Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

vThi, Dinh Ngo; Ha, Nguyen Thi Thu; Dang, Quy Tran; Koike, Katsuaki; Trong, Nhuan Mai

doi:10.3390/rs11060716

Cited by 17 publications

(4 citation statements)

References 51 publications

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“…In particular, determining the type of land use can be an easy task to conduct. Remotely monitoring allows the collection of data about the area of observation into a single database, which facilitates the work of all stakeholders [15].…”

Section: Discussionmentioning

confidence: 99%

Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

Rakhmonov,

Khaidarov,

Samarkhanov

et al. 2023

E3S Web Conf.

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Regular analysis of soil moisture is important for assessing the condition of soil fertility for adaptation to climate change, for the subsequent determination of the reasons for the deterioration of soil conditions, and for the preservation of sustainable agricultural development. Traditional field methods for studying land are laborious and costly. The aim of this scientific work is to develop an effective methodology for using Sentinel-2 satellite data to analyse changes in the condition of land moisture in the Republic of Uzbekistan, using the example of the pilot region under study in the Jizzakh region. The proposed methodology can be adapted in other regions for the effective monitoring of land resources. To conclude, the process of planning and organizing work on the conservation of crops is greatly simplified. It can be possible to save tens of thousands of hectares of highly productive land in agricultural circulation.

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Section: Discussionmentioning

confidence: 99%

Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

Rakhmonov,

Khaidarov,

Samarkhanov

et al. 2023

E3S Web Conf.

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“…The medium-resolution remote sensing images such as Landsat 8 OLI are popular for LULC mapping from local to global scales (Gad and Kusky, 2006;Thi et al, 2019), however, their utility is constrained by limitations inherent in their spatial and temporal resolutions as well as spectral responses, all of which affect visual image interpretation and hence the overall and class-specific accuracies. Because the details of an image are captured by visual inspection, we used well-trained image interpreters with expert knowledge of the study landscape to ensure a dependable identification of the image objects.…”

Section: Image Resolution Visual Image Interpretation and Lulc Accuracymentioning

confidence: 99%

“…We selected the RF classifier for this study because of its excellent predictive performance and computational efficiency with high-dimensional data (Janitza and Hornung, 2018). The RF is increasingly being used as a classifier of choice for LULC classification of remotely sensed data (e.g., Gislason et al, 2006;Belgiu and Drăguţ, 2016;Thi et al, 2019). The RF is a refinement of ensemble machine learning methods for reducing prediction variance using recursive binary partitioning and bootstrap aggregation (Breiman, 2001).…”

Section: Introductionmentioning

confidence: 99%

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Ojwang,

Ogutu,

Said

et al. 2024

Front. Remote Sens.

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Mapping land use and land cover (LULC) using remote sensing is fundamental to environmental monitoring, spatial planning and characterising drivers of change in landscapes. We develop a new, general and versatile approach for mapping LULC in landscapes with relatively gradual transition between LULC categories such as African savannas. The approach integrates a well-tested hierarchical classification system with the computationally efficient random forest (RF) classifier and produces detailed, accurate and consistent classification of structural vegetation heterogeneity and density and anthropogenic land use. We use Landsat 8 OLI imagery to illustrate this approach for the Extended Greater Masai Mara Ecosystem (EGMME) in southwestern Kenya. We stratified the landscape into eight relatively homogeneous zones, systematically inspected the imagery and randomly allocated 1,697 training sites, 556 of which were ground-truthed, proportionately to the area of each zone. We directly assessed the accuracy of the visually classified image. Accuracy was high and averaged 88.1% (80.5%–91.7%) across all the zones and 89.1% (50%–100%) across all the classes. We applied the RF classifier to randomly selected samples from the original training dataset, separately for each zone and the EGMME. We evaluated the overall and class-specific accuracy and computational efficiency using the Out-of-Bag (OOB) error. Overall accuracy (79.3%–97.4%) varied across zones but was higher whereas the class-specific accuracy (25.4%–98.1%) was lower than that for the EGMME (80.2%). The hierarchical classifier identified 35 LULC classes which we aggregated into 18 intermediate mosaics and further into five more general categories. The open grassed shrubland (21.8%), sparse shrubbed grassland (10.4%) and small-scale cultivation (13.3%) dominated at the detailed level, grassed shrubland (31.9%) and shrubbed grassland (28.9%) at the intermediate level, and grassland (35.7%), shrubland (35.3%) and woodland (12.5%) at the general level. Our granular LULC map for the EGMME is sufficiently accurate for important practical purposes such as land use spatial planning, habitat suitability assessment and temporal change detection. The extensive ground-truthing data, sample site photos and classified maps can contribute to wider validation efforts at regional to global scales.

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“…The approach was motivated by two factors. The first motivational factor is that band ratios in Landsat images are used for determining soil moisture coefficients and vegetation indices [5], which indicates that feature ratios contain additional information in the sample than features alone. The second motivational factor is that CNN models provide multiple levels of abstraction and generate a feature vector that combine low-level and high-level information of shapes in the image.…”

Section: Introductionmentioning

confidence: 99%

Multispectral Image Analysis using Convolution Neural Networks

Kulkarni

2023

IJACSA

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Machine learning (ML) techniques are used often to classify pixels in multispectral images. Recently, there is growing interest in using Convolution Neural Networks (CNNs) for classifying multispectral images. CNNs are preferred because of high performance, advances in hardware such as graphical processing units (GPUs), and availability of several CNN architectures. In CNN, units in the first hidden layer view only a small image window and learn low level features. Deeper layers learn more expressive features by combining low level features.In this paper, we propose a novel approach to classify pixels in a multispectral image using deep convolution neural networks (DCNNs). In our approach, each feature vector is mapped to an image. We used the proposed framework to classify two Landsat scenes that are obtained from New Orleans and Juneau, Alaska areas. The suggested approach is compared with the commonly used classifiers such as the Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). The proposed approach has shown the state-of-the-art results.

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Effective Band Ratio of Landsat 8 Images Based on VNIR-SWIR Reflectance Spectra of Topsoils for Soil Moisture Mapping in a Tropical Region

Cited by 17 publications

References 51 publications

Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

Classification of seasonal dynamics of soil moisture according to satellite data Sentinel-2, Jizzakh region, Uzbekistan

An integrated hierarchical classification and machine learning approach for mapping land use and land cover in complex social-ecological systems

Multispectral Image Analysis using Convolution Neural Networks

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