Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil

Cunha, Elias Rodrigues da; Santos, Celso Augusto Guimarães; Silva, Richarde Marques da; Bacani, Vitor Matheus; Teodoro, Paulo Eduardo; Panachuki, Elói; Oliveira, Naelmo de Souza

doi:10.1007/s10661-020-8093-9

Cited by 19 publications

(9 citation statements)

References 37 publications

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“…The assessment of overall accuracy and Kappa coefficient (Table 6) demonstrated the high capacity of the decision tree classifier (DTC) approach in integrating with different remote sensing-derived indices in producing multi-temporal LULC maps. Thus, this technique's findings are supposed to be compatible and reliable for modeling future LULC scenarios [27,[110][111][112]. As a consequence, the categorized images are valid for examining and forecasting the changing dynamics of LULCs in the study area [50].…”

Section: Accuracy Assessment Of the Lulc Classificationmentioning

confidence: 94%

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Selmy,

Kucher,

Mozgeris

et al. 2023

Remote Sensing

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Understanding the change dynamics of land use and land cover (LULC) is critical for efficient ecological management modification and sustainable land-use planning. This work aimed to identify, simulate, and predict historical and future LULC changes in the Sohag Governorate, Egypt, as an arid region. In the present study, the detection of historical LULC change dynamics for time series 1984–2002, 2002–2013, and 2013–2022 was performed, as well as CA-Markov hybrid model was employed to project the future LULC trends for 2030, 2040, and 2050. Four Landsat images acquired by different sensors were used as spatial–temporal data sources for the study region, including TM for 1984, ETM+ for 2002, and OLI for 2013 and 2022. Furthermore, a supervised classification technique was implemented in the image classification process. All remote sensing data was processed and modeled using IDRISI 7.02 software. Four main LULC categories were recognized in the study region: urban areas, cultivated lands, desert lands, and water bodies. The precision of LULC categorization analysis was high, with Kappa coefficients above 0.7 and overall accuracy above 87.5% for all classifications. The results obtained from estimating LULC change in the period from 1984 to 2022 indicated that built-up areas expanded to cover 12.5% of the study area in 2022 instead of 5.5% in 1984. This urban sprawl occurred at the cost of reducing old farmlands in old towns and villages and building new settlements on bare lands. Furthermore, cultivated lands increased from 45.5% of the total area in 1984 to 60.7% in 2022 due to ongoing soil reclamation projects in desert areas outside the Nile Valley. Moreover, between 1984 and 2022, desert lands lost around half of their area, while water bodies gained a very slight increase. According to the simulation and projection of the future LULC trends for 2030, 2040, and 2050, similar trends to historical LULC changes were detected. These trends are represented by decreasing desert lands and increasing urban and cultivated newly reclaimed areas. Concerning CA-Markov model validation, Kappa indices ranged across actual and simulated maps from 0.84 to 0.93, suggesting that this model was reasonably excellent at projecting future LULC trends. Therefore, using the CA-Markov hybrid model as a prediction and modeling approach for future LULC trends provides a good vision for monitoring and reducing the negative impacts of LULC changes, supporting land use policy-makers, and developing land management.

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Section: Accuracy Assessment Of the Lulc Classificationmentioning

confidence: 94%

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Selmy,

Kucher,

Mozgeris

et al. 2023

Remote Sensing

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“…An image segmentation technique using eCogniton 8.0 software was applied for LULC classification, as used by Cunha et al (2020) and Cunha et al (2021). This methodology was used to split an image into spectrally homogeneous areas to facilitate the determination of classes and the set of sampling regions.…”

Section: Lulc Classification and Assessment Accuracymentioning

confidence: 99%

“…This methodology was used to split an image into spectrally homogeneous areas to facilitate the determination of classes and the set of sampling regions. Evaluation of the assessment accuracy of the LULC classification was performed considering the omission, commission, global accuracy, user accuracy, producer accuracy, overall accuracy, and kappa coefficient, as proposed by Cunha et al (2020). After obtaining the LULC maps, an error matrix was obtained to validate the classification maps using a confusion matrix with omission and commission errors.…”

Section: Lulc Classification and Assessment Accuracymentioning

confidence: 99%

Urban forest loss using a GIS-based approach and instruments for integrated urban planning: A case study of João Pessoa, Brazil

et al. 2021

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Urban forests play an important role in the thermal comfort and overall life of local populations in large-and medium-sized cities. This study analyzes urban forest loss and maps land use and land cover (LULC) changes between 1991 and 2018 by evaluating the use of urban planning instruments for the mitigation of urban forest loss in João Pessoa, Brazil. For this purpose, satellite-derived LULC images from 1991, 2006, 2010 and 2018 and data on urban forest loss areas obtained using the Google Earth Engine were used. In addition, this paper also discusses the instruments used for integrated urban planning, which are (a) the legal sector, responsibility and nature; (b) the urban expansion process; and (c) the elements of urban infrastructure. The results show a clear shift in land use in the study area. The major changes in LULC classes occurred in urban areas and herbaceous vegetation, while the greatest loss was in arboreal/shrub vegetation. Thus, an increase in the pressure to occupy zones intended for environmental preservation could be estimated. Our results showed similar accuracies with other studies and more spatial details. The characteristics of the patterns, traces, and hotspots of urban expansion and forest cover loss were explored. We highlighted the potential use of this proposed framework to be applied and validated in other parts of the world to help better understand and quantify various aspects of urban-related problems such as urban forest loss mapping using instruments for integrated urban planning and low-cost approaches.

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“…Although the importance of studying the relationships between the runoff-erosion process and LULCC using remote sensing multiple gridded datasets is well-understood, determining the spatial distribution of the runoff-erosion process is an essential prerequisite for the establishment of erosion management plans in any catchment. The advancement of agriculture and the influence of different LULC scenarios has been significantly studied [7][8][9][10]; however, research involving the impacts of LULC on runoff-erosion processes using estimated satellite data and runoff-erosion models in some regions of the planet, such as Brazil, is still scarce [11][12][13]. In addition, the published studies did not carry out estimates of runoff and sediment yield considering different LULC scenarios at watershed scales.…”

Section: Introductionmentioning

confidence: 99%

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

Fonseca

Al‐Ansari

Silva

et al. 2022

IJGI

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Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome.

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Mapping LULC types in the Cerrado-Atlantic Forest ecotone region using a Landsat time series and object-based image approach: A case study of the Prata River Basin, Mato Grosso do Sul, Brazil

Cited by 19 publications

References 37 publications

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Detecting, Analyzing, and Predicting Land Use/Land Cover (LULC) Changes in Arid Regions Using Landsat Images, CA-Markov Hybrid Model, and GIS Techniques

Urban forest loss using a GIS-based approach and instruments for integrated urban planning: A case study of João Pessoa, Brazil

Investigating Relationships between Runoff–Erosion Processes and Land Use and Land Cover Using Remote Sensing Multiple Gridded Datasets

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