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Land Use - Science topic

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Are there any examples of good practices and new paradigms that you come across when considering agricultural land use (especially the measures taken by developing countries)?
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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.
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Working with SWAT modeling in mining regions. The Land Use of the mining region has to be fine enough to understand the impact of change of land disturbance by mining on hydrology.
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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.
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I am suppose to predict land use/land cover changes using rainfall, climate and socio-economic data. what regression model can be recommended ?
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Hi Morrison
Does your dependent variable have a separate value? In other words, can it only have one of two values ​​(0 or 1, true or false, black or white, green or not green, etc.)? In this case, you may want to use logistic regression to analyze your data.
Kind Regards
Ernur
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Kind request to receive some templates on interviews, surveys, questionnaires for the fieldwork related to the Impact of land-use changes on soil erosion intensity?
We are planning to use standard methodologies for the assessment of Soil erosion on the field. In addition to that, we will use some photo materials (repeat photography), google maps, google earth...
We are looking for some good examples you implemented earlier for collecting data using interview techniques, questioners, working with Focus Groups.
It is not a problem to include interested parties to be with our team one of the authors, being interested to participate in the preparation of the interview techniques, questioners, working with Focus Groups (online ZOOM with us); including the analysis of the received inputs.
Looking forward to your reply,
Dr. Sc. Velibor SPALEVIC
1, Novaka Miloseva, 81000 Podgorica, Montenegro
Mobile/Viber/WhatsApp: +382 67 201 222
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Dear Dev,
I am happy you are interested. As soon as we collect all the necessary materials, I will call you.
Velibor
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Hello everyone!
I am about to start some analysis for a paper I am preparing. I want to evaluate the possible relationship between land use variables and changes in fish funcional diversity.
However, for land use changes, I only have one measureament per year, as for the fish community, I have four samplings per year. My dataset comprehends 20 years of data, for both land use and fish metrics. (i.e I have 20 outcomes for each land use variable, and 80 measuraments for fish functional indexes)
So, my question is: is it correct to repeat the measurements for land use variables to matche the four measuraments I have for fish samplings? Initially I thought about using some kind of GLM or betaregressions (considering my response variable - fish functioanl indexes).
I really appreciate any help!
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Andrew Paul McKenzie Pegman thank you so much for your answer :))
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I want to do land use land cover classification using land sat images and random forest algorithm. Please, suggest an available potential software with some tutorials/ manuals to accomplish the research.
Thanks in advance
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try scikit-learn
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Hello guys!
I am looking for a way to build land-use models for the future 2050 and 2100. In addition to social and economic variables, I would like to include in these models the effects of climate change following the scenarios of SSPs 4.5 and 8.5. Do you have any suggestions for me? What are the limitations of these models?
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The IntErO model
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
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Can somebody please provide links to papers consisting of tables with maximum canopy storage values against corresponding landuses? Classification of landuse as used by ESRI is preferrable.
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Hope you get some idea from the following paper
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What is the purpose, usefulness and outcome of climatic simulations, particularly those of ecological LUC measures such as the Sahel afforestation ("Great Green Wall")?
How reliable are they and how can they have an impact on decision making? There are many simulations of the intended afforestation or restoration of the Sahel region, raising warning flags of heat waves and flooding since many years. However, we find that they are based on somewhat unrealistic, hydro-ecologically not feasible vegetational assumptions.
Now, after many years of simulations the most recent study (Camara et al, Atmosphere 2022, 13, 421) at least finds that reforestation should help to improve the climate over the reforested area. - Could scientific results have caused delays in starting badly needed restoration measures? Maybe even worse, as an indirect consequence have an affect on drought events?
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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”).
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What is the Site-Web do you recommend as a researcher to download satellite images to extract Land Uses and Land Covers (LULC) for future scenarios?
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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).
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I want to do logistic regression analysis in R studio with glm function. I have categorical independent values as lithology, land use, aspect. I would like to be sure that does r automatically create dummy variables for my categorical data. Or do I have to create dummy variables for all categoric variables?
Best regards.
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How to Create Dummy Variables in R (with Examples) (marsja.se)
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-So long as there are adequate and reliable datasets, it is possible to show the effects of land use and land cover changes on river flows in a given time period/s.
- The same is true for showing the effect of climate change on river flows using appropriate models, if there is data.
But, how can one show the combined effects of climate change and land-use and land cover changes on a given river catchment/flow?
Which of the existing tools and models could be helpful in this regard?
Thanks for your time and usual help!
Aklile
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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.
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How sustainable is it to generate too many transportation improvements on a poorly created land use development plan?
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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.
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I have LULC and ET data for the past three decades and have predicated LULC for future time period (e.g., 2025-2055). Now, I want to predict ET using land use land cover changes.
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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.
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Let's say, built up areas increased by 3400% from 1970 to 2000.
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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.
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i am working on relation between urban heat island and land use/ land cover. my intention is to use environmental critical index but i do not know how to determine it. please i need your help
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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)
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Instead of doing supervised classification to produce a land use map from satellite images, can I just use an existing land use map (in shapefile format) to predict land use change(Just convert the shapefile to raster then undergo land use prediction)? Furthermore, does the land use type need to be general? For example, the land use consists of forest, agricultural, built-up and water bodies only. What if I want to be more specific like breaking down agriculture to several types like husbandry, palm oil and aquaculture?
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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.
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Does altitude changes the relation between land use and lichen sp. richness
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Thank you Steffen, Your work is very impressive.. i will go through these.
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Dear Researchers,
These days, the implications of future socio-economic development on climate change mitigation, adaptation, and land use are explored using SSPs. How it is better than RCPs?
Regards
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I am comparing the soil quality of different land use at watershed scale, and in need of (if) a tool help to calculate the indices?
Thank you
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You may refer to the paper of Vasu et al. 2016. Geoderma ,282, 70-79.
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I assumed that the hybridization of two langur species in Bhutan is happening due to habitat modification and land use change. I wanted to determine how land use change has contributed towards habitat modification.
Thanks
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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.
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Within EU4Sevan project we are looking for the successful cases of implementation of PES for agricultural and sustainable land-use practices to adapt and scale-up for the lake Sevan basin. Any references to relevant studies in the area of PES in Agriculture, PES in Sustainable land-use and PES for ecosystems/agro-ecosystems would be highly appreciated and welcomed.
Thank you.
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I attempted to do land use classification (CART classifier in GEE) using the 2020 South African National Land Cover Map as training data (73 classes) (although only 53 of the classes occur in my study area).
The overall accuracy is only 40 - 45 %.
I saw in the map publisher's accuracy report that they decreased their classes and understand that my low accuracy is ascribed to the large quantity of classes I am trying to use.
What is your experience with land use classification in GEE and how can I attempt to improve the overall classification accuracy?
This student thanks you in advance.
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I have the rainfall data of different stations within the basin and the land use map.I am new to hec hms and i am having several doubts on how to operate the model. can anyone help me with few answers? thank u in advance
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That is a good question.
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I wish to prepare a land-use map for a certain region of a city (approx 800 sq km). What is a good number of GCPs? I will be defining around 5-6 different land-use types. I plan to use Semi-automatic Classification tool in QGIS for my purpose.
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GCP= Ground Control Points use for image georeference. But I think You ask for Ground truth data that support image classification. You need to collect two types of data. One for image analysis and the other for accuracy assessment. Actually, as I know there is no definition for the number of ground truth locations. But it is better to collect points that are uniformly distributed over your entire area.
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Hi everyone,
I'm trying to create different future land use land cover (LULC) scenarios. How can I manage to create such scenarios in ArcGIS?
I'd be so glad as well if you just could tell me other possible tools that I can use for creating different LULC scenarios and export them to ArcGIS?
Thanks in advance
Best of luck
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LCM
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Assalamu alaikum. I’m an undergrade student working on his academic thesis.
I want to know about the land use ratio of The Turag River of last 20-30 years. More specifically about the land filling activity that is occurring for decades. Can I do it using Google Earth or USGS? Or do I need other GIS software’s such as ArcGIS/Erdas Imagine or other applications? Please Mention.
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For LULC data from USGS, It shows me this error and logging in NASA earth data it doesn't help either, so if you can help me in this that would be really appreciated.
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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
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I need any who can help me to analyze the land use/cover change using the intensity analysis framework
Thank you
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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.
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We want to prepare village level crop maps (based on the land use maps) using Google Earth Engine and machine learning algorithm. There are around 27 major crops in Maharashtra.
  1. Can there be as many as 27 classes in the classification?
  2. On what basis can we decide the maximum number of classes algorithm can support?
  3. What is the optimum number of classes to achieve maximum accuracy?
  4. What is the number of ground truth points required for each class? What sample size is good sample size?
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Thanks for the question. The answers also interest me
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Dear Researcher,
It is well-establshed fact that the link between land use and the climate is complex. For example, land cover, as shaped by land-use practices, affects the global concentration of greenhouse gases. While land-use change is an important driver of climate change, as changing climate can lead to changes in land use and land cover. Although, the understanding of the interactions between climate and land-use change is improving, continued scientific investigation is still needed. I request you to kindly share your opinions and evidence from the published research regarding various methods available that help distinguishes and decide whether the disaster occurred due to climate change or land cover change. Thank you!
Yours Sincerely,
Aman Srivastava
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In recent time, climate change has played a significant role in disaster, however, climate change agreviates disasters rather than cause them. Land use\land cover on the other hand has been scientifically proven to contribute to disasters
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LUCL: BHUVAN
LULC: Other Sources
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Vikram Sharma Order the maps from Bhuvan portal and send the signed MoU to NRSC.
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Hello guys, I am new to remote sensing and GIS.
I am stuck in one problem. Actually, I had carried out the land use land cover classification but the main problem is to make the land use land cover classification at different altitudes.
I already had the DEM but doesn't know how to determine the values for the different classes at various altitudinal ranges.
Thanks in advance.
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You can use several methods to create altitude variations.
  • Reclassify DEM
  • Generate Contour, TIN
Then extract each land-use type to reclassified altitude range via any GIS software.
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To assess the accuracy of LULC classified images, a confusion matrix is created and overall accuracy, Kappa coefficient and user's and producer's accuracy are to be calculated. In a confusion matrix, which among user's and producer's accuracy is significant? Also, if we multiply user's and producer's accuracy (for eg. user's accuracy is 80% and the producer's accuracy is 78%, on multiplying 80% and 78% we get 62.4%), can we draw any discussion on the classified image?
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Overall accuracy of the remote sensing product can be calculated by adding all the diagonal elements divided by the overall sample units used for accuracy in the error matrix. If overall accuracy is more than 85% then we can say that it is meaningful but it depends upon your application. User accuracy refers how actually classified map is real on the ground. For example your user accuracy is 80% means your classified item is 80% of mapped area in actually that items other may not referred to that item. Producer accuracy refer to the classification scheme. There must more users and producer accuracy. If your user, overall, producer accuracy are high then kappa coefficient will also high.
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Can anyone please help with the process where area can be considered during land use classification? For e.g. If you want to classify forest, you need to take into account 0.5 ha area and 10% canopy cover. how do we set those criteria during classification? Is it under object based classification? I am classifying green space of urban area and want to consider only more than 10 sq m. If you have any ideas, I would sincerely appreciate.
Thank you
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Hi Anisa,
I'd start out with the following steps:
  1. Detect green / relevant pixels. This could be done with traditional image processing or semantic segmentation with NNs. (I'd start with the first option and get more "sophisticated" if the first solution doesn't work well enough)
  2. Filter / smooth results, e.g, reject outliers
  3. Cluster relevant pixels
  4. Analyse each cluster to get spatial information like the area covered by the associated pixels
  5. Use knowledge about spatial extend of clusters to acquire more features for the classification e.g. by leveraging different data sources
  6. Build a classifier
I hope I understood your problem correctly and that this helps.
Best,
Fabian
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Hi there!
I'm hoping there's an expert out there to help me. I'm doing a masters research project using ArcGIS. Put simply, I am creating land use change scenarios where I am converting a proportion of arable land into woodland. For example, I take 20% of arable land in my study area and convert it to woodland.
I would like to know, is there a way in GIS where I can randomly select 20% of the area within a land use, and then reclassify it to a new land class?
Many thanks!
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You can select random points by ArcGIS
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Hi! I am listing published works on Land Use and Land Cover classification of orbital remote sensing images in which labeled samples for training the classifier and/or assessing the classification accuracy were collected based on VANT/DRONE data. As this information is not usually straightforward in the keywords I am a bit stumped. Any known paper that used that type of data? Suggestions are greatly appreciated.
Thank you!
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Hello,
I am trying to find high detail information about the land cover/land use maps openly distributed by governamental agencies in all Eurasian countries. I am also interested in crop type maps and forest type maps, as well as marine ecosystem maps. I already know about initiatives like Open Street Map, but this is not always complete. There are also many dataset available from Copernicus, but this is not always enough (and it doesn't cover all countries). Basically, I think that I could find most European scale datasets when English search is possible. But my problem is mainly for countries of different languages, where the search by a non native speaker is more difficult despite computer translation. So with everyones collaboration, maybe we could list the ressources available in your own countries (or the fact that the data is not openly available is also interesting, so that I stop looking). Information on how to download the data (in the language of the site) would be a generous addition to providing the url.
Many thanks
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Dear Pietro,
Yes, this is exactly the type of information that I am looking for. Thank you.
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Hello
i want to perform CA Markov using IDRISI selva. but this annoying error always appears. does anybody know how can i fix this error?
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1.the input layer perhaps need reclass
2.check your file name
3.make sure the output file in the default path
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Background: In the field investigation of the headwaters stream, we discover that the land use intensity is positively correlated with the distance from the headwaters. Compared with the natural stream, the fish community composition in the disturbed stream was significantly changed. Question: how to quantify the impact of land use intensity on β diversity while excluding the effect of distance decay?
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I would like to know how to use quantitative land use intensity to explore the impact on diversity. Rather than exploring diversity based on different land use types (e.g., forests, agricultural land and urban land). Thank you very much for your replay. @Uzoma Darlington Chima
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Hello!
A question. Does anyone happen to know of any studies on the influence of surrounding land use (natural habitat, mass-flowering crops, other agriculture, etc.) on the sex ratio of the offspring produced by solitary bees (possibly specifically mason bees but not necessarily)?
To date, I have only found studies that indicate that larger individuals produce more females and that the proportion of females decreases through the season.
Thanks in advance!
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Actors may favour hard policy instruments (i.e. their policy preferences) if they can thus protect the interests (e.g. resources) of 'their' sector, thus in a way protecting 'their' sector and 'their' interests.
I'm looking for literature, especially from the land-use sector, that backs this idea.
Thank you!
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The following work may help you.
Michael Howlett, Governance Modes, Policy Regimes and Operational Plans: A Multi-Level Nested Model of Policy Instrument Choice and Policy Design
Policy Sciences, Vol. 42, No. 1 (Feb., 2009), pp. 73-89.
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I have a vector layer with buffers (all same size) around my sample points. I also have a land-use map of the area with 5 different classes. I have the land-use map in both rastar (Geotiff) and vector (by converting rastar using polygenize) format. I wish to calculate percentages of different land-use types in these buffers for two cases -
(1) buffers do not overlap
(2) some buffers overlap
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Dear all,
Does anyone of you know a tool for dissolving/eliminate small polygons by combining them with larger adjacent polygons? In the past I used the tool "Dissolve Adj. Polys" by Jenness Enterprise (http://www.jennessent.com/arcview/dissolve_adjacent_polys.htm) and it worked very well. However, it worked with ArcView3.x and now with the ArcGIS 10.x I don't know how to do it and setting minimum mapping unit quickly and efficiently.
Do you know a tool or software for this operation?
Thanks
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Dear Reuben
thanks for your reply, but I am working with a land use map, not a TIN.
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I know binary variables are not supposed to be modelled using linear regression models. However, I have come across many journal reviewers that prefer the so-called "linear probability model".
However, if an econometric model estimates a conditional mean of your dependent variable, then, can a binary dependent variable be interpreted as a %?
For example, if my land-use dependent variable is 1 if land use is agriculture and I estimate the impact of a national park on agriculture using a linear model, how would you estimate a coefficient, say, of b=0.025?
Thanks.
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It depends on the link function and on the sturctual model. For a binomial model the link is usually -but not neccessarily- the logit. This means that with a usual linear model the intercept coefficient is a log odds and other coefficients are log odds ratios.
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Could you please tell me how to perform Constrained Analysis of Principal Coordinates (CAP, Anderson and Willis, 2003) , which only based on environmental data, to test whether land use type (represents different land use intensities) influenced the environmental characteristics of the streams, using R software?
The details are as follows:
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data(varespec)#species data data(varechem)#environmental data ## Basic Analysis vare.cap <- capscale(varespec ~ N + P + K + Condition(Al), varechem, dist="bray") vare.cap plot(vare.cap) anova(vare.cap)
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I don’t know what kind of data should be replaced by “varespec” when performing CAP analysis based on environmental data only?
If it is grouping data, what is the format of the grouping data?
Although it took me a long time, this problem has not been solved yet.
If possible, please share me the R code or the learning links.
Thank you very much!
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Environment variables are indeed complicated and need be analyzed with greater precision to the objective in mind. It becomes more complicated when you are attempting 'Constrained Parameters. CAP is in fact similar to Redundancy Analysis but, allows non-Eucidean dissimilarity and despite this, the analysis is strictly linear and metric. There is similar one in Principal Coordinate Analysis (PCoA) which is multidimensional scaling. This explores and visualizes similarities or dissimilarities of data. This starts with similarity/dissimilarity matrix and assigns location in a low dimensional space. I am not sure whether this would help you...
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i am currently working for my project "IMPACT OF URBANIZATION ON AIR QUALITY USING REMOTE SENSING" . now i want to analyze between urban area and air pollution . LAND USE REGRESSION model is the only method to determine or some other ways available. please help me with that!! thank you.
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@ Santhosh,
Please note that the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform has been used to monitor atmospheric properties, including air pollution in the form of aerosols, since late February 2000. This instrument provides global coverage and is still operational today; it therefore offers a huge source of data and information on those issues. You will find a general description of the instrument, mission and products here:
In particular, you may want to browse through the large collection of papers dedicated to that issue and archived here:
Look, in particular, for papers dealing with atmospheric pollution in India, if that's your region of interest. If you require access to MISR data, then visit this web site:
Lastly, keep in mind that a successor instrument to MISR, called MAIA, will be launched in 2022 and will be dedicated to monitoring air pollution in cities. You can learn more about this upcoming project here:
I hope this may help you. Best regards, Michel.
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Hi everyone,
I want to ask a (not so) quick question about ecosystem services economics, urban planning, sustainable water drainage and cost-benefit analysis.
Whenever comparing different scenarii of urban greening or urban planning, in a cost-benefit anaysis (CBA), opportunity costs have to be integrated to represent the whole array of costs associated with a plan. The definition by Buchanan (1992) is good enough here : "Opportunity cost is the evaluation placed on the most highly valued of the rejected alternatives or opportunities", which is to say, it is the value that is sacrificed in any choice in a decision making situation. This cost is usually valued as the highest benefits that could have been produced by a choice, but that was forgone by the actual choice.
Now, when the subjects of a CBA, that is, the scenarii, are firm or private indivdual investments, the notion of opportunity cost is pretty thorough : it is the net revenue from the best forgone investment. And that's it.
However when dealing with urban planning, as with many other real world economics, it becomes trickier. The opportunity cost of using urban space is almost exclusively represented in the literature with land or real estate prices. The underlying hypothesis there is that construction would always be the next best profitable option for urban land, and that land prices are its best reflection. Fair enough, although it may debatable, as not all the alternatives of land use for a given land have a clear net benefit flow, far from it. Who knows, perhaps, at times, the next most profitable option for public welfare would be represented by the net flow of ecosystem services generated by a natural conservation option ?..
Anyhow, what if we are valuating water drainage systems scenarios ? Small urban green bioswales alongside the roads, designed for managing sustainably rainwater and stormwater ? It does not really eats up property lands, but rather public space, streets and sidewalks. What about linear street parking spaces, if those are to be converted for creating urban parks and whatnot. Real estate prices would not be a fitting approximation for the lost foregone best benefit in these situations, it seems to me.
I have not found any alternatives in the literature for taking into account the opportunity costs of open or public space. Do you have any insights on this matter ? Any literature on geographic economics or urban economics modelling that deals differently with this ?
Many thanks for any contribution
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Very good question. The opportunity costs of using public space for greening depends on situations and countries.
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As can be seen in the graphic t, there are certain points. (Such as S17-S18). If we give an example on s17, how can I find the land use rate of s17 around 5 km square? Use data on the basis of province by district are available on the internet, but how do I calculate the land use around a coordinate we want?
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There is a simple process. First you have to create a 5 km buffer around the point. after used tool Extract by mask using Buffer layer and land-use layer.
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What tools in ENVI should I use to perform post-classification sorting of Landsat 8 image? My goal is to incorporate ancillary data (i.e. DEM) in the post-classification process to enhance the accuracy of the supervised classification. My study area has 7 classes, 6 of these are vegetation types. I found that in some studies, the use of ancillary data could be helpful in improving land use discrimination and classification accuracy.
Can someone please suggest the steps and/or recommend studies that used ENVI for this purpose?
Thank you!
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Yes you are right Dannica.. sorry I missed that. than you can convert the null values basically into 0 or below 0 in to some logical number well ahead of your total number of classes.
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Dear Colleagues!
Are there any possible ways to map seasonal land-use trajectories?
If yes
then
(1) Which remote sensing datasets are more perfect for seasonal land-use trajectories?
(2) Which land cover dynamic models or frameworks could be used to analyze seasonal land-use trajectories?
I highly appreciate your answers, suggestions, and recommendations.
thanks, in anticipation
Kind Regards!
Arfan
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Puede ser muy útil interpretar y procesar imágenes en cada una de las 4 estaciones climáticas e ir al campo para validar lo observado y ptocesado en laboratorio.
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Dear all I need your scientific suggession please help me??
Before 30 years one area is covered by forest. But at this time part of it is changed to farmlands. All data needed for carbon stock estimation (for both biomass and soil) is collected from both land use (area remained as forest and area converted from forest to farm land).
The area of both land use which is remained as forest and converted from forest to farmland is known. I need to estimate the carbon dioxide released to the atmospher in association with the conversion of forest to farmlands sithin the span of 30 years. Please What method and procedures you suggest me as I get a reliable information for my research?
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this is for my thesis project, so this is the title of my thesis,
the effect of land use on hydrological components in Muar, Johor, Malaysia
any ideas, guidance, and suggestions all? Thank You, :)
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very interesting
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Landsat, sentinel and USGS satellite images have varying accuracy for identification of urban built up dynamics ? Which one is best suited for India
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Dear @Ashish Mishra,
Apart from distilling what is common to all land-use/land-cover classes, producing robust result in your task is dependent upon the classification method and resolution of the images used.
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I am looking for methods, theories, algorithms to divide an irregular shaped area (such as a parcel, a plot of land, a polygon) into equal multiple parts in size, or equivalant parts in land value, land use, land cover. Is there any standard procedure to subdivide such an area in theory, and taking into account the characteristics of the area?
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Dear Dr. Walter = plot the shape on a millimetric paper, or try to find the coordinates of the parcel vertexes (latitude and longitudes in metric system )
. Using the formula of (( 2 Area = SUM ( Xn-1 * Yn ) –SUM ( Yn – Xn-1)) divided by 2 you will obtain the area of the parcel , then draw a line from any vertex draw a line to cut the circumference of the parcel at a point , say P with unknown coordinates (X .., Y ..) , make two equations, one for each of the required parts, but contain the un known point. Solve the problem to get the values of the coordinates of point P . this procedure is common in classical geometry .
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Respected sir/mam,
I have using satellite image of Landsat 8 and 5 for LULC classification. I am confused that which software and method are the best for LULC classification?
Please help me.
Thanks and Regards
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Most GIS and RS software have the standard classification methods (ArcGIS, QGIS, ENVI, Eedas etc).
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Hello all,
I have two raster layers of the same area. one with land-use values (3 types) and the other with vegetation index (also 3 types). both rasters have the same resolution and same geo reference. now, I wish to replace the pixels with a certain value of land use (only one out of the three) with the vegetation index values. can anyone suggest a way to do that?
if necessary, I also have the land-use layer as a shapefile of polygons.
thanks!
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Namaa,
You may find the correlation between the 2 layers and then simply use the raster calculator.
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To develop a pedestrian collision frequency prediction model, I need to collect pedestrian volume data at intersections. However, these data are not available and I need to predict them. Most of the pedestrian volume prediction models need observed/sample data. However, due to time limitations, I cannot gather data and I am looking for some surrogate measures which I can use to estimate the pedestrian volume based on the built environment such as land-use, census data, etc.
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Dear Reza,
You can enjoy syntactical analysis as a predictor of pedestrian movement patterns.
Best luck,
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I want to develop several LULC maps of my study area for the last 30 years. But the fact is, there is no land-use data available for the previous years officially (except a few high-resolution Google Map). If I develop a site training dataset for land use classification using the old satellite images by interpretation from the local experts, will it be methodologically sound?
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Please have a look on the following paper: Twentieth century land resilience in Montenegro and consequent hydrological response.
To study the magnitude of land degradation, desertification or resilience in Montenegro throughout the 20th and early-21st centuries, we rephoto-graphed the landscapes recorded on 48 historical photographs dating back to between 1890 and 1985, and analysed in a semi-quantitative way the land use and cover changes that had occurred using an expert rating system (six correspondents). Time-series of hydrology and population density were analysed for the period since 1948 and were compared with the changes observed using repeat photography.
I think you may find some ideas or material for discussion for your research.
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Hey all,
I am trying to add two models (Unified equilibrium land-use transport & Bus operator behavior model). Unified equilibrium land-use transport model is a minimization problem subject to multiple equality constraints. However, Bus operator behavior model is a maximization problem subject to one inequality constraints. For the purpose of combining both models, I just need to change user travel cost (C_ijk) in the first model, perhaps. But, I am not sure how to add common constraint (equality or inequality) in the combined model? Both of these models as based on nested-logit-theory.
Any help will be appreciated.
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Following ^^
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I am trying to create polygon shapefile with land use from Second military survey using Arcmap. However, I really have hard time with reading the map, more correctly with boundaries between different land use areas because of very similar colors. Small lines marking the slope steepness inside of the areas are not helping either. Thank you in advance,
Ivan.
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You are basically attempting to extract overlapping features from a very low contrast image. It requires multiple stages, and depends on what software you have available. The best 'out of the box' toolset in in Harris ENVI, but commercial and Open Source GIS applications like ArcGIS, QGIS, GRASS have builtin or plug-ins that offer some capability, and there are tool sets like ImageJ ( https://imagej.net/Welcome ). It is useful to consider the original means of production when selecting the order of the algorithms ( https://imagej.net/Techniques and https://imagej.net/Category:Segmentation ) in your processing chain ( multiple presses from copper plates? Application of pen inkings and color washes on paper?, etc. ). In the end there will still be some further consolidation of these results to make vectors. It is possible, but a steep learning curve.
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How to identify the individual climate (Precipitation, Tmax & Tmin daily data of 15 years period) and basin parameters (Land Use, Soil and Slope) contributing and its relative importance to streamflow using the Boosted Regression Trees in R (or any other model)?
With respect to climate parameters, the comparison can be straightforward made with the streamflow statistically. However, the basin parameters considered (Land Use with 8 classes, Soil with 6 different types and Slope range from 0-86.2°) doesn’t match up with the long time series data of streamflow.
So, considering the above condition, on what basis can the basin parameters along with the climate variables be made to identify the contribution and its relative significance to streamflow.
Any help would be highly appreciated.
Thank you
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I would select the period of streamflow data that corresponds to the time period of your basin variables. And then fit them together in a BRT model. You can use the gbm package and caret package in R to fit your models. In your output, your explanatory variables (climate and basin variables) will be ordered according to the importance in explaining your streamflow data.
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We are trying to map land cover classes on a watershed. We have selected training sites (during a field campaign in early 2017) and extracted their spectral profiles based on a Landsat 8 image acquired at the time of field surveying.
In order to assess the land cover changes, we wanted to map the same cover classes at a previous year. Since our training sites might not be relevant, we wanted to perform supervised classification using endmembers spectra instead of ROIs. When importing those spectra inside ENVI's Endmember Collection toolbox, it appears that only Spectral Angle Mapper and Spectral Information Divergence classifiers could be used. Common algorithms such as Maximum Likelihood or Mahalanobis distance fail, returning the following error message :
Problem: the selected algorithm requires that the collected endmember spectra all contain an associated covariance. ENVI is unable to continue because some of the endmembers collected to not have their covariance.
Could anyone help here ? Actually is our method relevant ? How can we possibly perform supervised classification using Maximum Likelihood/ Mahalanobis classifiers on some older satellite images ?
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Roland Yonaba Aline de Matos Valerio Roland questioned himself as to whether the method is relevant. It is not. Methods such as SVM or Mahalanobis distance or Random Forest rely on a data distribution, thus the covariance that is derived from the many pixels in a region of interest taken for training. With the endmembers, you have only one example of each endmember spectrum. SAM and other methods can work with the endmembers because they are just calculating a distance in spectral space between each image pixel spectrum and the endmember spectrum.
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I want to create a large scale land use land cover change map of a village of India in the 10-year interval. Which one is the appropriate satellite image for this. and anybody kindly explains the detailed procedure.
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There is a wide variety of Sensors are available for land use/land cover classification. However, selecting an appropriate sensor is always depends on your level of classification (eg, level 4, level 3...level 1). If you need to do level 4 classification I recommend to use Spot 5 or IRS P6 LISS 4 images (both these missions are launched 2002-2003 period). But you have to pay for the same. If you are looking for a open source one then I recommend Sentinel 2A with 10m spatial resolution. However 2A was launched in 2015 so you won't get 10 year interval. Again if you are ready with moderate resolution images then you can use both Landsat 7 EMT+ and 8 OLI instruments. You can get 15m multi spectral images through pan-sharpening with 10 year temporal resolution. For land use /land cover classification level please refer USGS classification manual or NRSC classification manual.
best wishes.
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I need land use (forestry, farmland, rangeland, glaciers, waterbodies, barren regions, settlements) dataset for a project. I need it as baseline/ observational data for historical changes in land mentioned land use systems.
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Dear @Rizwan Karim,
In response to your question, thought you might find this link useful https://earthexplorer.usgs.gov/
Stay safe and ask for further help if needed.
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I am looking for land-use or land-cover data that matches all those requirements:
- global scale
- high resolution (better than 0.5°)
- with past / current data AND future projections for the CMIP5 scenarii (RCPs)
The Land Use Harmonization data (https://luh.umd.edu/index.shtml) matches those requirements (with 0.5° resolution for the CMIP5 data, 0.25° for CMIP6) but I wonder if the same kind of data exists at higher resolution.
Thank you
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The best option for future projections of land-use / land-cover data at global scale could be from CMIP6 models where they have a separate intercomparison project (MIP) for land use (LUMIP).
Further information is available from WCRPs website.
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Dear Fellows.
I have created land-use maps of two time periods. I have created the change map which shows all the changes from one class to the other, however, I want to create the transition maps, thus from on land-use class to the other ( individual maps), eg, a map showing cropland transition from 1990 to 2000, from 200 to 2010. something like this for the individual land use transitions. please how can I do this, any help? I am working with Arcmap.
Thank you
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Dear Nasir
Thank you very much, I have placed the request for the full test, I hope to receive it and address this issue
Thank you
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I have read some papers in where the authors had performed redundancy analysis (RDA) to identify the relationship between water quality and land use. However, I am confused as to how they are setting up their datasets.
My idea is that one of them is water quality parameters (e.g., pH, TN, TP, ammonia, conductivity, etc.), but what is the other dataset? Percentage of land use/land cover in a specific area (e.g., watershed, research area)?
The way I see it, land use/land cover, is not the explanatory variables. Still, there are limited classes that can be present in an area, and more water quality parameters (e.g., datasets that have measurements for 5 or 6 years).
Please let me know your thoughts and comments.
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Thank you Albert K. Smith , I will look at it and let you know if I am able to find answers to my questions.
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Since last year, the Global Land Cover facility mantained by the University of Maryland is offline and all Land Use and Land Cover data that was hosted there is not longer available.
I was wondering if there is any new server where I could find these data, as I would like to get access to them.
Thanks
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You can download land use land cover data in free from this site.
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When we analyse Land Use/Land Cover along years, there are alot of techniques but when monitoring Land cover change along agricultural season, Is NDVI index adequate?
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try the tasseled cap transform. It is supposed to show the crop evolution.
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I'm working with a mechanistic model to predict mosquito abundance with temperature input. I also have a raster with land-use classification for the same area and I now want to know how land-use dictates mosquito abundance (as land-use influences micro-climate). I think I should correct for spatial autocorrelation, but am struggling with how to do that best. One of my ideas is to find out up to what lag distance autocorrelation is present/significant and then do a ANOVA+post-hoc only on cells sampled x lag distance apart.
However, I'm confused about the different approaches amongst different functions/packages in R. On the one hand I tried lm.morantest, which computes Moran's I over the residuals of a linear model (in my case mosquito vs land-use). On the other hand I came across the raster package function , which calculates Moran's I just over the variable values (so either autocorrelation within the mosquito raster or in the land-use raster). Also sp.correlogram from the spdep package takes a variable vector as input instead of a linear model. The latter two functions give almost double the amount of autocorrelation compared to the first method.
So, I think I understand why you would check for autocorrelation in the residuals, but why in the variable input? Part of the correlation you're finding with the latter method might already be explained by your other variables as you're doing for example in linear regression? Are there options for doing something like a correlogram but with model residuals instead of variable input?
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for checking for spatial autocorrelation between two variables...lookup bivariate Moran's I..in Geoda..its very easy to do. You can check our publication which uses it. http://mires-and-peat.net/pages/volumes/map26/map2604.php
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Different transportation, land-use, environmental, and other corresponding planners propose their work for new city urban plans in order to address the existing problems by identifying the major gaps they had. The existing land-use was following bad planning principles and it is already a failed plan. When planners integrate their proposed spatial plan to existing land use, they will have a real challenge in aligning. So, as a planner, what would be your choice to align the proposal you have with the existing land-use?
  1. Shall i follow the standard plan i have and remove the existing unplanned land-uses( NB: the compensation cost may be too large) ? or
  2. Shall i accept the existing situation as it is and compromise the planning standards?
What is your suggestion?
Any help is appreciated very much.
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From a southern perspective, I will go for option 2. Like @Luis Fernando indicated, we need to rethink our understanding of city making especially in the global south. Obviously the city is being shaped largely by the people as opposed to 'our plans' - let's plan from the peoples perspective (inclusive, participatory planning). Miraftab's piece on 'insurgent planning' will offer you a clearer insight of my point of departure. Hope this helps
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I am new to this gigs and have gone through few recent and old articles about land use/land cover change its impact and ecosystem services that they provide. At this point, I want to start and list the hypothesis and theories that are not tested in the realm of land use land cover change and ecosystem services. Land use land cover change is rapid in many parts of the world, but i am curious if there are untested theories and hypothesis that can be tested in more stable kind of land use/land cover ( slow to no change for years). Land use/land cover and ecosystem services modellers, please fill us in this area so that many new comers like myself will have easy access to dig right in for some of the untested theories and hypothesis to work with.
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Dear all,
I am working on Chernozem soil development in Hungary and I was wondering if there is any chance that Chernozem soils developed from/through/by/after Early Neolithic land-use, deforestation or slash/burn agriculture?
Groundwater controlled meadow soils -> land-use -> Chernozem
Thank you for any suggestion and ideas!
Best wishes
Michael
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It seems to be more and more clean that Chernozem is semi-anthropogenic in its origin due to the large scale human impact in the Neolithic and post-Neolithic period. Extensive Neolithic agriculture minimally contributed to reduce woodland canopy and caused black soil development. There are some recent papers (like Vyslouzilova et al. 2014, 2016), which recorded this phenomenon with exact tools of geoarchaeology.
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I'm working on diversity of mesofauna under different forest land use.
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I know, generally, climate models cover the atmosphere, oceans, land and ice-covered regions of the planet. But does the model consider the change in land-use of a particular region when estimating the projections for 100 years?
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Most models use fixed land surface characteristics for climate projections. Because, the input data required for land use transitions involves a complex interplay of many factors. For example, a land use or cover transition from bareground or natural forest to agricultural or urban requires huge estimations to be used for projections. Such estimations also impart huge uncertainty to the outputs of climate models. Thence, most prefer to fix the land use/ cover type fixed to the current scenario. Hopefully, with increased observation information and improved computational resources, future climate models may consider implementing these land use transitions in climate model simulations. For more information, please read https://www.sciencedirect.com/science/article/abs/pii/S2212095517300858.