High Spatial Resolution Satellite Images Research Articles

Predicting aboveground biomass in Arctic landscapes using very high spatial resolution satellite imagery and field sampling

ABSTRACTRemote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R2 0.21–0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R2 0.33–0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling.

Delineation of Built-Up Areas from Very High-Resolution Satellite Imagery Using Multi-Scale Textures and Spatial Dependence

Very high spatial resolution (VHR) satellite images possess several advantages in terms of describing the details of ground targets. Extracting built-up areas from VHR images has received increasing attention in practical applications, such as land use planning, urbanization monitoring, geographic information database update. In this study, a novel method is proposed for built-up area detection and delineation on VHR satellite images, using multi-resolution space-frequency analysis, spatial dependence modelling and cross-scale feature fusion. First, the image is decomposed by multi-resolution wavelet transformation, and then the high-frequency information at different levels is employed to represent the multi-scale texture and structural characteristics of built-up areas. Subsequently, the local Getis-Ord statistic is introduced to model the spatial patterns of built-up area textures and structures by measuring the spatial dependence among frequency responses at different spatial positions. Finally, the saliency map of built-up areas is produced using a cross-scale feature fusion algorithm, followed by adaptive threshold segmentation to obtain the detection results. The experiments on ZY-3 and Quickbird datasets demonstrate the effectiveness and superiority of the proposed method through comparisons with existing algorithms.

FUNÇÃO ALOMÉTRICA DE BIOMASSA COM IMAGENS DE SATÉLITE DE ALTA RESOLUÇÃO ESPACIAL

O presente estudo teve como objetivo ajustar funções para estimar por meio de dados derivados de imagens de satélite de alta resolução espacial, a biomassa total de Eucalyptus em escala local e regional. Para este fim, foram ajustados modelos, combinando os valores de biomassa estimada por meio de parcelas do inventário florestal associadas com índices de vegetação (IV) baseados em imagem do satélite Pléiades. A função com que apresentou melhor performance na estimativa da biomassa total, foi aquela que utilizou como variável independente o IV-SAVI, apresentando um coeficiente de determinação (R2) de 64,6%, no entanto, sem grande diferença para o NDVI e SR. As funções ajustadas poderão ser utilizadas em regiões que apresentem as mesmas espécies, clima e propriedades locais parecidas com as do presente estudo. Esta abordagem pode ser usada como uma ferramenta de baixo custo para produzir estimativas de biomassa em escala local e regional.

The Effect of the Gully Land Consolidation Project on Soil Erosion and Crop Production on a Typical Watershed in the Loess Plateau

The Gully Land Consolidation Project (GLCP) was launched to create more arable land by excavating soil from the slopes on both sides of gullies, combined with simultaneous comprehensive gully prevention and control measures. The purpose of the GLCP is to increase crop production and reduce soil erosion to achieve ecological and agricultural sustainability. In this study, we assess the effects of the GLCP on soil erosion and crop production by studying the BaoChengGou Watershed in the Loess Plateau, primarily by means of high spatial-resolution satellite images (taken by the GF-1 and ZY-3 satellites) combined with the InVEST model and field investigations. Sloping cropland, sparse forestland, and natural grassland are the main land use types in the study area. After implementing the GLCP, consolidated land in the cropland increased by 7.35%, an increase that has come largely at the expense of grassland and forestland. The GLCP has markedly reduced soil erosion in the BaoChengGou Watershed, especially in the sense that soil erosion intensity was also reduced significantly in the project region on the whole, despite intensifying in certain places, such as excavated slopes; furthermore, it has improved crop yields in the study area by 10.9%. Comprehensive measurement shows the GLCP to be scientific, reasonable, and clearly efficacious. This study presents findings regarding the positive significance of the GLCP in promoting ecological and agricultural sustainability in the Loess Plateau.

Species Discrimination of Plantations in Subtropical China Using 4-Band VHR Imagery and an Operational Image Analysis Framework

Species information of forest plantations is of central interest to forest managers, especially under the current pressing demand for more structurally and functionally healthy and diverse plantations in China. In this study, five evergreen forest types were discriminated using a 4-band very high spatial resolution (VHR) satellite image with an operational image analysis framework which particularly involves a two-step feature selection (TSFS) method and image textural orientations. Two hundred and twenty-five object-based spectral and textural features were retrieved as candidates for the TSFS method to select the most influential feature subsets. Five schemes using random forest classification with optimal user-defined parameters were tested and compared. Both qualitative and quantitative assessment results indicated that the best scheme is the integration of spectral and textural features with individual orientations. The feature selection rate as low as 4% for the optimal scheme reflected the high effectiveness of the TSFS. The geometric intersections showed that many archive even-aged pure coniferous plantations were in fact mixed with nonconiferous species with different proportions (8.05–16.02%) and spatial aggregation patterns over very rugged terrain. Special management strategies are needed to perform the transition of pure coniferous plantations into more structurally balanced coniferous-broadleaved mixed woodland in subtropical China.

Coastal wetland classification with multiseasonal high-spatial resolution satellite imagery

ABSTRACTAccurate mapping of wetland distribution is required for wetland conservation, management, and restoration, but remains a challenge due to the complexity of wetland landscapes. This research employed four seasons of multispectral images from Gaofen-1 satellite to map wetland land-cover distribution in Hangzhou bay coastal wetland (245 km2) in China. Maximum likelihood classifier (MLC), random forest (RF), and the expert-based approach were examined based on spectral, spatial, and phenological features. The results showed that land-cover classification accuracies of 83.9% using RF and 90.3% using the expert-based approach were obtained, and they had higher accuracy than MLC, which had an overall accuracy of only 63.3%. The high classification accuracy for nine land-cover classes using the expert-based approach indicated the important role of expert knowledge from the phenological features in improving wetland classification accuracy. As high spatial resolution satellite images become more easily obtainable, effective use of temporal information of different sensor data will be valuable for detailed land-cover classification with higher accuracy. The approach to establish expert rules from multitemporal images provides a new way to improve land-cover classification in different terrestrial ecosystems.

Reflectance reference target at Järvselja, Estonia for the calibration of optical remote sensing sensors and lessons learned

A calibration target for the support of airborne and satellite measurements was built at the Järvselja site for ecological and remote sensing studies located in southeastern Estonia. The calibration target is a 10 × 10 m concrete panel which is protected by a removable roof. Optical properties of the panel are carefully studied in order to serve as a reference in spectroscopic remote sensing measurements. In this study we report the spectral distribution of reflectance in the wavelength range of 350–2500 nm, the angular distribution of directional reflectance and the linear polarization of the reflected radiation. The reflectance spectrum of the panel is smooth in the visible and NIR wavelengths but with steep decrease at wavelengths less than 400 nm. Variations of reflectance over the panel surface are less than 1%. The radiation is partly polarized in forward scattering direction. No temporal changes of optical properties have been observed. The calibration target improves the metrological quality of airborne spectral measurements at the Järvselja test site and neighboring areas. It can also be used for ground truth to aid the validation of adjacency correction algorithms for data from high spatial resolution satellite images, as well as a reference for the registration of airborne and satellite images. It can also serve as an elevation reference for both lidar and radar altimeter measurements.

Decadal biomass and area changes in a multi-species meadow in Singapore: application of multi-resolution satellite imagery

AbstractDue to the dearth of information regarding current and changing health of seagrass habitat in the Indo-Pacific region, prior research into global trends of seagrass habitat health has included little data from this region, even though it contains the highest abundance and species diversity of seagrass globally. This study evaluates the suitability of four satellite sensors [Worldview-2 (WV2), Advanced Land Imager (ALI), Enhanced Thematic Mapper+ (ETM+), Operational Land Imager (OLI)] for determining trends in seagrass habitat extent over the past decade in Singapore’s largest seagrass meadow, and thus contributes incrementally to the data available for regional or global analyses of seagrass habitat health. Using all four sensors, we find that seagrass bed extent at Pulau Semakau, Singapore, declined 37% from 2001 to 2015 at an average rate of 3.9% year−1. Using very high spatial resolution satellite images, we calculate that, although bed extent decreased 18% from April 2011 to June 2013, median meadow biomass increased, indicating that complex meadow dynamics may be mediating seagrass response to anthropogenic and environmental pressures. From a technological perspective, we find that, despite their lower spatial resolution, freely available satellite images can be used to measure the extent of a narrow, multi-species seagrass bed and to determine decadal trends reliably.

Functions for aboveground biomass estimation derived from satellite images data in Mediterranean agroforestry systems

Forest biomass has been having an increasing importance in the world economy and in the evaluation of the forests development and monitoring. The main goal of this study is the development of functions for the estimation of aboveground biomass, using crown cover as independent variable, for Quercus rotundifolia, Quercus suber and Pinus pinea in agroforestry systems, both for monospecies and multispecies stands, based on Portuguese data. Crown cover per specie was derived from crown horizontal projection obtained by processing very high spatial resolution satellite images (Quickbird and Worldview-2), with contrast split segmentation method and object-oriented classification. The stand species composition distinguished species and monospecies from multispecies stands. The best model was the one with crown cover and dummy variables for composition as explanatory variables, reflecting the differences between species and stand structure. Aboveground biomass with this function should ideally be calculated with the grid areas applied in this study, though similar accuracies can be obtained for other grid sizes.

Calibrating a hydrological model in a regional river of the Qinghai–Tibet plateau using river water width determined from high spatial resolution satellite images

The potential for estimating streamflow in large continental rivers from satellite observations of river hydraulic variables has been well recognized. However this has not been widely applied to smaller regional rivers. In this study, we investigated the reliability of calibrating a hydrological model for the upper Yalongjiang River Basin on the Qinghai–Tibet Plateau, based solely on river water surface widths derived from four high spatial resolution commercial satellite images: two from QuickBird and one each from IKONOS and WorldView-1. The model was forced using satellite-based meteorological datasets and calibrated using remotely sensed river widths. In situ gauged data are not used for modeling, making the approach applicable for ungauged basins. To shift the calibration objective from streamflow to river width, the hydrological model is connected to the at-a-station hydraulic geometry (AHG) relationship. Generalized likelihood uncertainty estimation was used for automatic calibration and to better quantify simulation uncertainty. After constraining the model parameter space solely to the river width observations, the Nash–Sutcliffe Efficiency values for the streamflow simulated using the calibrated model reached 64.3% in the calibration period and 55.8% in the validation period. When calibrating the model using river width datasets with designed measurement error associated with riparian canopy, the influences of error on model simulation were less than expected. This was because the effect of the error was counteracted by adjustment of the AHG parameters. Both superimposing updated ranges for the AHG parameters and further constraining the model output using hydrological signature information following calibration reduced simulation uncertainty, particularly with use of the latter method. This study demonstrates the potential for application of the proposed calibration method to regional ungauged basins lacking ground streamflow observation data, and provides insights into how remote sensing data can be more effectively integrated with hydrological modeling.

When Socio-Economic Plans Exacerbate Vulnerability to Physical Coastal Processes on the South East Coast of India

Industrial and urban development along the coast may exacerbate the changes induced by oceano-climatic processes to such an extent that the coast becomes uninhabitable. This paper presents a baseline study carried out on the 360 km long Coromandel coast of Tamil Nadu's (TN) Bay of Bengal coast, Southeast India. About 30% of the 72 million inhabitants of the state of TN live in low-lying coastal areas within a variety of ecosystems such as sandy beaches and dunes, wetlands, mudflats and mangroves. Exponential demographic growth combines with dense urban agglomerations, ever-growing industrial and harbour areas, aquaculture and luxury tourism expansion increasing vulnerabilities to coastal erosion and extreme meteorological events. We report here how very high spatial resolution satellite (VHSRS) images, freely provided by Google Earth engine or from the public database offered by GIS software, may be used to impartially and significantly survey coastal land cover (LC) transformations at fine scales over a number of years using preliminary visual interpretation. The study also gathered demographic data, historical records on strength and trajectories of the cyclones that have hit the TN coast since 1900, and decadal series of daily ERA-InterimiLCMWF waves and Mercator-Ocean oceanic currents. Our results demonstrate a tangle of environmental and human activities and situations subject to specific coastal hazards, particularly severe during the Northeast monsoon season. The potential risks for coastal ecosystems and local communities are finally discussed.

FULLY CONVOLUTIONAL NETWORK BASED SHADOW EXTRACTION FROM GF-2 IMAGERY

Abstract. There are many shadows on the high spatial resolution satellite images, especially in the urban areas. Although shadows on imagery severely affect the information extraction of land cover or land use, they provide auxiliary information for building extraction which is hard to achieve a satisfactory accuracy through image classification itself. This paper focused on the method of building shadow extraction by designing a fully convolutional network and training samples collected from GF-2 satellite imagery in the urban region of Changchun city. By means of spatial filtering and calculation of adjacent relationship along the sunlight direction, the small patches from vegetation or bridges have been eliminated from the preliminary extracted shadows. Finally, the building shadows were separated. The extracted building shadow information from the proposed method in this paper was compared with the results from the traditional object-oriented supervised classification algorihtms. It showed that the deep learning network approach can improve the accuracy to a large extent.

Land-use scene classification based on a CNN using a constrained extreme learning machine

ABSTRACTClassifying land-use scenes from high-resolution remote-sensing imagery with high quality and accuracy is of paramount interest for science and land management applications. In this article, we proposed a new model for land-use scene classification by integrating the recent success of convolutional neural network (CNN) and constrained extreme learning machine (CELM). In the model, the fully connected layers of a pretrained CNN have been removed. Then, CNN works as a deep and robust convolutional feature extractor. After normalization, deep convolutional features are fed to the CELM classifier. To analyse the performance, the proposed method has been evaluated on two challenging high-resolution data sets: (1) the aerial image data set consisting of 30 different aerial scene categories with sub-metre resolution and (2) a Sydney data set that is a large high spatial resolution satellite image. Experimental results show that the CNN-CELM model improves the generalization ability and reduces the training time compared to state-of-the-art methods.

Presenting an Object-Based Approach Using Image Edges to Detect Building Boundaries from High Spatial Resolution Images

Although various building detection algorithms from high spatial resolution satellite (HSRS) images have been presented in recent years, there are yet some difficulties to detect building boundaries for mapping purposes. The present study aims to propose a new approach to detect building boundaries from HSRS images by focusing on higher detection rates. The approach utilizes the idea of object-based image processing. However, it has an innovative vision using image edges instead of traditional image segments as objects. To evaluate the efficiency of the proposed approach, two datasets which have different contrast between building and non-building areas, are used: the first dataset has a high contrast between building and non-building areas (HC) and second has a low contrast (LC). The results are compared with the results of two segmentation-based algorithms, i.e., classification based on edge-based segmentation (CBES) and classification based on multi-resolution segmentation (CBMS). The comparisons indicate higher efficiency of the proposed approach for the HC dataset with 6–14% higher detection rate (lower omission error) than the two segmentation-based algorithms. For the LC dataset, the proposed approach is already more efficient than CBMS with 10–25% higher detection rate. However, it has lower efficiency than CBES with 15–36% higher omission errors. Though, the proposed approach is generally more robust than CBES and CBMS algorithms based on standard deviation values of evaluation metrics.

Above-ground biomass estimation for Quercus rotundifolia using vegetation indices derived from high spatial resolution satellite images

ABSTRACTThe estimation of vegetation parameters, such as above-ground biomass, with high accuracy using remote sensing data, represents a promising approach. The present study develops models to estimate and map above-ground biomass of Mediterranean Quercus rotundifolia stands using one QuickBird satellite image in pan-sharpened mode, with four multispectral bands (blue, green, red and near infrared) and a spatial resolution of 0.70 m. The satellite image was orthorectified, geometrically and radiometrically corrected. Object-oriented classification methods and multi-resolution segmentation were used to derive a vegetation mask per forest species. Data from forest inventory (24 plots) and vegetation indices (NDVI, EVI, SR and SAVI) derived from high spatial resolution satellite images were used for an area of 133 km2, in southern Portugal. The statistical analysis included correlation, variance analysis and linear regression. The linear regression models fitted included the arithmetic mean and the median values of the vegetation indices per inventory plot as explanatory variables. The overall results of the fitted models show a trend of better performance for those with the median value of the vegetation index as the explanatory variable. The best fitted model (R2 = 75.3) was associated with the Simple Ratio (SR) median value as an explanatory variable. A Quercus rotundifolia above-ground biomass map was produced.

Monitoring the Change in Urban Vegetation in 13 Chilean Cities Located in a Rainfall Gradient. What is the Contribution of the Widespread Creation of New Urban Parks?

The well-being of people living in cities is strongly dependent on the existence of urban vegetation because of the ecosystem services or benefits it provides. This is why governments develop plans to create green spaces, plant trees, promote the maintenance of vegetation in private spaces and also monitor their status over time. In Latin America, and particularly in Chile, the increase of urban vegetation has been stimulated through different initiatives and regulations. However, development of monitoring programs at the national level is scarce, so it is yet unknown if these initiatives and regulations have had positive effects. In this article, we monitor the change in urban vegetation in 13 Chilean cities located in a latitudinal gradient of practically zero to almost 1800 mm of annual rainfall. We calculated the trends in NDVI (2000-2016) as an indicator of change in urban greenery using data from the MODIS Subsets platform. Likewise, to assess whether the initiatives have had an effect we quantified the number of urban parks existing at the beginning of the period and how many were created during the study period. For this, we analysed official databases and high spatial resolution satellite images. Armed with said data, we assessed whether these new parks had impacted the tendency toward change in urban greenery. The results indicate that, in general, Chilean cities vary greatly inter-annually in urban greenery and have lost urban vegetation in the last 16 years, with significant losses in four of those cities. Two cities located in desert ecosystems represent an exception and showed positive trends in their urban vegetation. The rainfall in cities has an impact on the amount of vegetation, but not on their tendency to change, i.e. there are cities with loss of vegetation at all levels of precipitation. The creation of parks has not been able to reverse negative trends, which indicates the prevalence of other drivers of change that are not sufficiently compensated by initiatives and regulations that seek to increase urban vegetation. Today, planning and management of urban vegetation is a challenge for urban sustainability and must be addressed systematically, integrally and implemented via urban regulations. It is imperative to focus on cities in extenso, taking into consideration residential areas, private spaces, peri-urban areas, etc. Likewise, climate in each city, inter-annual variability and future changes must also be considered when designing green areas to make them resilient, prevent increases in maintenance costs and provide benefits for the inhabitants in perpetuity.

Estimation and dynamics of above ground biomass with very high resolution satellite images in Pinus pinaster stands

Biomass estimation is a tool for evaluating stands and forest dynamics. Traditional indirect methods use forest inventories and allometric functions at tree level to evaluate biomass at plot level, and an extrapolation method to assess an area. The goal of this study was the development of allometric functions for Pinus pinaster with crown horizontal projection derived from very high spatial resolution satellite images as an independent variable, as well as their application to the analysis of above ground biomass dynamics. The fitted functions show a good performance. The function used to estimate the above ground biomass per grid in 2004, 2007 and 2011 for the study area enable the evaluation of their temporal dynamics. From 2004 to 2007 it decreased in 90.5% of the study area, due to forest fires and cuts to control the pinewood nematode; from 2007 to 2011 increased in 45.6% and decreased in 51.6%, the latter corresponding to cuts to control the aforementioned disease. In 76.4% of the burnt areas, natural regeneration resulted in an increase of above ground biomass. The method's main advantages are the simultaneous evaluation of small or large areas and, when implemented in a GIS, it allows straightforward monitoring over a short period of time.

Multiobjective Genetic Optimization of Terrain-Independent RFMs for VHSR Satellite Images

Rational polynomial coefficients (RPCs) biases and over-fitting phenomenon are two major issues in terrain-independent rational function models. These problems degrade the accuracy of extracted spatial information from very high spatial resolution (VHSR) satellite images. This study particularly focused on overcoming the over-fitting problem through an optimal term selection approach. To this end, multiobjective genetic algorithm was used in order to optimize three effective objective functions: the RMSE of ground control points (GCPs), the number, and the distribution of both RPCs and GCPs. Finally, the technique for order of preference by similarity to ideal solution, as an efficient multicriteria decision-making method, was applied to select the best solution, i.e., the optimum terms of RPCs, through the ranking of solutions in the optimum set. The performance of the proposed method was evaluated by using three VHSR images acquired by GeoEye-1, Worldview-3, and Pleiades satellite sensors. Experimental results show that subpixel accuracy can be nearly achieved in all data sets, when over-fitting problem is addressed. The optimal selected terms leaded to a significant improvement compared to the original RPCs. Indeed, our method, which is independent of GCPs distribution, not only requires a small number of GCPs, but also leads to a 30% to 75% improvement when compared to the original RPCs. This improvement in VHSR images, usually makes no more need to remove the RPCs biases.

Estimation and Validation of Actual Evapotranspiration for Wheat Crop Using SEBAL Model over Hisar District, Haryana, India

Evapotranspiration (ET) is one of the complex, but essential components of the hydrologic cycle. Advances in remote sensing (RS) and geographical information systems (GIS) have enabled us to estimate ET spatially. In the present study, both, RS and GIS tools have been utilized to estimate the actual crop ET by surface energy balance algorithm for land (SEBAL) model using high spatial resolution satellite image Landsat7 ETM+ for Hisar district, Haryana in north India. Previously calibrated and validated SEBAL model with lysimeter data within the same agroclimatic zone were used in the study. Derived actual ET from lysimeter data validated SEBAL method was again validated using Penman-Montieth (PM) method for the study area located in the same agro-climatic zone. Based on the primary and secondary data analysis, it can be inferred that SEBAL ET is the best spatial ET estimation model for Hisar district or regions having similar agro-climatic conditions. Validation of SEBAL ET with ground-observed lysimeter data showed high coefficient of correlation (R 2 = 0.91). Validation using the PM method also showed high coefficient of correlation (R 2 = 0.835). Other statistical parameters (RMSE = 0.583, NRMSE = 0.236) also showed good agreement between actual SEBAL ET c and PM ET c (crop evapotranspiration). It was also found that any prior knowledge about the crops, their types and cropping seasons is not required for the estimation of actual ET by SEBAL model.

Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia

Revegetation of abandoned aquaculture regions should be a priority for any integrated coastal zone management (ICZM). This paper examines the potential of a matchless time series of 20 very high spatial resolution (VHSR) optical satellite images acquired for mapping trends in the evolution of mangrove forests from 2001 to 2015 in an estuary fragmented into aquaculture ponds. Evolution of mangrove extent was quantified through robust multitemporal analysis based on supervised image classification. Results indicated that mangroves are expanding inside and outside ponds and over pond dykes. However, the yearly expansion rate of vegetation cover greatly varied between replanted ponds. Ground truthing showed that only Rhizophora species had been planted, whereas natural mangroves consist of Avicennia and Sonneratia species. In addition, the dense Rhizophora plantations present very low regeneration capabilities compared with natural mangroves. Time series of VHSR images provide comprehensive and intuitive level of information for the support of ICZM.