1,487
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Agricultural expansion into forest reserves in Zambia: a remote sensing approach

, , , , , , , & show all
Article: 2213203 | Received 04 Oct 2022, Accepted 08 May 2023, Published online: 19 May 2023

References

  • Chauvin ND, Mulangu F, Porto G. 2012. Food production and consumption trends in Sub-Saharan Africa: prospects for the transformation of the agricultural sector. New York, NY: UNDP Regional Bureau for Africa, Vol 2; p. 74.
  • Chomba B, Tembo O, Mutandi K, Makano A. 2012. Drivers of deforestation, identification of threatened forests and forest cobenefits other than carbon from REDD + implementation in Zambia. A consultancy report prepared for the Forestry Department and the Food and Agriculture Organization of the United Nations under the national UN-REDD Programme. Ministry of Lands, Natural Resources and Environmental Protection. Lusaka, Zambia.
  • DeFries RS, Rudel T, Uriarte M, Hansen M. 2010. Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geosci. 3(3):178–181. http://www.nature.com/ngeo/journal/v3/n3/suppinfo/ngeo756_S1.html.
  • Du S, Wang Q, Guo L. 2014. Spatially varying relationships between land-cover change and driving factors at multiple sampling scales. J Environ Manage. 137:101–110.
  • Duro DC, Franklin SE, Dubé MG. 2012. A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery. Remote Sens Environ. 118:259–272.
  • Ernst C, Philippe M, Astrid V, Catherine B, Musampa C, Pierre D. 2013. National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005. Glob Chang Biol. 19(4):1173–1187.
  • Ezeh AC, Bongaarts J, Mberu B. 2012. Global population trends and policy options. Lancet. 380(9837):142–148.
  • FAOSTAT. 2021. [accessed 2022 June 21]. http://www.fao.org/faostat/en/.
  • Feddema JJ, Oleson KW, Bonan GB, Mearns LO, Buja LE, Meehl GA, Washington WM. 2005. The importance of land-cover change in simulating future climates. Science. 310(5754):1674–1678.
  • Felegari S, Sharifi A, Moravej K, Amin M, Golchin A, Muzirafuti A, Tariq A, Zhao N. 2021. Integration of Sentinel 1 and Sentinel 2 satellite images for crop mapping. Appl Sci. 11(21):10104.
  • Foley J, Ramankutty N, Brauman K. 2011. Solutions for a cultivated planet. Nature 478:337–342.
  • Ghorbanian A, Kakooei M, Amani M, Mahdavi S, Mohammadzadeh A, Hasanlou M. 2020. Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS J Photogramm Remote Sens. 167:276–288.
  • Handavu F, Chirwa PWC, Syampungani S. 2019. Socio-economic factors influencing land-use and land-cover changes in the miombo woodlands of the Copperbelt province in Zambia. Forest Policy and Econ. 100:75–94.
  • Hu P, Sharifi A, Tahir MN, Tariq A, Zhang L, Mumtaz F, Shah SH. 2021. Evaluation of vegetation indices and phenological metrics using time-series MODIS data for monitoring vegetation change in Punjab, Pakistan. Water. 13(18):2550.
  • Hussain S, Lu L, Mubeen M, Nasim W, Karuppannan S, Fahad S, Tariq A, Mousa BG, Mumtaz F, Aslam M. 2022a. Spatiotemporal variation in land use land cover in the response to local climate change using multispectral remote sensing data. Land. 11(5):595.
  • Hussain S, Qin S, Nasim W, Bukhari MA, Mubeen M, Faha S, Raza A, Abdo HG, Tariq A, Mousa BG, et al. 2022b. Monitoring the dynamic changes in vegetation cover using spatio-temporal remote sensing data from 1984 to 2020. Atmosphere. 13(10):1609.
  • Jayne TS, Mather D, Mghenyi E. 2010. Principal challenges confronting smallholder agriculture in sub-Saharan Africa. World Development. 38(10):1384–1398.
  • Jucker T, Caspersen J, Chave J, Antin C, Barbier N, Bongers F, Dalponte M, van Ewijk KY, Forrester DI, Haeni M, et al. 2017. Allometric equations for integrating remote sensing imagery into forest monitoring programmes. Glob Chang Biol. 23(1):177–190.
  • Juel A, Groom GB, Svenning JC, Ejrnæs R. 2015. Spatial application of Random Forest models for fine-scale coastal vegetation classification using object based analysis of aerial orthophoto and DEM data. Int J Appl Earth Obs Geoinf. 42:106–114.
  • Kalaba FK, Quinn CH, Dougill AJ, Vinya R. 2013. Floristic composition, species diversity and carbon storage in charcoal and agriculture fallows and management implications in Miombo woodlands of Zambia. For Ecol Manage. 304:99–109.
  • Kamwi J, Cho M, Kaetsch C, Manda S, Graz F, Chirwa PWC. 2018. Assessing the spatial drivers of land use and land cover change in the protected and communal areas of the Zambezi Region, Namibia. Land. 7(4):131.
  • Kindu M, Schneider T, Teketay D, Knoke T. 2015. Drivers of land use/land cover changes in Munessa-Shashemene landscape of the south-central highlands of Ethiopia. Environ Monit Assess. 187(7):452.
  • Kotsiantis SB, Zaharakis ID, Pintelas PE. 2006. Machine learning: a review of classification and combining techniques. Artif Intell Rev. 26(3):159–190.
  • Lambin EF, Turner BL, Geist HJ, Agbola SB, Angelsen A, Bruce JW, Coomes OT, Dirzo R, Fischer G, Folke C, et al. 2001. The causes of land-use and land-cover change: moving beyond the myths. Global Environ Change. 11(4):261–269.
  • Lebourgeois V, Dupuy S, Vintrou É, Ameline M, Butler S, Bégué A. 2017. A combined random forest and OBIA classification scheme for mapping smallholder agriculture at different nomenclature levels using multisource data (simulated Sentinel-2 time series, VHRS and DEM). Remote Sens. 9(3):259.
  • Li X, Wang Y, Li J, Lei B. 2016. Physical and socio-economic driving forces of land-use and land-cover changes: a case study of Wuhan City, China. Discrete Dyn Nat Soc. 2016:1–11.
  • Lindsey PA, Nyirenda VR, Barnes JI, Becker MS, McRobb R, Tambling CJ, Taylor WA, Watson FG, t‘Sas-Rolfes M. 2014. Underperformance of African protected area networks and the case for new conservation models: insights from Zambia. PLoS One. 9(5):e94109.
  • Maples S. 2021. Google Earth Engine 101: an introduction for complete beginners. California, USA: Stanford Geospatial Center.
  • Mayes MT, Mustard JF, Melillo JM. 2015. Forest cover change in Miombo Woodlands: modeling land cover of African dry tropical forests with linear spectral mixture analysis. Remote Sens Environ. 165:203–215.
  • McIver DK, Friedl MA. 2001. Estimating pixel-scale land cover classification confidence using nonparametric machine learning methods. IEEE Trans Geosci Remote Sensing. 39(9):1959–1968.
  • McNicol IM, Ryan CM, Mitchard ETA. 2018. Carbon losses from deforestation and widespread degradation offset by extensive growth in African woodlands. Nat Commun. 9(1):3045.
  • Misaki E, Apiola M, Gaiani S, Tedre M. 2018. Challenges facing sub‐Saharan small‐scale farmers in accessing farming information through mobile phones: a systematic literature review. E J Info Sys Dev Countries. 84(4):e12034.
  • Molinario G, Hansen MC, Potapov PV. 2015. Forest cover dynamics of shifting cultivation in the Democratic Republic of Congo: a remote sensing-based assessment for 2000-2010. Environ Res Lett. 10(9):094009.
  • Mukul SA, Herbohn J. 2016. The impacts of shifting cultivation on secondary forests dynamics in tropics: a synthesis of the key findings and spatio temporal distribution of research. Environ Sci Policy. 55:167–177.
  • Mutanga O, Kumar L. 2019. Google earth engine applications. Remote Sens. 11(5):591.
  • Phan TN, Kuch V, Lehnert LW. 2020. Land cover classification using Google Earth Engine and random forest classifier—The role of image composition. Remote Sens. 12(15):2411.
  • Phiri D, Chanda C, Nyirenda VR, Lwali CA. 2022. An assessment of forest loss and its drivers in protected areas on the Copperbelt province of Zambia: 1972–2016. Geomatics Nat Hazards Risk. 13(1):148–166.
  • Phiri D, Morgenroth J. 2017. Developments in Landsat land cover classification methods: a review. Remote Sens. 9(9):967.
  • Phiri D, Morgenroth J, Xu C. 2019a. Four decades of land cover and forest connectivity study in Zambia—An object-based image analysis approach. Int J Appl Earth Obs Geoinf. 79:97–109.
  • Phiri D, Morgenroth J, Xu C. 2019b. Long-term land cover change in Zambia: an assessment of driving factors. Sci Total Environ. 697:134206.
  • Phiri D, Simwanda M, Salekin S, Nyirenda VR, Murayama Y, Ranagalage M. 2020. Sentinel-2 data for land cover/use mapping: a review. Remote Sens. 12(14):2291.
  • Pimentel D, Pimentel M. 2006. Global environmental resources versus world population growth. Ecol Econ. 59(2):195–198.
  • Quintero-Gallego ME, Quintero-Angel M, Vila-Ortega JJ. 2018. Exploring land use/land cover change and drivers in Andean mountains in Colombia: a case in rural Quindío. Sci Total Environ. 634:1288–1299.
  • Ramankutty N, Foley JA, Olejniczak NJ. 2002. People on the land: changes in global population and croplands during the 20th century. AMBIO: J Human Environ. 31(3):251–257.
  • Schneibel A, Stellmes M, Röder A, Finckh M, Revermann R, Frantz D, Hill J. 2016. Evaluating the trade-off between food and timber resulting from the conversion of Miombo forests to agricultural land in Angola using multi-temporal Landsat data. Sci Total Environ. 548–549:390–401.
  • Searchinger TD, Estes L, Thornton PK, Beringer T, Notenbaert A, Rubenstein D, Heimlich R, Licker R, Herrero M. 2015. High carbon and biodiversity costs from converting Africa’s wet savannahs to cropland. Nature Clim Change. 5(5):481–486.https://www.nature.com/articles/nclimate2584#supplementary-information.
  • Sharifi A, Mahdipour H, Moradi E, Tariq A. 2022. Agricultural field extraction with deep learning algorithm and satellite imagery. J Indian Soc Remote Sens. 50(2):417–423.
  • Syampungani S, Chirwa PW, Akinnifesi FK, Sileshi G, Ajayi OC. 2009. The miombo woodlands at the cross roads: potential threats, sustainable livelihoods, policy gaps and challenges. Paper presented at The Natural Resources Forum. 33(2):150–159.
  • Syampungani S. 2008. Vegetation change analysis and ecological recovery of the Copperbelt miombo woodland of Zambia [PhD thesis]. Stellembosch University.
  • Tariq A, Siddiqui S, Sharifi A, Shah SHIA. 2022. Impact of spatio-temporal land surface temperature on cropping pattern and land use and land cover changes using satellite imagery, Hafizabad District, Punjab, Province of Pakistan. Arab J Geosci. 15(11):1045–2021.
  • Tariq A, Yan J, Gagnon AS, Riaz KM, Mumtaz F. 2022. Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest. Geo-Spatial Information Sci. 13(2):1–19.
  • Turner W, Rondinini C, Pettorelli N, Mora B, Leidner AK, Szantoi Z, Buchanan G, Dech S, Dwyer J, Herold M, et al. 2015. Free and open-access satellite data are key to biodiversity conservation. Biol Conserv. 182:173–176.
  • Vinya R. 2012. Preliminary study on the drivers of deforestation and potential for REDD + in Zambia. A consultancy report prepared for Forestry Department and FAO under the national UN-REDD + Programme Ministry of Lands & Natural Resources. Lusaka Zambia.
  • Woodcock CE, Allen R, Anderson M, Belward A, Bindschadler R, Cohen W, Gao F, Goward SN, Helder D, Helmer E, et al. 2008. Free access to landsat imagery. Science. 320(5879):1011–1011.
  • Zamani A, Sharifi A, Felegari S, Tariq A, Zhao N. 2022. Agro-climatic zoning of saffron culture in Miyaneh City by using WLC method and remote sensing data. Agriculture. 12(1):118.
  • ZamStats. 2010. Zambia 2010 Census of population and housing. GRZ, Lusaka, Zambia. Retrieved from Lusaka, Zambia.
  • Zulu D, Ellis RH, Culham A. 2019. Collection, consumption, and sale of Lusala (Dioscorea hirtiflora)—a Wild Yam—by rural households in Southern Province, Zambia. Econ Bot. 73(1):47–63.