Cover Type Information Research Articles

An Adaptive Change Threshold Selection Method Based on Land Cover Posterior Probability and Spatial Neighborhood Information

Change threshold selection (CTS) plays an important role in land cover change detection. The traditional CTS methods are mainly proposed by using the information contained in grayscale histogram distributions or pixel neighborhoods. However, land cover is highly spatially heterogeneous, and changes in different land cover types are characterized by different magnitudes. Unfortunately, few CTS studies have considered the effects of both land cover type and spatial heterogeneity on CTS, potentially leading to false alarms or missed alarms. To address this challenge, we propose an adaptive CTS method based on land cover posterior probability and spatial neighborhood information (LCSN). First, the posterior probability of the change magnitude in each land cover type is calculated according to a Bayesian criterion to integrate the land cover type information. Second, the posterior probability is calculated using a bilateral filtering method to construct the spatial surface based on the land cover type and spatial neighborhood information. Finally, the degree of difference between the spatial surface and the change magnitude map is taken as the final threshold. The proposed LCSN method is verified with Landsat 8-Operational Land Imager (OLI) images and IKONOS images. The experimental results show that the LCSN method is effective in reducing the pseudo changes and identifying changes in land cover types with low grayscale values in the corresponding change magnitude maps.

Fusion of Moderate Resolution Earth Observations for Operational Crop Type Mapping

Crop type inventory and within season estimates at moderate (<30 m) resolution have been elusive in many regions due to the lack of temporal frequency, clouds, and restrictive data policies. New opportunities exist from the operational fusion of Landsat-8 Operational Land Imager (OLI), Sentinel-2 (A & B), and Sentinel-1 (A & B) which provide more frequent open access observations now that these satellites are fully operating. The overarching goal of this research application was to compare Harmonized Landsat-8 Sentinel-2 (HLS), Sentinel-1 (S1), and combined radar and optical data in an operational, near-real-time (within 24 h) context. We evaluated the ability of these Earth observations (EO) across major crops in four case study regions in United States (US) production hot spots. Hindcast time series combinations of these EO were fed into random forest classifiers trained with crop cover type information from the Cropland Data Layer (CDL) and ancillary ground truth. The outcomes show HLS achieved high (>85%) accuracies and the ability to provide insight on crop location and extent within the crop season. HLS fused with S1 had, at times, a higher accuracy (5–10% relative overall accuracy and kappa increases) within season although the combination of fused data was minimal at times, crop dependent, and the accuracies tended to converge by harvest. In cloud prone regions and certain temporal periods, S1 performed well overall. The growth in the availability of time dense moderate resolution data streams and different sensitivities of optical and radar data provide a mechanism for within season crop mapping and area estimates that can help improve food security.

A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm

Abstract. The MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aboard the two Earth Observing System (EOS) satellites Terra and Aqua, provide aerosol information with nearly daily global coverage at moderate spatial resolution (10 and 3 km). Almost 15 years of aerosol data records are now available from MODIS that can be used for various climate and air-quality applications. However, the application of MODIS aerosol products for air-quality concerns is limited by a reduction in retrieval accuracy over urban surfaces. This is largely because the urban surface reflectance behaves differently than that assumed for natural surfaces. In this study, we address the inaccuracies produced by the MODIS Dark Target (MDT) algorithm aerosol optical depth (AOD) retrievals over urban areas and suggest improvements by modifying the surface reflectance scheme in the algorithm. By integrating MODIS Land Surface Reflectance and Land Cover Type information into the aerosol surface parameterization scheme for urban areas, much of the issues associated with the standard algorithm have been mitigated for our test region, the continental United States (CONUS). The new surface scheme takes into account the change in underlying surface type and is only applied for MODIS pixels with urban percentage (UP) larger than 20 %. Over the urban areas where the new scheme has been applied (UP > 20 %), the number of AOD retrievals falling within expected error (EE %) has increased by 20 %, and the strong positive bias against ground-based sun photometry has been eliminated. However, we note that the new retrieval introduces a small negative bias for AOD values less than 0.1 due to the ultra-sensitivity of the AOD retrieval to the surface parameterization under low atmospheric aerosol loadings. Global application of the new urban surface parameterization appears promising, but further research and analysis are required before global implementation.

Spatial scaling of net primary productivity using subpixel information

Spatial scaling is of particular importance in remote sensing applications to terrestrial ecosystems where spatial heterogeneity is the norm. Surface parameters derived at different resolutions can be considerably different even though they are derived using the same algorithms. This article addresses issues related to spatial scaling of net primary productivity (NPP). The main objective is to develop algorithms for spatial scaling of NPP using subpixel information. NPP calculations were performed using the Boreal Ecosystem Productivity Simulator (BEPS). The area of interest is near Fraserdale, Ontario, Canada. It is found from this investigation that lumped (coarse resolution) calculations can be considerably biased (by +14.9% on average) from the distributed (fine resolution) case. Based on these results, algorithms for removing these biases in lumped NPP are developed using subpixel land cover type information. The correlation between the distributed NPP and lumped NPP is improved from r 2=0.16 to r 2=0.59 after the correction. In addition, subpixel leaf area index (LAI) information is used to reduce the remaining biases. After the LAI correction, the correlation is further improved to r 2=0.90.

Conceptual ecological models for the Long Island pitch pine barrens: implications for managing rare plant communities

Fire dependent pitch pine ( Pinus rigida Mill.) and scrub oak ( Quercus ilicifolia Wang.) barrens are found on coarse-textured, droughty soils in the northeastern United States. These communities are globablly rare and, in many areas, dependent on active management to sustain them. We used historic and recent aerial photographs of the Central Pine Barrens in New York to develop transition matrices and trajectories of vegetation change. A vegetation map for 1990 provided pre-burn cover type information for a 1,200 ha area burned by an extremely severe wildfire in August 1995. Geographic information system analyses, a fire-history study, post-wildfire research and existing literature provided additional information for development of three conceptual models that describe our understanding of vegetation change in response to fire and land clearing. A simulation model was then used to predict the amount of each cover type in 2046. The reasonableness of the prediction was assessed based on our understanding of pine barrens vegetation dynamics. In 1938, following a period of frequent, intense wildfires of the region, 90% of vegetation in the study area was open canopy barrens (including dwarf pine plains, pitch pine–scrub oak woodland, heath, pine–heath woodland, and scrub oak shrubland). Between 1938 and 1994, wildfires decreased in size, the average area burned per year generally declined, and there were no fires in 70% of the study area. As a result, barrens decreased to ∼45% of study area vegetation. In August 1995, an unusually severe summer wildfire converted 810 ha of woodland and forest into scrub oak shrubland, and increased barrens to 60% of study area vegetation. Most areas of heath and pine–heath, which comprised 24% of barrens vegetation in 1996, are successional types that developed on formerly cleared land. In the absence of extensive burning, we expect barrens vegetation to be converted to closed-canopy forest as coppice trees and new seedlings mature. Recent changes in fire regimes are likely to result in a loss of barrens vegetation that exceeds predictions of the simulation model. Careful use of prescribed fire and/or mechanical treatments to simulate the effects of fire are recommended for future restoration and maintenance of pitch pine barrens, and for protection of human life and property.

A Shortwave Infrared Modification to the Simple Ratio for LAI Retrieval in Boreal Forests: An Image and Model Analysis

In preparation for new satellite sensors, such as VEGETATION on SPOT-4 and the MODerate Resolution Imaging Spectrometer (MODIS), we investigate the potential of the shortwave infrared (SWIR) signal to improve Leaf Area Index (LAI) retrieval in the boreal forests of Canada. Our study demonstrates that an empirical SWIR modification to the simple ratio (SR) vegetation index, termed the reduced simple ratio (RSR), has the potential to unify deciduous and conifer species in LAI retrieval, shows increased sensitivity to LAI, and demonstrates an improved correlation with LAI in individual jack pine and black spruce canopies. The unification of deciduous and conifer species suggests the possibility of not requiring a cover type stratification prior to retrieving LAI information from remotely sensed data, and has impact where no cover type information will be made or where the mix of cover types within a pixel is unknown. We use a geometric–optical canopy reflectance model to quantify the potential variation in jack pine and black spruce canopy reflectance caused by differences in background reflectance. The modeling study supports the results from the image analysis of the RSR showing increased sensitivity to LAI and reducing background effects in these conifer canopies.

Land cover from multiple thematic mapper scenes using a new enhancement‐classification methodology

The purpose of this paper is to test a methodology for extracting land cover information from high‐resolution satellite images over large areas. The use of multiple scenes brings additional complications which are related to atmospheric, phenological, spectral, and classification legend aspects of the data set. The approach is based on a supervised digital mosaicking of the input scenes and on a guided unsupervised classification of the mosaic. The goal of the development was to produce a methodology which is robust, minimally sensitive to the biases of the analyst, and capable of extracting all land cover type information contained in the satellite data. The mosaicking procedure aims to achieve radiometric consistency across the mosaic for key cover types. The classification procedure, named enhancement‐classification method (ECM), operates on three (or less) input channels and produces a classification in which virtually the entire relevant spectral content has been extracted. The above procedures were employed to produce a land cover classification of the BOREAS transect from six Landsat thematic mapper images. Initial assessment indicated that the classification accuracy of the mosaics varies with cover type (44–82% for forest cover types). The variability appears related to residual radiometric effects, lack of unique spectral land cover signatures, and vegetation phenology. Thus nonremote sensing (or multitemporal) information is likely to be required for consistent accuracies. While the two‐step procedure could be successfully automated under some conditions, the input of an analyst will remain essential until more experience is obtained, especially with classification across multiple scenes.

Wildlife habitat analysis for ‘sambar’ (Cervus unicolor) in Kanha National Park using remote sensing

Abstract In the present study habitat suitability analysis has been done in the census sectors of two ranges of Kanha National Park. Each sector was evaluated with respect to habitat parameters, viz., water, cover types (food and shelter values) and terrain. Cover type information was obtained from maps generated by visual interpretation of Landsat Thematic Mapper (TM) false colour composite on a 1:50 000 scale, and understorey information as supplemented from cover maps generated by using aerial photographs on a 1:10 000 scale and ground sampling. Waterbodies were taken from field records and terrain information from Survey of India toposheets. Four suitability ratings were assigned to each sector using habitat parameters. The results revealed that among 16 sectors in the Kisli range 3 sectors each were found to be in a very good, good and moderately suitable category and 7 sectors were rated under low suitability for sambar. In the Kanha range, out of 28 sectors, 12 were found to be very good, 6 were ‘g...