Search

PlanetScope satellite data with a 3-m resolution and near-daily global coverage have been increasingly used for land surface monitoring, ranging from land cover change detection to vegetative biophysics characterization and ecological assessments. Similar to other satellite data, effective screening of clouds and cloud shadows in PlanetScope images is a prerequisite for these applications, yet remains challenging as PlanetScope has 1) fewer spectral bands than other satellites hindering the use of traditional methods, and 2) inconsistent radiometric calibration across satellite sensors making the cloud/shadow detection using fixed thresholds unrealistic. To address these challenges, we developed a SpatioTemporal Integration approach for Automatic Cloud and Shadow Screening ('STI-ACSS'), including two steps: (1) generating initial masks of clouds/shadows by integrating both spatial (i.e. cloud/shadow indices of an individual PlanetScope image) and temporal (i.e. reflectance outliers in PlanetScope image time series) information with an adaptive threshold approach; (2) a two-step fine-tuning on these initial masks to derive final masks by integrating morphological processing with an object-based cloud and cloud shadow matching. We tested STI-ACSS at six tropical sites representative of different land cover types (e.g. forest, urban, cropland, savannah, and shrubland). For each site, we evaluated the performance of STI-ACSS with reference to the manual masks of clouds/shadows, and compared it with four state-of-the-art methods, namely Function of mask (Fmask), Automatic Time-Series Analysis (ATSA), Iterative Haze Optimized Transformation (IHOT) and the default PlanetScope quality control layer. Our results show that, across all sites, STI-ACSS 1) has the highest average overall accuracy (98.03%), 2) generates an average producer accuracy of 95.53% for clouds and 89.48% for cloud shadows, and 3) is robust across sites and seasons. These results suggest the effectiveness of using STI-ACSS for cloud/shadow detection for PlanetScope satellites in the tropics, with potential to be extended to other satellite sensors with limited spectral bands. • An automatic cloud and cloud shadow screening method was developed for PlanetScope. • The method was rigorously evaluated across diverse land cover types in the tropics. • The method was compared with the other four state-of-the-art methods. • Our method has the highest accuracy and is insensitive to land cover types. [ABSTRACT FROM AUTHOR]

This study compared the suitability of LIDAR (LIght Detection And Ranging) data, three-band multispectral data, and LIDAR data integrated with multispectral information, for classifying spatially complex vegetation in the Aspen Parkland of western Canada. Classifications were performed for both a) general vegetation classes limited to three major formations of deciduous forest, shrubland and grassland, and b) eight detailed vegetation classes including upland mixed prairie and fescue grasslands, closed and semi-open aspen forests, western snowberry and silverberry shrublands, and fresh and saline riparian (lowland) meadows. A Digital Elevation Model (DEM) and Surface Elevation Model (SEM) developed from LIDAR data incorporated both topographic and biological biases in community positioning across the landscape. Using multispectral data, the original digital image mosaic, its hybrid color composite, and an intensity–hue–saturation (IHS) image were each tested. Final vegetation classification was done through integration of information from both digital images and LIDAR data to evaluate the improvement in classification accuracy. Among the land cover schedules with three and eight classes of vegetation, classification from the multispectral imagery, specifically the hybrid color composite image, had the highest accuracy, peaking at 74.6% and 59.4%, respectively. In contrast, the LIDAR classification schedules led to an average classification accuracy of 64.8% and 52.3%, respectively, for the general and detailed vegetation data. Subsequent integration of the LIDAR and digital image classification schedules resulted in accuracy improvements of 16 to 20%, resulting in a superior final accuracy of 91% and 80.3%, respectively, for the three and eight classes of vegetation. A final land cover map including 8 classes of vegetation, fresh and saline water, as well as bare ground, was created for the study area with an overall accuracy of 83.9%, highlighting the benefit of integrating LIDAR and multispectral imagery for enhanced vegetation classification in heterogenous rangeland environments. [Copyright &y& Elsevier]

Small islands face environmental issues directly or indirectly related to land cover/land use changes (LCLUC), such as natural hazards, climate change, loss of biodiversity and proliferation of invasive alien species, some of which are caused by direct human exploitation. A Land Cover/Land Use Change (LCLUC) detection approach based on PCA and vegetation indices derived from low cost high-resolution RapidEye multispectral satellite data and available vegetation maps was developed to assess vegetated/forested aboveground carbon stocks and their changes in Madeira Island, Portugal, for the period between December 2009 and August 2011 due to catastrophic events occurred in 2010. During this period, the identified LCLUC revealed a relevant decrease of vegetated areas (especially those dominated by native/endemic communities) substituted by increases of non-vegetated and human-managed vegetated/forested areas. In particular, there was a decrease of 2% of vegetated areas, 30% of which were represented by native/endemic vegetation. The largest and most accurate LCLUC detected were used to estimate changes in aboveground biomass carbon (AGC) stocks. In 2010 more than 25,000 Mg of AGC stocks may have been released. Both relevant LCLUC and AGC stocks depletion in such short period of time may have been strongly enhanced by two catastrophic events that affected Madeira in February (flashflood and landslides) and August 2010 (wildfires). This straightforward and cost-effective methodological approach may be successfully applied in remote territories such as islands or mountainous areas, where the logistic and economic costs associated to periodic and standard airborne remote sensing campaigns for mapping, assessing and monitoring aboveground biomass carbon stocks are generally unaffordable for most decision-makers and land managers in these territories. • RapidEye spectral-, thematic- and vegetation condition-based change detection • Methodology for carbon accounting in remote and impervious territories • 20,000 Mg C stock depletion in 2010 in Madeira Island, Portugal • 2010 wildfires and landslides contributed to C depletion in Madeira. • We address Sustainable Development Goals 13 and 15, namely targets 15.1, 15.5 and 15.8. [ABSTRACT FROM AUTHOR]

Over the last 20 years, substantial amounts of grassland have been converted to other land uses in the Northern Great Plains. Most of land cover/land use (LCLU) assessments in this region have been based on the U.S. Department of Agriculture - Cropland Data Layer (USDA - CDL), which may be inconsistent. Here, we demonstrate an approach to map land cover utilizing multi-temporal Earth Observation data from Landsat and MODIS. We first built an annual time series of accumulated growing degree-days (AGDD) from MODIS 8-day composites of land surface temperatures. Using the Enhanced Vegetation Index (EVI) derived from Landsat Collection 1's surface reflectance, we then fit at each pixel a downward convex quadratic model to each year's progression of AGDD (i.e. , EVI = α + β × AGDD − γ × AGDD2). Phenological metrics derived from fitted model and the goodness of fit then are submitted to a random forest classifier (RFC) to characterize LCLU for four sample counties in South Dakota in three years (2006, 2012, 2014) when reference point datasets are available for training and validation. To examine the sensitivity of the RFC to sample size and design, we performed classifications under different sample selection scenarios. The results indicate that our proposed method accurately mapped major crops in the study area but showed limited accuracy for non-vegetated land covers. Although all RFC models exhibit high accuracy, estimated land cover areas from alternative models could vary widely, suggesting the need for a careful examination of model stability in any future land cover supervised classification study. Among all sampling designs, the "same distribution" models (proportional distribution of the sample is like proportional distribution of the population) tend to yield best land cover prediction. RFC used only the most eight important variables (e.g. , three fitted parameter coefficients [α, β, and γ]; maximum modeled EVI; AGDD at maximum modeled EVI; the number of observations used to fit CxQ model; and the number of valid observations) have slightly higher accuracy compared to those using all variables. By summarizing annual image time series through land surface phenology modeling, LCLU classification can embrace both seasonality and interannual variability, thereby increasing the accuracy of LCLU change detection. • Land surface phenology (LSP) modeled using Landsat EVI and MODIS LST • Land cover from random forest classification (RFC) using only phenometrics as input • Combinations of reference data augmented by CDL used for training/validation • LSP-RFC accurately identified major crops compared to the Cropland Data Layer (CDL). • Caveat: alternative RFC training designs yielded widely varying area estimates. [ABSTRACT FROM AUTHOR]

Land use and land cover maps provide fundamental information that has been used in different kinds of studies, ranging from climate change to city planning. However, despite substantial efforts in recent decades, large-scale 30-m land cover maps still suffer from relatively low accuracy in terms of land cover type discrimination (especially for the vegetation and impervious types), due to limits in relation to the data, method, and design of the workflow. In this work, we improved the land cover classification accuracy by integrating free and public high-resolution Google Earth images (HR-GEI) with Landsat Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+) imagery. Our major innovation is a hybrid approach that includes three major components: (1) a deep convolutional neural network (CNN)-based classifier that extracts high-resolution features from Google Earth imagery; (2) traditional machine learning classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)) that are based on spectral features extracted from 30-m Landsat data; and (3) an ensemble decision maker that takes all different features into account. Experimental results show that our proposed method achieves a classification accuracy of 84.40% on the entire validation dataset in China, improving the previous state-of-the-art accuracies obtained by RF and SVM by 4.50% and 4.20%, respectively. Moreover, our proposed method reduces misclassifications between certain vegetation types, and improves identification of the impervious type. Evaluation applied over an area of around 14,000 km2 confirms little improvement for land cover types (e.g., forest) of which the classification accuracies are already over 80% when using traditional machine learning approaches, yet improvements in accuracy of 7% for cropland and shrubland, 9% for grassland, 23% for impervious and 25% for wetlands were achieved when compared with traditional machine learning approaches. The results demonstrate the great potential of integrating features of datasets at different resolutions and the possibility to produce more reliable land cover maps. • Combining Google Earth images and Landsat data for land cover mapping • Fusing high-resolution spatial features and medium-resolution spectral features • Improving the previous highest OA from 80% to 84% on all samples in China • Reducing confusions among different vegetation and impervious types • Validating the method with 5 selected regions with a total area of about 14,000 km2 [ABSTRACT FROM AUTHOR]