scholarly journals Anthropogenic influences on morphological changes in the Progo River, Daerah Istimewa Yogyakarta Province, Indonesia

2020 ◽  
Vol 4 (3) ◽  
pp. 205-223
Author(s):  
Akhmad Zamroni ◽  
Bayurohman Pangacella Putra ◽  
Haris Nur Eka Prasetya
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Morphological Changes ◽  
Land Use Changes ◽  
Google Earth ◽  
Sediment Supply ◽  
Average Height ◽  
Anthropogenic Influences ◽  
Sand Mining ◽  
River Sand

Changes in the river morphology require knowledge of the suite of drivers that control it, whether natural or human. The study aims to analyze the anthropogenic influences on morphological changes in the Progo River using Google Earth Images. It is essential to know the recent changes in the morphology of the Progo River so that stakeholders can make policies to control human activities that influence the morphology changes of the Progo River. The study area is located in Bantul Regency, Daerah Istimewa Yogyakarta Province, Java Island, Indonesia. The size of ​​the Progo River watershed is around 17,432 square kilometers. Google Earth Images analysis is carried out to analyze the morphological changes of the Progo River from 2012 to 2019. The result shows that land-use changes due to dam construction affected the sediment supply downstream of the dam. In addition, land-use changes around the Progo River due to the opening of agricultural land and settlement areas had an effect on decreasing the infiltration area, so that the number of trees holding the soil from erosion was reduced, producing more eroded sediment that flowed to the river. Sand mining in the river could cause the deepening of water depths and a decrease in the average height of the riverbed.

scholarly journals Anthropogenic influences on Zambian water quality: hydropower and land-use change

2021 ◽  
Author(s):  
R. Scott Winton ◽  
Cristian R. Teodoru ◽  
Elisa Calamita ◽  
Fritz Kleinschroth ◽  
Kawawa Banda ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Field Data ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Surface Water Quality ◽  
Anthropogenic Influences ◽  
Hydropower Dams ◽  
Local Impacts ◽  
Zambezi River

Hydropower dams along with urban and agricultural land-use changes are altering surface water quality in the Zambezi River Basin, Zambia. Field data reveal local impacts and point to monitoring needs for safeguarding water resources under pressure.

scholarly journals Controlling Factors of Badland Morphological Changes in the Emilia Apennines (Northern Italy)

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 539
Author(s):  
Paola Coratza ◽  
Carlotta Parenti
Keyword(s):  
Land Use ◽  
Forest Cover ◽  
Agricultural Land ◽  
Drainage Basin ◽  
Morphological Changes ◽  
Land Use Changes ◽  
Northern Italy ◽  
Drainage Basins ◽  
Drainage Networks ◽  
Intensive Land Use

Badlands are typical erosional landforms of the Apennines (Northern Italy) that form on Plio-Pleistocene clayey bedrock and rapidly evolve. The present study aimed at identification and assessment of the areal and temporal changes of badlands within a pilot area of the Modena Province (Emilia Apennines), where no previous detailed investigation has been carried out. For this purpose, a diachronic investigation was carried out to map the drainage basin and the drainage networks of the linear erosion features in the study area during the last 40 years, and to evaluate changes in badlands drainage basins morphometry and surface, land use and pluviometry. The investigation carried out indicated a general stabilisation trend of the badlands in the study area. In fact, a reduction in the bare surface area from 6187.1 m2 in 1973 to 4214.1 m2 in 2014 (31%), due to an intensified revegetation process around the badland areas, has been recorded. This trend, in line with the results of research carried out in other sector of the Northern Apennines, is mainly due to intensive land use changes, mostly the increase in forest cover and the reduction of agricultural land, that occurred in the study area from the 1970s onwards.

scholarly journals Changes in Coastal Agricultural Land Use in Response to Climate Change: An Assessment Using Satellite Remote Sensing and Household Survey Data in Tien Hai District, Thai Binh Province, Vietnam

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 627
Author(s):  
Duong H. Nong ◽  
An T. Ngo ◽  
Hoa P. T. Nguyen ◽  
Thuy T. Nguyen ◽  
Lan T. Nguyen ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Management Strategies ◽  
Household Survey ◽  
Extreme Weather Events ◽  
Agricultural Land Use ◽  
Landsat 8 ◽  
Land Uses

We analyzed the agricultural land-use changes in the coastal areas of Tien Hai district, Thai Binh province, in 2005, 2010, 2015, and 2020, using Landsat 5 and Landsat 8 data. We used the object-oriented classification method with the maximum likelihood algorithm to classify six types of land uses. The series of land-use maps we produced had an overall accuracy of more than 80%. We then conducted a spatial analysis of the 5-year land-use change using ArcGIS software. In addition, we surveyed 150 farm households using a structured questionnaire regarding the impacts of climate change on agricultural productivity and land uses, as well as farmers’ adaptation and responses. The results showed that from 2005 to 2020, cropland decreased, while aquaculture land and forest land increased. We observed that the most remarkable decreases were in the area of rice (485.58 ha), the area of perennial crops (109.7 ha), and the area of non-agricultural land (747.35 ha). The area of land used for aquaculture and forest increased by 566.88 ha and 772.60 ha, respectively. We found that the manifestations of climate change, such as extreme weather events, saltwater intrusion, drought, and floods, have had a profound impact on agricultural production and land uses in the district, especially for annual crops and aquaculture. The results provide useful information for state authorities to design land-management strategies and solutions that are economic and effective in adapting to climate change.

Impacts of agricultural land use changes on pesticide use in French agriculture

2016 ◽  
Vol 80 ◽  
pp. 113-123 ◽  
Author(s):  
Nicolas Urruty ◽  
Tanguy Deveaud ◽  
Hervé Guyomard ◽  
Jean Boiffin
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Agricultural Land Use ◽  
Pesticide Use

scholarly journals Assessing the Opportunity Cost of Carbon Stock Caused by Land-Use Changes in Taiwan

Land ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 1240
Author(s):  
Ming-Yun Chu ◽  
Wan-Yu Liu
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Carbon Storage ◽  
Carbon Emissions ◽  
Opportunity Cost ◽  
Carbon Stock ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Market Price ◽  
The Government

As compared with conventional approaches for reducing carbon emissions, the strategies of reducing emissions from deforestations and forest degradation (REDD) can greatly reduce costs. Hence, the United Nations Framework Convention on Climate Change regards the REDD strategies as a crucial approach to mitigate climate change. To respond to climate change, Taiwan passed the Greenhouse Gas Reduction and Management Act to control the emissions of greenhouse gases. In 2021, the Taiwan government has announced that it will achieve the carbon neutrality target by 2050. Accordingly, starting with focusing on the carbon sink, the REDD strategies have been considered a recognized and feasible strategy in Taiwan. This study analyzed the net present value and carbon storage for various land-use types to estimate the carbon stock and opportunity cost of land-use changes. When the change of agricultural land to artificial forests generated carbon stock, the opportunity cost of carbon stock was negative. Contrarily, restoring artificial forests (which refer to a kind of forest that is formed through artificial planting, cultivation, and conservation) to agricultural land would generate carbon emissions, but create additional income. Since the opportunity cost of carbon storage needs to be lower than the carbon market price so that landlords have incentives to conduct REDD+, the outcomes of this study can provide a reference for the government to set an appropriate subsidy or price for carbon sinks. It is suggested that the government should offer sufficient incentives to reforest collapsed land, and implement interventions, promote carbon trading policies, or regulate the development of agricultural land so as to maintain artificial broadleaf forests for increased carbon storage.

scholarly journals Uncertainties in the land-use flux resulting from land-use change reconstructions and gross land transitions

2017 ◽  
Vol 8 (1) ◽  
pp. 91-111 ◽  
Author(s):  
Anita D. Bayer ◽  
Mats Lindeskog ◽  
Thomas A. M. Pugh ◽  
Peter M. Anthoni ◽  
Richard Fuchs ◽  
...  
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Terrestrial Ecosystem ◽  
Grid Cell ◽  
Carbon Stocks ◽  
Dynamic Global Vegetation Model ◽  
Soil C ◽  
Tropical Regions ◽  
C Stocks

Abstract. Land-use and land-cover (LUC) changes are a key uncertainty when attributing changes in measured atmospheric CO2 concentration to its sinks and sources and must also be much better understood to determine the possibilities for land-based climate change mitigation, especially in the light of human demand on other land-based resources. On the spatial scale typically used in terrestrial ecosystem models (0.5 or 1°) changes in LUC over time periods of a few years or more can include bidirectional changes on the sub-grid level, such as the parallel expansion and abandonment of agricultural land (e.g. in shifting cultivation) or cropland–grassland conversion (and vice versa). These complex changes between classes within a grid cell have often been neglected in previous studies, and only net changes of land between natural vegetation cover, cropland and pastures accounted for, mainly because of a lack of reliable high-resolution historical information on gross land transitions, in combination with technical limitations within the models themselves. In the present study we applied a state-of-the-art dynamic global vegetation model with a detailed representation of croplands and carbon–nitrogen dynamics to quantify the uncertainty in terrestrial ecosystem carbon stocks and fluxes arising from the choice between net and gross representations of LUC. We used three frequently applied global, one recent global and one recent European LUC datasets, two of which resolve gross land transitions, either in Europe or in certain tropical regions. When considering only net changes, land-use-transition uncertainties (expressed as 1 standard deviation around decadal means of four models) in global carbon emissions from LUC (ELUC) are ±0.19, ±0.66 and ±0.47 Pg C a−1 in the 1980s, 1990s and 2000s, respectively, or between 14 and 39 % of mean ELUC. Carbon stocks at the end of the 20th century vary by ±11 Pg C for vegetation and ±37 Pg C for soil C due to the choice of LUC reconstruction, i.e. around 3 % of the respective C pools. Accounting for sub-grid (gross) land conversions significantly increased the effect of LUC on global and European carbon stocks and fluxes, most noticeably enhancing global cumulative ELUC by 33 Pg C (1750–2014) and entailing a significant reduction in carbon stored in vegetation, although the effect on soil C stocks was limited. Simulations demonstrated that assessments of historical carbon stocks and fluxes are highly uncertain due to the choice of LUC reconstruction and that the consideration of different contrasting LUC reconstructions is needed to account for this uncertainty. The analysis of gross, in addition to net, land-use changes showed that the full complexity of gross land-use changes is required in order to accurately predict the magnitude of LUC change emissions. This introduces technical challenges to process-based models and relies on extensive information regarding historical land-use transitions.

scholarly journals Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

2019 ◽  
Vol 8 (10) ◽  
pp. 454 ◽  
Author(s):  
Junfeng Kang ◽  
Lei Fang ◽  
Shuang Li ◽  
Xiangrong Wang
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Cellular Automata ◽  
Markov Model ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Remote Sensing Images ◽  
Mapreduce Framework ◽  
Construction Land ◽  
Model Based

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

scholarly journals Determinants of Land Use in Maine with Projections to 2050

1999 ◽  
Vol 16 (2) ◽  
pp. 82-88 ◽  
Author(s):  
Thomas E. Mauldin ◽  
Andrew J. Plantinga ◽  
Ralph J. Alig
Keyword(s):  
Land Use ◽  
Agricultural Land ◽  
Land Use Changes ◽  
Soil Characteristics ◽  
Land Area ◽  
Usda Forest Service ◽  
Tax Assessment ◽  
Land Rents ◽  
Land Use Policies ◽  
Percentage Basis

Abstract Data on land use in Maine are assembled from USDA Forest Service inventories, the Census of Agriculture, and other sources. Regression analysis is used to estimate the relationships between land use and determinants of land use such as land rents and soil characteristics. The fitted models are used to project changes in land use to 2050. We project declines in private timberland area, though these losses are small on a percentage basis. Continued declines in agricultural land area and increases in urban land area also are projected. Land use policies that influence land rents such as preferential tax assessment programs can be used to deter socially undesirable land use changes. North. J. Appl. For. 16(2):82-88.

scholarly journals Spatio-Temporal Variation Analysis of Landscape Pattern Response to Land Use Change from 1985 to 2015 in Xuzhou City, China

2018 ◽  
Vol 10 (11) ◽  
pp. 4287 ◽  
Author(s):  
Yantao Xi ◽  
Nguyen Thinh ◽  
Cheng Li
Keyword(s):  
Land Use ◽  
Landscape Pattern ◽  
Urban Areas ◽  
Vegetation Index ◽  
Agricultural Land ◽  
Local Level ◽  
Land Use Changes ◽  
Landscape Patterns ◽  
Rapid Urbanization ◽  
Barren Land

Rapid urbanization has dramatically spurred economic development since the 1980s, especially in China, but has had negative impacts on natural resources since it is an irreversible process. Thus, timely monitoring and quantitative analysis of the changes in land use over time and identification of landscape pattern variation related to growth modes in different periods are essential. This study aimed to inspect spatiotemporal characteristics of landscape pattern responses to land use changes in Xuzhou, China durfing the period of 1985–2015. In this context, we propose a new spectral index, called the Normalized Difference Enhanced Urban Index (NDEUI), which combines Nighttime light from the Defense Meteorological Satellite Program/Operational Linescan System with annual maximum Enhanced Vegetation Index to reduce the detection confusion between urban areas and barren land. The NDEUI-assisted random forests algorithm was implemented to obtain the land use/land cover maps of Xuzhou in 1985, 1995, 2005, and 2015, respectively. Four different periods (1985–1995, 1995–2005, 2005–2015, and 1985–2015) were chosen for the change analysis of land use and landscape patterns. The results indicate that the urban area has increased by about 30.65%, 10.54%, 68.77%, and 143.75% during the four periods at the main expense of agricultural land, respectively. The spatial trend maps revealed that continuous transition from other land use types into urban land has occurred in a dual-core development mode throughout the urbanization process. We quantified the patch complexity, aggregation, connectivity, and diversity of the landscape, employing a number of landscape metrics to represent the changes in landscape patterns at both the class and landscape levels. The results show that with respect to the four aspects of landscape patterns, there were considerable differences among the four years, mainly owing to the increasing dominance of urbanized land. Spatiotemporal variation in landscape patterns was examined based on 900 × 900 m sub-grids. Combined with the land use changes and spatiotemporal variations in landscape patterns, urban growth mainly occurred in a leapfrog mode along both sides of the roads during the period of 1985 to 1995, and then shifted into edge-expansion mode during the period of 1995 to 2005, and the edge-expansion and leapfrog modes coexisted in the period from 2005 to 2015. The high value spatiotemporal information generated using remote sensing and geographic information system in this study could assist urban planners and policymakers to better understand urban dynamics and evaluate their spatiotemporal and environmental impacts at the local level to enable sustainable urban planning in the future.

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