, T. (2015). Employing ground and satellite-based QuickBird data and random forest to discriminate five tree species in a Southern African Woodland. Geocarto International, 30(4), 457–471.
   Web of Science ®Google Scholar
• Adelabu, S., Mutanga, O., & Adam, E. (2015). Testing the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods. Geocarto International, 30(7), 810–821.
   Web of Science ®Google Scholar
• Alig, R., Stewart, S., Wear, D., Stein, S., & Nowak, D. (2010). Conversions of Forest Land: Trends, Determinants, Projections, and Policy Considerations. In: J. M. Pye, H. M. Rauscher, Y. Sands, D. C. Lee, J. S. Beatty, eds. Advances in threat assessment and their application to forest and rangeland management. General Technical Report: PNW-GTR-802. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest and Southern Research Stations, 1–26.
   Google Scholar
• Al-Khaier, F. (2003, March). Soil salinity detection using satellite remote sensing. Thesis (MSc). International Institute for Geo-information Science and Earth Observation, Enschede .
   Google Scholar
• Baldeck, C. A., Colgan, M. S., Féret, J. B., Levick, S. R., Martin, R. E., & Asner, G. P. (2014). Landscape‐scale variation in plant community composition of an African savanna from airborne species mapping. Ecological Applications, 24(1), 84–93.
   PubMed Web of Science ®Google Scholar
• Belgiu, M., & Drăguţ, L. (2016). Random forest in remote sensing: A review of applications and future directions. ISPRS Journal of Photogrammetry and Remote Sensing, 114, 24–31.
   Web of Science ®Google Scholar
• Blackburn, G. A. (1998). Spectral indices for estimating photosynthetic pigment concentrations: A test using senescent tree leaves. International Journal of Remote Sensing, 19(4), 657–675.
   Web of Science ®Google Scholar
• Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.
   Web of Science ®Google Scholar
• Buschmann, C., & Nagel, E. (1993). In vivo spectroscopy and internal optics of leaves as basis for remote sensing of vegetation. International Journal of Remote Sensing, 14(4), 711–722.
   Web of Science ®Google Scholar
• Carter, G. A. (1994). Ratios of leaf reflectances in narrow wavebands as indicators of plant stress. Remote Sensing, 15(3), 697–703.
   Web of Science ®Google Scholar
• Chavez, P. S. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1035.
   Web of Science ®Google Scholar
• Chen, C., Liaw, A., & Breiman, L. (2004). Using random forest to learn imbalanced data. Berkeley: University of California, 110, 1–12.
   Google Scholar
• Cho, M. A., Malahlela, O., & Ramoelo, A. (2015). Assessing the utility WorldView-2 imagery for tree species mapping in South African subtropical humid forest and the conservation implications: Dukuduku forest patch as case study. International Journal of Applied Earth Observation and Geoinformation, 38, 349–357.
   Web of Science ®Google Scholar
• Clerici, N., Valbuena Calderón, C. A., & Posada, J. M. (2017). Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia. Journal of Maps, 13(2), 718–726.
   Web of Science ®Google Scholar
• Clevers, J. G., & Gitelson, A. A. (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and-3. International Journal of Applied Earth Observation and Geoinformation, 23, 344–351.
   Web of Science ®Google Scholar
• Crist, E. P., & Cicone, R. C. (1984). A physically-based transformation of Thematic Mapper data-The TM Tasseled Cap. IEEE Transactions on Geoscience and Remote Sensing, 22(3), 256–263.
   Web of Science ®Google Scholar
• Dalponte, M., Bruzzone, L., Vescovo, L., & Gianelle, D. (2009). The role of spectral resolution and classifier complexity in the analysis of hyperspectral images of forest areas. Remote Sensing of Environment, 113(11), 2345–2355.
   Web of Science ®Google Scholar
• Davies, L. (2013). Call to save the yellowwood - South Africa’s National Tree. African wildlife conservation. Retrieved from https://www.africanbudgetsafaris.com/blog/save-south-africas-national-tree-the- yellowwood/
   Google Scholar
• Delegido, J., Verrelst, J., Alonso, L., & Moreno, J. (2011). Evaluation of sentinel-2 red-edge bands for empirical estimation of green LAI and chlorophyll content. Sensors, 11(7), 7063–7081.
   PubMed Web of Science ®Google Scholar
• Dhau, I., Adam, E., Mutanga, O., Ayisi, K., Abdel-Rahman, E. M., Odindi, J., & Masocha, M. (2017). Testing the capability of spectral resolution of the new multispectral sensors on detecting the severity of grey leaf spot disease in maize crop. Geocarto International, 33(11),1–14.
   Web of Science ®Google Scholar
• Eredics, A., Németh, Z. I., Rákosa, R., Rasztovits, E., Móricz, N., & Vig, P. (2015). The effect of soil moisture on the reflectance spectra correlations in beech and sessile oak foliage. Acta Silvatica et Lignaria Hungarica, 11(1), 9–25.
   Google Scholar
• Farjon, A. (2010). A handbook of the world’s conifers. Leiden: Koninklijke Brill.
   Google Scholar
• Fassnacht, F. E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L. T., … Ghosh, A. (2016). Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 186, 64–87.
   Web of Science ®Google Scholar
• Fensholt, R., Rasmussen, K., Nielsen, T. T., & Mbow, C. (2009). Evaluation of earth observation based long term vegetation trends—Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sensing of Environment, 113(9), 1886–1898.
   Web of Science ®Google Scholar
• Forkuor, G., Dimobe, K., Serme, I., & Tondoh, J. E. (2017). Landsat-8 vs. Sentinel-2: Examining the added value of Sentinel-2’s red-edge bands to land-use and land-cover mapping in Burkina Faso. GIScience & Remote Sensing, 55(3),1–24.
   Web of Science ®Google Scholar
• Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS-MODIS. Remote Sensing of Environment, 58(3), 289–298.
   Web of Science ®Google Scholar
• Gitelson, A. A., Keydan, G. P., & Merzlyak, M. N. (2006). Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves. Geophysical Research Letters, 33(11), 1-5.
   PubMed Web of Science ®Google Scholar
• Government Gazette., 2016. Notice of the list of protected tree species under the national forests act, 1998 (act no. 84 of 1998).
   Google Scholar
• Gray, C. J., Shaw, D. R., Gerard, P. D., & Bruce, L. M. (2008). Utility of multispectral imagery for soybean and weed species differentiation. Weed Technology, 22(4), 713–718.
   Web of Science ®Google Scholar
• Grieve, G. R. H., & Downs, C. T. (2015). A checklist of the plants of the forests and grasslands in the Weza district, southern KwaZulu-Natal and a review of their status in the Red Data List. Koedoe, 57(1), 1–7.
   Web of Science ®Google Scholar
• Hart, L. A., Grieve, G. R. H., & Downs, C. T. (2013). Fruiting phenology and implications of fruit availability in the fragmented Ngele Forest Complex, KwaZulu-Natal, South Africa. South African Journal of Botany, 88, 296–305.
   Web of Science ®Google Scholar
• Henrich, V., Götze, E., Jung, A., Sandow, C., Thürkow, D., & Gläßer, C., 2009. Development of an Online indices-database: Motivation, concept and implementation. Proceedings of the 6th EARSeL Imaging Spectroscopy SIG Workshop Innovative Tool for Scientific and Commercial Environment Applications. Tel Aviv. Israel, 16–19.
   Google Scholar
• Henrich, V., Krauss, G., Götze, C., & Sandow, C. (2012). AK Fernerkundung. Bochum, 10,4–5.
   Google Scholar
• Hojas-Gascon, L., Belward, A., Eva, H., Ceccherini, G., Hagolle, O., Garcia, J., & Cerutti, P. (2015). Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(7), 417.
   Google Scholar
• Hudak, A. T., & Brockett, B. H. (2004). Mapping fire scars in a southern African savannah using Landsat imagery. International Journal of Remote Sensing, 25(16), 3231–3243.
   Web of Science ®Google Scholar
• Immitzer, M., Atzberger, C., & Koukal, T. (2012). Tree species classification with random forest using very high spatial resolution 8-band WorldView-2 satellite data. Remote Sensing, 4(9), 2661–2693.
   Web of Science ®Google Scholar
• Immitzer, M., Vuolo, F., & Atzberger, C. (2016). First experience with sentinel-2 data for crop and tree species classifications in Central Europe. Remote Sensing, 8(3), 166–193.
   Web of Science ®Google Scholar
• Jurgens, C. (1997). The modified normalized difference vegetation index (mNDVI) a new index to determine frost damages in agriculture based on Landsat TM data. International Journal of Remote Sensing, 18(17), 3583–3594.
   Web of Science ®Google Scholar
• Kawamura, K., Akiyama, T., Yokota, H. O., Tsutsumi, M., Yasuda, T., Watanabe, O., & Wang, S. (2005). Comparing MODIS vegetation indices with AVHRR NDVI for monitoring the forage quantity and quality in Inner Mongolia grassland, China. Grassland Science, 51(1), 33–40.
   Google Scholar
• Khoshgoftaar, T. M., Golawala, M., & Van Hulse, J., 2007, October. An empirical study of learning from imbalanced data using random forest. 19th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2007), Rai, 2310–317.
   Google Scholar
• Korpela, I., Ørka, H. O., Maltamo, M., Tokola, T., & Hyyppä, J. (2010). Tree species classification using airborne LiDAR–effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type. Silva Fennica, 44(2), 319–339.
   Web of Science ®Google Scholar
• Laurin, G. V., Puletti, N., Hawthorne, W., Liesenberg, V., Corona, P., Papale, D., … Valentini, R. (2016). Discrimination of tropical forest types, dominant species, and mapping of functional guilds by hyperspectral and simulated multispectral Sentinel-2 data. Remote Sensing of Environment, 176, 163–176.
   Web of Science ®Google Scholar
• Lavender, S., & Lavender, A. (2015). Practical handbook of remote sensing. New York: CRC Press.
   Google Scholar
• Li, F., Miao, Y., Feng, G., Yuan, F., Yue, S., Gao, X., … Chen, X. (2014). Improving estimation of summer maize nitrogen status with red edge-based spectral vegetation indices. Field Crops Research, 157, 111–123.
   Web of Science ®Google Scholar
• Madonsela, S., Cho, M. A., Mathieu, R., Mutanga, O., Ramoelo, A., Kaszta, Z., … Wolff, E. (2017). Multi-phenology WorldView-2 imagery improves remote sensing of savannah tree species. International Journal of Applied Earth Observation and Geoinformation, 58, 65–73.
   Web of Science ®Google Scholar
• Malahlela, O., Cho, M. A., & Mutanga, O. (2014). Mapping canopy gaps in an indigenous subtropical coastal forest using high-resolution WorldView-2 data. International Journal of Remote Sensing, 35(17), 6397–6417.
   Web of Science ®Google Scholar
• Malthus, T. J., Andrieu, B., Danson, F. M., Jaggard, K. W., & Steven, M. D. (1993). Candidate high spectral resolution infrared indices for crop cover. Remote Sensing of Environment, 46(2), 204–212.
   Web of Science ®Google Scholar
• Marot, C. (2017). Weza-Ngele Forest: Home to the endangered Cape parrot. South African Tourism [online]. Retrieved from http://www.southafrica.net/za/en/articles/entry/article-southafrica.net-weza-ngele-forest
   Google Scholar
• Marshall, V., Lewis, M., & Ostendorf, B., 2012. Do additional bands (coastal, NIR-2, red- edge and yellow) in WorldView-2 multispectral imagery improve discrimination of an Invasive Tussock, Buffel Grass (Cenchrus Ciliaris). Proceedings of the International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne 39, 1–5.
   Google Scholar
• Martins, G. D., Galo, M. D. L. B. T., & Vieira, B. S. (2017). Detecting and mapping root-knot nematode infection in coffee crop using remote sensing measurements. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(12), 5395–5403.
   Web of Science ®Google Scholar
• Matongera, T. N., Mutanga, O., Dube, T., & Sibanda, M. (2017). Detection and mapping the spatial distribution of bracken fern weeds using the Landsat 8 OLI new generation sensor. International Journal of Applied Earth Observation and Geoinformation, 57, 93–103.
   Web of Science ®Google Scholar
• Metternicht, G. (2003). Vegetation indices derived from high-resolution airborne videography for precision crop management. International Journal of Remote Sensing, 24(14), 2855–2877.
   Web of Science ®Google Scholar
• Mucina, L., & Rutherford, M. C. (2006). The vegetation of South Africa, Lesotho and Swaziland. South African National Biodiversity Institute. South African National Biodiversity Institute: Pretoria.
   Google Scholar
• Munyati, C. (2017). The potential for integrating Sentinel-2 MSI with SPOT 5 HRG and Landsat 8 OLI imagery for monitoring semi-arid savannah woody cover. International Journal of Remote Sensing, 38(17), 4888–4913.
   Web of Science ®Google Scholar
• Mutanga, O., Adam, E., & Cho, M. A. (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. International Journal of Applied Earth Observation and Geoinformation, 18, 399–406.
   Web of Science ®Google Scholar
• Negash, L., & van Staden, J. (2003). Vegetative propagation of the threatened East African yellowwood (Podocarpus falcatus). South African Journal of Botany, 69(2), 170–175.
   Web of Science ®Google Scholar
• Odindi, J., Mutanga, O., Rouget, M., & Hlanguza, N. (2016). Mapping alien and indigenous vegetation in the KwaZulu-Natal Sandstone Sourveld using remotely sensed data. Bothalia-African Biodiversity & Conservation, 46(2), 1–9.
   Google Scholar
• Olsen, J. L., Ceccato, P., Proud, S. R., Fensholt, R., Grippa, M., Mougin, E., … Sandholt, I. (2013). Relation between seasonally detrended shortwave infrared reflectance data and land surface moisture in semi-arid sahel. Remote Sensing, 5(6), 2898–2927.
   Web of Science ®Google Scholar
• Omer, G., Mutanga, O., Abdel-Rahman, E. M., & Adam, E. (2015). Performance of support vector machines and artificial neural network for mapping endangered tree species using worldview-2 data in Dukuduku forest, South Africa. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(10), 4825–4840.
   Web of Science ®Google Scholar
• Peerbhay, K. Y., Mutanga, O., & Ismail, R. (2013). Commercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa. ISPRS Journal of Photogrammetry and Remote Sensing, 79, 19–28.
   Web of Science ®Google Scholar
• Peerbhay, K. Y., Mutanga, O., & Ismail, R. (2015). Random forests unsupervised classification: The detection and mapping of solanum mauritianum infestations in plantation forestry using hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6), 3107–3122.
   Web of Science ®Google Scholar
• Perrin, M., 2014. Ecology and conservation biology of the Cape Parrot Poicephalus robustus in afromontane forests of South Africa. BOU’s 2014 annual conference, Leicester.
   Google Scholar
• Qiu, S., He, B., Yin, C., & Liao, Z., 2017. Assessments of Sentinel-2 vegetation red-edge spectral bands for improving land cover classification. ISPRS-international archives of the photogrammetry, remote sensing and spatial information sciences, Wuhan (pp.871–874).
   Google Scholar
• Sibanda, M., Mutanga, O., & Rouget, M. (2016). Discriminating rangeland management practices using simulated HyspIRI, landsat 8 OLI, Sentinel-2 MSI, and VENµS spectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(9), 3957–3969.
   Web of Science ®Google Scholar
• Sinha, S., Sharma, L. K., & Nathawat, M. S. (2015). Improved land-use/land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing. The Egyptian Journal of Remote Sensing and Space Science, 18(2), 217–233.
   Web of Science ®Google Scholar
• Sist, P., Chave, J., & Rutishauser, E., 2015. Tropical forest degradation in the context of climate change: Increasing role and research challenges. Our Common Future under Climate Change, Paris, (pp. 260–271).
   Google Scholar
• Thenkabail, P. S., Gumma, M. K., Teluguntla, P., & Mohammed, I. A. (2014). Hyperspectral remote sensing of vegetation and agricultural crops. Photogrammetric Engineering & Remote Sensing (PE&RS), 80(8), 697–723.
   Web of Science ®Google Scholar
• Thomlinson, J. R., Bolstad, P. V., & Cohen, W. B. (1999). Coordinating methodologies for scaling landcover classifications from site-specific to global: Steps toward validating global map products. Remote Sensing of Environment, 70(1), 16–28.
   Web of Science ®Google Scholar
• Tian, F., Brandt, M., Liu, Y. Y., Verger, A., Tagesson, T., Diouf, A. A., … Fensholt, R. (2016). Remote sensing of vegetation dynamics in drylands: Evaluating vegetation optical depth (VOD) using AVHRR NDVI and in situ green biomass data over West African Sahel. Remote Sensing of Environment, 177, 265–276.
   Web of Science ®Google Scholar
• Tsalyuk, M., Kelly, M., & Getz, W. M. (2017). Improving the prediction of African savanna vegetation variables using time series of MODIS products. ISPRS Journal of Photogrammetry and Remote Sensing, 131, 77–91.
   PubMed Web of Science ®Google Scholar
• Vrieling, A., De Leeuw, J., & Said, M. Y. (2013). Length of growing period over Africa: Variability and trends from 30 years of NDVI time series. Remote Sensing, 5(2), 982–1000.
   Web of Science ®Google Scholar
• Wirminghaus, J. O., Downs, C. T., Perrin, M. R., & Symes, C. T. (2001). Abundance and activity patterns of the Cape Parrot (Poicephalus robustus) in two afromontane forests in South Africa. African Zoology, 36(1), 71–77.
   Web of Science ®Google Scholar
• Xie, Y., Sha, Z., & Yu, M. (2008). Remote sensing imagery in vegetation mapping: A review. Journal of Plant Ecology, 1(1), 9–23.
   Web of Science ®Google Scholar
• Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017, 11–21.
   Web of Science ®Google Scholar
• Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 39(7), 1491–1507.
   Web of Science ®Google Scholar
• Zhou, Z., Yang, Y., & Chen, B. (2018). Estimating Spartina alterniflora fractional vegetation cover and aboveground biomass in a coastal wetland using SPOT6 satellite and UAV data. Aquatic Botany, 144, 38–45.
   Web of Science ®Google Scholar