Land Use - Science topic

Lawrence, thank you for taking the time to comment and share ideas that could be linked to those in this article.

Take a look at this article when you have time, you may find some good food for thoughts within it.

Respectfully yours;

Lucio

Go through it

Good day Prem, thank you for writing. Wish you find some good food for thoughts in it.

James, thank you for commenting. The reason I wrote that article in 2002 was that I saw moved towards a single focus on human causes only isolated from even the idea that earth is more than humans.

All the aspects you highlighted Jams are consistent with the implication of the simple formula I shared in the article and the piece by piece puzzle implications.

I am glad to hear that Eloihi.

In later years, I extended that thinking to share more sustainability related ideas from the market point of view. If you take the eco-economic development model as one based on perfect green market theory, a new world appears....

You may find some good food for thoughts in the following articles:

Use Landsat imagery in Google Earth Engine with machine learning-based supervised classification algorithms (RF, SVM, CART) is highly effective for land use and land cover change detection, even without field data. You can achieve compartively accurate results by splitting your data into training and validation samples but if you have field data it would be of more help.

While Sentinel-2 offers a higher temporal resolution, we prefer Landsat data for its longer historical record.

Felix M. Delmonte

Feng Frederic Deng

To optimize zoning policies and urban development regulations for promoting vertical development and better land use efficiency, cities can focus on a few key strategies. First, they can encourage high-density, mixed-use zoning, which allows for residential, commercial, and recreational spaces in the same area, making better use of limited land. Secondly, by relaxing height restrictions on buildings, cities can promote vertical growth instead of horizontal sprawl. Lastly, offering incentives for green building practices and efficient land use can help create more sustainable and livable urban areas. These approaches can reduce urban sprawl, lower environmental impact, and enhance overall urban resilience.

Remote sensing support the identification of land uses while GIS enabe the creation of land use maps. Some remote sensing softwares can be linked to GIS softwares and some data formats are identified by GIS so the user can further process remote sensing data or outputs from remote sensing softwares into GIS environments.

Philip Ojo

Hi ,

Geospatial techniques play a crucial role in land use planning and management by providing valuable insights and capabilities.

Geospatial techniques, which involve the collection, analysis, and visualization of geographic data, offer numerous benefits in the context of land use planning and management:

1. Data Integration and Visualization:

Geospatial techniques enable the integration of diverse data layers, such as land use maps, soil data, infrastructure networks, and environmental information. This comprehensive view allows planners to visualize and analyze the interrelationships between different factors, leading to more informed decision-making.

2. Land Use Suitability Analysis:

Geospatial techniques can assess the suitability of land for various uses based on specific criteria. By overlaying different data layers and applying suitability models, planners can identify areas that are most appropriate for development, conservation, or other designated purposes.

3. Land Use Change Monitoring:

Geospatial techniques facilitate the monitoring of land use changes over time. By comparing historical and current land use maps, planners can track trends, identify areas of rapid development or conversion, and assess the effectiveness of land use policies.

4. Environmental Impact Assessment:

Geospatial techniques support environmental impact assessments by providing detailed information about the natural resources and ecosystems within a given area. Planners can use this data to evaluate the potential impacts of proposed land use changes on the environment and develop mitigation strategies.

5. Public Participation and Engagement:

Geospatial tools can enhance public participation in land use planning processes. Interactive maps and online platforms allow stakeholders to visualize proposed plans, provide feedback, and contribute to the decision-making process.

So , we can say that Geospatial techniques empower land use planners with data-driven insights, enabling them to make informed decisions and manage land resources sustainably.

By leveraging geospatial technologies, planners can optimize land use allocation, minimize environmental impacts, and foster sustainable development.

The integration of geospatial techniques into land use planning and management practices promotes transparency, collaboration, and evidence-based decision-making.

ConvLSTM models are recommended for predicting spatiotemporal land use/land cover due to their ability to capture both spatial and temporal dependencies in sequential data effectively.

Hello good time

I work in the field of watershed management, watershed safety and health. can i help?

Thanks for sharing. Wishing you every success in your task.

Based on my experience, I used Landsat 8 OLI/TIRS Collection 2 atmospherically corrected surface reflectance data for my purpose of extracting NDVI, LST, NDBSI and Wetness. You can see more about the detail of Landsat Product from this site: https://developers.google.com/earth-engine/datasets/catalog/landsat-8

I agree with Nikolaos Tziokas. If you did your own LULC classifications, you can try to repeat your analysis changing algorithm parameters to improve them. But, if you downloaded a LULC dataset, there is not much to do.

Kindly review the literature to gain an understanding of the different threshold values, which may not necessarily apply to your study area.

It is also recommended that you apply NDBI on your imagery and verify the classes against values using Google Earth images and/or field verification.

Nigerian politicians find themselves in a strong position of power and wealth due to their connections with the oil and gas industries in Nigeria.

Nigeria's economic potential is constrained by many structural issues, including inadequate infrastructure, tariff and non-tariff barriers to trade, obstacles to investment, lack of confidence in currency valuation, and limited foreign exchange capacity.The rising land costs and accessibility to urban land has become a serious issue affecting agricultural production with over 70% of the citizens living on less than US$1 a day. Land use planning is the process of regulating the use of land by a central authority. Usually, this is done to promote more desirable social and environmental outcomes as well as a more efficient use of resources. Inadequate information/data is one of the major problems facing land use planning and management in Nigeria. Field investigation reveals lack of data on land use and no comprehensive and up-to-date plan or map showing land use pattern and structure of ownership in most areas.Interventions include; helping poor people protect and enhance their resource base, improving access to agricultural land through resettlement schemes, and ensuring food security of the vulnerable, including women, minorities and indigenous groups. present land use planning and management practices in Nigeria and highlights the challenges and implications on agricultural production. It is revealed that land transformation is a common phenomenon in Nigeria. The land use management has been wholly concerned with the granting of statutory right of occupancy and approval of plans to use land for different purposes without adequate monitoring of its outcomes. The lack of monitoring is attributable to a number of factors including lack of interests to adopt alternative land use systems. To create conducive environment for the present and future generations, it is essential to fundamentally rethink on land use control mechanism, policy, and action. To achieve such a favorable environment, it is prerequisite that land use laws should be enforced by the policy makers. It is also important to involve relevant stakeholders in the process of environmental planning to share their interests and opinions.

https://www.researchgate.net/publication/291195303_Assessment_of_Land_Use_Planning_and_Development_in_Nigeria_Challenges_and_Policy_Implications_on_Agriculture#:~:text=It%20is%20revealed%20that%20land,than%20US%241%20a%20day.

Repeated cropping is unlikely to produce any substantial benefits because the C inputs are largely rapidly metabolised

@Ahmad so you are giving answer based on ChatGPT, and additionally, you didn't tested this by yourself. Do not give false information to people.

Sure. You can fine land use raster data for 2016 and 2019 here (https://resourcewatch.org/data/explore/cit049-Urban-Land-Use-India_1?section=Discover&selectedCollection=&zoom=3&lat=0&lng=0&pitch=0&bearing=0&basemap=dark&labels=light&layers=%255B%257B%2522dataset%2522%253A%2522e354f7b8-0fca-422f-bf12-fc6189812bec%2522%252C%2522opacity%2522%253A1%252C%2522layer%2522%253A%25228efadc4d-7663-4666-ae69-fdf2dccd0cbd%2522%257D%255D&aoi=&page=1&sort=most-viewed&sortDirection=-1).

Xuyang Bai

Hi,

utilizing an Artificial Neural Network (ANN) to predict land use based on raster data is a potent application of machine learning in geospatial analysis. This approach involves training a model on historical raster data, such as population, precipitation, Digital Elevation Model (DEM), and existing land use, and then leveraging this trained model to forecast future land use patterns using forthcoming raster data containing parameters like population, precipitation, and temperature.

To practically implement this approach, you can follow these steps:

As for case studies and code examples, you can explore academic journals, conferences, and online repositories like GitHub. Many researchers have indeed explored this approach, but the specific code examples might not be readily available due to variations in dataset availability and implementation choices. Look for papers that discuss their methodology in detail; they often provide insights into the data preprocessing, model architecture, and evaluation techniques used.

Agricultural soils are essential resources for food production and play a vital role in global food security. This makes the application of soil conservation agricultural practices extremely important. These practices aim to reduce soil erosion, increase the amount of organic matter, improve soil structure and promote water retention in the soil. It is important to highlight the importance of public policies that legislate on these practices to ensure the maintenance of these soils.

From Climate Change to Global Environmental Change. Very early on, well-advised researchers understood that environmental issues are intertwined and cannot be understood according to compartmentalized disciplinary approaches. In this respect, and as an example, this relatively old chapter spoke of “Global Environmental Change.”

Fleagle, R. G., & Fleagle, R. G. (2001). Global environmental change. Eyewitness: Evolution of the Atmospheric Sciences, 101-107.

Abstract: "During the 1970s research had advanced the frontiers of understanding for a broad array of environmental problems: climate, stratospheric ozone, air quality, the carbon cycle, water resources, biological diversity, soil properties, and still others. For the most part each was studied independently of the others using more or less unique observation systems and by separate groups of scientists. However, it was evident that environmental problems were becoming more and more interdisciplinary and that observations were needed that could specify the whole phenomenon of interest, often on a sub-global or global scale, phenomena identified under “global environmental change.”

Comments. Question: How accelerated in the scientific integration of disciplines; would it be at the level of research? Personal answer: Very little to say nothing at all despite the very timid attempts of scientific organizations with mixed success, such as those of the IPCC for instance.

The study interval would have to be before the Anthropocene, the main field is Paleo-climatology, from there the individual ecosystem components. https://www.ncei.noaa.gov/products/paleoclimatology

Thanks everyone for the assistance, much appreciated!

Fragmentation confines pollinators to patches of vegetation that ultimately become clumped :)

Salma Bano

Urban sprawl refers to the uncontrolled expansion of urban areas into surrounding rural or undeveloped areas, resulting in a reduction of open space and increased land use. In the context of high mountain towns along the Karakorum Highway, urban sprawl can have significant implications for land use patterns and property values.

One of the main implications of urban sprawl in these towns is the loss of open space and natural resources. As urban areas expand into surrounding rural areas, the natural landscapes and resources that make these towns unique can be lost or degraded. This can include forests, agricultural land, water resources, and wildlife habitats, all of which are critical to the well-being of local communities and their economies.

Another implication of urban sprawl is changes in land use patterns. As urban areas expand, more land is converted from agricultural or natural areas to residential or commercial use, resulting in changes to the character of the landscape and potential conflicts between different land uses. For example, as more land is developed for residential or commercial use, there may be increased competition for resources such as water or land, leading to conflicts between different land users.

Urban sprawl can also have significant impacts on property values in high mountain towns. As the character of the landscape changes and open space is lost, the desirability of the area can decline, leading to a decrease in property values. Alternatively, if development is well-planned and managed, property values may increase as the area becomes more attractive to tourists and investors.

Overall, the implications of urban sprawl for land use patterns and property values in high mountain towns along the Karakorum Highway depend on the specific context and the way in which development is managed. Careful planning and management can help to minimize the negative impacts of urban sprawl and ensure that the unique natural and cultural resources of these areas are protected and preserved for future generations.

Neelabha Roy

The buffer distance to examine the roadside land use effect on crashes in India should be based on the size of the study area, the crash density, and the characteristics of the land use environment. Generally, a buffer of at least 500 meters or more should be used to ensure that the effect of roadside land use on crashes is adequately captured.

The built environment and land use are often used interchangeably, but each refers to different concepts. Built environment refers to the physical structure of the environment, whereas land use refers to how land is used. The built environment is the physical structure of the environment, including the buildings, roads, and infrastructure that make up the landscape. It is designed and built by humans to suit their needs and can also be enhanced with plants, trees, and other landscaping features. Buildings, roads, bridges, parks, and other structures are part of the built environment. This environment is constantly changing as society's needs evolve, and it is often seen as a reflection of the values and culture of the people who inhabit it. On the other hand, land use refers to how land is used. It involves using land for agricultural, residential, commercial, industrial, or other purposes. Land use decisions are typically based on economic factors, such as the availability of resources, the cost of land, and the demand for certain types of land. Land use decisions also consider environmental factors, such as the impact on ecosystems, water resources, and air quality. In conclusion, the built environment refers to the physical structure of the environment, while land use refers to how land is used. Both are essential components of the environment and have a significant impact on the way that people live and interact with the environment.

References:

Just a guess, but in any simulation model, depending on the described situation, it is entirely possible that some input(s) could dominate in relation to the the others, to the extent they have almost no influence at all - ie. the forestation factor contribution may be overwhelmed by extremes of morphological factors like slope, the soils, or the weather patterns ( we have an area nearby which has mountainous 'V' valleys, impervious clay soils, and 273+ inches of near continuous rain a year, so trees or not doesn't make much difference).

Thank you for the feedback.

Exactly, we search for more "bio-related" data. (habitats of animals, plant growth, climate, precipitation, etc.)

Best regards from Poland!

follow this link

Thank you all for your response and interest. I have some ideas but I need more details. I think I will use the Markov chain to predict change in LULC but I need a suggestion for free software.

Is it possible to obtain a scientific interpretation through satellite images? If you want, you can obtain high-resolution sensors through this site:

Dear Chenyuan,

Here you are a dissertation about it

and on this webpage, you can find most of the algorithms you could need

Cheers,

Ivan

My 2002 proposal at https://www.ecoseeds.com/cool.html about taking care of Global Warming by replanting desert areas to increase rainfall, was adopted by the Saudi Government in August, 2010, and 200 million acres were set aside as Ecological Restoration Preserves. Story at https://www.ecoseeds.com/Saudi-note-final.pdf

Last year the "Saudi Green Initiative" got started to begin the planting of 10 billion trees, and today that Initiative was broadened and renamed at COP27 as the "Middle East Green Initiative".

Joining this morning and speaking at the opening session were Kuwait, China, India, Pakistan, Sudan, Morocco, etc.

Since you are in Pakistan, and your country has joined, maybe there could be some work to replant the Thal desert.

By putting a native cover back on the land and selling the carbon credits produced by the plants sequestering soil carbon, could allow the oil companies to sell Carbon Neutral oil, could make the people grazing that marginal area 10-20X their annual income per hectare.

That is what Occidental Petroleum did last year, they sold two million barrels of Carbon Neutral oil to India, after they bought carbon offsets to cancel out the carbon in the soil.

The opening of the Middle East Green Initiative today, used a similar set of two pictures from Saudi Arabia, as I have posted here from Oman. Have vegetation and you have rainfall. Little or no vegetation and little or no rainfall.

Good afternoon.

This is a common problem in wastewater treatment impact assessment. To the best of my knowledge I have not found a database for wastewater treatment specific of the petrochemical industry.

If you don't expect wastewater treatment to have a significant contribution to the life cycle of your product/service/good, you can use a proxy database as a simplification approach to fill the data gap.

I like the approach of Fernández-daCosta et. al (2018) :

"Due to absence of data for treatment of wastewater as an effluent of the processes in question, the impacts of wastewater are assumed to be those

of wastewater as an effluent of soft fireboard production. This process was chosen as it has the median impacts out of a group of class 3, industrial

wastewater treatment processes in the Ecoinvent database"

This can help avoiding an under-estimation of the total life cycle impacts while being not too conservative.

Kind regards,

Gustavo Rangel

Well, in case of land changes study, u can use google earth, topography map..

If you have actual water quality data: One thing I've done in the past is used QGIS (free GIS software) with the GRASS plugin (also free), and GIS layers for land use (from the government), to determine the land uses in each sub-watershed. And then compared water quality data to the percentage of land uses. This was not very easy, but the results were somewhat informative.

I have results published here: https://tigerprints.clemson.edu/joe/vol54/iss3/14/

Dear Geoff, we have gone off the relevant question here, so this will be my last comment to your outside the question comments….

With respect to your previous comments and the current comments….

You original comments and critic of the working of the traditional market and its negative social and environmental consequences are consistent with my point of view because that means that we have been trying to use a distorted market in environmental terms to address environmental problems, a big inconsistency. However since GDP thinking is part of the distorted traditional market thinking, then unless it is made consistent with the relevant environmental sustainability problem like green GDP produced by green market growth where green producers and green consumers meet at the green market price, then the GDP based on distorted thinking is also distorted….

Before the Brundtland Commission report in 1987 I was thinking about how to correct the distorted traditional market model to transition it from a fully dirty market nature to a fully clean market nature using sustainability thinking, and then the commission formally called for correcting the social and environmental issues associated with the traditional market development model…. Then I realized that since 1776 to 1987 most economists knew or should have known the assumption of environmental and social neutrality assumptions and the assumption of population dynamic neutrality assumptions in the long term were going to lead to over production, over consumption and over population problems, but they remained silent. So I started a one man project to slowly but sure document the short comings of the traditional market models in simple terms and shared ideas on how to go beyond traditional business as usual model as the Brundtland commission asked in terms of red market thinking, green market thinking and sustainability market thinking….. Then came, Kyoto protocol, then came 2012 Rio +20 and the supposed move to green markets, then came the world of dwarf green markets a la environmental externality management and the flipping of traditional perfect market thinking…..And with all those events came paradigm shift knowledge gaps that needed to be closed….

Below I shared some of my articles related to the issues described above with some good food for thoughts on beyond business as usual

Did Adam Smith Miss the Chance to State the Goal and Structure of Sustainability Markets in His Time? If Yes, Which Could Be Some of the Possible Reasons Behind That?

What If Markets Have Always Been Distorted? Would It Then Be a Good Fix to Add Fair Trade Margins to Correct Distorted Agricultural Market Prices?

Complex and Man-made Markets: Are We Currently Approaching Sustainability in a Backward and More Chaotic Way in Terms of Economic Thinking?

Beyond Traditional Market Thinking: What is the Structure of the Perfect Green market?

Sustainability thought 165: How can we show that the overpopulation framework a la ecological overshoot is a subset of the most distorted market price possible framework? What are the main implications of this?

From Traditional Markets to Green Markets: A Look at Markets Under Perfect Green Market Competition

Sustainability thoughts 126: Are environmental externality management based production and consumption bundles inconsistent with green pareto efficiency and with pareto efficiency principles at the same time? If yes, why?

The Flipping of Traditional Economic Thinking: Contrasting the Working of Dwarf Green Market Thinking with that of Green Market Thinking to Highlight Main Differences and Implications

Look, please, to Paulo Pereira's from Mykolas Romeris university, Vilnius, Lithuania, publications.

Hi Morrison Kenie Lahai,

A detailed presentation of the techniques used in Australia to solve this problem can be found in:

Land Cover Mapping using Digital Earth Australia

doi:10.3390/data4040143

Hope it helps,

L.A. Sanabria

Protection of agricultural land in China relies more on government control than on legal regulation. In general, rule of law is much weaker than political control in China. So, if you want to study cases from developing countries, you need to go beyond legal regulations. Political control, clan, customs, etc. are very important in those countries.

Yaseen A Al-Janabi What I meant by agricultural land use was somewhat different, but thanks for your comment anyway and I agree with your point about the introduction of contemporary irrigation systems. Because land and water management practices specifically start in the field not outside of it.

The range of spatial resolution you are talking is availble but not free of cost such as Compsat, LISS-IV, Quick bird, Planetscope etc. Free of cost data from Sentinel-2 available at 10 m spatial resolution which could be benficial for you. Please try.

You are most welcome for further queries.

Dear Dev,

I am happy you are interested. As soon as we collect all the necessary materials, I will call you.

Velibor

Andrew Paul McKenzie Pegman thank you so much for your answer :))

try scikit-learn

www.geasci.org/IntErO Predicting sediment yield at the catchment scale is one of the main challenges in geomorphologic research. The research of the calculation of soil erosion intensity and torrents in Yugoslavia was initiated by the team of the researchers from the Jaroslav Cerni Institute for the Development of Water Resources in 1947. The first method that was developed in Yugoslavia was the Method for the Quantitative Classification of Erosion - MQCE (1954). The erosion intensity from the river basin was calculated by computing the amount of sediment that reaches the downstream part at the lowest point of the basin (H min). The process of the methodology development for investigation erosion process, mapping, sediment calculating and torrent classification continuous and resulted with the establishment of the Erosion Potential Method – EPM in 1968. The IntErO model (Spalevic, 2011) uses the Erosion Potential Method (Gavrilovic, 1972) in its algorithm background. The IntErO model, an upgrading of the Surface and Distance Measuring (Spalevic, 1999; Spalevic et al, 1999) and River Basins (Spalevic, 1999; Spalevic et al, 2000) programs and can be used for handling a large number of data with the processing of 27 inputs, returning, after the calculations, 22 final result parameters (Coefficient of the river basin form, A; Coefficient of the watershed development, m; Average river basin width, B; (A)symmetry of the river basin, a; Density of the river network of the basin, G; Coefficient of the river basin tortuousness, K; Average river basin altitude, Hsr; Average elevation difference of the river basin, D; Average river basin decline, Isr; The height of the local erosion base of the river basin, Hleb; Coefficient of the erosion energy of the river basin’s relief, Er; Coefficient of the region’s permeability, S1; Coefficient of the vegetation cover, S2; Analytical presentation of the water retention in inflow, W; Energetic potential of water flow during torrent rains, 2×gDF^½; Maximal outflow from the river basin, Qmax; Temperature coefficient of the region, T; Coefficient of the river basin erosion, Z; Production of erosion material in the river basin, Wyear; Coefficient of the deposit retention, Ru; Real soil losses, Gsp; Real soil losses per km2. For calculations model needs the following NUMERICAL VALUES as inputs: (1) The surface of River basin area, F [km²]; (2) The length of the watershed (perimeter), O [km]; (3) Natural length of the main watercourse, Lv [km]; (4) The shortest distance between the fountainhead and mouth, Lm [km]; (5) The total length of the main watercourse with tributaries of I and II class, ΣL [km]; (6) River basin length measured by a series of parallel lines, Lb [km]; (7) The area of the larger river basin part, Fv [km²]; (8) The area of the smaller river basin part, Fm [km²]; (9) Contour lines length, Liz [km]; (10) The area between the two neighboring contour lines, fiz [km²]. The model considers factors related to lithology (rocks permeability by percentage: fp, permeable; fpp, semipermeable; fo, low permeability) and soil type (erodibility coefficient, Y); topographic and relief data (I coefficient), monthly mean and annual precipitation (P coefficient), temperatures annual averages (t coefficient); land cover data (Xa coefficient of the River basin planning): 1. Bare lands; 2. Plough-lands; 3. Meadows; 4. Mountain pastures; 5. Orchards and vineyards; 6. Degraded forests; 7. Well-constituted forests; and finally the state of erosion patterns, and development of the watercourse network (Φ coefficient). The IntErO model can be characterized as semi-quantitative because it is based on a combination of descriptive and quantitative procedures. Compared to other semi-quantitative methods, this is the most quantitative because it uses descriptive evaluation for three parameters only: soil erodibility, soil protection, and the extent of erosion in the catchment.

SOME EXAMPLES OF THE IntErO APPLICATION: Spalevic, V.; Barovic, G.; Vujacic, D.; Curovic, M.; Behzadfar, M.; Djurovic, N.; Dudic, B.; Billi, P. The Impact of Land Use Changes on Soil Erosion in the River Basin of Miocki Potok, Montenegro. Water 2020, 12, 2973. https://doi.org/10.3390/w12112973 Sakuno, N.R.R., Guicardi, A.C.F., Spalevic, V., Avanzi, J.C., Silva, M.L.N., Mincato, R.L. (2020): Adaptation and application of the erosion potential method for tropical soils. Revista Ciência Agronômica, 51(1):  e20186545. Epub February 03, 2020. Link: http://www.scielo.br/pdf/rca/v51n1/1806-6690-rca-51-01-e20186545.pdf Mohammadi, M.; Khaledi Darvishan, A.K.; Spalevic, V.; Dudic, B.; Billi, P. Analysis of the Impact of Land Use Changes on Soil Erosion Intensity and Sediment Yield Using the IntErO Model in the Talar Watershed of Iran. Water 2021, 13, 881. https://doi.org/10.3390/w13060881 Ouallali, A,; Aassoumi, H.; Moukhchane, M.; Moumou, A.; Houssni, M.; Spalevic, V.; Keesstra, S. (2020) Sediment mobilization study on Cretaceous, Tertiary and Quaternary lithological formations of an external Rif catchment, Morocco, Hydrological Sciences Journal, 65:9, 1568-1582, https://doi.org/10.1080/02626667.2020.1755435 (link to download the peper) Spalevic, V., Zejak, D., Curovic, M., Glisic, I., Radovic, A. (2021): Analysis of the impact of fruit growing development on the intensity of soil erosion and runoff: Case study of Krusevo, Bijelo Polje, Montenegro. Agriculture and Forestry, 67 (2): 37-51 (link to download the paper) Tavares, A.S.; Uagoda, R.E.S.; Spalevic, V.; Mincato, R.L. (2021): Analysis of the erosion potential and sediment yield using the IntErO model in an experimental watershed dominated by karst in Brazil. Agriculture and Forestry, 67 (2): 153-16 (link to download the paper) Spalevic, V. (2019): Assessment of soil erosion processes by using the ‘IntErO’ model: Case study of the Duboki Potok, Montenegro. Journal of Environmental Protection and Ecology 20 (2): 657–665. Link to the paper: http://www.jepe-journal.info/journal-content/vol-20-no2 Direct link to the paper: (link) Chalise, D.; Kumar, L.; Spalevic, V.; Skataric, G. Estimation of Sediment Yield and Maximum Outflow Using the IntErO Model in the Sarada River Basin of Nepal. Water 2019, 11, 952. https://doi.org/10.3390/w11050952 Khaledi Darvishan, A., Mohammadi, M., Skataric, G., Popovic, S., Behzadfar, M., Rodolfo Ribeiro Sakuno, N., Luiz Mincato, R., Spalevic, V. (2019): Assessment of soil erosion, sediment yield and maximum outflow, using IntErO model (Case study: S8-IntA Shirindarreh Watershed, Iran). Agriculture and Forestry, 65 (4), 203-210. https://doi.org/10.17707/AgricultForest.65.4.18 El Mouatassime, S., Boukdir, A., Karaoui, I., Skataric, G., Nacka, M., Khaledi Darvishan, A., Sestras, P., Spalevic, V. (2019): Modelling of soil erosion processes and runoff for sustainable watershed management: Case study Oued el Abid Watershed, Morocco. Agriculture and Forestry 65 (4), 241-250. https://doi.org/10.17707/AgricultForest.65.4.22 Nikolic, G., Spalevic, V., Curovic, M., Khaledi Darvishan, A., Skataric, G., Pajic, M., Kavian, A., & Tanaskovik, V. (2018). Variability of Soil Erosion Intensity Due to Vegetation Cover Changes: Case Study of Orahovacka Rijeka, Montenegro. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 47(1), 237-248. https://doi.org/10.15835/nbha47111310 Khaledi Darvishan, A., Derikvandi, M., Aliramaee, R., Khorsand, M., Spalevic, V., Gholami, L., Vujacic, D. (2018): Efficiency of INTERO model to predict soil erosion intensity and sediment yield in Khamsan representative watershed (West of Iran). AGROFOR International Journal, 3 (2): 22-31. https://doi.org/10.7251/AGRENG1802022K Vujacic, D., Barovic, G., Djekovic, V., Andjelkovic, A., Khaledi Darvishan, A., Gholami, L., Jovanovic, M. and Spalevic, V. (2017): Calculation of Sediment Yield using the “River Basin” and “Surface and Distance” Models: A Case Study of the Sheremetski Potok Watershed, Montenegro. Journal of Environmental Protection and Ecology, 18(3): 1193-1202. Link: http://www.jepe-journal.info/journal-content/vol-18-no-3 Spalevic, V., Lakicevic, M., Radanovic, D., Billi, P., Barovic, G., Vujacic, D., Sestras, P., Khaledi Darvishan, A. (2017): Ecological-Economic (Eco-Eco) modelling in the river basins of Mountainous regions: Impact of land cover changes on sediment yield in the Velicka Rijeka in Montenegro. Notulae Botanicae Horti Agrobotanici Cluj-Napoca: 45(2):602-610. https://doi.org/10.15835/nbha45210695 Spalevic, V., Radanovic, D., Skataric, G., Billi. P., Barovic, G., Curovic, M., Sestras, P., and Khaledi Darvishan A. (2017): Ecological-economic (eco-eco) modelling in the mountainous river basins: Impact of land cover changes on soil erosion. Agriculture and Forestry, 63 (4): 9-25. https://doi.org/10.17707/AgricultForest.63.4.01 Khaledi Darvishan A., Behzadfar M., Spalevic V., Kalonde P., Ouallali A., Mouatassime E. S., (2017) Calculation of sediment yield in the S2-1 watershed of the Shirindareh river basin, Iran, Agriculture and Forestry, 63 (3): 23-32. https://doi.org/10.17707/AgricultForest.63.3.03 Vujacic, D., Spalevic, V. (2016): Assessment of Runoff and Soil Erosion in the Radulicka Rijeka Watershed, Polimlje, Montenegro. Agriculture and Forestry, 62 (2): 283-292. https://doi.org/10.17707/AgricultForest.62.2.25 Spalevic, V., Curovic, M., Barovic, G., Vujacic, D., Tunguz, V. and Djurovic, N. (2015): Soil erosion in the river basin of Provala, Montenegro. Agriculture and Forestry, 61(4): 133-143. https://doi.org/10.17707/AgricultForest.61.4.14 Barovic, G. and Spalevic, V. (2015): Calculation of runoff and soil erosion intensity in the Rakljanska Rijeka watershed, Polimlje, Montenegro. Agriculture and Forestry, 61(4): 109-115. https://doi.org/10.17707/AgricultForest.61.4.11 Barovic, G., Leandro Naves Silva, M., Veloso Gomes Batista, P., Vujacic, D., Soares Souza, W., Cesar Avanzi, J., Behzadfar M., Spalevic, V. (2015): Estimation of sediment yield using the IntErO model in the S1-5 Watershed of the Shirindareh River Basin, Iran. Agriculture and Forestry (61): 3: 233-243. https://doi.org/10.17707/AgricultForest.61.3.23 Vujacic, D., Barovic, G., Tanaskovikj, V., Kisic, I., Song, X., Silva, M.L.N. and Spalevic, V. (2015). Calculation of runoff and sediment yield in the Pisevska Rijeka Watershed, Polimlje, Montenegro. Agriculture and Forestry, 61 (2): 225-234. https://doi.org/10.17707/AgricultForest.61.2.20 Behzadfar, M., Tazioli, A., Vukelic-Shutoska, M., Simunic, I. Spalevic, V. (2014): Calculation of sediment yield in the S1 - 1 Watershed, Shirindareh Watershed, Iran. Agriculture and Forestry, 60 (4): 207-216. Link: http://www.agricultforest.ac.me/paper.php?journal_id=185&id=2355 Spalevic, V., Railic, B., Djekovic, V., Andjelkovovic, A. and Curovic, M. (2014): Calculation of the Soil Erosion Intensity and Runoff of the Lapnjak Watershed, Polimlje, Montenegro. Agriculture and Forestry, 60 (2): 261- 271. Link: http://www.agricultforest.ac.me/paper.php?journal_id=182&id=2300 Spalevic, V., Radanovic, D., Behzadfar, M., Djekovic, V., Andjelkovic, A., Milosevic, N (2014): Calculation of the sediment yield of the Trebacka rijeka, Polimlje, Montenegro. Agriculture and Forestry, 60 (1): 259-272. Link: http://www.agricultforest.ac.me/paper.php?journal_id=181&id=2255 Spalevic, V., Hubl, J. Hasenauer, H. and Curovic, M. (2014): Calculation of soil erosion intensity in the Bosnjak Watershed, Polimlje River Basin, Montenegro. The 5th International Symposium “Agrosym 2014”, Jahorina, 23-26 October 2014, Bosnia and Herzegovina, p 730-738. Spalevic, V., Grbovic, K., Gligorevic, K., Curovic, M. and Billi, P. (2013): Calculation of runoff and soil erosion on the Tifran watershed, Polimlje, North-East of Montenegro. Agriculture and Forestry, 59 (4): 5-17. Link: http://www.agricultforest.ac.me/paper.php?journal_id=176&id=2212 Spalevic, V., Simunic, I., Vukelic-Sutoska, M., Uzen, N., Curovic, M. (2013): Prediction of the soil erosion intensity from the River Basin Navotinski, Polimlje (Northeast Montenegro). Agriculture and Forestry, 59 (2): 9-20. Link: http://www.agricultforest.ac.me/paper.php?journal_id=174&id=2167 Spalevic, V., Mahoney, W., Djurovic, N., Uzen, U. and Curovic, M. (2012): Calculation of soil erosion intensity and maximum outflow from the Rovacki River Basin, Montenegro. Agriculture and Forestry, 58(3): 7-21. Link: http://www.agricultforest.ac.me/paper.php?journal_id=171&id=2108 Spalevic, V., Curovic, M., Borota, D. and Fustic, B. (2012): Soil erosion in the River Basin Zeljeznica, area of Bar, Montenegro. Agriculture and Forestry, 54 (1-4): 5-24. Link: http://www.agricultforest.ac.me/paper.php?journal_id=157&id=2015

Hope you get some idea from the following paper

Dear Lorenz Hübner ,

Land related factors contributie to climate change include through carbon dioxide emissions from loss of tropical forest, from degradation of ecosystems and soil; nitrous oxide emissions from excess fertilizer applied in farming; methane emissions from ruminants, rice farming, biomass burning and landfills etc. Responding to climate change involves two possible approaches: reducing and stabilizing the levels of heat-trapping greenhouse gases in the atmosphere (“mitigation”) and/or adapting to the climate change already in the pipeline (“adaptation”).

Hi Brahim Abdelkebir in this site you can download land use/cover type of the of your study area through this site:

This application provides access to individual GeoTIFF scenes files from the Sentinel-2 10m Land Use/Land Cover timeseries (2017-2021).

The process of land use and land cover change and its effects on climate change and river water is of particular importance. Hence, there are different ways for the successful operation of your research in today's world. And one of these ways is the survey operation and comment (Dr. Jerzy Lechnio & Dr.Michaelmary Chukwu ) in this direction.

The term effectiveness itself depends on time and the conditions, especially in human intervened environments. From this point of view solutions recommended would be temporal by definition. On the other hand we have to start from somewhere. I argue that even a poor land use planning would sustained and protected by the local authorities without changing it by the pressure from rent seeking political motives and voters then the transportation modelers/planners may be able to develop fair good solutions.

I think, evapotranspiration (ET) is directly related to water body area, temperature, and wind speed. LULC can directly help to caculate water body area. LULC is one component, ET is one vector of many components. Thus, predicting ET with LULC is one issue of weak or partial correlation in statistics. You need to enhance the cost function of machine learning optimization or the set of statistics estimation criteria.

Leovigildo Aparecido Costa Santos Spatial resolution 30m, max likelihood algorithm and supervised classification method were used. I know the area very well, it has not undergone that intense urbanization but there are quite of exposed soils. It has semi-arid conditions and thus increasing damming by government in effort to increase water supply. Increase in terms of Km² as you suggested, best captures the whole situation on the ground very well. Thank you all, your inputs are insightful.

The Environmental Criticality Index (ECI) is used to identify environmentally critical areas based on the ratio between LST and NDVI. The LST and NDVI layers used to derive the ECI Equation were first normalized using the histogram equalization method, resulting in a 1–255 pixel value range. The higher the ECI value, the more environmentally critical the area is.

very low (≤0.5), low (0.5–1), moderate (1–1.5), high (1.5–2.0), and very high (≥2.0).

ECI = LST (Stretched 1–255)/NDVI (Stretched 1–255)

It depends who made the land use map, and the various aspects of data quality, and more important if you intend on using a time series, if all the datasets have a consistent methodology. For instance you might have 5 datasets, but halfway through they split some categories and added others. Scale is important also. And how many categories in the classification - easy if it's binary or trinary, like impervious surface, agricultural, and natural, but we have something like 32 in our county, USGS has https://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=fsbdev3_048169 for the nation - because your looking for transitions between pairs of uses.

Thank you Steffen, Your work is very impressive.. i will go through these.

Many thanks for the question and answer Kemal Ersayin , Blaine Tomkins

You may refer to the paper of Vasu et al. 2016. Geoderma ,282, 70-79.

Two approaches are often used in studying ecosystem dynamics. The first and ideal type is “Temporal monitoring”, where the dynamics of ecosystem components (e.g. soil, plant, animal, etc.) are examined over time at a single site. This is feasible where long-term data are available and changes in ecosystem components over time can be directly measured. If there are data already collected on the species of concern in the site before the land use change, you can do enumeration of the species or the enumeration of any other ecosystem component that is of interest to you, and compare your results with those of previous enumerations. An alternative approach is to use the ‘’spatial analogue’’ method. The Spatial Analogue method involves spatial sampling on sites that are subject to different land uses but operating within a similar environment and on similar soil types. In the case of spatial analogue technique, you can do an enumeration of species in the introduced land use type and compare your result with the result of an enumeration done in a reference ecosystem within the same environment. You can then use the Sorensen similarity index to compare the species between the reference ecosystem and the introduced land use type to ascertain the extent of similarity or dissimilarity in their species composition.

Dear Cindy Viviers

These links might be helpful, have a look:

1. https://www.mdpi.com/2072-4292/13/17/3501/pdf

2. https://www.analyticsvidhya.com/blog/2021/04/google-earth-engine-machine-learning-for-land-cover-classification-with-code/

Kind Regards

That is a good question.

LCM

Hi Dear

there are many research word have been done on your study area. please have a look following:

you can find needed answers.

I recomend Google Earth Engine (GEE). It helps you to get data like landsat, sentinel, modis etc easily. There are lots of examples codes available for performing NDVI, LULC and Change Detection over time. If you're interested then I can provide my codes

GIS and remote sensing techniques have opened up wide range of avenues for effective land use and land cover mapping. The remote sensing data combined with field survey data can provide a unique and hybrid database for optimal mapping of land use and land cover.

Thanks for the question. The answers also interest me

Vikram Sharma Order the maps from Bhuvan portal and send the signed MoU to NRSC.

You can use several methods to create altitude variations.

Then extract each land-use type to reclassified altitude range via any GIS software.