Applications of ALOS PALSAR for monitoring biophysical parameters of a degraded black mangrove (Avicennia germinans) forest

Within the last few decades mangrove forests worldwide have been experiencing high annual rates of loss and many of those that remain have undergone considerable degradation. To understand the condition of these forests, various optical remote sensing platforms have been used to map and monitor these wetlands, including the use of these data for biophysical parameter mapping. For many mangrove forests a reliable source of optical imagery is not possible given their location in quasi-permanent cloud cover or smoke covered regions. In such cases it is recommended that Synthetic Aperture Radar (SAR) be considered. The purpose of this investigation was to examine the relationships between various ALOS-PALSAR modes, acquired from eight images, and mangrove biophysical parameter data collected from a black mangrove (Avicennia germinans) dominated forest that has experienced considerable degradation. In total, structural data were collected from 61 plots representing the four common stand types found in this degraded forest of the Mexican Pacific: tall healthy mangrove (n = 17), dwarf healthy mangrove (n = 15), poor condition mangrove (n = 13), and predominantly dead mangrove (n = 16).Based on backscatter coefficients, significant negative correlation coefficients were observed between filtered single polarization ALOS PALSAR (6.25 m) HH backscatter and Leaf Area Index (LAI). When the dead stands were excluded (n = 45) the strength of these relationships increased. Moreover, significant negative correlation coefficients were observed with stand height, Basal Area (BA) and to a lesser degree with stem density and mean DBH. With the coarser spatial resolution dual-polarization and quad polarization data (12.5 m) only a few, and weaker, correlation coefficients were calculated between the mangrove parameters and the filtered HH backscatter. However, significant negative values were once again calculated for the HH when the 16 dead mangrove stands were removed from the sample. Conversely, strong positive significant correlation coefficients were calculated between the cross-polarization HV backscatter and LAI when the dead mangrove stands were considered. Although fewer in comparison to the HH correlations, a number of VV backscatter based relationships with mangrove parameters were observed from the quad polarization mode and, to a lesser extent, with the one single VV polarization data.In addition to backscatter coefficients, stepwise multiple regression models of the mangrove biophysical parameter data were developed based on texture parameters derived from the grey level co-occurrence matrix (GLCM) of the ALOS data. A similar pattern to the backscatter relationships was observed for models based on the single polarization unfiltered data, with fairly strong coefficients of determination calculated for LAI and stem height when the dead stands were excluded. In contrast, similar coefficients of determination with biophysical parameters were observed for the dual and quad polarization multiple regression models when the dead stands were both included and excluded from the analyses. An estimated mangrove LAI map of the study area, derived from a multiple regression model of the quad polarization texture parameters, showed comparable spatial patterns of degradation to a map derived from higher spatial resolution optical satellite data.     

The nature and possible causes of land cover change (1984 –1996) along a rainfall gradient in southeastern Botswana

Land cover change analysis was undertaken in semi-arid southeastern Botswana. The aim was to determine how remotely sensed data could be applied over time and under different rainfall regimes to help assess the relative significance of biophysical and human factors in causing land cover change in a rapidly evolving developing world context. To this purpose, land cover changes were studied along an east (hardveld)-west (sandveld) gradient of decreasing rainfall and decreasing population density. Three years of Thematic Mapper imagery from 1984, 1994 and 1996, covering the period from the 1980s drought to the 1990s ‘normal’ rainfall regime were analysed using supervised classification techniques. Land cover change analysis revealed that over a large part of the study area the dry and more biophysically vulnerable western sandveld showed greater vegetation recovery than the eastern hardveld with its more productive soils and higher rainfall. Underlying causes behind this apparent reversal of trends are inferred to be mainly socioeconomic in nature and particularly related to higher population density due to the rise of salaried urban occupation opportunities in the hardveld. This work concludes that, while biophysical causes of change are important, the human dimension is regarded as being more significant especially where human factors negate otherwise positive biophysical effects in an agrarian developing country.     

Remote sensing monitoring of land restoration interventions in semi-arid environments with a before–after control-impact statistical design

Restoration interventions to combat land degradation are carried out in arid and semi-arid areas to improve vegetation cover and land productivity. Evaluating the success of an intervention over time is challenging due to various constraints (e.g. difficult-to-access areas, lack of long-term records) and the lack of standardised and affordable methodologies. We propose a semi-automatic methodology that uses remote sensing data to provide a rapid, standardised and objective assessment of the biophysical impact, in terms of vegetation cover, of restoration interventions. The Normalised Difference Vegetation Index (NDVI) is used as a proxy for vegetation cover. Recognising that changes in vegetation cover are naturally due to environmental factors such as seasonality and inter-annual climate variability, conclusions about the success of the intervention cannot be drawn by focussing on the intervention area only. We therefore use a comparative method that analyses the temporal variations (before and after the intervention) of the NDVI of the intervention area with respect to multiple control sites that are automatically and randomly selected from a set of candidates that are similar to the intervention area. Similarity is defined in terms of class composition as derived from an ISODATA classification of the imagery before the intervention. The method provides an estimate of the magnitude and significance of the difference in greenness change between the intervention area and control areas. As a case study, the methodology is applied to 15 restoration interventions carried out in Senegal. The impact of the interventions is analysed using 250-m MODIS and 30-m Landsat data. Results show that a significant improvement in vegetation cover was detectable only in one third of the analysed interventions, which is consistent with independent qualitative assessments based on field observations and visual analysis of high resolution imagery. Rural development agencies may potentially use the proposed method for a first screening of restoration interventions.     

Detecting biophysical properties of a semi-arid grassland and distinguishing burned from unburned areas with hyperspectral reflectance

This paper examines the utility of hyperspectral remote sensing to detect fresh and dry biomass, water content and plant area index of burned and unburned grassland in southern California. Contrary to many previously published reports, the normalized difference vegetation index (NDVI, a vegetation greenness index) was not a good indicator of any of these important biophysical properties in either the burned or unburned area, especially after regeneration. Rather, the water band index (WBI, an index of water status in vegetation) showed better promise of estimating these biophysical properties in this semi-arid ecosystem. Despite the post-regeneration similarities in visual and harvested values of these two areas, we found that the full range of hyperspectral reflectance in ‘visible to infrared’ (400–1000 nm) wavelengths when used in a cluster analysis can readily differentiate the burned and unburned areas. This demonstrates the utility of hyperspectral remote-sensing in mapping subtle features that may not be detectable from conventional remote-sensing indices (e.g. NDVI) alone.     

An individual-based model study of anchovy early life history in the northern Humboldt Current system

We used an individual-based model of anchovy (Engraulis ringens) early life history coupled with hydrodynamic outputs from the regional oceanic modeling system (ROMS) to investigate the factors driving variability in egg and larval survival rates in the northern Humboldt upwelling region off Peru. Individuals were released within a coastal area and followed for a period of 30 days. Those that were still in the coastal area at that time were considered as retained. We investigated the spatial and temporal variability in the release locations of the individuals retained, and compared these to observed egg concentration patterns reconstructed from a 40-year period of monitoring. A first set of simulations using passive particles to represent anchovy eggs and larvae revealed a large sensitivity of the results to the initial vertical distribution of particles. We then conducted two additional sets of simulations that included the effect of egg buoyancy, larval vertical swimming behavior and lethal temperature. We obtained (1) maximal coastal retention close to the surface in winter and in deeper layers in summer, (2) a large influence of egg buoyancy and of larval vertical behavior on coastal retention in all seasons, (3) a partial match between dates and locations of enhanced retention and observed egg concentration patterns and (4) a low effect of lethal temperature on survival except when associated with high egg density. The model suggests that an optimal temporal spawning pattern for maximizing coastal retention would have two maximums, the most significant in austral winter and the second in summer. This pattern agrees roughly with observed spawning seasonality, but with temporal discrepancy of about two months in the peaks of both series. Spatially, we obtained higher retention from 10 S to 20 S, whereas the observed maximum egg concentration was located between 6°S and 14°S. Among the three sets of simulations, the one taking into account larval vertical swimming behavior lead to the best match with the data.     

Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval

ESA’s upcoming Sentinel-2 (S2) Multispectral Instrument (MSI) foresees to provide continuity to land monitoring services by relying on optical payload with visible, near infrared and shortwave infrared sensors with high spectral, spatial and temporal resolution. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods, which ideally should provide uncertainty intervals for the predictions. Statistical learning regression algorithms are powerful candidats for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. In this paper, we focus on a new emerging technique in the field of Bayesian nonparametric modeling. We exploit Gaussian process regression (GPR) for retrieval, which is an accurate method that also provides uncertainty intervals along with the mean estimates. This distinct feature is not shared by other machine learning approaches. In view of implementing the regressor into operational monitoring applications, here the portability of locally trained GPR models was evaluated. Experimental data came from the ESA-led field campaign SPARC (Barrax, Spain). For various simulated S2 configurations (S2-10m, S2-20m and S2-60m) two important biophysical parameters were estimated: leaf chlorophyll content (LCC) and leaf area index (LAI). Local evaluation of an extended training dataset with more variation over bare soil sites led to improved LCC and LAI mapping with reduced uncertainties. GPR reached the 10% precision required by end users, with for LCC a NRMSE of 3.5–9.2% (r²: 0.95–0.99) and for LAI a NRMSE of 6.5–7.3% (r²: 0.95–0.96). The developed GPR models were subsequently applied to simulated Sentinel images over various sites. The associated uncertainty maps proved to be a good indicator for evaluating the robustness of the retrieval performance. The generally low uncertainty intervals over vegetated surfaces suggest that the locally trained GPR models are portable to other sites and conditions.     

High-resolution spatial assessment of population vulnerability to climate change in Nepal

Current, spatially explicit, and high-resolution assessments of population vulnerability to climate change and variability in developing countries can be difficult to create due to lack of data or financial and technical capacity constraints. We propose a comparative, multiple-approach framework to assess the spatial variation of population vulnerability to climatic changes using several high-resolution variables related to climate, topography, and socioeconomic conditions with an objective to detect the spatial variability of climate vulnerability in Nepal. Nepal is one of the most vulnerable countries to the effects of climate change due to frequent climatic hazards and poor socio-economic capacity. We used a climate vulnerability index (CVI) approach to derive climate vulnerability maps at the one-kilometer resolution and test an additive and a principal components-based composite method of data aggregation. In this work, we attempt to answer three questions. 1) How do different methods of assessment inform the spatial variation of the climate vulnerability in Nepal? 2) How do different variables interact to shape climate vulnerability in Nepal? 3) What proportions of the population in Nepal are vulnerable to climatic disasters and why? Our analysis uncovered significant spatial variations in population vulnerability to climate change across Nepal, with the highest vulnerability being experienced by the High Mountain region followed by the regions in the lower elevations. We find that although the lack of adaptive capacity is the biggest cause of population vulnerability to climate change in Nepal, a resilient community is shaped by both biophysical and socioeconomic characteristics. By performing an iterative sensitivity analysis of our thirteen variables both at the aggregate level (nationally) as well as at the more disaggregated (physiographic region) level, we contribute to identifying important, multi-scalar driving factors for vulnerability that can be employed as leverage points for lowering vulnerability at different scales. After performing analyses at multiple regions, we conclude that region-specific variable selection is needed for more detailed assessments and in order to prioritize adaptation strategies at scales that go beyond the hierarchy of administrative divisions.     

Transport of plankton and particles between the Chukchi and Beaufort Seas during summer 2002, described using a Video Plankton Recorder

A key goal of the Western Arctic Shelf Basin Interactions program is to understand how physical and biological processes together impact shelf–basin exchange of biological, chemical, and physical properties. High-resolution vertical distributions of plankton and particles were obtained using an Auto Video Plankton Recorder from 29 locations on the Chukchi Shelf, in the deep Beaufort Sea, and across the Beaufort–Chukchi Shelf-break during a cruise on the USCGC Healy in July–August, 2002. Coincident velocity estimates were collected using hull-mounted acoustic Doppler current profilers. Images of plankton and particles were extracted automatically and identified manually to taxa and type. Copepods, diatom chains, decaying diatoms, marine snow, and radiolarians were the most abundant categories observed. Distinct regional differences in abundance were observed that were associated with different oceanographic regimes and with the prevailing circulation in the region. Vertical distributions were closely associated with the physical structure of the water column. A sharp horizontal discontinuity in abundance of all categories between shelf and basin was observed, located over the shelf break and potentially established and maintained by transport of plankton and particles along-shelf to the east rather than northwards towards the basin. Barrow Canyon and the shelf and shelf-break east of Barrow Canyon had very high concentrations of plankton and particles, especially marine snow, that may have resulted from elevated production on the eastern Chukchi Shelf that subsequently was advected out of Barrow Canyon and to the east. Comparisons of downward flux, estimated from particle sinking rates based on individual marine snow particle size, and horizontal velocities suggested that much of the marine snow carbon was sinking to the benthos of the Chukchi Sea prior to being advected off-shelf. Velocities and plankton concentrations together indicated that little off-shelf flux of plankton or particles to the basin was occurring except in an eddy located off of the Beaufort Shelf.