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State-of-the-Art in Land Cover Classification and Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Agriculture and Vegetation".

Deadline for manuscript submissions: 15 February 2025 | Viewed by 28602

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Prof. Dr. Jingxiong Zhang

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Guest Editor

School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: geostatistics; remote rensing; digital terrain analysis; vegetation mapping; land cover
Special Issues, Collections and Topics in MDPI journals

Dr. Won-Ho Nam

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Guest Editor

School of Social Safety and Systems Engineering, Hankyong National University, Anseong 17579, Republic of Korea
Interests: irrigation and drainage engineering; agricultural drought and water resource management; drought monitoring, mitigation, planning, and policy; risk and vulnerability management; remote sensing for drought monitoring and management; soil moisture and hydrologic/watershed modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover classification and mapping have seen great advances methodologically and technically, with continuing improvements in conceptualization, models and methods, remote sensing science and systems, instrumentation, computer algorithms, and implementations. However, due to complexity in semantics, scale, phenology, and other characteristics inherent to land cover, there remain many challenging issues. Certain cover types (e.g., vegetation) are more difficult to classify and map than others (e.g., water bodies). Inconsistency and variability are common, even for experienced image analysts. Scientific rigor, technical sophistication, higher accuracy, and cost-effectiveness are extremely important and much needed, as are guidelines and overviews of good practice in these aspects of practice.

This Special Issue (SI), amended from its earlier theme on vegetation classification and mapping, aims to bring together multidisciplinary scientists and specialists for concerted research on concepts, models, methods, algorithms, and practicalities concerning the classification and mapping of land cover. Strategic key research foci will be thoroughly discussed with the aim of identifying bottlenecks to breakthroughs. The SI will facilitate communications among researchers and practitioners on topics of mutual interest. Such topics include, but are not limited to, the following:

Classification system, harmonization, interoperability, and standards;
Semantics and thematic resolution;
Conceptualization of land cover as fields vs. objects;
Multi-resolution, proportional, and fuzzy representations of land cover;
Models of scale and minimum mapping units (MMU);
Upscaling and downscaling;
Sampling design for reference data acquisition;
Image interpretation, interpreter variability, consistency, and quality assurance;
Training datasets for machine learning oriented for land cover mapping;
Spectral, spatial, and temporal features and their informativeness;
Phenology and time series analysis;
Statistical vs. rule-based classification methods;
Physics-informed and explainable machine learning in land cover classification and mapping;
Fusion of sensors, data, features, and classifiers;
Data cubes of existing land cover products and land cover primitives;
Well targeted re-mapping of land cover;
Accuracy metrics and assessments for pixels, classes, and all problem domains;
Uncertainty characterization;
Thematic and regional case studies of cropland, grassland, shrub, forest, wetland, impervious surfaces, water bodies, and other broad cover types;
Best practice in land cover classification and mapping.

Prof. Dr. Jingxiong Zhang
Prof. Dr. Won-Ho Nam
Guest Editors

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

classification
mapping
classification systems
land cover
remote-sensing images
scale
resolution
semantics
spectral–spatial–temporal features
phenology
pattern analysis
machine learning
rule bases
accuracy metrics and assessment
uncertainty
confusion matrix
mixed pixels
sampling
reference samples
image interpretation

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Published Papers (10 papers)

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Research

19 pages, 8123 KiB

Open AccessArticle

Identification and Analysis of Long-Term Land Use and Planting Structure Dynamics in the Lower Yellow River Basin

by Shengzhe Hong, Yu Lou, Xinguo Chen, Quanzhong Huang, Qianru Yang, Xinxin Zhang, Haozhi Li and Guanhua Huang

Remote Sens. 2024, 16(13), 2274; https://doi.org/10.3390/rs16132274 - 21 Jun 2024

Cited by 1 | Viewed by 656

Abstract

Accurate identification of the spatio-temporal planting structure and analysis of its driving factors in an irrigation district are the important bases for scientific and reasonable utilization of irrigation water resources. In pursuit of this goal, the training sample migration method combined with the random forest algorithm were used to classify land use and planting structure over 2001–2022 in the lower Yellow River Basin. Moreover, an econometric regression model was applied to quantify the driving factors of the change in the crop-planted area. The results illustrated that the identification method has relatively high accuracy in identifying historical periods of land use and planting structures, with the average kappa coefficient equating to 0.953. From 2001 to 2022, the area of cultivated land was the largest, with the proportion of the total area increasing from 45.72% to 58.12%. The planted area of winter wheat–summer maize rotation increased from 74.84% to 88.11% of the cultivated land. While the planted area of cotton declined by 96.36%, about 50% of cotton planting was converted to the winter wheat–summer maize rotation planting. The government policies about grain purchase and storage were the dominant factors for the change in the crop-planted area. This resulted in an increase of 63.32 × 10³ ha and 63.98 × 10³ ha in the planted area of winter wheat and summer maize, respectively. The findings are of great significance to the allocation of water resources in irrigation districts of the lower Yellow River Basin. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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26 pages, 16418 KiB

Open AccessArticle

Comparison and Assessment of Different Land Cover Datasets on the Cropland in Northeast China

by Peipei Cui, Tan Chen, Yingjie Li, Kai Liu, Dapeng Zhang and Chunqiao Song

Remote Sens. 2023, 15(21), 5134; https://doi.org/10.3390/rs15215134 - 27 Oct 2023

Cited by 4 | Viewed by 2220

Abstract

The provision of precise and dependable information regarding the extent and distribution of cropland is imperative for the evaluation of food security, agricultural planning, and resource management. Cropland is an important component of land cover type and is offered in multiple existing global/regional land cover products. However, global-scale accuracy evaluation may not be representative of class-specific or local-area accuracy, such as in Northeast China, which is an important grain-producing region of China and has various types of cultivated land (e.g., wheat, rice) and diverse terrains. It poses a great challenge in generating precise cropland classification by automated mapping. Thus, it is indispensable to evaluate the accuracy and reliability of these various land cover datasets before using them. In this study, we collected thirteen sets of global or national-scale land cover datasets. Through the visual interpretation of high-resolution images, ground “truth” samples were collected to evaluate the data accuracy across Northeast China. The overall accuracy (OA) evaluation results in Phase-2020 show that CLCD has the highest value with 0.914, followed by GlobeLand30 (0.906), GLC_FCS30 (0.902), and Esri (0.896) for cropland classification in Northeast China. CGLS-LC100 has the lowest OA (0.710). For the commission and omission errors of six datasets in Phase-2020, CGLS-LC100 has an obvious overestimation (larger commission error), while the two national-scale datasets (CLCD and CLUDs) perform relatively better. In terms of spatial consistency, high spatial agreement among the nine Phase-2015 datasets or in the six Phase-2020 datasets could be discovered in traditional agricultural regions like the Sanjiang–Songnen–Liaohe Plain, and low agreement is found in the transition areas of mountains (hills) and plains with the mixed landscape of forest (grassland) and farmland. In the aspect of comparison pairwise data, CLCD is in good agreement with GLC_FCS30, GlobeLand30, and Esri, while CGLS-LC100 is in the poorest agreement with any other dataset. The comparison and evaluation results are expected to provide a reference on which aspects and to what extent these land cover products may be consistent and guide the cropland data product selection for Northeast China. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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19 pages, 9454 KiB

Open AccessArticle

R-Unet: A Deep Learning Model for Rice Extraction in Rio Grande do Sul, Brazil

by Tingyan Fu, Shufang Tian and Jia Ge

Remote Sens. 2023, 15(16), 4021; https://doi.org/10.3390/rs15164021 - 14 Aug 2023

Viewed by 2146

Abstract

Rice is one of the world’s three major food crops, second only to sugarcane and corn in output. Timely and accurate rice extraction plays a vital role in ensuring food security. In this study, R-Unet for rice extraction was proposed based on Sentinel-2 and time-series Sentinel-1, including an attention-residual module and a multi-scale feature fusion (MFF) module. The attention-residual module deepened the network depth of the encoder and prevented information loss. The MFF module fused the high-level and low-level rice features at channel and spatial scales. After training, validation, and testing on seven datasets, R-Unet performed best on the test samples of Dataset 07, which contained optical and synthetic aperture radar (SAR) features. Precision, intersection, and union (IOU), F1-score, and Matthews correlation coefficient (MCC) were 0.948, 0.853, 0.921, and 0.888, respectively, outperforming the baseline models. Finally, the comparative analysis between R-Unet and classic models was completed in Dataset 07. The results showed that R-Unet had the best rice extraction effect, and the highest scores of precision, IOU, MCC, and F1-score were increased by 5.2%, 14.6%, 11.8%, and 9.3%, respectively. Therefore, the R-Unet proposed in this study can combine open-source sentinel images to extract rice timely and accurately, providing important information for governments to implement decisions on agricultural management. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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18 pages, 4721 KiB

Open AccessArticle

Early Crop Mapping Based on Sentinel-2 Time-Series Data and the Random Forest Algorithm

by Peng Wei, Huichun Ye, Shuting Qiao, Ronghao Liu, Chaojia Nie, Bingrui Zhang, Lijuan Song and Shanyu Huang

Remote Sens. 2023, 15(13), 3212; https://doi.org/10.3390/rs15133212 - 21 Jun 2023

Cited by 19 | Viewed by 3348

Abstract

Early-season crop mapping and information extraction is essential for crop growth monitoring and yield prediction, and it facilitates agricultural management and rapid response to agricultural disasters. However, training classifiers by remote sensing classification features for early crop prediction can be challenging, as early-season mapping can only use remote sensing image data during part of the crop growth period. In order to overcome this limitation, this study takes the Sanjiang Plain as an example to investigate the earliest identification time of rice, maize and soybean based on Sentinel-2 time-series data and the random forest classification algorithm. Crop information extraction was then performed. Following the analysis of the remote sensing classification features by the random forest importance approach and the subsequent normalization, the optimal features greater than or equal to 0.5 have yielded quite results in early crop mapping, and their overall accuracy was the highest in early-season mapping. The overall accuracy was observed to improve by 5% for 10 to 20 days of delay. In addition, rice, maize, and soybean were mapped at the irrigation transplanting period (10 May), jointing stage (9 July) and flowering (29 July), with an overall accuracy of 90.4%, 90.0% and 90.9%, respectively. This study shows that features suitable for early crop classification can be selected by random forest importance analysis as well as the ability of remote sensing to extract crop acreage information within the reproductive period. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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21 pages, 5121 KiB

Open AccessArticle

Spatiotemporal Landscape Pattern Analyses Enhanced by an Integrated Index: A Study of the Changbai Mountain National Nature Reserve

by Ying Zhang, Jingxiong Zhang, Fengyan Wang and Wenjing Yang

Remote Sens. 2023, 15(7), 1760; https://doi.org/10.3390/rs15071760 - 24 Mar 2023

Cited by 2 | Viewed by 8740

Abstract

The analysis of spatiotemporal changes of landscape patterns is of great significance for forest protection. However, the selection of landscape metrics is often subjective, and existing composite landscape metrics rarely consider the effects of spatial correlation. A more objective approach to formulating composite landscape metrics involves proper weighting that incorporates spatial structure information into integrating individual conventional metrics selected for building a composite metric. This paper proposes an integrated spatial landscape index (ISLI) based on variogram modeling and entropy weighting. It was tested through a case study, which sought to analyze spatiotemporal changes in the landscape pattern in the Changbai Mountains over 30 years based on six global land-cover products with a fine classification system at 30 m resolution (GLC_FCS30). The test results confirm: (1) spatial structure information is useful for weighting conventional landscape pattern metrics when constructing ISLI as validated by correlation analysis between the incorporated conventional metrics and their variogram ranges. In terms of the range parameters of different land cover types, broadleaf forest and needleleaf forest have much larger range values than those of other land cover types; (2) DIVISION and PLAND, two of the conventional landscape metrics considered for constructing ISLI, were assigned the greatest weights in computing ISLI for this study; and (3) ISLI values can be used to determine the dominant landscape types. For the study area, ISLI values of broadleaf forests remained the largest until 2020, indicating that forest landscape characteristics were the most prominent during that period. After 2020, the dominance of needleleaf forest gradually increased, with its ISLI value reaching a maximum of 0.91 in 2025. Therefore, the proposed ISLI not only functions as an extension and complement to conventional landscape metrics but also provides more comprehensive information concerning landscape pattern dynamics. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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23 pages, 5281 KiB

Open AccessArticle

Characterizing Uncertainty and Enhancing Utility in Remotely Sensed Land Cover Using Error Matrices Localized in Canonical Correspondence Analysis Ordination Space

by Yue Wan, Jingxiong Zhang, Wangle Zhang, Ying Zhang, Wenjing Yang, Jianxu Wang, Okafor Somtoochukwu Chukwunonso and Asurapplullige Milani Tharuka Nadeeka

Remote Sens. 2023, 15(5), 1367; https://doi.org/10.3390/rs15051367 - 28 Feb 2023

Cited by 1 | Viewed by 1695

Abstract

In response to uncertainty in remotely sensed land cover products, there is continuing research on accuracy assessment and analysis. Given reference sample data, accuracy indicators are commonly estimated based on error matrices, from which areal extents of different cover types are also estimated. There are merits to explore the ways utilities of land cover products may be further enhanced beyond map face values and conventional area estimation. This paper presents an integrative method (CCAErrMat) for uncertainty characterization and utility enhancement. This works through reference-map cover type co-occurrence analyses based on error matrices localized in canonical correspondence analysis (CCA) ordination space rather than in geographic space to overcome the sparsity of reference sample data. The aforementioned co-occurrence analyses facilitate quantification of accuracy indicators, identification of correctly classified and perfectly misclassified pixels, and prediction of reference class probabilities, all at individual pixels. Moreover, these predicted reference class probabilities are used as auxiliary variables to formulate model-assisted area estimation, further enhancing map utilities. Extensions to CCAErrMat are also investigated as a way to bypass the pre-computing of map class occurrence pattern indices as candidate explanatory variables for CCAErrMat, leading to two variant methods: CCACCAErrMat and CNNCCAErrMat. A case study based in Wuhan municipality, central China was undertaken to compare the proposed method against alternative methods, including CCA-separate and CNN-separate. The advantages of CCAErrMat and CCACCAErrMat were confirmed. The proposed method is recommendable for characterizing uncertainty and enhancing utilities in land cover maps by analyzing locally constrained error matrices. The method is also cost-effective in terms of reference sample data, as requirements for them are similar to those for conventional accuracy assessments. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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23 pages, 4349 KiB

Open AccessArticle

Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities

by Wangle Zhang, Jiwen Wang, Hate Lin, Ming Cong, Yue Wan and Jingxiong Zhang

Remote Sens. 2023, 15(2), 481; https://doi.org/10.3390/rs15020481 - 13 Jan 2023

Cited by 3 | Viewed by 1794

Abstract

There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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20 pages, 1402 KiB

Open AccessArticle

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

by Huaqiao Xing, Bingyao Chen and Miao Lu

Remote Sens. 2022, 14(24), 6280; https://doi.org/10.3390/rs14246280 - 11 Dec 2022

Cited by 8 | Viewed by 2232

Abstract

Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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33 pages, 21761 KiB

Open AccessArticle

Evaluation of Decision Fusions for Classifying Karst Wetland Vegetation Using One-Class and Multi-Class CNN Models with High-Resolution UAV Images

by Yuyang Li, Tengfang Deng, Bolin Fu, Zhinan Lao, Wenlan Yang, Hongchang He, Donglin Fan, Wen He and Yuefeng Yao

Remote Sens. 2022, 14(22), 5869; https://doi.org/10.3390/rs14225869 - 19 Nov 2022

Cited by 8 | Viewed by 2221

Abstract

Combining deep learning and UAV images to map wetland vegetation distribution has received increasing attention from researchers. However, it is difficult for one multi-classification convolutional neural network (CNN) model to meet the accuracy requirements for the overall classification of multi-object types. To resolve these issues, this paper combined three decision fusion methods (Majority Voting Fusion, Average Probability Fusion, and Optimal Selection Fusion) with four CNNs, including SegNet, PSPNet, DeepLabV3+, and RAUNet, to construct different fusion classification models (FCMs) for mapping wetland vegetations in Huixian Karst National Wetland Park, Guilin, south China. We further evaluated the effect of one-class and multi-class FCMs on wetland vegetation classification using ultra-high-resolution UAV images and compared the performance of one-class classification (OCC) and multi-class classification (MCC) models for karst wetland vegetation. The results highlight that (1) the use of additional multi-dimensional UAV datasets achieved better classification performance for karst wetland vegetation using CNN models. The OCC models produced better classification results than MCC models, and the accuracy (average of IoU) difference between the two model types was 3.24–10.97%. (2) The integration of DSM and texture features improved the performance of FCMs with an increase in accuracy (MIoU) from 0.67% to 8.23% when compared to RGB-based karst wetland vegetation classifications. (3) The PSPNet algorithm achieved the optimal pixel-based classification in the CNN-based FCMs, while the DeepLabV3+ algorithm produced the best attribute-based classification performance. (4) Three decision fusions all improved the identification ability for karst wetland vegetation compared to single CNN models, which achieved the highest IoUs of 81.93% and 98.42% for Eichhornia crassipes and Nelumbo nucifera, respectively. (5) One-class FCMs achieved higher classification accuracy for karst wetland vegetation than multi-class FCMs, and the highest improvement in the IoU for karst herbaceous plants reached 22.09%. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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21 pages, 4740 KiB

Open AccessArticle

A Multitemporal Mountain Rice Identification and Extraction Method Based on the Optimal Feature Combination and Machine Learning

by Kaili Zhang, Yonggang Chen, Bokun Zhang, Junjie Hu and Wentao Wang

Remote Sens. 2022, 14(20), 5096; https://doi.org/10.3390/rs14205096 - 12 Oct 2022

Cited by 7 | Viewed by 1869

Abstract

The quick and precise assessment of rice distribution by remote sensing technology is important for agricultural development. However, mountain rice is limited by the complex terrain, and its distribution is fragmented. Therefore, it is necessary to fully use the abundant spatial, temporal, and spectral information of remote sensing imagery. This study extracted 22 classification features from Sentinel-2 imagery (spectral features, texture features, terrain features, and a custom spectral-spatial feature). A feature selection method based on the optimal extraction period of features (OPFSM) was constructed, and a multitemporal feature combination (MC) was generated based on the separability of different vegetation types in different periods. Finally, the extraction accuracy of MC for mountain rice was explored using Random Forest (RF), CatBoost, and ExtraTrees (ET) machine learning algorithms. The results show that MC improved the overall accuracy (OA) by 3–6% when compared to the feature combinations in each rice growth stage, and by 7–14% when compared to the original images. MC based on the ET classifier (MC-ET) performed the best for rice extraction, with the OA of 86%, Kappa coefficient of 0.81, and F1 score of 0.95 for rice. The study demonstrated that OPFSM could be used as a reference for selecting multitemporal features, and the MC-ET classification scheme has high application potential for mountain rice extraction. Full article

(This article belongs to the Special Issue State-of-the-Art in Land Cover Classification and Mapping)

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