Multispectral Digital Imagery Research Articles

REMOTE SENSING OF URBAN DEVELOPMENT AND ENVIRONMENTAL LANDSCAPE: A STUDY OF GREEN COVER LUNGS OF DELHI AND SURROUNDING CITY REGIONS

Abstract. The remote sensing satellites have now a days been used for creation of geospatial databases of the earth surface features and natural resources as the land use land cover mapping and monitoring periodically at the local, regional, and global levels using geospatial technologies. The Land cover consider to the type of features present on the surface of the earth. Whereas, the land use considers to the human activity or the economic function associated with a specific piece of land. The supervised classification based on the maximum likelihood classifier technique has been applied to process the digital multi-spectral imagery for LULC classification. The built-up area was about 11.95 per cent in 1977 which increased to 19.74 per cent in 1999 and further increased to about 24.54 per cent in 2009. So, the urban landscape’s urban built-up land was composed by areas of intensive use with much of land covered by built-up structures. The cities, towns, villages, strip development along highways; transportation, power, and communication facilities; shopping centres, industrial and commercial complexes, and institutions found located in the urban areas have been developed over the periods. The forests cover area accounted to about 1.43 per cent in 1977 which increased to about 10.04 per cent in 1999. The forest cover regeneration over the Delhi Ridge recognized as a significant contributor to carbon sequestration, micro-climate change, biodiversity restoration for the Delhi’s urban development and rejuvenation of environmental landscape. So, the remote sensing imagery have commonly been used for urban landscape monitoring and environmental modelling for sustainable development of Delhi and adjoining city regions.

Assessment of Cork Oak Decline Using Digital Multispectral Imagery in Relation with in Situ Crown Condition

Cork oak in Maamora forest is experiencing the dieback phenomenon. The evaluation of the latter in this forest has gained the importance over time and with the solicitation of managers to objectify its phytosanitary situation. Aiming at prioritizing management actions, remote sensing seems to be an effective tool to inquire about stands’ health conditions and their evolution. To this end, this study aims at mapping and validating health status of cork oak stands in Maamora. Sentinel 2 images in 2015 and 2020 were processed to calculate the differential normalized difference water index (NDWI), revealing vegetation moisture variation caused by drought. A statistical method based on thresholds was used to map cork oak dieback stands, those with no changes and those recovered. Results have shown that 54.63% of cork oak in Maamora forest have not changed in terms of phytosanitary situation between 2015 and 2020, 31.10% of oak stands are afflicted by a slight decline and 12.97% by a severe decline. Areas with slight or strong recovery remain minimal and represent 1.04% and 0.25% respectively. Ground data indicated that the map generated displayed a good distinction between stands severely and slightly declined with a global accuracy of 66.66%. Therefore, further research elaborating an advanced vegetation index reflecting the various factors of dieback would be of much importance.

Seasonal Timing for Estimating Carbon Mitigation in Revegetation of Abandoned Agricultural Land with High Spatial Resolution Remote Sensing

Dryland salinity is a major land management issue globally, and results in the abandonment of farmland. Revegetation with halophytic shrub species such as Atriplex nummularia for carbon mitigation may be a viable option but to generate carbon credits ongoing monitoring and verification is required. This study investigated the utility of high-resolution airborne images (Digital Multi Spectral Imagery (DMSI)) obtained in two seasons to estimate carbon stocks at the plant- and stand-scale. Pixel-scale vegetation indices, sub-pixel fractional green vegetation cover for individual plants, and estimates of the fractional coverage of the grazing plants within entire plots, were extracted from the high-resolution images. Carbon stocks were correlated with both canopy coverage (R2: 0.76–0.89) and spectral-based vegetation indices (R2: 0.77–0.89) with or without the use of the near-infrared spectral band. Indices derived from the dry season image showed a stronger correlation with field measurements of carbon than those derived from the green season image. These results show that in semi-arid environments it is better to estimate saltbush biomass with remote sensing data in the dry season to exclude the effect of pasture, even without the refinement provided by a vegetation classification. The approach of using canopy cover to refine estimates of carbon yield has broader application in shrublands and woodlands.

Enhancing a eucalypt crown condition indicator driven by high spatial and spectral resolution remote sensing imagery

Individual crown condition of Eucalyptus gomphocephala was assessed using two classification models to understand changes in forest health through space and time. Using high resolution (0.5 m) digital multispectral imagery, predictor variables were derived from textural and spectral variance of all pixels inside the crown area. The results estimate crown condition as a surrogate for tree health against the total crown health index. Crown condition is derived from combining ground-based crown assessment techniques of density, transparency, dieback, and the regrowth of foliage. This object-based approach summarizes the pixel data into mean crown indices assigned to crown objects which became the carrier of information. Models performed above expectations, with a significant weighted Cohen's kappa (κ>0.60 and p<0.001) using 70% of available data. Using in situ data for model development, crown condition was predicted forwards (2010) and backwards (2007) in time, capturing trends in crown condition and identifying decline in the healthiest between 2008 and 2010. The results confirm that combining spectral and textural information increased model sensitivity to small variations in crown condition. The methodology provides a cost-effective means for monitoring crown condition of this or other eucalypt species in native and plantation forests.

Dieback classification modelling using high-resolution digital multispectral imagery and in situ assessments of crown condition

Quantifying dieback in forests is useful for land managers and decision makers seeking to explain spatial disturbances and understand the cyclic nature of forest health. Crown condition is assessed as reference to dieback in terms of the density, transparency, extent and in-crown distribution of foliage. At 20 sites in the Yalgorup National Park, Western Australia, a total of 80 Eucalyptus gomphocephala crowns were assessed both in situ (2008) and using two acquisitions (2008 and 2010) of airborne imagery. Each tree was assessed using four crown-condition indices: Crown Density, Foliage Transparency, the Crown Dieback Ratio and Epicormic Index combined into a single index called the Total Crown Health Index (TCHI). The airborne imagery is like value calibrated then classified and modelled using in situ canopy condition assessments resulting in a quantification of crown-condition change over time. Comparison of Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI) and a novel Red-Edge Extrema Index (REEI) suggests that the latter is more suited to classification applications of this type.

Managing drought-induced mortality in Pinus radiata plantations under climate change conditions: A local approach using digital camera data

Pinus radiata D. Don is an internationally important plantation species. The predicted increases in the frequency, duration and/or severity of drought and heat stress associated with climate change could challenge the commercial viability of P. radiata plantations in several countries where existing plantations are grown in districts already vulnerable to periods of drought. We undertook an empirical approach to examine silvicultural and site factors associated with a significant drought-induced mortality event in southern New South Wales, Australia. The aim was to identify local, practical, risk prevention options for P. radiata plantations exposed to the climatic conditions predicted for this region. Our approach did not rely on ground-based assessment of plots but rather a total census of dead trees across two study areas totalling 10,000 ha of P. radiata, in compartments ranging in age from 0 to 35+ years. Dead tree density counts were derived both manually and automatically from high spatial resolution digital multispectral imagery acquired using a Leica ADS40 Airborne Digital Sensor. The results showed a strong correlation between dead tree densities obtained by the manual detection and the automated process ( R = 0.95, P < 0.0001). Two modelling approaches were applied: random forests (RF) and generalised additive models (GAM). For our study sites, both methodologies identified a similar set of parameters, with time since planting in unthinned stands being the most influential variable, and terrain variables playing a smaller role. Specifically, the models identified a threshold age at around 17–18 years for stands on good quality sites and before age 16 on poorer quality sites before the on-set of catastrophic mortality under severe drought conditions. Both modelling techniques also identified similar trends with respect to elevation and slope, and indirectly, site quality. These site attributes being likely to contribute to water availability. If stands in this area are to be planted at 1000 stems ha −1 and thinning schedules cannot be met, then we recommend avoiding sites having a mean elevation of below 600 m or to establish these sites with drought-tolerant genotypes. These recommendations cannot be extrapolated beyond the range of the data. However, the application of robust image classification techniques (e.g. automated tree counts) to high spatial resolution digital imagery, the increasing availability of high resolution climatic, terrain and edaphic GIS datasets, as well as readily available spatial modelling packages, helps off-set the limitations in transferability of our empirical approach.

Employing airborne multispectral digital imagery to map Brazilian pepper infestation in South Texas

A study was conducted in south Texas to determine the feasibility of using airborne multispectral digital imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral digital imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral digital imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.

Understanding patterns of vegetation degradation at meaningful scales within saline landscapes

AbstractUnderstanding patterns of vegetation degradation in saline catchments has been a major challenge to ecohydrology researchers in the conservation biology, geomorphology and hydrology disciplines. Hydrologists have measured streamflow quantity and quality at point locations and modelled at the landscape or macro‐scale. Botanists have investigated species diversity, structure and patterns at the micro‐scale in transect studies. Yet, the reach‐scale is widely recognized as the most appropriate scale for management and considering geomorphology. As a result, links between streamflow salinity and vegetation condition and river health remain poorly understood in a spatial sense. This study uses digital multi‐spectral imagery (DMSI) collected along a freshening downstream salinity gradient in a southwestern Australian catchment affected by secondary dryland salinity. DMSI was collected over 59 km in a 1200‐m swath at the micro‐scale (2‐m pixels), and calibrated against field transect data from 12 sites across three sections (degraded, transitional and ‘locally healthy’) to map vegetation condition. Results show that measures of mean annual salinity cannot account for variability in vegetation condition, but peak salinity concentration maintained over a length of time similar to greenhouse trials of plant mortality (∼60 days) appears to be a physiologically meaningful descriptor of streamflow salinity. The 60‐day peak salinity concentration showed high correlation to patterns of vegetation condition and published plant salinity tolerance values. Remote‐sensed imagery that captures vegetative condition in combination with physiologically meaningful descriptors of streamflow salinity provides a powerful tool to identify the spatial patterns of vegetation degradation at meaningful management scales within a saline catchment. Copyright © 2010 John Wiley &amp; Sons, Ltd.

Comparison of hyperspectral imagery with aerial photography and multispectral imagery for mapping broom snakeweed

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.

Evaluating Airborne Multispectral Digital Video to Differentiate Giant Salvinia from Other Features in Northeast Texas

Giant Salvinia (Salvinia molesta) is one of the world’s most invasive aquatic weeds. We evaluated the accuracy of airborne multispectral digital video imagery for separating giant salvinia from other aquatic and terrestrial features at a study site located in northeast, Texas. The five-band multispectral digital video imagery was subjected to an unsupervised computer analysis to derive a thematic map of the infested area. User’s and producer’s accuracies of the giant salvinia class were 74.6% and 87.2%, respectively. Aerial multispectral digital videography has potential as a remote sensing tool for differentiating giant salvinia from other terrestrial and aquatic features.

Forest inventory research at the Canadian Wood Fibre Centre: Notes from a research coordination workshop, June 3–4, 2009, Pointe Claire, QC

As part of its mandate to improve forest sector competitiveness, the Canadian Wood Fibre Centre (CWFC) has directed its research program towards the enhancement of forest inventory tools and systems to enable the spatial identification and forecasting of forest value. A June 2009 workshop attended by inventory researchers and provincial specialists from across the country reviewed current research needs and made recommendations for improvement of the CWFC-funded research program over the next 2- to 5-year period. Current efforts appear well positioned to improve stand-level inventory detail through cost-effective, semi-automated interpretation and quantification of tree species, size, and distribution. Means of incorporating specific attributes to better convey value, such as branch size and wood density, are being actively explored. More effort and resources are required to meet needs for better predictive models, sampling systems that incorporate data from a wide array of available sources, and proactive technology transfer and uptake of research results. Key words: forest inventory, value chain optimization, fibre quality, LiDAR, multispectral digital imagery

Estimating stand volume in broad-leaved forest using discrete-return LiDAR: plot-based approach

Quantitative assessment of forests is important at a variety of scales for forest planning and management. This study investigated the use of small-footprint discrete-return lidar for estimating stand volume in broad-leaved forest at plot level. Field measurements were conducted at 20 sample plots in the study area in western Japan, composed of temperate broad-leaved trees. Five height variables and two density variables were derived from the lidar data: 25th, 50th, 75th, and 100th percentiles, and mean of laser canopy heights as height variables (h 25, h 50, h 75, h 100, h mean); and ground fraction and only-and-vegetation fraction (d GF, d OVF) as density variables, defined respectively as the proportion of laser returns that reached the ground, and the proportion of only echoes (i.e., single pulse returns for which the first and last pulses returned from the same point) within vegetation points. In addition, the normalized difference vegetation index (NDVI), which is often used as an estimator for leaf area index (LAI) and above-ground biomass, was derived from multispectral digital imagery as an alternative density variable (d NDVI). Nonlinear least-square regression with cross-validation analysis was performed with single variables and combinations; a total of 23 models were studied. The best prediction was found when h 75 and d OVF were used as independent variables, resulting in adjusted R 2 of 0.755 and root-mean-square error (RMSE) of 41.90 m3 ha−1, corresponding to 16.4% of the mean stand volume, better than or comparable to the prediction models of previous studies.

Mapping crop ground cover using airborne multispectral digital imagery

Empirical relationships between remotely sensed vegetation indices and canopy density information, such as leaf area index or ground cover (GC), are commonly used to derive spatial information in many precision farming operations. In this study, we modified an existing methodology that does not depend on empirical relationships and extended it to derive crop GC from high resolution aerial imagery. Using this procedure, GC is calculated for every pixel in the aerial imagery by dividing the perpendicular vegetation index (PVI) of each pixel by the PVI of full canopy. The study was conducted during the summer growing seasons of 2007 and 2008, and involves airborne and ground truth data from 13 agricultural fields in the Southern High Plains of the USA. The results show that the method described in this study can be used to estimate crop GC from high-resolution aerial images with an overall accuracy within 3% of their true values.

Use of high resolution digital multi-spectral imagery to assess the distribution of disease caused byPhytophthora cinnamomion heathland at Anglesea, Victoria

Disease caused by the soilborne plant pathogen Phytophthora cinnamomi causes long-term floristic and structural changes in native vegetation communities in Australia. Key components of the management of this disease are to know where it occurs and the rate at which it spreads. The distribution of P. cinnamomi has generally been assessed as locality points of infestation and mapping the extent of diseased vegetation in any area is difficult and costly. This study was undertaken in P. cinnamomi-infested heathland communities in southern Victoria, Australia, where the symptoms of P. cinnamomi arise as a mosaic within healthy vegetation. We investigated the potential to improve the efficiency and effectiveness of mapping and monitoring vegetation affected by P. cinnamomi using digital multi-spectral imaging. This technique was developed for the purposes of monitoring vegetation and provides a single, seamless ortho-rectified digital image over the total area of interest. It is used to spatially quantify small differences in the characteristics of vegetation. In this study, the symptoms of disease caused by P. cinnamomi infestation were related to differences in the imagery and were used to map areas of infestation. Comparison of the digital multi-spectral imaging indications with on-ground observations gave moderate accuracy between the datasets (k = 0.49) for disease and healthy indications. This study demonstrates the ability of the technique to determine disease extent over broad areas in native vegetation and provides a non-invasive, cost effective tool for management.

Situational Waste in Landscape Watering: Residential and Business Water Use in an Urban Utah Community1

Abstract: Landscape water conservation is an important issue for municipalities throughout the Western United States, and especially in Utah as rapid growth strains existing water supplies. We conducted interdisciplinary research in Layton, Utah, that aimed at understanding patterns of landscape water use among households and businesses. The research project involved three basic tasks. First, a landscape “water budget” was developed by producing a calibrated and classified mosaic of landscape type and area from airborne multispectral digital imagery, integrating this information with Layton City parcel boundary data to determine landscape vegetated areas per lot, and estimating irrigation needs derived from reference evapotranspiration (ETo) obtained using weather data for the Salt Lake City metropolitan region. Second, utilizing Layton water billing data, water use for each household and business was identified and categorized as “conserving,”“acceptable” or “wasteful” by determining how much the water applied varied from actual landscape plant need. Third, surveys were administered to a random stratified sample of households and businesses in the study area to investigate various factors that were hypothesized to be predictive of wasteful watering practices. This paper primarily focuses on analysis of the household and business survey data, which explores factors affecting urban landscape water use from a human behavioral perspective. We found that the most significant factors predicting actual water use were the type of irrigation system and whether the location was a household or business. Attitudinal and motivational characteristics were not consistently associated with water use. We found that wasteful watering is the result of many factors embedded in the complex context of urban landscapes. This implies that water conservation programs should identify potential wasteful users through analyses of water billing data and direct water conservation measures at these users by focusing on site‐specific evaluations and recommendations. Water audits or water checks are one such tool that some communities have employed to help people understand and assess the quantity of water needed by and applied to their landscapes. This approach provides an opportunity to evaluate situational constraints at particular locations and design appropriate strategies for reducing water waste.

Monitoring cross-border trails using airborne digital multispectral imagery and interactive image analysis techniques

The objective of this study was to evaluate image-based procedures for monitoring cross-border foot trails in the US – Mexico border zone in eastern San Diego County using airborne remote sensing techniques. Specifically, digital multi-spectral and multi-temporal imagery from an airborne digital multi-spectral imaging system, digital image processing, and visual image analysis techniques were explored in the context of detecting and delineating new trail features and updating trail GIS layers. Three trail updating approaches: map-to-image (M-I) overlay, map and image-to-image (M/I-I) differencing, map and image-to-image (M/I-I) swiping and two types of spectral transform, PCA and NDVI, were tested and compared. The M-I overlay was found to be the most reliable trail updating approach. The optimal image enhancement method for the M-I overlay approach varied with vegetation structure. PCA imagery yielded better results than NDVI imagery in a highly disturbed area and NDVI imagery performed better in a densely vegetated area. The M/I-I swiping approach was useful for distinguishing misregistered extant trails from new trail features.

Forest health and vitality: the detection and monitoring of Pinus patula trees infected by Sirex noctilio using digital multispectral imagery

The Eurasian woodwasp, Sirex noctilio, causes considerable tree mortality in commercial pine plantations in southern KwaZulu-Natal, South Africa. Broad-scale visual assessments of infestation provided by forest managers are currently used to measure forest health and vitality. The effectiveness of visual assessments is questionable because they are qualitative, subjective and dependent on the skill of the surveyor. Remote sensing technology provides a synoptic view of the canopy and thus offers an alternative to the conventional methods of monitoring forest health and vitality. In this study, high resolution (0.5 × 0.5m) digital multispectral imagery (DMSI) was acquired over commercial Pinus patula trees of varying age classes, which had been ground assessed and ranked on an individual tree crown basis using a severity scale. The severity scale was based on a hierarchy of decline symptoms that are visibly apparent on the infested tree and are represented in this study as the green, red and grey stages. A ...

Development and validation of canopy reflectance-based crop coefficient for potato

The present paper describes the development and validation of canopy reflectance-based crop coefficients for potato using canopy reflectance (measured using hand-held radiometers and high resolution multispectral digital imagery), and extensive crop biophysical sampling in selected potato growing fields in and around Kimberly, Idaho, during 1998 and 1999 seasons. Daily crop evapotranspiration was estimated using basal and canopy reflectance-based crop coefficients, and a hydrologic water balance was conducted in the plant root zone. Independent measurements of actual soil moisture measurements were made by neutron probe and gravimetric methods, and used to validate the simulated results. The results validate the reflectance-based crop coefficient method. High resolution multispectral aerial imagery was used to highlight the spatial variability of actual crop water demand in the study fields.

Surveying kaolin-treated cotton plots with airborne multispectral digital video imagery

Airborne multispectral digital video imagery was evaluated as tool for surveying kaolin-treated cotton ( Gossypium hirsutum L.) plots, with emphasis on decision support. Images of experimental plots were obtained on 12 June 2002 and 16 June 2004 with an electronic digital video imaging system acquiring blue (447–455 nm), green (555–565 nm), red (625–635 nm), near-infrared (814–826 nm), and mid-infrared (1631–1676 nm) imagery. We qualitatively and quantitatively evaluated the single band images and color and false color composite images to compare the image responses of treated plots with the image responses of untreated plots. Blue, green, and red imagery separated the treated plots from the untreated plots (Dunnett's test and t-test; P = 0.05), with the blue imagery being the most responsive to the treatment. Treated plots were readily differentiated from untreated plots with natural color and false color composite imagery. This separation was attributed to the images that were sensitive to some portion of the visible spectrum (i.e., blue, green, and red light). These results indicate that airborne electronic imaging systems have great potential as tools for surveying cotton fields treated with kaolin particle film, supporting application of the imagery as a decision support tool.

Agricultural Applications of High-Resolution Digital Multispectral Imagery

This paper analyses the potential of a high-resolution airborne remote sensing system, the Digital Multi-Spectral Imagery (DMSI), for detecting canola growth variability within a field to help farmers for future incorporation of the system into sitespecific crop management approaches for agriculture. Transect sampling within a canola field of a broad acre agricultural property in the South West of Western Australia was conducted synchronous with the capture of one-meter spatial resolution DMSI. Four individual bands (blue, green, red, and NIR) and five image transformations namely the Normalized Difference Vegetation Index (NDVI), Normalized Difference Vegetation Index ‐ Green (NDVI-green), Soil Adjusted Vegetation Index (SAVI), Photosynthetic Vigor Ratio (PVR) and Plant Pigment Ratio (PPR) of DMSI were investigated. Canola density was correlated with the four individual bands and five image transformations, while the LAI was correlated with the four individual bands. The NDVI-green, red and near-infrared bands of DMSI produced the best correlations with the density of canola, whereas the LAI had significant (� � 0.05) negative correlations with the blue (� 0.93) and red (� 0.89) DMSI bands, and a significant positive correlation were found with the nearinfrared band (0.82).