Abstract
A majority of the rural population in Ethiopia depends on agriculture. Land use changes during the past couple of decades are mostly linked to agricultural development attributed to factors such as population pressure and environmental changes. Mapping land use/land cover (LULC ) to analyze the type, rate, and extent of changes in land use patterns has far reaching significance for policy/decision makers and resource managers to provoke the wide range of applications at regional scales for erosion, landslide, land planning, forest management, and ecosystem conservation. The focus of this chapter is to depict quick and practical approaches to generate spatially and temporally quantified information on land cover dynamics using high-resolution satellite images for the years (1973–2008) in Batena watershed and its environs in southwestern Ethiopia. To quantify the magnitude of LULC change, supervised classification technique was applied using Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images employing Bayesian maximum likelihood classifier (MLC) with the aid of ground truth training sites. A majority/minority analysis was used for smoothing the classification results and the accuracy of image classification was carried out by means of a confusion matrix generated through geographic information system (GIS) overlay of the classified maps and the test samples. The classification accuracy was further verified by the strong kappa statistical estimate of more than 90 % as a measure of overall agreement between image and reference data. The final output of remote sensing imagery revealed five land cover classes: Grazing land, bush land, mixed forest, dominantly cultivated agricultural land, and water body. It has been discovered that, there were more active LULC change processes in the area in the first study period (1973–1984) than the second study period (1984–1995) and the third study period (1995–2003). On the other hand, areal extent of cultivated and uncultivated agricultural land has been on a steady decline from 39.7 % in 1995 to 41.4 % in 2003 and a mere 50.1 % in 2008. In the first period, nearly half of the landscape underwent land cover change with more than 17 % of the entire landscape experiencing agricultural expansion. In the second period, the extent of the changes was limited to less than 1/3 of the total area with a smaller amount of agricultural area expansion than before. Though the rate of land cover change was observed to vary across the three periods of study, a general decline of forest cover and amplified increase of agricultural lands of more than 41.7 % was found in the area.
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References
Bauer M, Yuan F, Saway K (2003) Multi-temporal landsat image classification and change analysis of land cover in the twin cities (Minnesota) metropolitan area. Workshop on the analysis of multi-temporal remote sensing images, Italy
Bernstein R (1983) Image geometry and rectification. Chapter 21 in manual of remote sensing. In: Colwell RN (eds) Falls church. American Society of Photogrammetry, Virginia
Carlson T, Azofeifa S (1999) Satellite remote sensing of land use changes in and around San Jose, Costa Rica. Remote Sens Environ 70:247–256
Clevers JB (2004) Land cover classification with the medium resolution imaging spectrometer (MERIS). In: EARSeLeProceedings 3
Congalton R (1996) A review of assessing the accuracy of classification of remotely sensed data. Remote Sens Environ 37:35–46
Crippen R (1989a) A simple spatial filtering routine for the cosmetic removal of scan-line noise from landsat tm p-tape imagery. Photogram Eng Remote Sens 55(3):327–331
Currit N (2005) Development of a remotely sensed, historical land-cover change data base forrural Chihuahua, Mexico. Int J Appl Earth Obs Geoinf 7:232–247
Degelo S (2007) Analysis of biomass degradation as an indicator of environmental challenge of Bilate watershed
Dimyati (1995) An analysis of landuse/landcover change using the combination of MSS landsat and land use map—case of Yogyakarta, Indonesia. Int J Remote Sens 17(5):913–944
Fashona M, Omojola A (2005) Climate change, human security and communal clashes in Nigeria. In: Int’l workshop on human security and climate change. Oslo, Norway
Getachew HE, Melesse AM (2012) Impact of land use /land cover change on the hydrology of Angereb Watershed, Ethiopia. Int J Water Sci 1(4):1–7. doi:10.5772/56266
Goldewijk K, Ramankutty NK (2004) Land cover change over the last three centuries due to human activities: the availability of new global data sets. Geojournal 61:335–344. Kluwer Academic Publishers, The Netherlands
Guerschman J, Paruelo J, Bela C, Giallorenzi M, Pacin F (2003) Land cover classification in the Argentine Pampas using multi-temporal Landsat TM data. Int J Remote Sens 24:3381–3402
Heinen JT, Lyon JG (1989) The effects of changing weighting factors on the calculation of wildlife habitat index values: a sensitivity analysis. Photogram Eng Remote Sens 55(10):1445–1447
Jensen JR (1996) Introductory digital processing: a remote sensing perspective, 2nd edn. Prentice-Hall, Upper Saddle River
Kassa T (2009) Watershed hydrological responses to changes in land use and land cover, and management practices at Hare Watershed, Ethiopia
Lambin EF, Geist HJ, Lepers E (2003) Dynamics of land-use and land-cover change in tropical regions. Ann Rev Environ Res 28:205–241
Loppiso S (2010) Assessment of land use land cover dynamics and its impact on soil loss: using GIS and remote sensing, in Shashogo Woreda, Southern Ethiopia
Lu D, Mausel P, Brondízio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25:2365–2401
Lupo F, Reginster I, Lambin EF (2001) Monitoring land-cover changes in West Africa with SPOT vegetation: impact of natural disasters in 1998–1999. Int J Remote Sens 22:2633–2639
Mango L, Melesse AM, McClain ME, Gann D, Setegn SG (2011a) Land use and climate change impacts on the hydrology of the upper Mara River Basin, Kenya: results of a modeling study to support better resource management, special issue: climate, weather and hydrology of East African highlands. Hydrol Earth Syst Sci 15:2245–2258. doi:10.5194/hess-15-2245-2011
Mango L, Melesse AM, McClain ME, Gann D, Setegn SG (2011b) Hydro-meteorology and water budget of Mara River basin, Kenya: a land use change scenarios analysis, In: Melesse A (ed) Nile River basin: hydrology, climate and water use. Springer Science Publisher, Chapter 2, pp 39–68. doi:10.1007/978-94-007-0689-7_2
Melesse AM, Jordan JD (2003) Spatially distributed watershed mapping and modeling: land cover and microclimate mapping using landsat imagery part 1. J Spat Hydrol (e-journal) 3(2):1–29
Mendoza M, Bocco G, Bravo B (2002) Spatial prediction in hydrology: status and implication in the estimation of hydrological processes for applied research. Prog Phys Geogr 26(3):319–338
Mohammed H, Alamirew A, Assen M, Melesse AM (2013) Spatiotemporal mapping of land cover in Lake Hardibo Drainage Basin, Northeast Ethiopia: 1957–2007. Water conservation: practices, challenges and future implications. Nova Publishers, New York, pp 147–164
Moshen A (1999) Environmental landuse change detection and assessment using multi-temporal satelitte imageries. Zanjan University
Muzein B (2008) Remote sensing and gis for landcover/landuse change detection and analysis in the semi-natural ecosystem and agriculture landscapes of the Central Ethiopian Rift Valley. Fakultät Forst- Geo-und Hydrowissenschaften Institut Fernerkundung
Negash W (2014) Catchment dynamics and its impact on runoff generation: coupling watershed modelling and statistical analysis to detect catchment responses. Int J stat Anal Detect Catchment Responses 6:73–87
Ozbakir B, Bayram B, Acar U, Uzar M, Baz I, Karaz I (2007) Synergy between shoreline change detection and social profile of waterfront zones: a case study in Istanbul. In: Conference paper at the international conference for photogrammetry and remote sensing, Istanbul, Turkey 16–18 May
Petit C, Scudder T, Lambin E (2001) Quantifying processes of land-cover change by remote sensing: resettlement and rapid land-cover changes in southeastern Zambia. Int J Remote Sens 22:3435–3456
Prakasam C (2010) Land use and land cover change detection through remote sensing approach: a case study of Kodaikanal taluk, Tamilnadu. Int J Geomatics Geosci 1(2):189–206
Richards J (1999) Remote sensing digital image analysis: an introduction. Springer, Berlin, p 240
Rogana J, Chen D (2004) Remote sensing technology for mapping and monitoring land-cover and landuse change. Prog Plann 61:301–325
Sexton JO, Urban DL, Donohue MJ, Song C (2013) Long-term land cover dynamics by multi-temporal classification across the landsat-5 record. J Remote Sens Environ 128:246–258
Singh A (1989) Digital change detection techniques using remotely-sensed data. Int J Remote Sens 10(6):989–1003
Swinne E, Veroustaete F (2008) Extending the SPOT-VEGETATION NDV timeseries (1998–2006) back in time with NOAA-AVHRR data (1985–1998) for southern Africa. IEEE Trans Geosci Remote Sens 46(2):558–572
Tiwari MK, Saxena A (2011) Change detection of land use/landcover pattern in an around Mandideep and Obedullaganj area, using remote sensing and GIS. Int J Technol Eng Syst 2(3):398–402
Verburg P, Schot P, Dijst M, Veldkamp (2004) Lan duse change modelling: current practices and research priorities. Geo Journal 51(4):309–324
Verburg P, Soepboer W, Veldkamp A, Limpiada R, Espaldon V, Mastura S (2002) Modelling the spatial dynamics of land use: the CLUE-S model. Environ Manage 30(3):391–405
Verbyla (1986) Potential prediction bias in regression and discriminate analysis. Can J Forest Res 16:1255–1257
Wondie M, Scrhneider W, Melesse AM, Teketay D (2011) Spatial and temporal land cover changes in the Simen mountains national park, a world heritage site in Northwestern Ethiopia. Remote Sens 3:752–766. doi:10.3390/rs3040752
Wondie M, Schneider W, Melesse AM, Teketay D (2012) Relationship among environmental variables and land cover in the Simen Mountains national park, a world heritage site in Northern Ethiopia. Int J Remote Sens Appl (IJRSA) 2(2):36–43
Yu W, Gu S, Zhao XQ, Xiao J, Tang Y, Fang J, Jiang S (2011) High positive correlation between soil temperature and NDVI from 1982 to 2006 in alpine meadow of the three river sources region of Qinghai-Tibetan plateau. Int J Appl Earth Obs Geoinf 13(4):528–535
Zsuzsanna D, Bartholy J, Pongracz R, Barcza Z (2005) Analysis of landuse/land-cover change in the Carpathian region based on remote sensing techniques. Phys Chem Earth 30:109–115
Acknowledgments
The authors acknowledge the International Water Management Institute (IWMI) “Nile Basin Development Challenge of the Consultative Group on International Agricultural Research program for water and food (NBDC-CGIAR-CPWF)”, Horn of Africa Regional Environment Center and Network, Demand Driven Action Research Program (HoA-REC/N-DDAR), and the Ethiopian Ministry of Education for their financial support to conduct this research work.
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Ayele, G.T., Demessie, S.S., Mengistu, K.T., Tilahun, S.A., Melesse, A.M. (2016). Multitemporal Land Use/Land Cover Change Detection for the Batena Watershed, Rift Valley Lakes Basin, Ethiopia. In: Melesse, A., Abtew, W. (eds) Landscape Dynamics, Soils and Hydrological Processes in Varied Climates. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-319-18787-7_4
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