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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 68058

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Special Issue Editor

Dr. Fernando Vicente-Guijalba

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Remote Sensing Consultant, C/ Princep de Viana 7, Barcelona, 08001 Barcelona, Spain
Interests: SAR; interferometry; remote sensing; land cover; mapping; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Land cover and vegetation mapping represents an invaluable product for land use and land management. From the local to global scale, land cover products help to plan and optimize the limited resources our planet provides. Remote sensing techniques have shown their capabilities in obtaining reliable and recurrent information regarding the nature and condition of surfaces. The broad diversity of technologies also allows us to sense different aspects of the surface, such as moist conditions, biochemical and structural elements, etc. Moreover, the dynamic nature of particular land surfaces driven by seasonal (e.g., crop rotation from winter to summer) or trend changes (e.g., land cover transition due to the climatic change) means that we must frequently observe the areas in order to track the cover changes with the aim of maintaining an updated cover map.

This Special Issue is focused on compiling the state-of-the-art research that specifically addresses aspects of the LC (land cover) and vegetation mapping from a remote sensing perspective, including but not limited to research on a regional to global scale, the role of passive and active sensors, capabilities and limitations in detecting similar type of covers, new technologies such as interferometric products, and state-of-the art algorithms for classification. Review contributions are welcomed as well as papers describing new measurement concepts and sensors.

Dr. Fernando Vicente-Guijalba
Guest Editor

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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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote sensing
land cover and land use
land cover dynamics
vegetation mapping and identification
machine learning
EO data classification
mapping
EO data fusion and assimilation

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16 pages, 6311 KiB

Open AccessArticle

An Operational Radiometric Correction Technique for Shadow Reduction in Multispectral UAV Imagery

by Xavier Pons and Joan-Cristian Padró

Remote Sens. 2021, 13(19), 3808; https://doi.org/10.3390/rs13193808 - 23 Sep 2021

Cited by 8 | Viewed by 3851

Abstract

This study focuses on the recovery of information from shadowed pixels in RGB or multispectral imagery sensed from unmanned aerial vehicles (UAVs). The proposed technique is based on the concept that a property characterizing a given surface is its spectral reflectance, i.e., the ratio between the flux reflected by the surface and the radiant flux received by the surface, and this ratio is usually similar under direct-plus-diffuse irradiance and under diffuse irradiance when a Lambertian behavior can be assumed. Scene-dependent elements, such as trees, shrubs, man-made constructions, or terrain relief, can block part of the direct irradiance (usually sunbeams), in which part of the surface only receives diffuse irradiance. As a consequence, shadowed surfaces comprising pixels of the image created by the UAV remote sensor appear. Regardless of whether the imagery is analyzed by means of photointerpretation or digital classification methods, when the objective is to create land cover maps, it is hard to treat these areas in a coherent way in terms of the areas receiving direct and diffuse irradiance. The hypothesis of the present work is that the relationship between irradiance conditions in shadowed areas and non-shadowed areas can be determined by following classical empirical line techniques for fulfilling the objective of a coherent treatment in both kinds of areas. The novelty of the presented method relies on the simultaneous recovery of information in non-shadowed and shadowed areas by the in situ spectral reflectance measurements of characterized Lambertian targets followed by smoothing of the penumbra area. Once in the lab, firstly, we accurately detected the shadowed pixels by combining two well-known techniques for the detection of the shadowed areas: (1) using a physical approach based on the sun’s position and the digital surface model of the area covered by the imagery; and (2) the image-based approach using the histogram properties of the intensity image. In this paper, we present the benefits of the combined usage of both techniques. Secondly, we applied a fit between non-shadowed and shadowed areas by using a twin set of spectrally characterized target sets. One set was placed under direct and diffuse irradiance (non-shadowed targets), whereas the second set (with the same spectral characteristics) was placed under diffuse irradiance (shadowed targets). Assuming that the reflectance of the homologous targets of each set was the same, we approximated the diffuse incoming irradiance through an empirical line correction. The model was applied to all detected shadowed areas in the whole scene. Finally, a smoothing filter was applied to the penumbra transitions. The presented empirical method allowed the operational and coherent recovery of information from shadowed areas, which is very common in high-resolution UAV imagery. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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22 pages, 2497 KiB

Open AccessArticle

Continental-Scale Land Cover Mapping at 10 m Resolution Over Europe (ELC10)

by Zander S. Venter and Markus A. K. Sydenham

Remote Sens. 2021, 13(12), 2301; https://doi.org/10.3390/rs13122301 - 11 Jun 2021

Cited by 66 | Viewed by 10870

Abstract

Land cover maps are important tools for quantifying the human footprint on the environment and facilitate reporting and accounting to international agreements addressing the Sustainable Development Goals. Widely used European land cover maps such as CORINE (Coordination of Information on the Environment) are produced at medium spatial resolutions (100 m) and rely on diverse data with complex workflows requiring significant institutional capacity. We present a 10 m resolution land cover map (ELC10) of Europe based on a satellite-driven machine learning workflow that is annually updatable. A random forest classification model was trained on 70K ground-truth points from the LUCAS (Land Use/Cover Area Frame Survey) dataset. Within the Google Earth Engine cloud computing environment, the ELC10 map can be generated from approx. 700 TB of Sentinel imagery within approx. 4 days from a single research user account. The map achieved an overall accuracy of 90% across eight land cover classes and could account for statistical unit land cover proportions within 3.9% (R² = 0.83) of the actual value. These accuracies are higher than that of CORINE (100 m) and other 10 m land cover maps including S2GLC and FROM-GLC10. Spectro-temporal metrics that capture the phenology of land cover classes were most important in producing high mapping accuracies. We found that the atmospheric correction of Sentinel-2 and the speckle filtering of Sentinel-1 imagery had a minimal effect on enhancing the classification accuracy (<1%). However, combining optical and radar imagery increased accuracy by 3% compared to Sentinel-2 alone and by 10% compared to Sentinel-1 alone. The addition of auxiliary data (terrain, climate and night-time lights) increased accuracy by an additional 2%. By using the centroid pixels from the LUCAS Copernicus module polygons we increased accuracy by <1%, revealing that random forests are robust against contaminated training data. Furthermore, the model requires very little training data to achieve moderate accuracies—the difference between 5K and 50K LUCAS points is only 3% (86% vs. 89%). This implies that significantly less resources are necessary for making in situ survey data (such as LUCAS) suitable for satellite-based land cover classification. At 10 m resolution, the ELC10 map can distinguish detailed landscape features like hedgerows and gardens, and therefore holds potential for aerial statistics at the city borough level and monitoring property-level environmental interventions (e.g., tree planting). Due to the reliance on purely satellite-based input data, the ELC10 map can be continuously updated independent of any country-specific geographic datasets. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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14 pages, 5241 KiB

Open AccessArticle

Mapping Mature Post-Agricultural Forests in the Polish Eastern Carpathians with Archival Remote Sensing Data

by Zofia Jabs-Sobocińska, Andrzej N. Affek, Ireneusz Ewiak and Mihai Daniel Nita

Remote Sens. 2021, 13(10), 2018; https://doi.org/10.3390/rs13102018 - 20 May 2021

Cited by 9 | Viewed by 5112

Abstract

Post-WWII displacements in the Polish Carpathians resulted in widespread land abandonment. Most of the pre-war agricultural areas are now covered with secondary forests, which will soon reach the felling age. Mapping their exact cover is crucial to investigate succession–regeneration processes and to determine their role in the landscape, before making management decisions. Our goal was to map post-agricultural forests in the Polish Eastern Carpathians using archival remote sensing data, and to assess their connectivity with pre-displacement forests. We used German Flown Aerial Photography from 1944 to map agricultural lands and forests from before displacements, and Corona satellite images to map agricultural lands which converted into the forest as a result of this event. We classified archival images using Object-Based Image Analysis (OBIA) and compared the output with the current forest cover derived from Sentinel-2. Our results showed that mature (60–70 years old) post-agricultural forests comprise 27.6% of the total forest area, while younger post-agricultural forests comprise 9%. We also demonstrated that the secondary forests fill forest gaps more often than form isolated patches: 77.5% of patches are connected with the old-woods (forests that most likely have never been cleared for agriculture). Orthorectification and OBIA classification of German Flown Aerial Photographs and Corona satellite images made it possible to accurately determine the spatial extent of post-agricultural forest. This, in turn, paves the way for the implementation of site-specific forest management practices to support the regeneration of secondary forests and their biodiversity. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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21 pages, 15952 KiB

Open AccessArticle

Performance Evaluation of Sentinel-2 and Landsat 8 OLI Data for Land Cover/Use Classification Using a Comparison between Machine Learning Algorithms

by Laleh Ghayour, Aminreza Neshat, Sina Paryani, Himan Shahabi, Ataollah Shirzadi, Wei Chen, Nadhir Al-Ansari, Marten Geertsema, Mehdi Pourmehdi Amiri, Mehdi Gholamnia, Jie Dou and Anuar Ahmad

Remote Sens. 2021, 13(7), 1349; https://doi.org/10.3390/rs13071349 - 1 Apr 2021

Cited by 86 | Viewed by 9343

Abstract

With the development of remote sensing algorithms and increased access to satellite data, generating up-to-date, accurate land use/land cover (LULC) maps has become increasingly feasible for evaluating and managing changes in land cover as created by changes to ecosystem and land use. The main objective of our study is to evaluate the performance of Support Vector Machine (SVM), Artificial Neural Network (ANN), Maximum Likelihood Classification (MLC), Minimum Distance (MD), and Mahalanobis (MH) algorithms and compare them in order to generate a LULC map using data from Sentinel 2 and Landsat 8 satellites. Further, we also investigate the effect of a penalty parameter on SVM results. Our study uses different kernel functions and hidden layers for SVM and ANN algorithms, respectively. We generated the training and validation datasets from Google Earth images and GPS data prior to pre-processing satellite data. In the next phase, we classified the images using training data and algorithms. Ultimately, to evaluate outcomes, we used the validation data to generate a confusion matrix of the classified images. Our results showed that with optimal tuning parameters, the SVM classifier yielded the highest overall accuracy (OA) of 94%, performing better for both satellite data compared to other methods. In addition, for our scenes, Sentinel 2 date was slightly more accurate compared to Landsat 8. The parametric algorithms MD and MLC provided the lowest accuracy of 80.85% and 74.68% for the data from Sentinel 2 and Landsat 8. In contrast, our evaluation using the SVM tuning parameters showed that the linear kernel with the penalty parameter 150 for Sentinel 2 and the penalty parameter 200 for Landsat 8 yielded the highest accuracies. Further, ANN classification showed that increasing the hidden layers drastically reduces classification accuracy for both datasets, reducing zero for three hidden layers. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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23 pages, 6809 KiB

Open AccessArticle

Long-Time Interval Satellite Image Analysis on Forest-Cover Changes and Disturbances around Protected Area, Zeya State Nature Reserve, in the Russian Far East

by Chulabush Khatancharoen, Satoshi Tsuyuki, Semyon V. Bryanin, Konosuke Sugiura, Tatsuyuki Seino, Viktor V. Lisovsky, Irina G. Borisova and Naoya Wada

Remote Sens. 2021, 13(7), 1285; https://doi.org/10.3390/rs13071285 - 27 Mar 2021

Cited by 11 | Viewed by 4748

Abstract

Boreal forest areas in the Russian Far East contained very large intact forests. This particular area is considered one of the most productive and diverse forests in the boreal biome of the world, and it is also home to many endangered species. Zeya State Nature Reserve is located at the southern margin of the boreal forest area in the Russian Far East and has rich fauna and flora. However, the forest in the region faced large-scale forest fires and clearcutting for timber recently. The information of disturbances is rarely understood. This study aimed to explore the effects of disturbance and forest dynamics around the reserve. Our study used two-year overlaid Landsat images from Landsat 5 Thematic Mapper (TM) and 8 Operational Land Imager (OLI), to generate forest-cover-change maps of 1988–1999, 1999–2010, and 2010–2016. In this paper, we analyze the direction of forest successional stages, to demonstrate the effectiveness of this protected area in terms of preventing human-based deforestation on the vegetation indices. The vegetation indices included the normalized burn ratio (NBR), the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI). The study provided information on the pattern of forest-cover change and disturbance area around the reserve. The NDWI was used to differentiate between water and non-water areas. The mean values of NBR and NDVI were calculated and determine the forest successional stages between burn, vegetation recovery, grass, mixed forest, oak forest, and birch and larch forest. The accuracy was assessed by using field measurements, field photos, and high-resolution images as references. Overall, our classification results have high accuracy for all three periods. The most disturbed area occurred during 2010–2016. The reserve was highly protected, with no human-disturbance activity. However, large areas from fire disturbance were found (137 km²) during 1999–2010. The findings also show a large area of disturbance, mostly located outside of the reserve. Mixed disturbance increased to almost 50 km² during 2010–2016, in the buffer zone and outside of the reserve. We recommend future works to apply our methods to other ecosystems, to compare the forest dynamics and disturbance inside and outside the protected area. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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23 pages, 9599 KiB

Open AccessArticle

Using UAV Imagery to Detect and Map Woody Species Encroachment in a Subalpine Grassland: Advantages and Limits

by Ludovica Oddi, Edoardo Cremonese, Lorenzo Ascari, Gianluca Filippa, Marta Galvagno, Davide Serafino and Umberto Morra di Cella

Remote Sens. 2021, 13(7), 1239; https://doi.org/10.3390/rs13071239 - 24 Mar 2021

Cited by 27 | Viewed by 4860

Abstract

Woody species encroachment on grassland ecosystems is occurring worldwide with both negative and positive consequences for biodiversity conservation and ecosystem services. Remote sensing and image analysis represent useful tools for the monitoring of this process. In this paper, we aimed at evaluating quantitatively the potential of using high-resolution UAV imagery to monitor the encroachment process during its early development and at comparing the performance of manual and semi-automatic classification methods. The RGB images of an abandoned subalpine grassland on the Western Italian Alps were acquired by drone and then classified through manual photo-interpretation, with both pixel- and object-based semi-automatic models, using machine-learning algorithms. The classification techniques were applied at different resolution levels and tested for their accuracy against reference data including measurements of tree dimensions collected in the field. Results showed that the most accurate method was the photo-interpretation (≈99%), followed by the pixel-based approach (≈86%) that was faster than the manual technique and more accurate than the object-based one (≈78%). The dimensional threshold for juvenile tree detection was lower for the photo-interpretation but comparable to the pixel-based one. Therefore, for the encroachment mapping at its early stages, the pixel-based approach proved to be a promising and pragmatic choice. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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20 pages, 10906 KiB

Open AccessArticle

Tropical Forest and Wetland Losses and the Role of Protected Areas in Northwestern Belize, Revealed from Landsat and Machine Learning

by Colin Doyle, Timothy Beach and Sheryl Luzzadder-Beach

Remote Sens. 2021, 13(3), 379; https://doi.org/10.3390/rs13030379 - 22 Jan 2021

Cited by 18 | Viewed by 5693

Abstract

Changes in land-use and land-cover, including both agricultural expansion and the establishment of protected areas, have altered the landscape pattern and extent of forest and wetland cover in the tropics. In Central America, land-use and land-cover change is also threatening the cultural resources of the region’s ancient Maya heritage since many ancient sites have been degraded by burning, deforestation, and plowing. In this study of Orange Walk District of northern Belize, from the 1980s to the present, we used multitemporal Landsat data with a random forest classifier to reveal trends in land-use and land-cover change and the increasing loss of forest and wetlands. We develop a random forest classifier that is time-generalized to map land-use and land-cover across the entire Landsat record, including Landsat 4, 5, 7, and 8, with a single algorithm. Including multiyear and seasonal composites was important for obtaining cloud-free coverage and distinguishing between different land-use and land-cover types. Early deforestation (1984–1987) was in small patches scattered across the landscape and likely driven by small scale agriculture such as milpa and smaller area tractor and horse-drawn plowing. The establishment of protected areas in the late 1980s and early 1990s allowed for forest regrowth in these areas, while wetland losses were high at 15%. The transition to industrial agriculture in the 2000s, however, drove a 43.6% expansion of agriculture and a 7.5% loss of forest and a 28.2% loss of wetlands during the ~15 years. Protected areas initiated in the 1980s led to a nearly 100 km² decrease in agriculture from 1984–1987 to 1999–2001, and they became essential refugia for habitat and maintaining ecosystem services. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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26 pages, 13502 KiB

Open AccessArticle

Vegetation Abundance and Health Mapping Over Southwestern Antarctica Based on WorldView-2 Data and a Modified Spectral Mixture Analysis

by Xiaohui Sun, Wenjin Wu, Xinwu Li, Xiyan Xu and Jinfeng Li

Remote Sens. 2021, 13(2), 166; https://doi.org/10.3390/rs13020166 - 6 Jan 2021

Cited by 12 | Viewed by 3257

Abstract

In polar regions, vegetation is especially sensitive to climate dynamics and thus can be used as an indicator of the global and regional environmental change. However, in Antarctica, there is very little information on vegetation distribution and growth status. To fill this gap, we evaluated the ability of both linear and nonlinear spectral mixture analysis (SMA) models, including a group of newly developed modified Nascimento’s models for Antarctic vegetated areas (MNM-AVs), in estimating the abundance of major Antarctic vegetation types, i.e., mosses and lichens. The study was conducted using WorldView-2 satellite data and field measurements over the Fildes Peninsula and its surroundings, which are representative vegetated areas in Antarctica. In MNM-AVs, we introduced secondary scattering components for vegetation and its background to account for the sparsity of vegetation cover and reassigned their coefficients. The new models achieved improved performances, among which MNM-AV3 achieved the lowest error for mosses (lichens) abundance estimation with RMSE = 0.202 (0.213). Compared with MNM-AVs, the linear model performed particularly poor for lichens (RMSE = 0.322), which is in contrast to the case of mosses (RMSE = 0.212), demonstrating that spectral signals of lichens are more prone to mix with their backgrounds. Abundance maps of mosses and lichens, as well as a map of moss health status for the entire study area, were then obtained based on MNM-AV3 with around 80% overall accuracy. Moss areas account for 0.7695 km² in Fildes and 0.3259 km² in Ardley Island; unhealthy mosses amounted to 40% (49%) of the area in the summer of 2018 (2019), indicating considerable environmental stress. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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19 pages, 10781 KiB

Open AccessArticle

Green Vegetation Cover Dynamics in a Heterogeneous Grassland: Spectral Unmixing of Landsat Time Series from 1999 to 2014

by Yuhong He, Jian Yang and Xulin Guo

Remote Sens. 2020, 12(22), 3826; https://doi.org/10.3390/rs12223826 - 21 Nov 2020

Cited by 26 | Viewed by 4136

Abstract

The ability to quantify green vegetation across space and over time is useful for studying grassland health and function and improving our understanding of the impact of land use and climate change on grasslands. Directly measuring the fraction of green vegetation cover is labor-intensive and thus only practical on relatively smaller experimental sites. Remote sensing vegetation indices, as a commonly-used method for large-area vegetation mapping, were found to produce inconsistent accuracies when mapping green vegetation in semi-arid grasslands, largely due to mixed pixels including both photosynthetic and non-photosynthetic material. The spectral mixture approach has the potential to map the fraction of green vegetation cover in a heterogeneous landscape, thanks to its ability to decompose a spectral signal from a mixed pixel into a set of fractional abundances. In this study, a time series of fractional green vegetation cover (FGVC) from 1999 to 2014 is estimated using the spectral mixture approach for a semi-arid mixed grassland, which represents a typical threatened, species-rich habitat in Central Canada. The shape of pixel clouds in each of the Landsat images is used to identify three major image endmembers (green vegetation, bare soil/litter, and water/shadow) for automated image spectral unmixing. The FGVC derived through the spectral mixture approach correlates highly with field observations (R² = 0.86). Change in the FGVC over the study period was also mapped, and green vegetation in badlands and uplands is found to experience a slight increase, while vegetation in riparian zone shows a decrease. Only a small portion of the study area is undergoing significant changes, which is likely attributable to climate variability, bison reintroduction, and wildfire. The results of this study suggest that the automated spectral unmixing approach is promising, and the time series of medium-resolution images is capable of identifying changes in green vegetation cover in semi-arid grasslands. Further research should investigate driving forces for areas undergoing significant changes. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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24 pages, 34801 KiB

Open AccessArticle

Radar Satellite Image Time Series Analysis for High-Resolution Mapping of Man-Made Forest Change in Chongming Eco-Island

by Zhihuo Xu and Yuexia Wang

Remote Sens. 2020, 12(20), 3438; https://doi.org/10.3390/rs12203438 - 19 Oct 2020

Cited by 7 | Viewed by 3360

Abstract

Forests are important oxygen sources and carbon sinks. They provide a series of ecosystem services that are crucial to eco-island sustainability, such as the protection of soils, conservation of biodiversity, and development of the eco-tourism, etc. Interestingly, Chongming eco-island is located in the borderlands between fresh- and saltwater environments, where the Yangtze River meets the East China Sea. Most forests in Chongming island are therefore man-made and very vulnerable to the ecological environment mixing of freshwater streams and rivers with salty ocean tides, and are affected by climate and human activity. Estimating and monitoring forest change within an estuary is required for the sustainable management of forest resources and forest-based eco-tourism. Most optical satellites are unsuitable for continuous forest mapping due to cloud cover and their relatively low spatial and temporal resolution. Here, using Sentinel-1 satellite carrying an imaging C-band synthetic aperture radar, radar vegetation index was derived to detect the forest dynamics on the island. Furthermore we quantified forest area change in three well known and the most strictly protected and representative areas, namely Dongping National Forest Park, Dongtan National Wetland Park, and Xisha National Wetland Park, in the Chongming eco-island over the past five years at 10-metre resolution. We recorded the early and mid summers when the forest canopies grew to the peak in the study areas. The planted forest in Dongping National Forest Park grew an area of 7.38 hectares from 2015 to 2019, and disappeared from an area of almost 2.59 hectares in 2018. The man-made forest of Xisha National Wetland Park increased at an area of almost 20.19 hectares over the past five years. The forest in Dongtan National Wetland Park increased to an area of almost 2.12 hectares in the period of 2015–2017 and 2018–2019. However, from 2017 to 2018, the area of planted forests in Dongtan National Wetland Park decreased by 1.35 hectares. This study shows man-made forest change can be measured and that evidence can be collected to show how the forest is altered by human activities, and informs forest management decision-making for Chongming eco-island. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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26 pages, 14027 KiB

Open AccessArticle

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks

by Biswajeet Pradhan, Husam A. H. Al-Najjar, Maher Ibrahim Sameen, Ivor Tsang and Abdullah M. Alamri

Remote Sens. 2020, 12(10), 1676; https://doi.org/10.3390/rs12101676 - 23 May 2020

Cited by 55 | Viewed by 9958

Abstract

Zero-shot learning (ZSL) is an approach to classify objects unseen during the training phase and shown to be useful for real-world applications, especially when there is a lack of sufficient training data. Only a limited amount of works has been carried out on ZSL, especially in the field of remote sensing. This research investigates the use of a convolutional neural network (CNN) as a feature extraction and classification method for land cover mapping using high-resolution orthophotos. In the feature extraction phase, we used a CNN model with a single convolutional layer to extract discriminative features. In the second phase, we used class attributes learned from the Word2Vec model (pre-trained by Google News) to train a second CNN model that performed class signature prediction by using both the features extracted by the first CNN and class attributes during training and only the features during prediction. We trained and tested our models on datasets collected over two subareas in the Cameron Highlands (training dataset, first test dataset) and Ipoh (second test dataset) in Malaysia. Several experiments have been conducted on the feature extraction and classification models regarding the main parameters, such as the network’s layers and depth, number of filters, and the impact of Gaussian noise. As a result, the best models were selected using various accuracy metrics such as top-k categorical accuracy for k = [1,2,3], Recall, Precision, and F1-score. The best model for feature extraction achieved 0.953 F1-score, 0.941 precision, 0.882 recall for the training dataset and 0.904 F1-score, 0.869 precision, 0.949 recall for the first test dataset, and 0.898 F1-score, 0.870 precision, 0.838 recall for the second test dataset. The best model for classification achieved an average of 0.778 top-one, 0.890 top-two and 0.942 top-three accuracy, 0.798 F1-score, 0.766 recall and 0.838 precision for the first test dataset and 0.737 top-one, 0.906 top-two, 0.924 top-three, 0.729 F1-score, 0.676 recall and 0.790 precision for the second test dataset. The results demonstrated that the proposed ZSL is a promising tool for land cover mapping based on high-resolution photos. Full article

(This article belongs to the Special Issue Remote Sensing for Land Cover and Vegetation Mapping)

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