scholarly journals Conserving Working Rangelands: A Social–Ecological Case Study from Northeastern Colorado

Land ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1399
Author(s):  
Jasmine E. Bruno ◽  
Stephen J. Leisz ◽  
Jake S. Bobula ◽  
María E. Fernández-Giménez
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Open Space ◽  
Classification Model ◽  
Ecological Change ◽  
Conservation Policies ◽  
Social Ecological ◽  
Land Use Decisions ◽  
Multi Method Approach ◽  
Study Sites

Land changes in rangeland systems cascade through interconnected social and ecological spheres, affecting both humans and the environment. This study applied a multi-method approach to examine the causes and consequences of change in two rangeland communities in northeastern (NE) Colorado. First, this study used a Random Forest supervised classifier to analyze 36 years of land-cover data and create a land-cover/use change classification model. Second, the research team analyzed transcripts of interviews with 32 ranchers, examining how ranchers’ adaptive strategies influence land-cover change trends. Lastly, the analysis integrated the quantitative and qualitative data, constructing a social–ecological rangeland change conceptual model. This study found that the cultivated area decreased in both study sites from 1984–2019, with 16.0% and 18.7% of each site transitioning out of the cultivated area. Moreover, 10.3% and 18.4% of each site, respectively, transitioned to herbaceous/grassland cover from 1984–2019. The qualitative analysis identified the role of conservation policies, such as open space programs, on land change. Also, despite the relatively small area that transitioned to developed cover—1.83% and 0.183% of each site—participants emphasized that the associated demographic and cultural shifts drive land-use change. This study highlights that while rangelands are undergoing social–ecological change, land-use decisions and land conservation programs can help mitigate the global trend of declining rangeland and grassland cover.

scholarly journals PREDIKSI SPASIAL DINAMIKA AREAL TERBANGUN KOTA SEMARANG DENGAN MENGGUNAKAN MODEL REGRESI LOGISTIK

2016 ◽  
Vol 18 (2) ◽  
pp. 95 ◽  
Author(s):  
Irmadi Nahib
Keyword(s):  
Land Use ◽  
Logistic Regression ◽  
Cellular Automata ◽  
Land Cover ◽  
Open Space ◽  
Land Use Changes ◽  
Binary Logistic Regression ◽  
Binary Logistic Regression Model ◽  
Rate Of Increase ◽  
The City

<p class="JudulABSInd"><strong>ABSTRAK</strong></p><p class="abstrak">Salah satu indikator perkembangan fisik wilayah kota dapat diidentifikasi melalui fenomena perubahan tutupan lahan bervegetasi menjadi lahan terbangun. Perubahan lahan tersebut dapat berdampak terhadap penurunan kualitas lingkungan, akibat berkurangnya ruang terbuka hijau. Kota Semarang dengan visi terwujudnya Semarang sebagai kota perdagangan dan jasa yang berbudaya menuju masyarakat sejahtera, merupakan  wilayah yang rentan mengalami perubahan penggunaan lahan yang cenderung kearah lahan terbangun. Penelitian ini mengintegrasikan model <em>Cellular Automata</em> (CA) dan regresi logistik biner untuk memprediksi dinamika lahan terbangun di Kota Semarang. Citra yang digunakan adalah Citra Ikonos 2002, Ikonos 2006 dan <em>Quic</em><em>kbird</em> 2012. Model CA pada penelitian ini digunakan untuk memprediksi sebaran penutup lahan tahun 2022 dan 2032 dengan mempertimbangkan jarak terhadap jalan, jarak terhadap sungai, jarak terhadap lahan terbangun, ketinggian, kepadatan penduduk, <em>evidence likelihood </em>perubahan lahan dan indeks pengembangan kelurahan yang diakomodasi dalam peta sub-model transisi hasil model regresi logistik biner. Hasil penyusunan model ini adalah peta prediksi penutup lahan dengan akurasi 78,21 % validitas model yang dihasilkan dapat dikategorikan “<em>moderate</em>” mengindikasikan bahwa peta yang dihasilkan dapat digunakan. Hasil pemodelan menunjukkan bahwa Kota Semarang pada tahun 2022 terjadi pertambahan luas lahan terbangun rata-rata 284 ha/tahun dan pada tahun 2032 rata-rata 226 ha/tahun.</p><p><strong><em>Kata </em></strong><strong><em>k</em></strong><strong><em>unci</em></strong><em>: </em><em>cellular automata, pemodelan, regresi logistik biner, lahan terbangun</em></p><p class="judulABS"><em><strong>ABSTRACT</strong></em></p><p class="Abstrakeng">One indicator of the physical development of the city can be identified by phenomenon of land expansion, vegetated land cover changes to be built-up area. The land use changes can impact to environmental degradation, due to reduced green open space. Semarang as a city of trade and services cultured toward a prosperous community, a region that is vulnerable to changes in land use tends toward small plots. This research integrates the model of Cellular Automata (CA) and binary logistic regression to predict the dynamics of builtup area in the city of Semarang. The image used is a Ikonos imagery (2002), Ikonos imagery (2006) and Quickbird (2012). Model CA in this research use to predict the distribution of land cover 2022 and 2032 with respect to: distance to roads, the distance to the river, the distance to the built-up area, elevation, population density, evidence likelihood of land use change and development villages index were accommodated in the map sub-model transition binary logistic regression model results. The results of this study are predictive maps of built-up area  with an accuracy of 78,21 % so that the validity of the resulting model can be categorized as "moderate", indicates that the probability map is valid. Modeling results showed that Semarang City in 2022 predicted rate of increase of  built-up area an average 284  ha / year and in 2032 rate of increase of built-up area an average 226 ha / year.</p><p><strong><em>Keywords</em></strong><em>: cellular automata, modelling, binary logistic regression, built-up area</em></p>

scholarly journals Machine Learning Techniques for Land Use/Land Cover Classification of Medium Resolution Optical Satellite Imagery Focusing on Temporary Inundated Areas

2020 ◽  
Vol 13 (1-2) ◽  
pp. 43-52
Author(s):  
Boudewijn van Leeuwen ◽  
Zalán Tobak ◽  
Ferenc Kovács
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Land Use ◽  
Land Cover ◽  
Classification Model ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Learning Techniques

AbstractClassification of multispectral optical satellite data using machine learning techniques to derive land use/land cover thematic data is important for many applications. Comparing the latest algorithms, our research aims to determine the best option to classify land use/land cover with special focus on temporary inundated land in a flat area in the south of Hungary. These inundations disrupt agricultural practices and can cause large financial loss. Sentinel 2 data with a high temporal and medium spatial resolution is classified using open source implementations of a random forest, support vector machine and an artificial neural network. Each classification model is applied to the same data set and the results are compared qualitatively and quantitatively. The accuracy of the results is high for all methods and does not show large overall differences. A quantitative spatial comparison demonstrates that the neural network gives the best results, but that all models are strongly influenced by atmospheric disturbances in the image.

scholarly journals Spatio-temporal analysis of land use/land cover dynamics in Sokoto Metropolis using multi-temporal satellite data and Land Change Modeller

2020 ◽  
Vol 52 (3) ◽  
pp. 306
Author(s):  
Murtala Dangulla ◽  
Latifah Abd Manaf ◽  
Firuz Ramli Mohammad
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Open Space ◽  
Urban Expansion ◽  
Land Use Land Cover ◽  
Land Change ◽  
Landsat Images ◽  
Green Area ◽  
Rate Of Increase ◽  
Green Areas

Urbanization is currently one of the most pressing environmental issues which cuts across all countries at unprecedented rates and intensities, with far reaching consequences on ecosystems, biodiversity and human wellbeing. This paper assessed urban expansion and land use/land cover changes in Sokoto metropolis, North-western Nigeria using Remote Sensing and GIS. Landsat images of 1990, 1999 and 2015 were processed for LULC classification and change detection using the Maximum Likelihood Classification, Post Classification Comparison techniques and the Land Change Modeler. The classification revealed five broad land cover classes which include Built-up Area, Farmland, Green Area, Open Space and Wetland/Water. The Built-up and Green areas continuously increased while Farmland and Open space decreased throughout the study period. The metropolis expanded radially at a faster rate between 1999 and 2015 with the highest rate of increase (1890.5ha per annum) recorded in the Built-up Area. This implies a doubling time of approximately 30 years at the expense of Farmland and Open space which may be completely exhausted in 40 and 29 years respectively. Infrastructural provision should thus align with the rate and direction of growth and where the Green Area is converted, replacement should be made to ensure continued supply and stability of the numerous ecosystem services green areas provide.

scholarly journals Response of Ground Beetle (Coleoptera: Carabidae) Communities to Effect of Urbanization in Southern Osaka: An Analytical Approach Using GIS

2021 ◽  
Vol 13 (13) ◽  
pp. 7134
Author(s):  
Jin-Wook Park ◽  
Cheol Min Lee
Keyword(s):  
Land Use ◽  
Species Richness ◽  
Urban Areas ◽  
Open Space ◽  
Ground Beetle ◽  
Negative Influence ◽  
Green Spaces ◽  
Gis Data ◽  
Open Land ◽  
Study Sites

Urbanization involves the profound alteration of original habitats and causes habitat loss and biodiversity decline. This study aims to clarify the response of ground beetle communities to the effect of urbanization in southern Osaka, Japan. In total, 2950 individuals from 53 species of ground beetle were collected in nine urban green areas. The categories of land use regarding the study sites were determined based on GIS data. The community index was not significantly different between areas. Urban areas and roads in land use mainly have a negative influence on ground beetles. Paddies, fields, parks and green spaces, and open space were positively correlated with species richness of forest species and large-sized species, and open space was positively correlated with species richness and the density of open land species. However, ground beetle communities in different areas of varying sizes did not group separately. These results suggest that changes in paddies, fields, parks and green spaces, forests, and open space associated with the expanding urban area and road greatly influenced species composition, and the community structure remained similar.

Conjugated evolution of regional social-ecological system driven by land use and land cover change

2018 ◽  
Vol 89 ◽  
pp. 213-226 ◽  
Author(s):  
Guangming Yu ◽  
Mengxing Li ◽  
Zhenfa Tu ◽  
Qiwu Yu ◽  
Yi Jie ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Ecological System ◽  
Social Ecological

scholarly journals LAND USE AND LAND COVER CLASSIFICATION USING HYPERSPECTRAL IMAGERY: EVALUATING THE PERFORMANCE OF SPECTRAL ANGLE MAPPER, SUPPORT VECTOR MACHINE AND RANDOM FOREST

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1841-1847 ◽  
Author(s):  
L. E. Christovam ◽  
G. G. Pessoa ◽  
M. H. Shimabukuro ◽  
M. L. B. T. Galo
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Random Forest ◽  
Land Cover ◽  
Principal Components ◽  
Classification Model ◽  
Support Vector ◽  
Classification Models ◽  
Spectral Angle Mapper ◽  
Spectral Angle

<p><strong>Abstract.</strong> Land Use and Land Cover (LULC) information is an important data source for modeling environmental variables, so it is essential to develop high quality LULC maps. The hundreds of continuous spectral bands gathered with hyperspectral sensors provide high spectral detail and consequently confirm hyperspectral remote sensing as an appropriate option for many LULC applications. Despite increased spectral detail, issues like high dimensionality, huge volume of data and redundant information, mean that hyperspectral image classification is a complex task. It is therefore essential to develop classification approaches that deals with these issues. Since classification results are directly dependent on the dataset used, it is fundamental to compare and validate the classification approaches in public datasets. With this in mind, aiming to provide a baseline, four classification models in the relatively new hyperspectral HyRANK dataset were evaluated. The classification models were defined with three well-known classification algorithms: Spectral Angle Mapper (SAM), Support Vector Machine (SVM) and Random Forest (RF). A classification model with SAM and another with RF were defined with the 176 surface reflectance bands. A dimensionality reduction with principal component analysis was carried out and a classification model with SVM and another with RF were defined using 14 principal components as features. The results show that SVM and RF algorithms outperformed by far the SAM in terms of accuracy, and that the RF is slightly better than the SVM in this respect. It is also possible to see from the results that the use of principal components as features provided an improvement in the accuracy of the RF and an improvement of 28% in the time spent fitting the classification model.</p>

scholarly journals Analisis Korelasi Perubahan Tutupan Vegetasi Terhadap Perubahan Suhu Udara di Kota Denpasar

2017 ◽  
Vol 11 (2) ◽  
pp. 166
Author(s):  
MAR’IE ABDA’U ZAL ◽  
I Wayan Nuarsa ◽  
NI WAYAN FEBRIANA UTAMI
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Air Temperature ◽  
Environmental Quality ◽  
Temperature Rise ◽  
Growth And Development ◽  
Open Space ◽  
Vegetation Coverage ◽  
The Impact

The rate of growth and development of Denpasar city increases the demand for land to supply the needs of urban facilities and infrastructure. This affected to the increase of conversion of vegetation coverage into built-up land cover. The conversion of the vegetation coverage impacts on urban environmental quality which is increase the rate of air temperature.  The purpose of this study is to examine the impact of vegetation coverage change on the air temperature change in Denpasar city in year of 2003, 2009 and 2015.  Remote sensing and regression statistic method were used in this study.  The results showed that the decrease of vegetation coverage influenced the increase of air temperature in Denpasar city. Statistically, the correlation can be projected on the equation y = 31,295-0,078x, where x and y are vegetaton coverege and air temperature respectively.  This equation shows that decresing of 1% vegetation coverege will increase 0,078 oC of air temperature. The effort to conceder in responding to the temperature rise that occurred in Denpasar is increasing the number of green open space. Based on the classification value of vegetation coverage and air temperature of Denpasar City, there are three categories of priority areas for green open space development that is high, medium and low priority. The development of green open space in each priority area can be adjusted to the characteristics of land use.

scholarly journals Boom Bust Economy and Social-ecological System Relationship

2020 ◽  
Author(s):  
Xiaohui Liu ◽  
Michael Ungar ◽  
Jen McRuer ◽  
Daniel Blais ◽  
Linda Theron ◽  
...  
Keyword(s):  
Water Quality ◽  
Land Use ◽  
Land Cover ◽  
Oil And Gas ◽  
Oil And Gas Industry ◽  
Ecological Systems ◽  
Ecological System ◽  
Gas Industry ◽  
Social Ecological Systems ◽  
Social Ecological

This paper reports on the changing dynamics of a small town&rsquo;s social-ecological system (SES) concerning oil and gas industry boom-bust economic cycles and both the vulnerability and resilience of the town over the past 30 years. With the goal to understand how resource-based single industry impact social-ecological systems, we developed indicators of human and environmental well-being and assessed them. Seven indicators include labor force distribution, education, oil price, household income, water quality, air quality, and land cover land use. Over this period, Drayton Valley, Canada quadrupled in size, with more than 20% of the population working in the oil and gas sector. Median income rose to 42% above the national average despite the population lagging national benchmarks for educational attainment. There have also been dramatic fluctuations in levels of fluoride, phosphorus, and other chemicals in water quality samples, implying a correlation with fossil fuel extractive activities over this period. Land cover land use change analysis shows a decreased area of water bodies, wetland, and forests, and increased built capital and agricultural land. While economic boom cycles have led to cash inflows, an exclusive focus on the benefits of the oil and gas industry may leave those dependent on the industry vulnerable to social and environmental risk factors during bust cycles that are beyond their control in the everchanging global oil economy. This phenomenon which has been referred to as the &ldquo;resource curse&rdquo; suggests the need to anticipate cyclical (or more sustained) periods of low levels of oil and gas production. These results suggest that single boom-bust economies impact every aspect of social-ecological systems. Therefore, a sustainable development plan that comprehensively considers not only economic growth, but also diversification, environment protection, and strategic land use planning is indispensable to ensure the long-term development of communities that depend upon extractive industries.

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