Perubahan Tutupan Lahan Setelah 14 Tahun Bencana Tsunami (Studi Kasus di Kecamatan Baitussalam)

Abstrak. Perubahan tutupan lahan mengakibatkan beberapa penggunaan lahan menjadi berubah, terutama pada lahan pertanian yang berubah menjadi non-pertanian. Perubahan penggunaan lahan saat ini sudah sering terjadi di beberapa daerah terutama pada lahan pertanian yang berubah menjadi lahan non-pertanian. Pasca Tsunami daerah yang terkena bencana dilakukan rehabilitasi dan rekontruksi, semua aktivitas tersebut berdampak kepada perubahan tutupan lahan. Perubahan tutupan lahan diperoleh dari overlay dengan kaedah union mulai dari tahun 2004 hingga tahun 2018. Hasil analisis menunjukkan bahwa perubahan tutupan lahan selama kurun waktu 14 tahun pasca Tsunami terbesar terjadi pada pemukiman, yaitu mengalami peningkatan sebesar 550,14 ha (76,96%). sedangkan Perubahan tutupan lahan terkecil yaitu semak belukar sebesar 66,41 ha (5,06%).Land Cover Changes after 14 years of the Tsunami Case Study at Kecamatan BaitussalamAbstract. Changes in land cover have caused some land use to change, especially on agricultural land that has turned into non-agricultural land. Post-tsunami areas affected by rehabilitation and reconstruction, all of these activities have an impact on land cover change. Changes in land cover were obtained from overlays with the unification method from 2004 to 2018. The results of the analysis showed that changes in land cover for 14 years after the Tsunami occurred mostly in settlements, which increased by 550.14 ha (76.96%). while the smallest land cover change is shrubs covering an area of 66.41 ha (5.06%).

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Land Use and Land Cover Change Detection and Prediction in the Kathmandu District of Nepal Using Remote Sensing and GIS

Sustainability ◽

10.3390/su12093925 ◽

2020 ◽

Vol 12 (9) ◽

pp. 3925 ◽

Cited By ~ 2

Author(s):

Sonam Wangyel Wang ◽

Belay Manjur Gebru ◽

Munkhnasan Lamchin ◽

Rijan Bhakta Kayastha ◽

Woo-Kyun Lee

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Agricultural Land ◽

Water Bodies ◽

Geographical Information ◽

Land Cover Changes ◽

Rapid Urbanization ◽

Urbanized Areas

Understanding land use and land cover changes has become a necessity in managing and monitoring natural resources and development especially urban planning. Remote sensing and geographical information systems are proven tools for assessing land use and land cover changes that help planners to advance sustainability. Our study used remote sensing and geographical information system to detect and predict land use and land cover changes in one of the world’s most vulnerable and rapidly growing city of Kathmandu in Nepal. We found that over a period of 20 years (from 1990 to 2010), the Kathmandu district has lost 9.28% of its forests, 9.80% of its agricultural land and 77% of its water bodies. Significant amounts of these losses have been absorbed by the expanding urbanized areas, which has gained 52.47% of land. Predictions of land use and land cover change trends for 2030 show worsening trends with forest, agriculture and water bodies to decrease by an additional 14.43%, 16.67% and 25.83%, respectively. The highest gain in 2030 is predicted for urbanized areas at 18.55%. Rapid urbanization—coupled with lack of proper planning and high rural-urban migration—is the key driver of these changes. These changes are associated with loss of ecosystem services which will negatively impact human wellbeing in the city. We recommend city planners to mainstream ecosystem-based adaptation and mitigation into urban plans supported by strong policy and funds.

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Modelled land use and land cover change emissions – a spatio-temporal comparison of different approaches

Earth System Dynamics ◽

10.5194/esd-12-635-2021 ◽

2021 ◽

Vol 12 (2) ◽

pp. 635-670

Author(s):

Wolfgang A. Obermeier ◽

Julia E. M. S. Nabel ◽

Tammas Loughran ◽

Kerstin Hartung ◽

Ana Bastos ◽

...

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Environmental Conditions ◽

Agricultural Land ◽

Environmental Changes ◽

Land Cover Changes ◽

Environmental Forcing ◽

The Impact ◽

Global Carbon

Abstract. Quantifying the net carbon flux from land use and land cover changes (fLULCC) is critical for understanding the global carbon cycle and, hence, to support climate change mitigation. However, large-scale fLULCC is not directly measurable and has to be inferred from models instead, such as semi-empirical bookkeeping models and process-based dynamic global vegetation models (DGVMs). By definition, fLULCC estimates are not directly comparable between these two different model types. As an important example, DGVM-based fLULCC in the annual global carbon budgets is estimated under transient environmental forcing and includes the so-called loss of additional sink capacity (LASC). The LASC results from the impact of environmental changes on land carbon storage potential of managed land compared to potential vegetation and accumulates over time, which is not captured in bookkeeping models. The fLULCC from transient DGVM simulations, thus, strongly depends on the timing of land use and land cover changes mainly because LASC accumulation is cut off at the end of the simulated period. To estimate the LASC, the fLULCC from pre-industrial DGVM simulations, which is independent of changing environmental conditions, can be used. Additionally, DGVMs using constant present-day environmental forcing enable an approximation of bookkeeping estimates. Here, we analyse these three DGVM-derived fLULCC estimations (under transient, pre-industrial, and present-day forcing) for 12 models within 18 regions and quantify their differences as well as climate- and CO2-induced components and compare them to bookkeeping estimates. Averaged across the models, we find a global fLULCC (under transient conditions) of 2.0±0.6 PgC yr−1 for 2009–2018, of which ∼40 % are attributable to the LASC (0.8±0.3 PgC yr−1). From 1850 onward, the fLULCC accumulated to 189±56 PgC with 40±15 PgC from the LASC. Around 1960, the accumulating nature of the LASC causes global transient fLULCC estimates to exceed estimates under present-day conditions, despite generally increased carbon stocks in the latter. Regional hotspots of high cumulative and annual LASC values are found in the USA, China, Brazil, equatorial Africa, and Southeast Asia, mainly due to deforestation for cropland. Distinct negative LASC estimates in Europe (early reforestation) and from 2000 onward in the Ukraine (recultivation of post-Soviet abandoned agricultural land), indicate that fLULCC estimates in these regions are lower in transient DGVM compared to bookkeeping approaches. Our study unravels the strong dependence of fLULCC estimates on the time a certain land use and land cover change event happened to occur and on the chosen time period for the forcing of environmental conditions in the underlying simulations. We argue for an approach that provides an accounting of the fLULCC that is more robust against these choices, for example by estimating a mean DGVM ensemble fLULCC and LASC for a defined reference period and homogeneous environmental changes (CO2 only).

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Analysis of Land Use Land Cover Changes Using Remote Sensing Data and Geographical Information Systems (GIS) at an Urban Set up of Damaturu, Nigeria

Journal of Science and Technology ◽

10.30880/jst.2020.12.02.003 ◽

2020 ◽

Vol 12 (2) ◽

Author(s):

Karagama Kolo Geidam ◽

◽

Nor Aizam Adnan ◽

Baba Alhaji Umar ◽

◽

...

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Agricultural Land ◽

Research Work ◽

Ground Truth ◽

Geographical Information ◽

Land Cover Changes ◽

Land Use Land Cover

Change detection is useful in many applications related to land use and land cover change (LULCC), such as shifting cultivation and landscape changes. Land degradation and desertification. Remote sensing technology has been used for the detection of the changes in land use land cover in Damaturu town Nigeria. The main objectives of this research is to derive the land use/cover change map of Damaturu town from 1986 to 2017 and to quantify land use/ land cover change in the study area. Methodology employed while carry the research includes three satellites images for the year 1986, 1998 and 2017 were downloaded from USGS websites and used for detecting the land cover changes. Ground truth points were collected using google images and used for verification of image classifications. The accuracy of images classification was checked using ground truth point which showed the overall accuracy of 84.6% and a kappa coefficient of 0.89 which indicated that the method of classification was accurate. In the process of the research work, an increased was recorded in the built-up area which rose from 7.2% to 22.0%, open space increased from 10.8 to 22.8%, vegetation from 4.0% to 9.7%, water bodies from 0.0% to 0.1% while agricultural land decreased from 78% to 45.4% due to increase in interest of building as a result of the expansion of the town. The study arrived at the conclusion that there has been a significant land use change due to increase in population and development interest in built up areas which resulted in increased of amount of agricultural land being converted to build up areas over the period of 31 years.

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