Canadian Landsat studies for monitoring resource development: A summary

This report summarizes a major paper reviewing Canadian work using Landsat imagery for monitoring the environmental effects of the development of both renewable and non-renewable resources. Landsat has definite advantages for studying resources. In particular, the data are able to show changes in surface cover indicating in many cases development or the exploitation of resources. Monitoring of areas and rates of change over time is possible. Outlined in the paper are examples related to three aspects of resource development. First is the use of Landsat for updating topographic maps for areas where recent developments have taken place. The second aspect is forestry, and examples related to the detection and measurement of clear cuts and recording of areas affected by spruce budworm are reported. Finally, studies of landscape changes produced by mining activities are presented.     

Using land use change trajectories to quantify the effects of urbanization on urban heat island

This paper proposed a quantitative method of land use change trajectory, which means the succession among different land use types across time, to examine the effects of urbanization on an urban heat island (UHI). To accomplish this, multi-temporal images from Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) of Xiamen City in China from 1987 to 2007 were selected. First, the land use change trajectories were extracted based on the classified images from different years. Then the land surface temperatures (LST) were retrieved and the magnitudes of the UHI were evaluated using the UHI intensity (UHII) indicator. Finally, the indices of the contribution to UHI intensity (CUHII) were constructed and calculated to quantify the effects of each land use change trajectory on the UHI during urbanization. The results demonstrated that the land use change trajectories and CUHII are effective and useful in quantifying the effects of urbanization on UHI. In Xiamen City, a total of 2218 land use change trajectories were identified and 530 of them were the existing urban or urbanization trajectories. The UHII presents a trend of continuous increase from 0.83 °C in 1987 to 2.14 °C in 2007. With respect to the effects of urban growth on UHI, the contribution of existing urban area to UHI decreased during urbanization. Prior to 2007, the existing urban area of trajectory NO. 44444 had the most significant effect on UHI with the greatest CUHII, while the value has decreased from 55.00% in 1987 to 13.03% in 2007 because of the addition of new urbanized area. In 2007, the greatest CUHII was replaced by a trajectory from farmland to built-up area (NO. 22224) with the CUHII of 21.98%, followed by the existing urban area of trajectory NO. 44444 with the CUHII of 13.03%. These results provide not only a new methodology to assess the environmental effects of urbanization, but also decision-supports for the planning and management of cities.     

Utility of landsat data in mineral exploration — a case study from Orissa,India

The utility of Landsat data in geosciences has been established beyond doubt. In this paper utility of Landsat data in mineral exploration has been highlighted.The geologic and geomorphic interpretation of Landsat data brought out two very significant and characteristic features in scene 152-046 in the southern part of Orissa. One was distinct tonal anomaly and other was occurance of flat planated surface devoid of vegetation and drainage on top of hills. This when checked on ground at Gandhmardan was found to be bauxite bearing lateritic plateau. When this known information was extrapolated over other regions in scenes 151-046 and 152-046, all other bauxitic — lateritic plateaus were picked up very easily on Landsat imagery.The information was further extrapolated in the southern scene No.152-047 where without doing much detailed geologic and geomorphic interpretation almost all other bauxitic — lateritic bodies occuring in the region were picked up. This proves the utility of Landsat data in mineral exploration.Moreover delineation of extent of such bodies could be done very accurately on Landsat imagery thus saving considerable time in the exploration programme.     

Estimating canopy cover in drylands with Landsat MSS data

The relationship between canopy cover and spectral characteristics of the corresponding areas was studied in a semi-arid savannah environment in Kordofan, The Sudan.The canopy cover was measured in 32 test plots through air photo interpretation. Achieved values were correlated with multitemporal Landsat MSS raw data and manipulated data.The highest correlation coefficients in general were obtained between crown cover and spectral data recorded during the dry season.The inverse relationship between amount of woody vegetation and nIR reflectance (MSS 6, MSS 7) was striking. This implied that other factors than a healthy foliage characterized the spectral responses.Destructive measurements of woody biomass were carried out to establish a relationship between woody wet weight and crown diameter for future biomass studies.     

Assessment of plant vitality detection through fluorescence and reflectance imagery

Recent studies of the vegetation fluorescence show that it can be successfully used as an intrinsic indicator of plant photosynthetic activity. With respect to the vegetation spectral reflectance, the chlorophyll (Chl) fluorescence is more specific as an observable of basic biophysical processes in the plant cells. Laser induced fluorescence is widely used in near range remote sensing, but it is not suitable for the global monitoring of vegetation. Decades of active fluorometry studies have collected useful information of leaf reaction to natural and anthropogenic stress. Still the passive fluorescence, the one that could be registered from satellite orbit has still to prove its advantage over widely used reflectance signature. The weakness of the signal and the lack of experience with passive fluorescence measurements require extensive technical, theoretical and experimental studies. New imaging fluorometres are to be designed for measuring steady state fluorescence in controlled and natural conditions.

In order to compare reflectance and steady state fluorescence sensitivity to stress impact, a set of experiments have been conducted under controlled illumination conditions in a bio-chamber, designed by the author’s team. The equipment allows plant vitality to be monitored both by passive fluorescence and spectral reflectance imaging. Different types of stress factors (heat and drought stress, acid impact) were investigated to demonstrate equipments ability in monitoring changes of fluorescence signal. Selected fluorescence images of foliage illustrate an early detection of plant dysfunction and the temporal and spatial spreading of the stress impact. Analysis shows that fluorescence imaging of green plants can be developed as a highly effective early warning remote sensing method, which could have application for an ecosystems’ monitoring along with high-spectral reflectance imagery.     

The role of image processing in mineral exploration: Why stop at Landsat?

Digital image processing procedures can be applied to data other than Landsat. Registered images of ancillary data such as may be derived from airborne magnetic and radiometric surveys provide new insights into existing data and extend the usefulness of satellite imagery. In times of increasing costs and difficulty of exploration better information handling is at a premium. It should be remembered that the field geologist is the ultimate end-user of most geological information, and as such the data should be easily useable and interpretable by him.     

A comparison of different regression models for downscaling Landsat and MODIS land surface temperature images over heterogeneous landscape

Remotely sensed high spatial resolution thermal images are required for various applications in natural resource management. At present, availability of high spatial resolution (<200 m) thermal images are limited. The temporal resolution of such images is also low. Whereas, coarser spatial resolution (∼1000 m) thermal images with high revisiting capability (∼1 day) are freely available. To bridge this gap, present study attempts to downscale coarser spatial resolution thermal image to finer spatial resolution using relationships between land surface temperature (LST) and vegetation indices over a heterogeneous landscape of India. Five regression based models namely (i) Disaggregation of Radiometric Temperature (DisTrad), (ii) Temperature Sharpening (TsHARP), (iii) TsHARP with local variant, (iv) Least median square regression downscaling (LMS_DS) and (v) Pace regression downscaling (PR_DS) are applied to downscale LST of Landsat Thematic Mapper (TM) and Terra MODIS (Moderate Resolution Imaging Spectroradiometer) images. All the five models are first evaluated on Landsat image aggregated to 960 m resolution and downscaled to 480 m and 240 m resolution. The downscale accuracy is achieved using LMS_DS and PR_DS models at 240 m resolution at 0.61 °C and 0.75 °C respectively. MODIS data downscaled from 1000 m to 250 m spatial resolution results root mean square error (RMSE) of 1.43 °C and 1.62 °C for LMS_DS and PR_DS models, respectively. The LMS_DS model is less sensitive to outliers in heterogeneous landscape and provides higher accuracy when compared to other models. Downscaling model is found to be suitable for agricultural and vegetated landscapes up to a spatial resolution of 250 m but not applicable to water bodies, dry river bed sand sandy open areas.     

Prospecting in glaciated terrain-integrating airborne and Landsat MSS

The effects of a glacially enriched zone of trace elements on soils and vegetation in the Thetford Mines area of Quebec were investigated using ground information plus digital Multispectral Scanner (MSS) data from airborne and Landsat sensors. The enriched zone was developed during the last glaciation when a southeastward flowing glacier eroded and dispersed an ultrabasic outcrop that had anomalous levels of Ni, Cu, Co, Cr, Mg and Fe.The dispersal train of enriched trace elements was detectable over an area at least 70 × 15 km ‘down-ice’ from the outcrop. In this zone total Ni concentrations in the soil ranged from background levels of 10 ppm to levels in excess of 1800 ppm. The dominant tree species, $\text{Abies}$$\text{balsamea}$ (balsam fir) and $\text{Picea glauca}$ (white spruce) reflect the soil anomaly with higher concentrations of trace elements in their tissue and lower concentrations of chlorophyll.An unsupervised enhancement of Landsat imagery showed that a tonal discontinuity was caused by a vegetation segregation related to the heavy metal enrichment soils. A detailed study based on Landsat MSS data was able to establish regional patterns of chlorophyll production by certain plant species closely related to the ultrabasic dispersal train. Multi-channel airborne MSS data confirmed the Landsat soil-plant patterns.     

Synthesis of geophysical data with space-acquired imagery: A review

Geophysical data obtained from ground and airborne platforms have been used in the development of regional geologic models for many years. Space-acquired data and imagery have a shorter but similar history of applications. All these data may be synthesized either manually or digitally. Manual synthesis methods consist of overlaying and comparing maps, whereas digital synthesis methods consist of computer storage and analysis of registered digital data sets.A data base may include topographic, geologic, soils, aeromagnetic, gravity, radiometric, electromagnetic and geochemical data, and Landsat, Seasat, and Heat Capacity Mapping Mission (HCMM) images, all of which can be evaluated individually or compared in multiple layers (overlays).Stereographic models, useful in the correlation and interpretation of geophysical data, have been created from Landsat images by using aeromagnetic, gravity, geochemical, or topographic values to offset Landsat pixels, thus introducing parallax and permitting stereoscopic viewing.Statistical correlation has been used to determine the applicability of specific data sets to the development of geologic or exploration models. Various arithmetic functions have proven useful in developing models from such data sets.     

Canadian landsat studies for monitoring hydrologic conditions and coastal environments: A summary

This report summarizes a major paper reviewing Canadian work using Landsat imagery for studying changes in lakes and in coastal environments. The nature of environmental change is discussed. For lakes and coastal environments, it is suggested that change is either seasonal, long term, short term or constant; examples of each are given. There is also an important distinction between natural and man-induced change. Outlined in the paper are studies of the filling of the LG2 Reservoir, water level and vegetation changes in the Peace-Athabasca Delta, possible vegetation changes due to the construction of Roberts Bank Port near Vancouver and measurement of surface suspended sediment concentration in the Bay of Fundy using chromaticity analysis.     

Enhanced Landsat and HCMM imagery for mineral exploration in contrasting areas of subtropical humid and semi-arid terrain

The role of multispectral and thermal imagery in mineral exploration is evaluated for two areas with favourable geological conditions within contrasting physical environments — one in the semi-arid low tree and shrub savannas of western Queensland and northern South Australia, the other in the humid subtropical forest zone of western Yunnan, China.For both areas Landsat imagery was used to identify structures and lithologies favourable for mineralization and locate ironstones/gossans indicative of mineralized bedrock. HCMM imagery was used alongside Landsat imagery to identify former and ephemeral drainage patterns important for the interpretation of geochemical data over covered ground in Australia. Interpretations were made of enhanced colour composites, outputs of MSS band ratios and colour rotated images generated from both NASA film products and CCTs. These were checked by field studies during which plant, soil and rock chip samples for subsequent metals analyses were collected from areas of anomalous vegetation and from barren ironstones. Some rock chip samples from Australia contained concentrations of copper or lead or small amounts of other metals. In China geobotanical anomalies over ironstones from which soil and rock samples yielded copper, lead, zinc, tin and silver suggest the presence of a major belt of potential multi-metal mineralization.     

Sensor data for forest policy information

NASA-Ames Research Center has investigated the role and performance capabilities of the Landsat Multi-Spectral Scanner (MSS) for forest policy analysis for the past four years in cooperation with the California Department of Forestry. A thorough series of studies, from a statewide land cover map to smaller, highly detailed studies including collateral data, have been conducted with a view to comprehensive forest policy needs. The strengths and limitations of MSS data have been evaluated. Some observations about the information needed from new satellite sensors such as the Thematic Mapper are discussed against this background.     

Monitoring of rice cropping intensity in the upper Mekong Delta,Vietnam using time-series MODIS data

Information on rice growing areas is important for policymakers to devise agricultural plans. This research explores the monitoring of rice cropping intensity in the upper Mekong Delta, Vietnam (from 2001 to 2007) using time-series MODIS NDVI 250-m data. Data processing includes three steps: (1) noise is filtered from the time-series NDVI data using empirical mode decomposition (EMD); (2) endmembers are extracted from the filtered time-series data and trained in a linear mixture model (LMM) for classification of rice cropping systems; and (3) classification results are verified by comparing them with the ground-truth and statistical data. The results indicate that EMD is a good filter for noise removal from the time-series data. The classification results confirm the validity of LMM, giving an overall accuracy of 90.1% and a Kappa coefficient of 0.7. The lowest producer and user accuracies were associated with single crop rain-fed rice class due to the mixed pixel problems. A strong yearly correlation at the district level was revealed in the MODIS-derived areas (R² ? 0.9). Investigation of interannual changes in rice cropping intensity from 2001 to 2007 showed a remarkable conversion from double to triple crop irrigated rice from 2001 to 2003, especially in the Thoai Son and Phu Tan districts. A big conversion from triple crop rice back to double crop rice cultivation was also observed in Phu Tan from 2005 to 2006. These changes were verified by visual interpretation of Landsat images and examination of NDVI profiles.     

Is the phenology-based algorithm for mapping deciduous rubber plantations applicable in an emerging region of northern Laos?

Updated information of rubber plantations is essential for assessing socioeconomic and environmental impacts, especially in the emerging region of northern tropics. Here, a phenological method was modified to detect rubber plantations using Landsat Operational Land Imager (OLI) imagery in Phongsaly Province of northern Laos, where it begun a rubber boom in the mid-2000s due to geo-economic cooperation. It highlighted the landscape and pixel differences of deciduous rubber plantations in the tri-temporal phases (i.e., pre-defoliation, defoliation, and foliation) during the dry season due to phenological changes. Six commonly used vegetation indices (VIs), including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Land Surface Water Index (LSWI), Atmospherically Resistant Vegetation Index (ARVI), Normalized Burn Ratio (NBR and NBR2) derived from OLI imagery during 2013–2016 were compared to determine the most suitable VI for discriminating the phenological differences of rubber plantations from natural forests. Then, the Differences of Normalized Burn Ratio (DNBR) was applied to generate the 30 m map of rubber plantations in 2016, by combining two masks of Landsat-derived forest and suitable elevation for rubber trees cultivation. The resultant map of rubber plantations had a classification accuracy of 93.7% and the Kappa coefficient of 0.848. Our study demonstrated the usefulness of the Landsat-derived tri-temporal phenological DNBR approach in an emerging region of northern Laos, despite requiring more scenes compared with single- and double temporal window methods.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

Surface temperature estimation in Singhbhum Shear Zone of India using Landsat-7 ETM+ thermal infrared data

Land surface temperature (LST) is an important factor in global change studies, heat balance and as control for climate change. A comparative study of LST over parts of the Singhbhum Shear Zone in India was undertaken using various emissivity and temperature retrieval algorithms applied on visible and near infrared (VNIR), and thermal infrared (TIR) bands of high resolution Landsat-7 ETM+ imagery. LST results obtained from satellite data of October 26, 2001 and November 2, 2001 through various algorithms were validated with ground measurements collected during satellite overpass. In addition, LST products of MODIS and ASTER were compared with Landsat-7 ETM+ and ground truth data to explore the possibility of using multi-sensor approach in LST monitoring. An image-based dark object subtraction (DOS3) algorithm, which is yet to be tested for LST retrieval, was applied on VNIR bands to obtain atmospheric corrected surface reflectance images. Normalized difference vegetation index (NDVI) was estimated from VNIR reflectance image. Various surface emissivity retrieval algorithms based on NDVI and vegetation proportion were applied to ascertain emissivities of the various land cover categories in the study area in the spectral range of 10.4–12.5 μm. A minimum emissivity value of about 0.95 was observed over the reflective rock body with a maximum of about 0.99 over dense forest. A strong correlation was established between Landsat ETM+ reflectance band 3 and emissivity. Single channel based algorithms were adopted for surface radiance and brightness temperature. Finally, emissivity correction was applied on ‘brightness temperature’ to obtain LST. Estimated LST values obtained from various algorithms were compared with field ground measurements for different land cover categories. LST values obtained after using Valor’s emissivity and single channel equations were best correlated with ground truth temperature. Minimum LST is observed over dense forest as about 26 °C and maximum LST is observed over rock body of about 38 °C. The estimated LST showed that rock bodies, bare soils and built-up areas exhibit higher surface temperatures, while water bodies, agricultural croplands and dense vegetations have lower surface temperatures during the daytime. The accuracy of the estimated LST was within ±2 °C. LST comparison of ASTER and MODIS with Landsat has a maximum difference of 2 °C. Strong correlation was found between LST and spectral radiance of band 6 of Landsat-7 ETM+. Result corroborates the fact that surface temperatures over land use/land cover types are greatly influenced by the amount of vegetation present.     

A study of forest vegetation dynamics in the south of the Krasnoyarskii Krai in spring

Remote sensing applications have greatly enhanced ability to monitor and manage in the areas of forestry. Accurate measurements of regional and global scale vegetation dynamics (phenology) are required to improve models and understanding of inter-annual variability in terrestrial ecosystem carbon exchange and climate–biosphere interactions. Study of vegetation phenology is required for understanding of variability in ecosystem. In this paper, monitoring of vegetation dynamics using time series of satellite data is presented. Vegetation variability (vegetation rate) in different topoclimatic areas is investigated. Original software using IDL interactive language for processing of satellite long-term data series was developed. To investigate growth dynamics vegetation rate inferred from remote sensing was used. All estimations based on annual time series of Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Vegetation rate for Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) was calculated using MODIS data. The time series covers spring seasons of each of 9 years, from 2000 to 2008. Comparison of EVI and NDVI derived growth rates has shown that NDVI derived rates reveal spatial structure better. Using long-term data of vegetation rates variance was estimated that helps to reveal areas with anomalous growth rate. Such estimation shows sensitivity degree of different areas to different topoclimatic conditions. Woods of heights depend on spatial topoclimatic variability unlike woods of lowlands. Principal components analysis shows vegetation with different rate conditions. Also it reveals vegetation of same type in areas with different conditions. It was demonstrated that using of methods for estimating the dynamic state of vegetation based on remote sensing data enables successful monitoring of vegetation phenology.     

Mapping recent agricultural developments in China from satellite data

Landsat data have been employed to study and map agricultural developments in three regions of China: 1) Pearl River delta; 2) Nen River basin; and 3) Xinjiang Autonomous Region. Manual interpretation procedures used in conjunction with multi-date Landsat images and collateral information permitted rice yields to be estimated for the Pearl River delta in 1978. A combination of manual and computer-assisted analyses of Landsat data of Northeast China revealed that more than 15,000 km² of agricultural land in a 184,500 km² study area had been reclaimed from rangeland and marshland. These analyses also indicated a shift in cropping practices, with the foodcrops wheat and corn replacing cash crops such as soybeans. In the arid west, Landsat image data provided valuable input to a geographic information system (GIS). It appears the GIS approach will prove useful for evaluating agricultural land potential in the remote areas of China.     

Remote-sensing technology applied to forest assessment in California

Remote-sensing technology has been thoroughly evaluated for the analysis of California forest policy. A statewide, 1.6-acre-resolution, digital land-cover data base of Landsat Multispectral Scanner (MSS) classification has been produced. Three major resource regions have been analyzed in detail and one of them geographically integrated with 12 other physical and socioeconomic data layers to model fire and reforestation problems, using a geographic information system (GIS). A study of GIS design criteria has been conducted and the California Department of Forestry, the cooperator in all of these studies, is presently evaluating the alternatives and implementing certain aspects of them.     

Watershed image segmentation and cloud classification from multispectral MSG-SEVIRI imagery

In this work a technique for cloud detection and classification from MSG-SEVIRI (Meteosat Second Generation-Spinning Enhanced Visible and Infra-red Imager) imagery is presented. It is based on the segmentation of the multispectral images using order-invariant watershed algorithms, which are applied to the corresponding gradient images, computed by a multi-dimensional morphological operator. To reduce the over-segmentation produced by the watershed method, a RAG (Region Adjacency Graph) based region merging technique is applied, using region dissimilarity functions. Once the objects present in the image have been segmented, they are classified using a multi-threshold method based on physical considerations that takes into account the statistical parameters inside each region.