Remote Sensing-based Analysis Research Articles

The impact of the 2015/2016 El Niño on global photosynthesis using satellite remote sensing.

The El Niño-Southern Oscillation exerts a large influence on global climate regimes and on the global carbon cycle. Although El Niño is known to be associated with a reduction of the global total land carbon sink, results based on prognostic models or measurements disagree over the relative contribution of photosynthesis to the reduced sink. Here, we provide an independent remote sensing-based analysis on the impact of the 2015-2016 El Niño on global photosynthesis using six global satellite-based photosynthesis products and a global solar-induced fluorescence (SIF) dataset. An ensemble of satellite-based photosynthesis products showed a negative anomaly of -0.7 ± 1.2 PgC in 2015, but a slight positive anomaly of 0.05 ± 0.89 PgC in 2016, which when combined with observations of the growth rate of atmospheric carbon dioxide concentrations suggests that the reduction of the land residual sink was likely dominated by photosynthesis in 2015 but by respiration in 2016. The six satellite-based products unanimously identified a major photosynthesis reduction of -1.1 ± 0.52 PgC from savannahs in 2015 and 2016, followed by a highly uncertain reduction of -0.22 ± 0.98 PgC from rainforests. Vegetation in the Northern Hemisphere enhanced photosynthesis before and after the peak El Niño, especially in grasslands (0.33 ± 0.13 PgC). The patterns of satellite-based photosynthesis ensemble mean were corroborated by SIF, except in rainforests and South America, where the anomalies of satellite-based photosynthesis products also diverged the most. We found the inter-model variation of photosynthesis estimates was strongly related to the discrepancy between moisture forcings for models. These results highlight the importance of considering multiple photosynthesis proxies when assessing responses to climatic anomalies.This article is part of a discussion meeting issue 'The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications'.

GIS and remote sensing-based analysis of the impacts of land use/land cover change (LULCC) on the environmental sustainability of Ekiti State, southwestern Nigeria

Analysis of land use/land cover change (LULCC) and their impacts on the natural environment is essential in policy decision for an effective solution to the sustainability of the earth system. This study employed supervised image classification (maximum likelihood) algorithm to map changes in land use/land cover for a period of 4.5 decades (1972–2017) in Ekiti State. Vegetation and temperature dynamics were examined for the selected years using normalized difference vegetation index (NDVI) and land surface temperature (LST), respectively. NDVI and LST were statistically compared to understand the potential impacts anthropogenic activities on the environment. Resultant LULC maps indicated a decrease in forests and woodlands at a change rate of 51.25 and 0.72% over the last 4.5 decades in Ekiti State, while built-up areas, croplands, rocks/bare soils and water bodies have increased about 267.58, 197.30, 714.11 and 4421.43%, respectively. Agricultural lands (now occupying 47.13%) are the major contributor to the net change in forests (decreasing). LST showed a negative correlation with NDVI (r = −0.672). Mean LST are in the order 22.7 °C (1984) < 23.84 °C (1991) < 27.17 °C (2017) < 28.16 °C (2000). As the LULC pattern is changing, its imprint is reflected on LST and NDVI. Built-up areas, rocks/bare soils exhibit the highest surface radiant temperature, while vegetated surfaces and water bodies recorded the least. The study demonstrated that changes in land covers through urban development have affected the natural functioning of ecosystems. As such, proper natural resource management and effective policies are required to ensure sustainable development.

Mapping urban residents’ vulnerability to heat in Abidjan, Côte d’Ivoire

The land use/land cover of the district of Cocody in Abidjan, Côte d’Ivoire, is under substantial pressure associated with ‘fast urbanization. This study conducts a detailed analysis of the impact of the changing land use/land cover in Cocody by assessing inhabitants’ social vulnerability to heat. The approach was based on integrated methods: remote sensing-based analysis of urban land cover helped to analyze the urbanization dynamic, while indicator-based analysis, through the integrated theoretical MOVE framework, helped to define vulnerability and identify indicators. Both methods combined allowed an analysis of the social vulnerability of Cocody inhabitants to extreme heat conditions. The results reveal that Cocody is experiencing rapid land use/land cover change dominated by rapid growth of buildings and a noticeable decrease in the natural environment. Cocody is highly vulnerable to extreme heat conditions, which could be aggravated by its increasing population, with a high percentage of children under five years of age (11.31%). Creating more green spaces is therefore recommended in the more susceptible areas to counter the harmful effects of the changing climate.

Recent structural evolution of Forni Glacier tongue (Ortles-Cevedale Group, Central Italian Alps)

ABSTRACTStructural glaciology yields important details about the evolution of glacier dynamics in response to climate change. The maps provided here document the occurrence and evolution of brittle and ductile structures on the tongue of Forni Glacier, Ortles-Cevedale Group, Central Italian Alps, between 2003 and 2014. Through the remote sensing-based analysis of structures, we found evidence of brittle fractures such as crevasses, faults and ring faults, and ductile structures such as ogives at the base of the icefall in the eastern glacier tongue. Although each of the three glacier tongues have evolved differently, a reduction in flow-related dynamics and an increase in the number of collapse structures occurred over the study period. Analysis of the glacier structural evolution based on the numbers and the locations of different structures, suggest a slowdown of glacier flow on the eastern tongue. The recent evolution of the glacier also suggests that the occurrence of a disintegration scenario is likely to worsen over the next decades.

Remote sensing-based analysis on temporal and spatial changes about environmental elements in the northwest of Junggar basin, China

This paper presents a study of revealing the environmental elements change during the process of local industrialization based on remote sensing technique in the western part of China. Spatio-temporal evolution of vegetation cover derived from NDVI and land surface water distribution was analyzed by time-series analysis of MSS and Landsat data from 1977 to 2011. Results show that remote sensing provide a way for monitoring the influence of local industrialization on regional environment elements in gobi region.

Multiple Stable States and Catastrophic Shifts in Coastal Wetlands: Progress, Challenges, and Opportunities in Validating Theory Using Remote Sensing and Other Methods

Multiple stable states are established in coastal tidal wetlands (marshes, mangroves, deltas, seagrasses) by ecological, hydrological, and geomorphological feedbacks. Catastrophic shifts between states can be induced by gradual environmental change or by disturbance events. These feedbacks and outcomes are key to the sustainability and resilience of vegetated coastlines, especially as modulated by human activity, sea level rise, and climate change. Whereas multiple stable state theory has been invoked to model salt marsh responses to sediment supply and sea level change, there has been comparatively little empirical verification of the theory for salt marshes or other coastal wetlands. Especially lacking is long-term evidence documenting if or how stable states are established and maintained at ecosystem scales. Laboratory and field-plot studies are informative, but of necessarily limited spatial and temporal scope. For the purposes of long-term, coastal-scale monitoring, remote sensing is the best viable option. This review summarizes the above topics and highlights the emerging promise and challenges of using remote sensing-based analyses to validate coastal wetland dynamic state theories. This significant opportunity is further framed by a proposed list of scientific advances needed to more thoroughly develop the field.

Remote Sensing Observations on Impact of Phailin Cyclone on Phytoplankton Distribution in Northern Bay of Bengal

Remote sensing-based analysis has been carried out to study the impact of “Phailin” cyclone on ocean phytoplankton distribution off Odisha coast and on the northern Bay of Bengal water. Oceansat-2 Ocean Colour Monitor (OCM) and MODISTerra sensors-derived chlorophyll images have been generated over the study area during October 2013. There has been observation of drastic change in the chlorophyll concentration of the north-western Bay of Bengal water with effect of the cyclone “Phailin,” which hit Gopalpur, Odisha coast on 12 October night at around 21:00 h IST. The postcyclone images of Oceansat-2 OCM data and retrieved chlorophyll concentration were observed to be very high, 3.0-4.0 mg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> in coastal water, which was around 0.5-1.0 mg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> during precyclone. Similarly, the postcyclone chlorophyll was around 0.80-1.50 mg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> in the offshore water, unusually high compared to precyclone concentration (<;0.60 mg/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</sup> ). The cyclone track of India Meteorological Department (IMD) has been superimposed on chlorophyll images and observed as proxy along the chlorophyll front with algal bloom features. There has been observation of cooling in the northern Bay of Bengal and off Odisha coast with impact of the induced upwelling, entrainment and mixing of the water column, as evident from the sea surface temperature (SST) data analysis. Interesting features such as cold core eddies and fronts have been visualized. This study is important to assess the impact of Phailin cyclone on ocean productivity on the local and basin scale and its deviation from seasonal trend as well.

Land surface dynamics and environmental challenges of the Niger Delta, Africa: Remote sensing-based analyses spanning three decades (1986–2013)

The Niger Delta, the largest river delta on the African continent, is one of the most densely populated river deltas globally and hosts the world's third largest mangrove forest. It is a major biodiversity hot spot of our planet. At the same time the delta is home to Africa's largest oil reserves and responsible for a skyrocketing GDP development of Nigeria since the 1970s. Nigeria ranks 13th among all oil producing countries, but oil exploitation also brought with it severe environmental degradation, leading to the delta's nomination for a place on the top 10 list of the “World's Worst Polluted Places Report” in 2013. Despite the outstanding importance of the region for Nigeria, Africa, and the international community most studies published focus mainly on topics of geology, geochemistry, and environmental toxicology. Studies employing earth observation satellite data to assess Niger Delta dynamics are rare. This paper aims at contributing to an overview of Niger Delta geography and environmental threats and challenges, as well as to an understanding of Niger Delta land surface dynamics from 1986 to 2013. Covering the complete delta, we present results of land cover change analyses, results of an assessment of coastline dynamics, as well as the manifestation of oil exploitation activity as expressed via oil access canal dredging and gas flaring, monitored within the 27 year time span investigated.

Remote sensing-based analysis of the planform changes in the Upper Amazon River over the period 1986–2006

An analysis of the planform changes of the Colombian reach of the Amazon River was carried out over a period of 19.9 years. Remote sensing image processing techniques were applied to Landsat images acquired in 1986, 1994, 2001 and, 2006. These images were selected based on minimal daily water level variations, while providing the widest temporal span. Plan view river changes and geomorphologic characteristics were examined to identify which channel pattern classification best represents this large tropical river system. Discharge was also analyzed to determine whether changes in the river's plan view are a direct response to variations in discharge. The system had a depositional tendency between 1986 and 2006, with a period where erosion was more intense than deposition between 1994 and 2001. Percent change in the plan view area of the system (1.4% yr−1) and the maximum migration rates (125 m yr−1) suggest that this reach of the Amazon is less active than reaches upstream and the downstream reach between the confluences of the Jutaí and Japurá Rivers. Variations in discharge appear to be responsible for deposition and erosion dynamics observed after this remote sensing analysis in the Colombian reach of the Amazon River. Characteristics including multiple channels with vegetated islands developed from within-channel deposition, meandering planform, lateral activity of channel margins, and the absence of islands with saucer-like morphology suggest a multichannel, meandering pattern for this reach of the Amazon, that corresponds to a laterally active anabranching river.

Landscape analysis of wetland plant functional types: The effects of image segmentation scale, vegetation classes and classification methods

Remote sensing-based analyses of vegetation function such as photosynthesis and productivity are challenging in wetlands with complex cover and difficult field access. Recent advances in object-based image analysis (OBIA) and machine-learning algorithms offer new image classification tools; however, few comparisons of different approaches have been discussed to date. We applied OBIA to delineate wetland plant functional types (PFTs) for Poyang Lake, the largest freshwater lake in China and Ramsar wetland conservation site, from a spring 2008 Landsat TM image. We targeted major PFTs that represent dominant vegetation groups along wetland inundation gradients and affect ecosystem biogeochemical cycles and ecological habitats. Classification results were compared among: a) several “small” object segmentation scales (with average object sizes 1350–9000m2); b) algorithms from six families of statistical machine-learning classifiers (Bayesian, Logistic, Neural Network, Decision Trees, K-Nearest Neighbors and Support Vector Machines) and c) two hierarchical levels of vegetation classification, a generalized 3-class set and a more specific 6-class set. We also examined the response of classification accuracy to four basic object-level texture metrics. The highest accuracies (>85–90%) and best agreement among algorithms occurred at coarser object scales rather than close-to-pixel scales. No single machine-learning algorithm was consistently superior at all scales, although support vector machine, k-nearest neighbor and artificial neural network most frequently provided the highest overall and PFT-specific accuracies. Including texture metrics had both positive and negative low-magnitude effects on classification accuracy that were not consistent among scale values, algorithms or PFT classes. Individual PFTs differed in scales at which they were best discriminated from others, reflecting their unique landscape positions, ecology of dominant species and disturbance agents. There was a 29–35% disagreement between mapped areas of generalized PFTs and their respective subclasses, suggesting potential mismatches between the ecological classification scheme and PFT landscape patch structure, and raising concern on error propagation in multi-scale classifications. We conclude that OBIA with machine-learning classifiers is useful for landscape vegetation analyses, however, considerations of spatial scale and image segmentation outcomes are critical in mapping PFTs and should be more thoroughly investigated in future work.

Defining the Early Bronze Age landscape: a remote sensing-based analysis of Hafit tomb distribution in Wadi Andam, Sultanate of Oman

High-resolution, freely available Google Earth software was successfully utilised in a remote-sensing survey to locate and map thousands of Hafit tombs in a study area centring on Wadi Andam in the Sultanate of Oman. This dataset was refined for reliability with ground-based fieldwork, and was analysed in the landscape using GIS software. The tombs are not randomly distributed across the study area; rather, the hydrology of the area appears to have been a dominant determining factor in the location of the graves. The structures were constructed in elevated, highly visible locations, possibly in an attempt to mark ownership of the landscape. The distribution of Hafit tombs is likely to provide some insight into the utilisation of the landscape in this period, with the evidence from Wadi Andam tentatively suggesting the presence of a nomadic pastoralist population. The development of this rapid, accurate and cost-effective methodology has the potential to greatly facilitate the survey and analysis of Hafit tombs across the Oman peninsula.

Estimating recharge distribution using remote sensing: A case study from the West Bank

Summary Estimating groundwater recharge to the aquifer system is a very important element in assessing the water resources in regions of the world with poorly developed hydrological measurement stations such as the West Bank. Previous studies have developed analytical recharge models that are based on the long-term annual rainfall data. These models have been shown to be inadequate and changes over shorter periods, e.g. monthly estimates, must be known in order to study the temporal distribution of recharge. The approach used in this research integrates data derived from satellite images (e.g. land cover, evapotranspiration, precipitation, and digital elevation model) with hydrogeological data in a Geographic Information System (GIS) model to identify surface recharge areas on a pixel-by-pixel basis. The Surface Energy Balance Algorithm for Land (SEBAL) is applied to time series of MODerate Resolution Imaging Spectroradiometer (MODIS) Level 3 data of reflectance and surface temperature measurements to estimate monthly evapotranspiration; precipitation is derived from the monthly data sets of the Tropical Rainfall Measuring Mission (TRMM); runoff is given assumed values of 1.0 mm month −1 and 0.5 mm month −1 for the months of January and February, respectively. Recharge is quantified from November until March by applying the water balance method where evapotranspiration estimates and runoff are subtracted from precipitation. Results show good agreement between available numbers reported in literature and the remote sensing-based analysis. Empirical models that are based on long term rainfall measurements result values between 800 and 836 MCM yr −1 while the remote sensing based model results a recharge estimate of between 650 and 750 MCM yr −1 .

Remote Sensing-Based Characterization of Settlement Structures for Assessing Local Potential of District Heat

In Europe, heating of houses and commercial areas is one of the major contributors to greenhouse gas emissions. When considering the drastic impact of an increasing emission of greenhouse gases as well as the finiteness of fossil resources, the usage of efficient and renewable energy generation technologies has to be increased. In this context, small-scale heating networks are an important technical component, which enable the efficient and sustainable usage of various heat generation technologies. This paper investigates how the potential of district heating for different settlement structures can be assessed. In particular, we analyze in which way remote sensing and GIS data can assist the planning of optimized heat allocation systems. In order to identify the best suited locations, a spatial model is defined to assess the potential for small district heating networks. Within the spatial model, the local heat demand and the economic costs of the necessary heat allocation infrastructure are compared. Therefore, a first and major step is the detailed characterization of the settlement structure by means of remote sensing data. The method is developed on the basis of a test area in the town of Oberhaching in the South of Germany. The results are validated through detailed in situ data sets and demonstrate that the model facilitates both the calculation of the required input parameters and an accurate assessment of the district heating potential. The described method can be transferred to other investigation areas with a larger spatial extent. The study underlines the range of applications for remote sensing-based analyses with respect to energy-related planning issues.

A remote sensing-based analysis on the impact of Wenchuan Earthquake on the core value of World Nature Heritage Sichuan Giant Panda Sanctuary

In this study, we used remotely sensed data, GIS modeling, and statistical methods to evaluate the damage caused by the Wenchuan Earthquake (May 12, 2008) to the giant panda (Ailuropoda melanoleuca) habitat in the World Nature Heritage Sichuan Giant Panda Sanctuary (WHSGPS) in China. A landscape ecological analysis found increases of landscape heterogeneity, complexity, and fragmentation in the giant panda habitat after the earthquake. A terrain analysis found that slope and elevation are directly associated with the distribution of the damaged areas. As slope and elevation increase, the size of the damaged area keeps increase until to a peak, and then starts to drop. The total area of the damaged vegetation in our study area is 114.26 km2, accounting for 3.78% of the study area; 30.46% of that 114.26 km2 is located in the Core Zone of WHSGPS. There are 18.57km2 of the damaged vegetation located in the identified suitable giant panda habitat, accounting for 1.75% of the total area of suitable giant panda habitats in the study area. Based on these findings, we conclude that the Wenchuan Earthquake does not have significant impact on the WHSGPS as a whole.

Remote Sensing-Based Analysis For Expansion of Construction Land and Cultivated Land Cut in recent 20 Years

Remote Sensing-Based Analysis For Expansion of Construction Land and Cultivated Land Cut in recent 20 Years