WorldView-2 Images Research Articles

Segmentation Scale Effect Analysis in the Object-Oriented Method of High-Spatial-Resolution Image Classification.

High-spatial-resolution images play an important role in land cover classification, and object-based image analysis (OBIA) presents a good method of processing high-spatial-resolution images. Segmentation, as the most important premise of OBIA, significantly affects the image classification and target recognition results. However, scale selection for image segmentation is difficult and complicated for OBIA. The main challenge in image segmentation is the selection of the optimal segmentation parameters and an algorithm that can effectively extract the image information. This paper presents an approach that can effectively select an optimal segmentation scale based on land object average areas. First, 20 different segmentation scales were used for image segmentation. Next, the classification and regression tree model (CART) was used for image classification based on 20 different segmentation results, where four types of features were calculated and used, including image spectral bands value, texture value, vegetation indices, and spatial feature indices, respectively. WorldView-3 images were used as the experimental data to verify the validity of the proposed method for the selection of the optimal segmentation scale parameter. In order to decide the effect of the segmentation scale on the object area level, the average areas of different land objects were estimated based on the classification results. Experiments based on the multi-scale segmentation scale testify to the validity of the land object’s average area-based method for the selection of optimal segmentation scale parameters. The study results indicated that segmentation scales are strongly correlated with an object’s average area, and thus, the optimal segmentation scale of every land object can be obtained. In this regard, we conclude that the area-based segmentation scale selection method is suitable to determine optimal segmentation parameters for different land objects. We hope the segmentation scale selection method used in this study can be further extended and used for different image segmentation algorithms.

ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

Attention Enhanced U-Net for Building Extraction from Farmland Based on Google and WorldView-2 Remote Sensing Images

High-resolution remote sensing images contain abundant building information and provide an important data source for extracting buildings, which is of great significance to farmland preservation. However, the types of ground features in farmland are complex, the buildings are scattered and may be obscured by clouds or vegetation, leading to problems such as a low extraction accuracy in the existing methods. In response to the above problems, this paper proposes a method of attention-enhanced U-Net for building extraction from farmland, based on Google and WorldView-2 remote sensing images. First, a Resnet unit is adopted as the infrastructure of the U-Net network encoding part, then the spatial and channel attention mechanism module is introduced between the Resnet unit and the maximum pool and the multi-scale fusion module is added to improve the U-Net network. Second, the buildings found on WorldView-2 and Google images are extracted through farmland boundary constraints. Third, boundary optimization and fusion processing are carried out for the building extraction results on the WorldView-2 and Google images. Fourth, a case experiment is performed. The method in this paper is compared with semantic segmentation models, such as FCN8, U-Net, Attention_UNet, and DeepLabv3+. The experimental results indicate that this method attains a higher accuracy and better effect in terms of building extraction within farmland; the accuracy is 97.47%, the F1 score is 85.61%, the recall rate (Recall) is 93.02%, and the intersection of union (IoU) value is 74.85%. Hence, buildings within farming areas can be effectively extracted, which is conducive to the preservation of farmland.

Resource-environment joint forecasting using big data mining and 3D/4D modeling in Luanchuan mining district, China

The Fourth generation industrial age and 5G + intelligent communication in the "Fourth Paradigm of Science" in the 21st century provide a new opportunity for research on the relationship between mining development and environmental protection. This paper is based on the theory of metallogenic geodynamics background, metallogenic process and quantitative evaluation and chooses the Luanchuan district as a case study, using deep-level artificial intelligence mining and three/four-dimensional (3D/4D) multi-disciplinary, multi-parameter and multi-scale modeling technology platform of geoscience big data (including multi-dimensional and multi-scale geological, geophysical, geochemical, hyperspectral and highresolution remote sensing (multi-temporal) and real-time mining data), carrying out the construction of 3D geological model, metallogenic process model and quantitative exploration model from district to deposit scales and the quantitative prediction and evaluation of the regional Mo polymetallic mineral resources, the aim is to realize the dynamic evaluation of highprecision 3D geological (rock, structure, hydrology, soil, etc.) environment protection and comprehensive development and utilization of mineral resources in digital and wisdom mines, it provides scientific information for the sustainable development of mineral resources and mine environment in the study area. The research results are summarized as follows: (1) The geoscience big data related to mineral resource prediction and evaluation of district include mining data such as 3D geological modeling, geophysics interpretation, geochemistry, and remote sensing modeling, which are combined with GeoCube3.0 software. The optimization of deep targets and comprehensive evaluation of mineral resources in Luanchuan district (500 km2, 2.5 km deep) have been realized, including 6.5 million tons of Mo, 1.5 million tons of W, and 5 million tons of Pb-Zn-Ag. (2) The 3D geological modeling of geology, mineral deposit, and exploration targeting is related to the mine environment. The data of exploration and mining in the pits of Nannihu – Sandaozhuang – Shangfang deposits and the deep channels of Luotuoshan and Xigou deposits show a poor spatial correlation between the NW-trending porphyryskarn deposits and the ore bodies. The NE-trending faults are usually tensional or tensional-torsional structures formed in the post-metallogenic period, which is the migration pathway of hydrothermal fluid of the related Pb-Zn deposit. There is a risk of groundwater pollution in the high-altitude Pb-Zn mining zones, such as the Lengshui and Bailugou deposits controlled by NE-trending faults are developed outside of porphyry-skarn types of Mo (W) deposits in the Luanchuan area. (3) Construction of mineral resources and environmental assessment and decision-making in intelligent digital mines: 3D geological model is established in large mines and associated with ancient mining caves, pit, and deep roadway engineering in the mining areas to realize reasonable orientation and sustainable development of mining industry. The hyperspectral database is used to construct three-dimensional useful and harmful element models to realize the association of exploration, mining, and mineral processing mineralogy for the recovery of harmful elements (As, Sb, Hg, etc.). 0.5 m resolution Worldview2 images are used to identify the distribution of Fe in the wastewater and slag slurry of important tailings reservoirs, so as to protect surface runoff and soil pollution.

Research of the Dual-Band Log-Linear Analysis Model Based on Physics for Bathymetry without In-Situ Depth Data in the South China Sea

The current widely used bathymetric inversion model based on multispectral satellite imagery mostly relies on in-situ depth data for establishing a liner/non-linear relationship between water depth and pixel reflectance. This paper evaluates the performance of a dual-band log-linear analysis model based on physics (P-DLA) for bathymetry without in-situ depth data. This is done using WorldView-2 images of blue and green bands. Further, the pixel sampling principles for solving the four key parameters of the model are summarized. Firstly, this paper elaborates on the physical mechanism of the P-DLA model. All unknown parameters of the P-DLA model are solved by different types of sampling pixels extracted from multispectral images for bathymetric measurements. Ganquan Island and Zhaoshu Island, where accuracy evaluation is performed for the bathymetric results of the P-DLA model with in-situ depth data, were selected to be processed using the method to evaluate its performance. The root mean square errors (RMSEs) of the Ganquan Island and Zhaoshu Island results are 1.69 m and 1.74 m with the mean relative error (MREs) of 14.8% and 18.3%, respectively. Meanwhile, the bathymetric inversion is performed with in-situ depth data using the traditional dual-band log-linear regression model (DLR). The results show that the accuracy of the P-DLA model bathymetry without in-situ depth data is roughly equal to that of the DLR model water depth inversion based on in-situ depth data. The results indicate that the P-DLA model can still obtain relatively ideal bathymetric results despite not having actual bathymetric data in the model training. It also demonstrates underwater microscopic features and changes in the islands and reefs.

Affordable Use of Satellite Imagery in Agriculture and Development Projects: Assessing the Spatial Distribution of Invasive Weeds in the UNESCO-Protected Areas of Cuba

The effective and regular remote monitoring of agricultural activity is not always possible in developing countries because the access to cloud-based geospatial analysis platforms or expensive high-resolution satellite images are not always available. Herein, using paid high-resolution satellite images first and then free medium-resolution satellite images, we aimed to develop a cost-free, affordable method for regularly mapping the spatial distribution of sicklebush (Dichrostachys cinerea), an archetypal allochthonous invasive plant in Cuba that is becoming impossible to control owing to its rapid growth in areas planted with sugar cane in the Trinidad-Valle de los Ingenios area (Cuba), a UNESCO World Heritage Site. Two types of images were used (WorldView-2 and Landsat-8); these were subjected to supervised classification, with accuracy values of 88.7% and 93.7%, respectively. Vegetation cover was first derived from the purchased WorldView-2 image, and this information was then used as the training field to obtain spectral signatures from the Landsat-8 free image so that Landsat images may be regularly used to monitor D. cinerea infestations. The results obtained in the spatial distribution map for sicklebush in the Landsat-8 images had an overall reliability of 93.7% and a Kappa coefficient reliability of 91.9%. These values indicate high confidence in the results, which suggests that sicklebush has invaded 52.7% of the total 46,807.26-ha area of the Trinidad-Valle de los Ingenios. This process proved extremely effective and demonstrated the benefits of using high-resolution spatial images from which information can be transferred to free satellite images with a larger pixel size.

Earthquake Aftermath from Very High-Resolution WorldView-2 Image and Semi-Automated Object-Based Image Analysis (Case Study: Kermanshah, Sarpol-e Zahab, Iran)

This study aimed to classify an urban area and its surrounding objects after the destructive M7.3 Kermanshah earthquake (12 November 2017) in the west of Iran using very high-resolution (VHR) post-event WorldView-2 images and object-based image analysis (OBIA) methods. The spatial resolution of multispectral (MS) bands (~2 m) was first improved using a pan-sharpening technique that provides a solution by fusing the information of the panchromatic (PAN) and MS bands to generate pan-sharpened images with a spatial resolution of about 50 cm. After applying a segmentation procedure, the classification step was considered as the main process of extracting the aimed features. The aforementioned classification method includes applying spectral and shape indices. Then, the classes were defined as follows: type 1 (settlement area) was collapsed areas, non-collapsed areas, and camps; type 2 (vegetation area) was orchards, cultivated areas, and urban green spaces; and type 3 (miscellaneous area) was rocks, rivers, and bare lands. As OBIA results in the integration of the spatial characteristics of the image object, we also aimed to evaluate the efficiency of object-based features for damage assessment within the semi-automated approach. For this goal, image context assessment algorithms (e.g., textural parameters, shape, and compactness) together with spectral information (e.g., brightness and standard deviation) were applied within the integrated approach. The classification results were satisfactory when compared with the reference map for collapsed buildings provided by UNITAR (the United Nations Institute for Training and Research). In addition, the number of temporary camps was counted after applying OBIA, indicating that 10,249 tents or temporary shelters were established for homeless people up to 17 November 2018. Based on the total damaged population, the essential resources such as emergency equipment, canned food and water bottles can be estimated. The research makes a significant contribution to the development of remote sensing science by means of applying different object-based image-analyzing techniques and evaluating their efficiency within the semi-automated approach, which, accordingly, supports the efficient application of these methods to other worldwide case studies.

Hierarchical mapping of Brazilian Savanna (Cerrado) physiognomies based on deep learning

The Brazilian Savanna, also known as Cerrado, is considered a global hotspot for biodiversity conservation. The detailed mapping of vegetation types, called physiognomies, is still a challenge due to their high spectral similarity and spatial variability. There are three major ecosystem groups (forest, savanna, and grassland), which can be hierarchically subdivided into 25 detailed physiognomies, according to a well-known classification system. We used an adapted U-net architecture to process a WorldView-2 image with 2-m spatial resolution to hierarchically classify the physiognomies of a Cerrado protected area based on deep learning techniques. Several spectral channels were tested as input datasets to classify the three major ecosystem groups (first level of classification). The dataset composed of RGB bands plus 2-band enhanced vegetation index (EVI2) achieved the best performance and was used to perform the hierarchical classification. In the first level of classification, the overall accuracy was 92.8%. On the other hand, for the savanna and grassland detailed physiognomies (second level of classification), 86.1% and 85.0% were reached, respectively. As the first work that intended to classify Cerrado physiognomies in this level of detail using deep learning, our accuracy rates outperformed others that applied traditional machine learning algorithms for this task.

The damage of urban vegetation from super typhoon is associated with landscape factors: Evidence from Sentinel-2 imagery

Many studies have investigated the impacts of typhoons on natural vegetation, but the influencing factor of urban vegetation damage from super typhoon is not clear. Therefore, this study investigated the vegetation damage patterns in eight cities affected by Typhoon Mangkhut (the 2nd strongest tropical storm worldwide in 2018) using the normalized difference vegetation index (NDVI) derived from Sentinel-2 images. The vegetation damage maps have an overall accuracy of 97% using the very high-resolution WorldView-3 images as reference data. The results show that (1) The typhoon-induced vegetation damage show high spatial heterogeneity in urban areas and varies with land cover types. Residential greenspace and street trees are more susceptible to typhoon disturbance than natural vegetation. (2) Wind intensity is still an important factor in urban vegetation damage (r2 = 0.43, P value <0.001). (3) Urban vegetation damage positively relates to vegetation sparseness for all cities (r: 0.39–0.89, P value <0.01), whereas negatively correlated to the height of surrounding buildings (r = −0.57, P value <0.01), suggesting that both biotic and abiotic factors of the urban environment have influences on the resistance of vegetation to storms. This study provides insights into the resistance and resilience of urban vegetation to strong typhoons that can be used for urban forestry planning and management.

The influence of window size on remote sensing-based prediction of forest structural variables

BackgroundDetermining the appropriate window size is a critical step in the estimation process of stand structural variables based on remote sensing data. Because the value of the reference laser and image metrics that affect the quality of the prediction model depends on window size. However, suitable window sizes are usually determined by trial and error. There are a limited number of published studies evaluating appropriate window sizes for different remote sensing data. This research investigated the effect of window size on predicting forest structural variables using airborne LiDAR data, digital aerial image and WorldView-3 satellite image.ResultsIn the WorldView-3 and digital aerial image, significant differences were observed in the prediction accuracies of the structural variables according to different window sizes. For the estimation based on WorldView-3 in black pine stands, the optimal window sizes for stem number (N), volume (V), basal area (BA) and mean height (H) were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. In oak stands, the R2 values of each moving window size were almost identical for N and BA. The optimal window size was 400 m2 for V and 600 m2 for H. For the estimation based on aerial image in black pine stands, the 800 m2 window size was optimal for N and H, the 600 m2 window size was optimal for V and the 1000 m2 window size was optimal for BA. In the oak stands, the optimal window sizes for N, V, BA and H were determined as 1000 m2, 100 m2, 100 m2 and 600 m2, respectively. The optimal window sizes may need to be scaled up or down to match the stand canopy components. In the LiDAR data, the R2 values of each window size were almost identical for all variables of the black pine and the oak stands.ConclusionThis study illustrated that the window size has an effect on the prediction accuracy in estimating forest structural variables based on remote sensing data. Moreover, the results showed that the optimal window size for forest structural variables varies according to remote sensing data and tree species composition.

WorldView-2 Satellite Image Classification using U-Net Deep Learning Model

Land cover maps are important documents for local governments to perform urban planning and management. A field survey using measuring instruments can produce an accurate land cover map. However, this method is time-consuming, expensive, and labor-intensive. A number of researchers have proposed using remote sensing, which generates land cover maps using an optical satellite image with various statistical classification procedures. Recently, artificial intelligence (AI) technology, such as deep learning, has been used in multiple fields, including satellite image classification, with satisfactory results. In this study, a WorldView-2 image of Terangun in Aceh Province, which was acquired on Aug 2, 2016, was classified using a commonly used deep-learning-based classification, namely, U-net. There were eight classes used in the experiment: building, road, open land (such as green open space, bare land, grass, or low vegetation), river, farm, field, aquaculture pond, and garden. For comparison, three classification methods: maximum-likelihood, random forest, and support vector machine, were performed compared to U-Net. A land cover map provided by the government was used as a reference to evaluate the accuracy of land cover maps generated using two classification methods. The results with 100 randomly selected pixels revealed that U-Net was able to obtain a 72% and 0.585 for overall and kappa accuracy, respectively; whereas, overall accuracy and kappa accuracy for the maximum likelihood, random forest and support vector machine methods were 49% and 0.148; 59% and 0.392; and 67% and 0. 511; respectively. Therefore, U-Net outperformed those three of classification methods in classifying the image.&#x0D;

A New Individual Tree Species Classification Method Based on the ResU-Net Model

Individual tree species (ITS) classification is one of the key issues in forest resource management. Compared with traditional classification methods, deep learning networks may yield ITS classification results with higher accuracy. In this research, the U-Net and ResNet networks were combined to form a Res-UNet network by changing the structure of the convolutional layer to the residual structure in ResNet based on the framework of the U-Net model. In addition, a second Res-UNet network named Res-UNet2 was further constructed to explore the effect of the stacking of residual structures on network performance. The Res-UNet2 model structure is similar to that of the Res-UNet model, but the convolutional layer in the U-Net model is created with a double-layer residual structure. The two networks proposed in this work were used to classify ITSs in WorldView-3 images of the Huangshan Mountains, Anhui Province, China, acquired in March 2019. The resulting ITS map was compared with the classification results obtained with U-Net and ResNet. The total classification accuracy of the ResU-Net network reached 94.29% and was higher than that generated by the U-Net and ResNet models, verifying that the ResU-Net model can more accurately classify ITSs. The Res-UNet2 model performed poorly compared to Res-UNet, indicating that stacking the residual modules in ResNet does not achieve an accuracy improvement.

Object-based modelling and accuracy assessment of Gordion tumuli using very high-resolution stereo satellite images

ABSTRACT Very high-resolution (VHR) stereo satellite images provide detailed spatial and spectral information for detecting and monitoring artificial or natural objects. This information can also be used for object-based change detection. This study aims to reveal the potential of three-dimensional (3D) information generated from VHR stereo satellite images to determine the physical changes that may occur on Gordion tumuli. For this purpose, point cloud data were generated from WorldView-3 and KOMPSAT-3 stereo satellite images. The accuracy of these datasets was tested using 161 check points (CPs). On the other hand, digital surface models (DSMs) generated from VHR stereo satellite images were used for object-based modelling of tumuli. The vertical accuracy of these DSMs was tested by comparing them with reference DSMs extracted from Unmanned Aerial System (UAS) images. As a result of the vertical accuracy assessment of the point cloud data, 0.16 m and 0.53 m root mean square error (RMSE) values were obtained for WorldView-3 and KOMPSAT-3 data, respectively. The average RMSE values obtained as a result of the model-based comparison of DSMs are 0.28 m for WorldView-3 and 0.57 m for KOMPSAT-3. These results demonstrate that the sub-metre level physical changes occurring on tumuli can be determined by VHR stereo satellite images.

Monitoring and assessment of endangered UNESCO World Heritage Sites using space technology: a case study of East Rennell, Solomon Islands

Space technology offers effective tools to monitor the status of World Natural Heritage sites. East Rennell Island, which covers approximately 37,000 hectares (ha) and a marine area extending three nautical miles into the sea, was added to the list of endangered World Natural Heritage sites in 2013. Deforestation and natural disasters have increasingly threatened sustainable development on the island. Based on analyses of multi-source time series of remote sensing data (e.g., MODIS and Worldview), the forest cover change on Rennell Island from 2000 to 2020 and its future trends were mapped and analysed using Sen + Mann–Kendall and Hurst index models. A land cover classification system derived from high-resolution Worldview images was developed as a baseline for monitoring and analysing future forest cover changes on the island. Our results showed that (1) the areas of vegetation degradation and improvement were basically equal from 2000 to 2020. (2) The forest cover change trend had weak continuity, and significant improvements could be achieved in areas with damaged vegetation given sufficient protection measures and financial input. (3) This heritage site has a strong vegetation regeneration ability, and human activities such as mining, logging, and road construction, which could greatly disturb the unique ecosystem, should be restricted.

Assessment of top of atmosphere reflectance generation algorithm for world view-2 high resolution images

Recently, there are many developments in remote sensing techniques using satellite imagery as a result of the variety of remotely sensed image sources with different spatial and spectral resolutions and from different sensors. Optical satellite products are affected by an error of payload, atmosphere (scattering and absorption), and variations relative to positions of Sun, Earth, and satellite during capturing data. Top of Atmosphere (TOA) correction is the process of removing the effects of variations relative to sensor error and positions of Sun, Earth, and satellite. In this paper, maximum benefits from high spectral and spatial resolution images are demonstrated and analyzed or compared with images from different. TOA correction algorithm, which was implemented using Python environment, is applied to high resolution images from WorldView-2 (WV-2) satellite. TOA reflectance is considered the first step in any algorithm dedicated to the change detection process. Reflectance conversion is also performed on the same images using Orfeo ToolBox (OTB), the open-source software. The performance and efficiency of the proposed algorithm are compared with that of the Orfeo ToolBox (OTB) TOA reflectance output. The achieved results show that the proposed algorithm, which is automatically performed, is faster and provides significant results for WV-2 images, and can be adapted to be applied on different optical satellite sensors.

Channel–spatial attention-based pan-sharpening of very high-resolution satellite images

The pan-sharpening process aims to generate a new synthetic output image preserving the spatial details of panchromatic and spectral details of the multi-spectral image inputs. Recently, deep learning-based methods show substantial success in the remote sensing field mostly with the application of traditional Convolutional Neural Networks (CNNs). Most of the traditional CNN-based approaches treat all the channels equitably and cannot learn the correlation. Attention mechanism which can learn the correlations among the channels has been proven to be effective in super-resolution and object detection tasks. In this research, we introduced a novel deep learning framework, channel–spatial attention-based method for pan-sharpening (CSAPAN), by designing a Densely residual attention module (RAM). Besides, we train our model in the high-frequency domain and up-sample the low-resolution multispectral images by using the pixel shuffle method before stacking with the panchromatic images for further feature extraction. We evaluated our proposed CSAPAN along with traditional methods and CNN-based methods in reduced and full resolution and obtained satisfactory quantitative and qualitative results on Pleiades, Worldview-2, and QuickBird-2 satellite image datasets.

Research on remote sensing prospecting technology based on multi-source data fusion in deep-cutting areas

Tectonically deep-cut areas are difficult to access due to the treacherous terrain, making it difficult to carry out fieldwork in these areas for geological prospecting. Liwu Copper-Zinc Mine is a medium-sized rich polymetallic deposit belonging to deep-cutting areas in tectonic structures. Complicated structures influence the ore development and formation in the region. With the introduction of remote sensing technology, geologic features and mineralisation abnormality information can be recognised effectively in such regions. This can provide scientific references for dividing potential metallogenic regions. Remote sensing images with sub-meter spatial resolutions were collected using the Worldview-2(WV-2) satellite to interpret mineralisation-related geologic bodies and analyse the distribution relationships of mineralisation-controlling structures. Multispectral near-infrared spectral information was acquired through the Landsat-8. Meanwhile, the alteration information of two hydrothermal solutions in the study area related to hydroxide radicals and iron staining was extracted via principal component analysis (PCA) based on Crosta technology. The alteration-related standard spectral information and the measured spectral information of known copper outcrops in the region were collected as guiding spectra. A comparative analysis between guiding spectra and GF-5 graphic information was conducted. Possible mineralisation information distributions in the study area were collected via a spectral angle mapper (SAM) and support vector machine (SVM). The results showed that the WV-2 images were able to accurately identify the mineralised geological bodies. The overall classification precisions of SAM- and SVM-based erratic rock mapping were 80.18% and 83.51%, respectively. The information on the hydroxyl iron staining anomaly had been well fitted to the actual alteration zone by field investigation. An analogy analysis between the multivariate remote sensing prospecting information collected by the above means and the existing typical mining areas in Liwu Region was conducted; the dense areas of mineral control structures and remote sensing anomalies were well integrated. The remote sensing metallogenic prediction model of the region was constructed on this basis. Meanwhile, the potential mineralisation was delineated at target zone 1. In conclusion, the presented technology is based on remote sensing data and the fusion of multi-resolutions. It can provide guidance and references for exploring potential metallogenic regions in deep-cutting areas, especially in fold-development regions.

Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map

Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km2 vs. ~195 km2) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery.

Identification of Construction Areas from VHR-Satellite Images for Macroeconomic Forecasts

This contribution focuses on the utilization of very-high-resolution (VHR) images to identify construction areas and their temporal changes aiming to estimate the investment in construction as a basis for economic forecasts. Triggered by the need to improve macroeconomic forecasts and reduce their time intervals, the idea arose to use frequently available information derived from satellite imagery. For the improvement of macroeconomic forecasts, the period to detect changes between two points in time needs to be rather short because early identification of such investments is beneficial. Therefore, in this study, it is of interest to identify and quantify new construction areas, which will turn into build-up areas later. A multiresolution segmentation followed by a kNN classification is applied to WorldView images from an area around the southern part of Berlin, Germany. Specific material compositions of construction areas result in typical classification patterns different from other land cover classes. A GIS-based analysis follows to extract specific temporal “patterns of life” in construction areas. With the early identification of such patterns of life, it is possible to predict construction areas that will turn into real estate later. This information serves as an input for macroeconomic forecasts to support quicker forecasts in future.