scholarly journals Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6617 ◽  
Author(s):  
Jesús A. Prieto-Amparán ◽  
Federico Villarreal-Guerrero ◽  
Martin Martínez-Salvador ◽  
Carlos Manjarrez-Domínguez ◽  
Griselda Vázquez-Quintero ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Temperate Forests ◽  
Secondary Forest ◽  
Primary Forest ◽  
Support Vector ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Kappa Index

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

scholarly journals Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico

2019 ◽  
Author(s):  
Jesús A Prieto-Amparán ◽  
Federico Villarreal-Guerrero ◽  
Martin Martínez-Salvador ◽  
Carlos Manjarrez-Domínguez ◽  
Griselda Vázquez-Quintero ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Temperate Forests ◽  
Secondary Forest ◽  
Primary Forest ◽  
Support Vector ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Kappa Index

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and, on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

scholarly journals Spatial near future modeling of land use and land cover changes in the temperate forests of Mexico

2019 ◽  
Author(s):  
Jesús A Prieto-Amparán ◽  
Federico Villarreal-Guerrero ◽  
Martin Martínez-Salvador ◽  
Carlos Manjarrez-Domínguez ◽  
Griselda Vázquez-Quintero ◽  
...  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Temperate Forests ◽  
Secondary Forest ◽  
Primary Forest ◽  
Support Vector ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Kappa Index

The loss of temperate forests of Mexico has continued in recent decades despite wide recognition of their importance to maintaining biodiversity. This study analyzes land use/land cover change scenarios, using satellite images from the Landsat sensor. Images corresponded to the years 1990, 2005 and 2017. The scenarios were applied for the temperate forests with the aim of getting a better understanding of the patterns in land use/land cover changes. The Support Vector Machine (SVM) multispectral classification technique served to determine the land use/land cover types, which were validated through the Kappa Index. For the simulation of land use/land cover dynamics, a model developed in Dinamica-EGO was used, which uses stochastic models of Markov Chains, Cellular Automata and Weight of Evidences. For the study, a stationary, an optimistic and a pessimistic scenario were proposed. The projections based on the three scenarios were simulated for the year 2050. Five types of land use/land cover were identified and evaluated. They were primary forest, secondary forest, human settlements, areas without vegetation and water bodies. Results from the land use/land cover change analysis show a substantial gain for the secondary forest. The surface area of the primary forest was reduced from 55.8% in 1990 to 37.7% in 2017. Moreover, the three projected scenarios estimate further losses of the surface are for the primary forest, especially under the stationary and pessimistic scenarios. This highlights the importance and probably urgent implementation of conservation and protection measures to preserve these ecosystems and their services. Based on the accuracy obtained and, on the models generated, results from these methodologies can serve as a decision tool to contribute to the sustainable management of the natural resources of a region.

scholarly journals Long-Term Land-Use/Land-Cover Change Increased the Landscape Heterogeneity of a Fragmented Temperate Forest in Mexico

Forests ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 1099
Author(s):  
Claudia K. Legarreta-Miranda ◽  
Jesús A. Prieto-Amparán ◽  
Federico Villarreal-Guerrero ◽  
Carlos R. Morales-Nieto ◽  
Alfredo Pinedo-Alvarez
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Secondary Forest ◽  
Edge Density ◽  
Primary Forest ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Machine Method ◽  
Northern Mexico

The temperate forests of northern Mexico possess a great diversity of unique and endemic species, with the greatest associations of pine-oak in the planet occurring within them. However, the ecosystems in this region had experienced an accelerated fragmentation process in the past decades. This study described and quantified the landscape fragmentation level of a degraded watershed located in this region. For that, data from the Landsat series from 1990, 2005 and 2017, classified with the Support Vector Machine method, were used. The landscape structure was analyzed based on six metrics applied at both, the landscape and class levels. Results show considerable gains in surface area for the land use land cover change (LULC) of secondary forest while the Primary Forest (PF) lost 18.1% of its area during 1990–2017. The PF increased its number of patches from 7075 to 12,318, increased its patch density (PD) from 53.51 to 58.46 # of patches/100 ha, and reduced its average patch size from 39.21 to 15.05 ha. This made the PF the most fragmented LULC from the 5 LULCs evaluated. In this study, strong fluctuations in edge density and PD were registered, which indicates the forests of northern Mexico have experienced a reduction in their productivity and have been subjected to a continuous degradation process due to disturbances such as fires, clandestine and non-properly controlled logging, among others.

scholarly journals Examining Land Use/Land Cover Change and Its Prediction Based on a Multilayer Perceptron Markov Approach in the Luki Biosphere Reserve, Democratic Republic of Congo

2021 ◽  
Vol 13 (12) ◽  
pp. 6898
Author(s):  
Opelele Omeno Michel ◽  
Yu Ying ◽  
Fan Wenyi ◽  
Chen Chen ◽  
Kachaka Sudi Kaiko
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Multilayer Perceptron ◽  
Secondary Forest ◽  
Biosphere Reserve ◽  
Primary Forest ◽  
Fallow Land ◽  
Land Use Land Cover ◽  
Landscape Metric

Villages within the Luki Biosphere Reserve and the surrounding cities have undergone rapid demographic growth and urbanization that have impacted the reserve’s natural landscape. However, no study has focused on the spatiotemporal analysis of its land use/land cover. The present research aims at providing a comprehensive analysis of land use/land cover change in the Luki Biosphere Reserve from the year 1987 to 2020, and to predict its future change for the year 2038. Landsat images were classified in order to provide land use/land cover maps for the years 1987, 2002, 2017 and 2020. Based on these maps, change detection, gradient direction, and landscape metric analyses were performed. In addition, land use/land cover change prediction was carried out using the Multilayer Perceptron Markov model. The results revealed significant land use/land cover changes in the Luki Biosphere Reserve during the study period. Indeed, tremendous changes in the primary forest, which lost around 17.8% of its total area, were noted. Other classes, notably savannah, secondary forest, built-up area, fallow land and fields had gained 79.35, 1150.36, 67.63, 3852.12 hectares, respectively. Based on the landscape metric analysis, it was revealed that built-up areas and fallow land and fields experienced an aggregation trend, while other classes showed disaggregation and fragmentation trends. Analysis further revealed that village expansion has significantly affected the process of land use/land cover change in the Luki Biosphere Reserve. However, the prediction results revealed that the primary forest will continue to increase while built-up area, fallow land and fields will follow a trend similar to a previous one. As for secondary forest and savannah, the forecast revealed a decrease of the extent during the period extending from 2020 to 2038. The present findings will benefit the decision makers, particularly in the sustainable natural resources management of the Luki Biosphere Reserve.

scholarly journals Retrieval of Land-Use/Land Cover Change (LUCC) Maps and Urban Expansion Dynamics of Hyderabad, Pakistan via Landsat Datasets and Support Vector Machine Framework

2021 ◽  
Vol 13 (16) ◽  
pp. 3337
Author(s):  
Shaker Ul Din ◽  
Hugo Wai Leung Mak
Keyword(s):  
Land Use ◽  
Support Vector Machine ◽  
Land Cover ◽  
Land Cover Change ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Support Vector ◽  
Land Use Land Cover ◽  
Landsat Images ◽  
Barren Land

Land-use/land cover change (LUCC) is an important problem in developing and under-developing countries with regard to global climatic changes and urban morphological distribution. Since the 1900s, urbanization has become an underlying cause of LUCC, and more than 55% of the world’s population resides in cities. The speedy growth, development and expansion of urban centers, rapid inhabitant’s growth, land insufficiency, the necessity for more manufacture, advancement of technologies remain among the several drivers of LUCC around the globe at present. In this study, the urban expansion or sprawl, together with spatial dynamics of Hyderabad, Pakistan over the last four decades were investigated and reviewed, based on remotely sensed Landsat images from 1979 to 2020. In particular, radiometric and atmospheric corrections were applied to these raw images, then the Gaussian-based Radial Basis Function (RBF) kernel was used for training, within the 10-fold support vector machine (SVM) supervised classification framework. After spatial LUCC maps were retrieved, different metrics like Producer’s Accuracy (PA), User’s Accuracy (UA) and KAPPA coefficient (KC) were adopted for spatial accuracy assessment to ensure the reliability of the proposed satellite-based retrieval mechanism. Landsat-derived results showed that there was an increase in the amount of built-up area and a decrease in vegetation and agricultural lands. Built-up area in 1979 only covered 30.69% of the total area, while it has increased and reached 65.04% after four decades. In contrast, continuous reduction of agricultural land, vegetation, waterbody, and barren land was observed. Overall, throughout the four-decade period, the portions of agricultural land, vegetation, waterbody, and barren land have decreased by 13.74%, 46.41%, 49.64% and 85.27%, respectively. These remotely observed changes highlight and symbolize the spatial characteristics of “rural to urban transition” and socioeconomic development within a modernized city, Hyderabad, which open new windows for detecting potential land-use changes and laying down feasible future urban development and planning strategies.

scholarly journals A Study of Diffusion Equation-Based Land-Use/Land-Cover Change Simulation

2021 ◽  
Vol 10 (6) ◽  
pp. 383
Author(s):  
Min Jin ◽  
Ruyi Feng ◽  
Lizhe Wang ◽  
Jining Yan
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Diffusion Equation ◽  
Land Cover Change ◽  
Change Process ◽  
Urban Land ◽  
Urban Land Use ◽  
Series Data ◽  
Land Cover Changes ◽  
Land Use Land Cover

Simulating and predicting the development and changes in urban land change can provide valuable references for the sustainable development of cities. However, the change process of urban land-use/land-cover is a complex process involving multiple factors and multiple relationships. This dilemma makes it very challenging to accurately simulate the results and to make predictions. In response to this problem, we started with the physical characteristics of the land-use/land-cover change process and constructed a diffusion equation to simulate and predict urban land-use/land-cover changes. The diffusion equation is used to describe the diffusion characteristics of the land-use/land-cover change process, which helps to understand the urban land-use/land-cover change process. The experimental results show that (1) the diffusion equation we constructed can simulate urban land-use/land-cover changes, (2) the simulation process of the model is not limited by the time interval of the time series data itself, and (3) the model only requires one parameter without other constraints.

The Implication of Land Use Land Cover Change on Biodiversity Conservation: An Overview from Protected Areas in Ethiopia

2021 ◽  
pp. 1-15
Author(s):  
Israel Petros Menbere ◽  
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Protected Areas ◽  
Biodiversity Conservation ◽  
Land Cover Change ◽  
Land Use Planning ◽  
National Park ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Planning And Management

Conversion of natural habitat to other forms of land use is the main threat to protected areas and biodiversity globally. The continued trend of land use land cover change in protected areas resulted in loss of a large portion of biodiversity, overexploitation by humans, transformation of natural land to human settlement, etc. In Ethiopia, the causes for land use land cover change in many protected areas are farmland expansion, deforestation, unsustainable grazing and settlement expansion, and are leading to loss of biodiversity and negative impacts of ecosystem services. In addition, Ethiopia’s protected areas entertain escalating threats and land cover changes due to human population growth, competing claims from the surrounding communities, incompatible investment, lack of environmental law enforcement, absence of complete plan and timely update for protected areas, etc. These have affected protected areas in the country namely the Bale Mountains National Park, Chocke Mountains, Babile Elephant sanctuary, Abijata Shalla Lakes National Park, Awash National Park and others. The continued land use land cover changes are aggravating ecosystem, soil and water resources degradation in mountainous protected areas while they are leading to biodiversity destruction and loss of forest cover in lowland protected areas. In order to halt and reduce the impact of land cover change on biodiversity conservation, undertaking complete land use planning and continuous monitoring of protected areas was found to be important. Similarly, integrating protected areas into the surrounding landscapes and a broader framework of national plans, promoting income generation means for communities surrounding protected areas, promoting biodiversity conservation directly linked to poverty alleviation, involving local communities and stakeholders in land use planning and sustainable management of protected areas, enhancing sound management in vulnerable mountain protected areas and restoring abandoned lands located in and around protected areas are crucial in the proper land use planning and management of protected areas. In addition, enhancing awareness creation and promoting natural resource information of protected areas and enhancing scientific study on land use land cover change pattern of protected areas are vital to undertake effective land use planning and management of protected areas in Ethiopia.

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

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.

scholarly journals Using GIS, Remote Sensing, and Machine Learning to Highlight the Correlation between the Land-Use/Land-Cover Changes and Flash-Flood Potential

2020 ◽  
Vol 12 (9) ◽  
pp. 1422 ◽  
Author(s):  
Romulus Costache ◽  
Quoc Bao Pham ◽  
Ema Corodescu-Roșca ◽  
Cătălin Cîmpianu ◽  
Haoyuan Hong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Land Cover Change ◽  
Flash Flood ◽  
Machine Learning Techniques ◽  
Land Cover Changes ◽  
Land Use Land Cover ◽  
Runoff Potential

The aim of the present study was to explore the correlation between the land-use/land cover change and the flash-flood potential changes in Zăbala catchment (Romania) between 1989 and 2019. In this regard, the efficiency of GIS, remote sensing and machine learning techniques in detecting spatial patterns of the relationship between the two variables was tested. The paper elaborated upon an answer to the increase in flash flooding frequency across the study area and across the earth due to the occurred land-use/land-cover changes, as well as due to the present climate change, which determined the multiplication of extreme meteorological phenomena. In order to reach the above-mentioned purpose, two land-uses/land-covers (for 1989 and 2019) were obtained using Landsat image processing and were included in a relative evolution indicator (total relative difference-synthetic dynamic land-use index), aggregated at a grid-cell level of 1 km2. The assessment of runoff potential was made with a multilayer perceptron (MLP) neural network, which was trained for 1989 and 2019 with the help of 10 flash-flood predictors, 127 flash-flood locations, and 127 non-flash-flood locations. For the year 1989, the high and very high surface runoff potential covered around 34% of the study area, while for 2019, the same values accounted for approximately 46%. The MLP models performed very well, the area under curve (AUC) values being higher than 0.837. Finally, the land-use/land-cover change indicator, as well as the relative evolution of the flash flood potential index, was included in a geographically weighted regression (GWR). The results of the GWR highlights that high values of the Pearson coefficient (r) occupied around 17.4% of the study area. Therefore, in these areas of the Zăbala river catchment, the land-use/land-cover changes were highly correlated with the changes that occurred in flash-flood potential.

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