Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery

Remote Sensing, 2012, 4(4), published online 27 March 2012

Jindong Wu and Marvin E. Bauer

“Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data.

QuickBird false color images before shadows were removed (left) and after spectral information was restored for shadow pixels (right).

QuickBird false color images before shadows were removed (left) and after
spectral information was restored for shadow pixels (right).

“A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0–1,301 g∙C∙m−2∙yr−1 depending on vegetative conditions and management intensity. The average annual net primary production per unit turfgrass cover by golf course grass (1,100.5 g∙C∙m−2) was much higher than that by regular lawn grass (771.2 g∙C∙m−2). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability.”

Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs

Remote Sensing, 2012, 4(4), 911-933; published online 29 March 2012

Junichi Susaki

“Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough textures, digital numbers (DNs) are quantized into several quantum values. Quantization using several interval widths is applied during segmentation, and for each quantization, areas with homogeneous values are labeled in an image.

Edge completion using filters: (a) non-completed edges, (b) segmentation result using non-completed edges, (c) completed edges, and (d) segmentation result using completed edges. All results were generated with Δdi = 40.

Edge completion using filters: (a) non-completed edges, (b) segmentation result using non-completed edges, (c) completed edges, and (d) segmentation result using completed edges. All results were generated with Δdi = 40.

“Edges determined from the homogeneous areas obtained at each quantization are then merged, and frequently observed edges are extracted. By using a “rectangular index”, regions whose shapes are close to being rectangular are thus selected as buildings. Experimental results show that the proposed algorithm generates more practical segmentation results than an existing algorithm does. Therefore, the main factors in successful segmentation of shadowed roofs are (1) combination of different quantization results, (2) selection of buildings according to the rectangular index, and (3) edge completion by the inclusion of non-edge pixels that have a high probability of being edges. By utilizing these factors, the proposed algorithm optimizes the spatial filtering scale with respect to the size of building roofs in a locality. The proposed algorithm is considered to be useful for conducting building segmentation for various purposes.”

Spatial Analysis of Remote Sensing Image Classification Accuracy

Remote Sensing of EnvironmentRemote Sensing of Environment, Volume 127, December 2012, Pages 237–246

Alexis Comber, Peter Fisher, Chris Brunsdon, and Abdulhakim Khmag

“Highlights

  • The confusion matrix provides no information on the spatial distribution of errors.
  • The spatial distribution of correspondence provides richer accuracy information.
  • Geographically weighted models were used to map Boolean and Fuzzy accuracy.
  • This is a methodological advance in accuracy assessment in remote sensing.

“The error matrix is the most common way of expressing the accuracy of remote sensing image classifications, such as land cover. However, it and the measures that can be calculated from it have been criticised for not providing any indication of the spatial distribution of errors. Other research has identified the need for methods to analyse the spatial non-stationarity of error and to visualise the spatial variation in classification uncertainty. This research uses geographically weighted approaches to model the spatial variations in the accuracy of both (crisp) Boolean and (soft) fuzzy land cover classes. Remotely sensed data were classified using a maximum likelihood classifier and a fuzzy classifier to predict Boolean and fuzzy land cover classes respectively. Field data were collected at sub-pixel locations and used to generate soft and crisp validation data. A Geographically Weighted Regression was used to analyse spatial variations in the relationships between observations of Boolean land cover in the field and land cover classified from remote sensing imagery. A geographically weighted difference measure was used to analyse spatial variations in fuzzy land cover accuracy. Maps of the spatial distribution of accuracy were created for fuzzy and Boolean classes. This research demonstrates that data collected as part of a standard remote sensing validation exercise can be used to estimate mapped, spatial distributions of accuracy that would augment standard accuracy measures reported in the error matrix. It suggests that geographically weighted approaches, and the spatially explicit representations of accuracy they support, offer the opportunity to report land cover accuracy in a more informative way.”

An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning

Remote Sensing, 2012, 4(4), 950-974; published online 30 March 2012

Harri Kaartinen , Juha Hyyppä , Xiaowei Yu , Mikko Vastaranta , Hannu Hyyppä , Antero Kukko , Markus Holopainen , Christian Heipke , Manuela Hirschmugl , Felix Morsdorf , Erik Næsset , Juho Pitkänen , Sorin Popescu , Svein Solberg , Bernd Michael Wolf, and Jee-Cheng Wu
Article:

“The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test.

Site A (Left) and Site B (Right), tree heights shown as color-coded canopy height model (CHM).

Site A (Left) and Site B (Right), tree heights shown as color-coded canopy
height model (CHM).

“Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future.”

Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data

Remote Sensing, 2012, 4(4), 810-829

Sandra Eckert

“Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression.

Overview and zoom map of the study area.

Overview and zoom map of the study area.

“Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information.”

Using MODIS-NDVI for the Modeling of Post-Wildfire Vegetation Response as a Function of Environmental Conditions and Pre-Fire Restoration Treatments

Remote Sensing, 2012, 4(3), 598-621

Jose Raul Romo Leon, Willem J.D. van Leeuwen, and Grant M. Casady

“Post-fire vegetation response is influenced by the interaction of natural and anthropogenic factors such as topography, climate, vegetation type and restoration practices. Previous research has analyzed the relationship of some of these factors to vegetation response, but few have taken into account the effects of pre-fire restoration practices. We selected three wildfires that occurred in Bandelier National Monument (New Mexico, USA) between 1999 and 2007 and three adjacent unburned control areas. We used interannual trends in the Normalized Difference Vegetation Index (NDVI) time series data derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) to assess vegetation response, which we define as the average potential photosynthetic activity through the summer monsoon. Topography, fire severity and restoration treatment were obtained and used to explain post-fire vegetation response.

Monsoon trend for each pixel within the fire and the reference areas were computed.

Monsoon trend for each pixel within the fire and the reference areas were computed. For Frijoles Canyon (A) the trend is calculated from 2008 to 2010, for Mid Elevation Mesas (B) from 2000 to 2010, and for Capulin (C) from 2006 to 2010.

“We applied parametric (Multiple Linear Regressions-MLR) and non-parametric tests (Classification and Regression Trees-CART) to analyze effects of fire severity, terrain and pre-fire restoration treatments (variable used in CART) on post-fire vegetation response. MLR results showed strong relationships between vegetation response and environmental factors (p < 0.1), however the explanatory factors changed among treatments. CART results showed that beside fire severity and topography, pre-fire treatments strongly impact post-fire vegetation response. Results for these three fires show that pre-fire restoration conditions along with local environmental factors constitute key processes that modify post-fire vegetation response.”

Aerial Survey and Spatial Analysis of Sources of Light Pollution in Berlin, Germany

Remote Sensing of EnvironmentRemote Sensing of Environment, Volume 126, November 2012, Pages 39–50

Helga U. Kuechly, Christopher C.M. Kyba, Thomas Ruhtz, Carsten Lindemann, Christian Wolter, Jürgen Fischer, and Franz Hölker

“Highlights

  • A 391 square kilometer urban light pollution map is produced with 1 m resolution.
  • Geospatial analysis of the map compares lighting to land use type.
  • Lighting associated with streets accounts for 1/3 of the total zenith uplight.
  • Land use types of differing areas are compared equivalently using mean brightness.
  • The utility of night aerial photography for light pollution studies is demonstrated.

“Aerial observations of light pollution can fill an important gap between ground based surveys and nighttime satellite data. Terrestrially bound surveys are labor intensive and are generally limited to a small spatial extent, and while existing satellite data cover the whole world, they are limited to coarse resolution. This paper describes the production of a high resolution (1 m) mosaic image of the city of Berlin, Germany at night. The dataset is spatially analyzed to identify the major sources of light pollution in the city based on urban land use data. An area-independent ‘brightness factor’ is introduced that allows direct comparison of the light emission from differently sized land use classes, and the percentage area with values above average brightness is calculated for each class. Using this methodology, lighting associated with streets has been found to be the dominant source of zenith directed light pollution (31.6%), although other land use classes have much higher average brightness. These results are compared with other urban light pollution quantification studies. The minimum resolution required for an analysis of this type is found to be near 10 m. Future applications of high resolution datasets such as this one could include: studies of the efficacy of light pollution mitigation measures, improved light pollution simulations, economic and energy use, the relationship between artificial light and ecological parameters (e.g. circadian rhythm, fitness, mate selection, species distributions, migration barriers and seasonal behavior), or the management of nightscapes. To encourage further scientific inquiry, the mosaic data is freely available at Pangaea: http://dx.doi.org/10.1594/PANGAEA.785492.”

Use of Satellite Data and GIS for Assessing the Agricultural Potentiality of the Soils South Farafra Oasis, Western Desert, Egypt

Arabian Journal of GeosciencesArabian Journal of Geosciences, Published Online 24 January 2012

Wael Ahmed Mohamed Abdel Kawy and Islam H. Abou El-Magd

“Overpopulation and food security are the main global problems alert decision makers. In developing countries, such problem put extra pressure for horizontal expansion for agricultural development. The rapid sprawl of urbanized areas on the alluvial land of the River Nile and delta to accommodate the population growth has encouraged governmental and private sector for agricultural expansion in the desert. Unless there are reliable information and accurate studies for land and soil suitability, there will be a collapse of such investment. To evaluate the potential suitability of soil for agriculture development in areas of the western desert, satellite images, geographic information, and field survey including soil profiles and artesian water samples with laboratory analysis were integrated to classify the soils according their suitability for specific crop. The main land qualities of the different mapping units and the crop requirement were rated and matched to obtain the current and potential land suitability using Automated Land Evaluation System “ALES”. The study found that the main physiographic units are plateaus, hilland, mountain, and depression floor. But there are three limiting parameters for land suitability which are the lack of nutrient elements, wind erosion vulnerability, and soil texture. The study concluded that the best crops adapted with the soil conditions and could be feasible for economic use are: (1) native vegetation such as agol, sand trees, sammar, halfaa, bawaal, qordaob, bardi, and qortom; (2) filed crops such as onion, garlic, watermelon and wheat; and (3) fruits such as olive and date palms.”

Esri and PCI Geomatics Imagery Grant Program to Support Natural Resources Management

Esri logoGIS and Imagery Software, Data, and Training to Be Awarded for Natural Resources Analysis Projects

Esri, PCI Geomatics, MDA, and RapidEye today announced their new Natural Resources Imagery Grant Program. The grant program will provide software, data, and training for detecting and analyzing land-cover change through the combined use of geographic information system (GIS), image processing, and remote-sensing technologies.

Designed to foster innovative approaches that solve natural resources management problems, the Natural Resources Imagery Grant Program will provide 20 grants valued at $100,000 each. The grant includes the following:

  • Esri GIS software and training
  • PCI Geomatics imagery processing and analysis software and training
  • MDA RADARSAT-2 synthetic aperture radar (SAR) imagery
  • RapidEye 5-meter multispectral imagery

“GIS and image processing are mission-critical technologies in natural resources management,” said Jack Dangermond, president, Esri. “This grant opportunity will help organizations expand their existing imagery or GIS infrastructure and more efficiently support sustainable land-use management.”

Companies, educational institutions, nongovernmental organizations (NGOs), state and regional governments, or tribal governments within the United States may apply. Eligible projects are those that focus on remotely sensed imagery beyond the visible spectrum. Preferred projects will also demonstrate increased efficiency, productivity, or accuracy.

“Technology leaders and innovators should be presented with opportunities to advance their resources projects,” said Terry Maloney, president and CEO, PCI Geomatics. “This imagery grant program will bring solutions to the natural resources industries through inventive and operational use of satellite imagery.”

Applications for the Natural Resources Imagery Grant Program will be accepted beginning in September 2012 and ending November 16, 2012. Learn more at esri.com/imagerygrant.

[Source: Esri press release]

Improved Support for Landsat Imagery in ArcGIS 10.1

Esri logoGIS Users Can Manipulate and Analyze Esri’s Landsat Imagery Services for Better Use with Geospatial Data

To assist scientists and land and resources managers in evaluating the earth’s changing landscape, Esri announced today that it has further improved support for Landsat imagery, including simplified workflows for ArcGIS 10.1 for Desktop and improvements in the World Landsat Services on ArcGIS Online. In addition, Esri and the Department of the Interior (DOI) worked closely to make all Landsat Global Land Survey (GLS) imagery, including the latest—GLS2010—available through dynamic, multispectral, multitemporal image services on ArcGIS Online.

“Technology barriers are coming down,” said Rachel Headley, PhD, Landsat project, United States Geological Survey (USGS) Earth Resources Observation and Science (EROS). “We are now enabling entirely new communities to share and enjoy the views of earth that Landsat has documented for more than four decades.”

Landsat 7, the current earth observation satellite, produces 30-meter-resolution, calibrated, multispectral imagery in 185 x 185-kilometer scenes. The imagery is free for use by everyone and has become a rich data resource for agriculture, forestry, natural resources exploration, and many other industries.

The existing Landsat image services were refined by adding the GLS 2010 dataset and improving the visual quality with radiometric enhancement. Ten services were added including the following:

  • A single service end point that combines 26 separate image services products
  • A service that returns tasseled cap transforms
  • A 15-meter panchromatic image
  • Services for better visualization such as a natural color combined with hillshading

“By combining Landsat imagery with a mashup of multiple data sources available through ArcGIS Online, such as bathymetric, world elevation services, and DeLorme datasets, as well as user-defined content, users can better understand the spatial relationship and interaction of ecosystems and urban development,” said Lawrie Jordan, Esri’s director of imagery. “ArcGIS allows people to analyze and use imagery for more than just an image backdrop to their GIS. It has become an integral part of their analysis of GIS data.”

Esri has also updated the easy-to-use web-based Landsat ChangeMatters viewer for visualizing, analyzing, and detecting change using these image services. For more information on Esri’s support for Landsat imagery, visit esri.com/landsat.

[Source: Esri press release]