Mapping of cropland, cropping patterns and crop types by combining optical remote sensing images with decision tree classifier and random forest

Geo-spatial Information Science, Journal Year: 2022, Volume and Issue: 26(3), P. 302 - 320

Published: July 21, 2022

Mapping and monitoring the distribution of croplands crop types support policymakers international organizations by reducing risks to food security, notably from climate change and, for that purpose, remote sensing is routinely used. However, identifying specific types, cropland, cropping patterns using space-based observations challenging because different have similarity spectral signatures. This study applied a methodology identify cropland including tobacco, wheat, barley, gram, as well following patterns: wheat-tobacco, wheat-gram, wheat-barley, wheat-maize, which are common in Gujranwala District, Pakistan, region. The consists combining optical images Sentinel-2 Landsat-8 with Machine Learning (ML) methods, namely Decision Tree Classifier (DTC) Random Forest (RF) algorithm. best time-periods differentiating other land cover were identified, then Landsat 8 NDVI-based time-series linked phenological parameters determine over region their temporal indices ML algorithms. was subsequently evaluated images, statistical data 2020 2021, field on patterns. results highlight high level accuracy methodological approach presented together techniques, mapping not only but also when validated at county level. These reveal this has benefits evaluating security adding evidence base studies use countries.

Language: Английский

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Land change modeler and CA-Markov chain analysis for land use land cover change using satellite data of Peshawar, Pakistan

Physics and Chemistry of the Earth Parts A/B/C, Journal Year: 2022, Volume and Issue: 128, P. 103286 - 103286

Published: Oct. 26, 2022

Language: Английский

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Spatio-temporal assessment of land use land cover based on trajectories and cellular automata Markov modelling and its impact on land surface temperature of Lahore district Pakistan

Environmental Monitoring and Assessment, Journal Year: 2022, Volume and Issue: 195(1)

Published: Nov. 17, 2022

Language: Английский

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Future Scenarios of Land Use/Land Cover (LULC) Based on a CA-Markov Simulation Model: Case of a Mediterranean Watershed in Morocco

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(4), P. 1162 - 1162

Published: Feb. 20, 2023

Modeling of land use and cover (LULC) is a very important tool, particularly in the agricultural field: it allows us to know potential changes area future consider developments order prevent probable risks. The idea give representation situations based on certain assumptions. objective this study make predictions watershed “9 April 1947”, years 2028, 2038 2050. Then, maps obtained with climate will be integrated into an agro-hydrological model water yield, sediment yield balance studied by 2050.The scenarios were created using CA-Markov forecasting model. results simulation LULC considered satisfactory, as shown values from kappa indices for agreement (κstandard) = 0.73, lack information (κno) 0.76, location at grid cell level (κlocation) 0.80. Future modeled indicate decrease areas wetlands, both which can seen warning crop loss. There is, other hand, increase forest that could advantage biodiversity fauna flora 1947” watershed.

Language: Английский

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Monitoring and predicting the influences of land use/land cover change on cropland characteristics and drought severity using remote sensing techniques

Environmental and Sustainability Indicators, Journal Year: 2023, Volume and Issue: 18, P. 100248 - 100248

Published: March 21, 2023

The Federal University of Technology at Akure (FUTA) in Nigeria is experiencing ongoing development that leading to the replacement agricultural and forestry land cover types. This study aimed assess predict changes use/land (LULC) types their impact on crop characteristics 17 plots FUTA from 1991 2031. Crop were evaluated using normalized difference vegetation index (NDVI), water (NDWI), moisture (NDMI), condition (VCI), watershed delineation, spectral Landsat imageries. change modeler TerraSet software was used future LULC scenarios. Results showed an increase built-up areas (15%) bare (14%), but a reduction 19% light 2021. predicted map illustrated decrease area (11%) (19%) NDVI values denoting health coverage extent, NDWI & NDMI indicating stress soil palm tree (Plot 1) had highest average indices (0.31, 0.34, 0.06, respectively), while mixed cropping cassava, cashew, potatoes 6) lowest (0.23, 0.28, −0.029 respectively). indicates Plot 1 (palm tree) better with higher green canopy lower compared 6 (cassava, potato vegetation). Drought analysis (VCI) drought became severe during 2001–2021 Plots 4 6. growing accelerated severity. advocates for sustainable use management manage field level.

Language: Английский

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Relation of land surface temperature with different vegetation indices using multi-temporal remote sensing data in Sahiwal region, Pakistan

Geoscience Letters, Journal Year: 2023, Volume and Issue: 10(1)

Published: July 26, 2023

Abstract At the global and regional scales, green vegetation cover has ability to affect climate land surface fluxes. Climate is an important factor which plays role in cover. This research aimed study changes relation of different indices with temperature using multi-temporal satellite data Sahiwal region, Pakistan. Supervised classification method (maximum likelihood algorithm) was used achieve based on ground-truthing. Our denoted that during last 24 years, almost 24,773.1 ha (2.43%) area been converted roads built-up areas. The increased coverage from 43,255.54 (4.24%) 1998 2022 area. Average (LST) values were calculated at 16.6 °C 35.15 for winter summer season, respectively. In average RVI, DVI, TVI, EVI, NDVI SAVI noted as 0.19, 0.21, 0.26, 0.28, 0.30 0.25 For LST relation, statistical linear regression analysis indicated kappa coefficient R 2 = 0.79 0.75 0.78 0.81 0.83 0.80 related LST. remote sensing (RS) technology can be monitor over time, providing valuable information sustainable use management. Even though findings provide significant references reasoned optimal resources through policy implications.

Language: Английский

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Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data

Heliyon, Journal Year: 2023, Volume and Issue: 9(2), P. e13212 - e13212

Published: Jan. 26, 2023

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 2017. Landsat data (Thematic mapper [TM]), 2000 2010 (Enhanced Thematic Mapper [ETM+]), 2013 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into classes termed snow, water, barren land, built-up area, forest, vegetation. method was built multitemporal images machine learning Support Vector Machine (SVM), Naive Bayes Tree (NBT) Kernel Logistic Regression (KLR). According results, area decreased 19,360 km2 (26.0%) 18,784 (25.2%) 2010, while increased 18,640 (25.0%) 26,765 (35.9%) due "One billion tree Project". our findings, SVM performed better than KLR NBT on all three accuracy metrics (recall, precision, accuracy) F1 score >0.89. demonstrated that concurrent reforestation land areas improved methods of sustaining RS GIS everyday forestry organization practices Khyber Pakhtun Khwa (KPK), Pakistan. results beneficial, especially at decision-making level for local or provincial government KPK understanding global scenario regional planning.

Language: Английский

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Modelling of land use and land cover changes and prediction using CA-Markov and Random Forest

Geocarto International, Journal Year: 2023, Volume and Issue: 38(1)

Published: May 3, 2023

We used the Cellular Automata Markov (CA-Markov) integrated technique to study land use and cover (LULC) changes in Cholistan Thal deserts Punjab, Pakistan. plotted distribution of LULC throughout desert terrain for years 1990, 2006 2022. The Random Forest methodology was utilized classify data obtained from Landsat 5 (TM), 7 (ETM+) 8 (OLI/TIRS), as well ancillary data. maps generated using this method have an overall accuracy more than 87%. CA-Markov forecast usage 2022, were projected 2038 by extending patterns seen A CA-Markov-Chain developed simulating long-term landscape at 16-year time steps 2022 2038. Analysis urban sprawl carried out (RF). Through Chain analysis, we can expect that high density low-density residential areas will grow 8.12 12.26 km2 18.10 28.45 2038, inferred occurred 1990 showed there would be increased urbanization terrain, with probable development croplands westward northward, growth centers. findings potentially assist management operations geared towards conservation wildlife eco-system region. This also a reference other studies try project arid are undergoing land-use comparable those study.

Language: Английский

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Prediction of flash flood susceptibility using integrating analytic hierarchy process (AHP) and frequency ratio (FR) algorithms

Frontiers in Environmental Science, Journal Year: 2023, Volume and Issue: 10

Published: Jan. 5, 2023

The landscape of Pakistan is vulnerable to flood and periodically affected by floods different magnitudes. aim this study was aimed assess the flash susceptibility district Jhelum, Punjab, using geospatial model Frequency Ratio Analytical Hierarchy Process. Also, considered eight most influential flood-causing parameters are Digital Elevation Model, slop, distance from river, drainage density, Land use/Land cover, geology, soil resistivity (soil consisting rocks formation) rainfall deviation. data collected weather stations in vicinity area. Estimated weight allotted each flood-inducing factors with help AHP FR. Through use overlay analysis, were brought together, value density awarded maximum possible score. According several areas region based on have been classified zones viz, very high risk, moderate low risk. In light results obtained, 4% area that accounts for 86.25 km 2 at risk flood. like Bagham, Sohawa, Domeli, Turkai, Jogi Tillas, Chang Wala, Dandot Khewra located elevation. Whereas Potha, Samothi, Chaklana, Bagrian, Tilla Jogian, Nandna, Rawal high-risk damaged badly history This first its kind conducted Jhelum District provides guidelines disaster management authorities response agencies, infrastructure planners, watershed management, climatologists.

Language: Английский

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Agricultural land suitability analysis of Southern Punjab, Pakistan using analytical hierarchy process (AHP) and multi-criteria decision analysis (MCDA) techniques

Cogent Food & Agriculture, Journal Year: 2024, Volume and Issue: 10(1)

Published: Jan. 16, 2024

Agricultural Land Suitability Analysis plays a pivotal role in sustainable land use planning, aiding decision-makers identifying areas most conducive to agriculture. This study employs systematic approach integrating Analytical Hierarchy Process and Multi-Criteria Decision techniques assess prioritize the suitability of agricultural Southern Punjab (Multan region). The methodology involves defining clear objectives, relevant criteria sub-criteria, establishing hierarchical structure conducting pairwise comparisons determine relative importance each factor. Our outcomes indicated that almost 43% area was highly suitable for agriculture, 27% moderately suitable, 16% marginally 8% less 6% not agriculture area. All lands had silty clay or type soil, which sandy loam soil Multan region. output is comprehensive map identifies Sensitivity analysis validation are incorporated enhance robustness reliability results. provides valuable tool planners policymakers make informed decisions regarding allocation, contributing practices resource management.

Language: Английский

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