Remote sensing techniques in estuaries and coastal zones an update

This paper reviews briefly the current state of the use of remote sensing in estuaries and coastal waters. This covers the established uses, the recent developments in relation to hyperspectral imaging systems (imaging spectrometers), interferometric SAR and lidar bathymetry. The opportunities that are expected to occur soon, as 1-m resolution space-flown systems become available, are also discussed.     

Remote sensing of temperature variations around major power plants as point sources of heat

Variations in land surface temperature (LST) around major point sources of heat were studied using the Tampa Bay region as a case study. LST in the Tampa Bay region, FL, USA, was retrieved from Landsat Thematic Mapper (TM) 6 and Enhanced Thematic Mapper Plus (ETM+) 6 high-gain thermal bands. The TM6 image data were obtained on 29 January (winter season) and 3 April 2007 (spring season). The ETM+6 data were obtained on 11 April 2007 (spring season). Spatial profiles of LST around four major fossil-fuelled power plants (FFPPs) were considered in this study. Temperatures were found to be highest at power plants and to decay to an average background temperature within 1.2–2.0 km from the FFPPs. The average background temperatures obtained in January and April were 17°C and 29°C, respectively. Results indicate that LST in close proximity to the FFPPs could be up to 10°C hotter than the surrounding areas. These findings suggest that FFPPs are significant heat sources and populations living within 1–2 km from an FFPP might be at significantly higher risk of heat-related illnesses and mortality.     

Remote sensing in ecological botany

At the XIIth International Botanical Congress, on July 4, 1975, a new direction in scientific methodology was evaluated for the first time within the framework of an International Union of Biological Sciences—remote sensing of vegetation and the environment. Remote sensing is a method of studying the composition, structure, dynamics, and productivity of ecosystems and the state of the biosphere by means of reflectance and emittance characteristics of the earth's surface measureable from aircraft and spacecraft, and the interpretation of such remotely sensed imagery. Remote cartography is conducted with aerial and space images with a scale of from 1: 1000 to 1:30 000 000. Phytomass can be measured by comparing the dependence of the phytocenometric characteristics with the magnitude of the remotely obtained signal. Phenology and dynamics are revealed by means of optical comparison of successive images. Structural ecological investigations can be based on spatial and factoral integration of ecosystems on single, remotely-sensed images. Remote sensors record spatial and temporal variability of the reflective and emissive characteristics of vegetative ground cover. Anthropogeneous effects are recognized by indication of vegetation clearing, fires, ploughing, overgrazing, water and air pollution, and water and wind erosion.     

Evaluation of spatio-temporal variations in chlorophyll-a in Lake Naivasha,Kenya: remote-sensing approach

Restoration of the ecosystem services and functions of lakes requires an understanding of the turbidity dynamics in order to arrive at informed environmental management decisions. The understanding of the spatio-temporal dynamics of turbidity requires frequent monitoring of the turbidity components such as chlorophyll-a concentration. In this study, we explored the use of Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-Aqua) satellite data in studying the spatio-temporal changes in chlorophyll-a concentration in Lake Naivasha, a turbid tropical system. The temporal trend of chlorophyll-a concentration over the study period in the lake was also evaluated. The temporal trend assessment was achieved through the removal of periodic seasonal interference using Seasonal-Trend decomposition based on the LOESS (Local Regression) procedure. The resultant chlorophyll-a concentration maps derived from MODIS-Aqua satellite data give an indication of the monthly spatial variation in chlorophyll-a concentration from 2002 to 2012. The results of regression analyses between satellite-derived chlorophyll-a and in situ measurements reveal a high level of precision, but with a measureable bias with the satellite underestimating actual in situ measurements (R² = 0.65, P < 0.001). Although the actual values of the chlorophyll-a concentrations are underestimated, the significant relationship between satellite-derived chlorophyll-a and in situ measurements provides reliable information for studying spatial variations and temporal trends. In 2009 and 2010, it was difficult to detect chlorophyll-a from the MODIS-Aqua imagery, and this coincided with a period of the lowest water levels in Lake Naivasha. An inverse relationship between de-seasoned water level and chlorophyll-a concentration was evident. This study shows that MODIS-Aqua satellite data provide useful information on the spatio-temporal variations in Lake Naivasha, which is useful in establishing general trends that are more difficult to determine through routine ground measurements.     

Remote sensing of foliar chemistry

Remotely sensed data are being used to estimate foliar chemical content as a result of our need for the information and our increasing ability to understand and measure foliar spectra. This paper reviews how stepwise multiple regression and deconvolution have been used to extract chemical information from foliar spectra, and concludes that both methods are useful, but neither is ideal. It is recommended that the focus of research be modeling in the long term and experimentation in the short term. Long-term research should increase our understanding of the interaction between radiation and foliar chemistry so that the focus of research can move from leaf model to canopy model to field experiment. Short-term research should aim to design experiments in which remotely sensed data are used to generate unambiguous and accurate estimates of foliar chemical content.     

Spatio-temporal remote sensing of vertical and seasonal chlorophyll-a:b ratio in Tamarix ramosissima

Remotely sensed spectral reflectance data have provided avenues for large-scale non-destructive estimation of temporal and spatial variations of physiological processes in plants. This study established the potential for tracking (chlorophyll) chl-a:b ratio in Tamarix ramosissima based on -leaf-scale photochemical reflectance index (PRI) at Fukang Station of Desert Ecology in the hinterland of the Junggar Basin, Xinjiang, northwest China. Leaves were sampled on a monthly basis over a 3-year growing period. T. ramosissima tolerance to the fragile arid conditions revealed higher coefficient of determination (R² > 0.6) between chl-a:b ratio and N content at each light condition. This implied a higher potential for irradiance acclimation through plasticity in photosynthetic apparatus, and hence an important attribute for colonizing wider desert ecological range. PRI was negatively correlated to chl-a:b ratio regardless of season but was more sensitive to changes in light condition. The modified PRI (PRI_mod, R₅₁₀–R₅₇₀ nm) performed better than the original PRI (PRI, R₅₃₁–R₅₇₀ nm) with R² improvement in all data sets of this species. These results implied that seasonality and leaf age, within canopy resource variation and the individual species must be considered when applying PRI_mod to estimate chl-a:b ratio. Application of empirical indices avails a non-destructive timely leaf-level, species and site-specific avenue of detecting vegetation status in arid ecosystems. Remote estimation of chl-a:b ratio obtained at leaf scale in this study could be scaled to ecosystem and global scale by effective estimation of spatial distribution and seasonal variation using other pigment-related vegetation index such as the normalized difference vegetation index, or combination of PRI and the water band index.     

Remote sensing for land-use planning

Human needs transform the land to different activities and the quantifiable information of these dominating activities are necessary to develop future planning. In this paper three techniques, geometrical shapes and growth concept, causal modelling concept, and category rate concept are developed which are used to assess built-up areas for 2001. The shapes of built-up areas for 13 places in a Tuni region of Andhrapradesh has been estimated by imagery and incorporated in order to estimate future built-up areas by the rating method. The variables which are caused by land growth are identified and used in causal modelling to estimate future built-up areas. The areas and rates of influencing variables were used as input in the Cross Classification method.     

Remote sensing with small satellites

Abstract

Some design issues relevant to the adaptation of small satellites for remote sensing applications are presented. Particular emphasis is placed on those areas which will necessitate the advancement of current small satellite technology. The aim of such a programme would be to provide a cost-effective but reliable way of obtaining remotely sensed data from a range of novel sensors, thus complementing the efforts of major missions towards the 21st century.     

Remote sensing of rice crop areas

Rice means life for millions of people and it is planted in many regions of the world. It primarily grows in the major river deltas of Asia and Southeast Asia, such as the Mekong Delta, known as the Rice Bowl of Vietnam, the second-largest rice-producing nation on Earth. However, Latin America, the USA, and Australia have extensive rice-growing regions. In addition, rice is the most rapidly growing source of food in Africa. Rice is therefore of significant importance to food security in an increasing number of low-income food-deficit countries. This review article gives a complementary overview of how remote sensing can support the assessment of paddy rice cultivation worldwide. This article presents and discusses methods for rice mapping and monitoring, differentiating between the results achievable using different sensors of various spectral characteristics and spatial resolution. The remote sensing of rice-growing areas can not only contribute to the precise mapping of rice areas and the assessment of the dynamics in rice-growing regions, but can also contribute to harvest prediction modelling, the analyses of plant diseases, the assessment of rice-based greenhouse gas (methane) emission due to vegetation submersion, the investigation of erosion-control-adapted agricultural systems, and the assessment of ecosystem services in rice-growing areas.     

Remote sensing in sea search and rescue

A classified United States Army line scanner (Texas Instruments Model AAS-24) mounted in a Mohawk aircraft was tested for its air-sea search and rescue potential. Within the environmental parameters indicated the instrument proved effective in identifying both small boats and individual swimmers. Positive identification was made during daylight and night sorties when the location of swimmers was known and in daylight trials when their location was unknown.     

Remote sensing and GIS activities in UNESCO

The role of UNESCO is to promote international peace and security through cooperation in the fields of education, science and culture. In view of eradicating illiteracy, overcoming unsustainability of natural resources, confronting environmental problems, safeguarding historical monuments, and preserving natural and cultural heritage, UNESCO develops international research programmes and makes best possible use of new advances in technology, namely remote sensing and GIS, in order to effectively carry out activities in the framework of its programmes. Part one describes remote sensing and GIS use in international programmes such as the Man and the Biosphere (MAB) programme for biosphere reserve development and ecosystem monitoring, management and conservation; the International Hydrological Programme (IHP) for surface and ground water research management; the International Geological Correlation Programme (IGCP) for the creation of a data base on the spectral signatures of rocks and soils; and in several projects concerning ocean investigation and climate observation conducted in the framework of the Intergovernmental Oceanographic Commission (IOC). In part two constraints of using the remote sensing and GIS technology are addressed and remaining difficulties in accessing and exchanging data, training issues and costs are discussed.     

Remote sensing of phytoplankton functional types

The principal goal in early missions of satellite-borne visible spectral radiometry (ocean colour) was to create synoptic fields of phytoplankton biomass indexed as concentration of chlorophyll-a. In the context of climate change, a major application of the results has been in the modelling of primary production and the ocean carbon cycle. It is now recognised that a partition of the marine autotrophic pool into a suite of phytoplankton functional types, each type having a characteristic role in the biogeochemical cycle of the ocean, would increase our understanding of the role of phytoplankton in the global carbon cycle. At the same time, new methods have been emerging that use visible spectral radiometry to map some of the phytoplankton functional types. Here, we assess the state of the art, and suggest paths for future work.     

Remote sensing in BOREAS: Lessons learned

The Boreal Ecosystem Atmosphere Study (BOREAS) was a large, multiyear internationally supported study designed to improve our understanding of the boreal forest biome and its interactions with the atmosphere, biosphere, and the carbon cycle in the face of global climate change. In the initial phase of this study (early 1990s), remote sensing played a key role by providing products needed for planning and modeling. During and after the main BOREAS field campaigns (1994 and 1996), innovative remote sensing approaches and analyses expanded our understanding of the boreal forest in four key areas: (1) definition of vegetation structure, (2) land-cover classification, (3) assessment of the carbon balance, and (4) links between surface properties, weather, and climate. In addition to six BOREAS special issues and over 500 journal papers, a principal legacy of BOREAS is its well-documented and publicly available database, which provides a lasting scientific resource and opportunity to further advance our understanding of this critical northern biome.     

Remote sensing of vegetation dynamics in West Africa

Vegetation dynamics and the lives of millions of people in West Africa are closely interlinked with each other. The high annual variability of the phenological cycle considerably affects the agricultural population with late rainfalls and droughts, often resulting in serious food crises. On the other hand, the rapidly growing population has a great need for space due to expanding cities and a low agricultural efficiency. This situation, together with a changing climate, has had a strong impact on vegetation dynamics in West Africa and will play a major role in the future. The dynamic nature of vegetation in the region has attracted a lot of remote-sensing-based research in the past 30 years and has lead to heated discussions. This review article gives a comprehensive overview of the studies on remotely sensed vegetation dynamics in West Africa. After an introduction to the specific situation for vegetation dynamics in West Africa, the applied sensors and their suitability for the region are outlined. Research on the assessment of different plant parameters, on phenological metrics as well as on the monitoring of agricultural areas is outlined and discussed. Furthermore, a major part of this review is dedicated to the analyses undertaken to assess vegetation trends in West Africa over the past 30 years and their potential human and climatic causes. Finally, identified research gaps and challenges for future studies are discussed.     

Remote sensing of water clarity in Tampa Bay

We examined the spatial and temporal variability of the Secchi Disk Depth (SDD) within Tampa Bay, Florida, using the Sea-viewing Wide Field-of-View Sensor (SeaWiFS) satellite imagery collected from September 1997 to December 2005. SDD was computed using a two-step process, first estimating the diffuse light attenuation coefficient at 490 nm, K_d(490), using a semi-analytical algorithm and then SDD using an empirical relationship with K_d(490). The empirical SDD algorithm (SDD = 1.04 × K_d(490)^− 0.82, 0.9 < SDD < 8.0 m, r² = 0.67, n = 80) is based on historical SDD observations collected by the Environmental Protection Commission of Hillsborough County (EPCHC) in Tampa Bay. SeaWiFS derived SDD showed distinctive seasonal variability, attributed primarily to chlorophyll concentrations and color in the rainy season and to turbidity in the dry season, which are in turn controlled by river runoff and winds or wind-induced sediment resuspension, respectively. The Bay also experienced strong interannual variability, mainly related to river runoff variability. As compared to in situ single measurements, the SeaWiFS data provide improved estimates of the “mean” water clarity conditions in this estuary because of the robust, frequent, and synoptic coverage. Therefore we recommend incorporation of this technique for routine monitoring of water quality in coastal and large estuarine waters like Tampa Bay.     

Remote sensing terminology: a reply

A point in the Letter by Curran (1983) on remote sensing terminology is expanded and a further point made.     

Remote sensing techniques for mangrove mapping

Different approaches to the classification of remotely sensed data of mangroves are reviewed, and five different methodologies identified. Landsat TM, SPOT XS and CASI data of mangroves from the Turks and Caicos Islands, were classified using each method. All classifications of SPOT XS data failed to discriminate satisfactorily between mangrove and non-mangrove vegetation. Classification accuracy of CASI data was higher than Landsat TM for all methods, and more mangrove classes could be discriminated. Merging Landsat TM and SPOT XP data improved visual interpretation of images, but did not enhance discrimination of different mangrove categories. The most accurate combination of sensor and image processing method for mapping the mangroves of the eastern Caribbean islands is identified.     

Remote sensing terminology: a reply

Abstract

The widespread misuse of the word ‘acronym’ is pointed out.