Earth resources: A continuing bibliography with indexes, issue 22, July 1979

This bibliography lists 390 reports, articles, and other documents introduced into the NASA scientific and technical information system between 1 April 1979 and 30 June 1979. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Mapping Chestnut Stands Using Bi-Temporal VHR Data

This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife

Earth Resources: A continuing bibliography with indexes, issue 36

This bibliography lists 576 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between October 1 and December 31, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Assessing and mapping of carbon in biomass and soil of mangrove forest and competing land uses in the Philippines

Mangrove forests provide many ecosystem goods and services, and are important carbon (C) sinks in the tropics. Yet, land use conversions in mangroves still continue, especially in Southeast Asia. Carbon stocks in biomass and soil as well as the soil emissions of greenhouse gases (GHG) are important parameters to quantify, monitor and map in mangrove area, and are vital inputs for assessing the impact of mangrove conversion on C budget. This study was conducted in a section of tropical intertidal zone in Honda Bay, Philippines, with the following objectives: 1) evaluate the biomass C stocks in mangrove forests and land uses that replaced mangroves, 2) examine the potential of Sentinel satellite radar and multispectral imagery for mapping the aboveground biomass in mangrove area, 3) investigate the soil C stocks and the potential of GIS-based Ordinary Kriging for mapping the C stocks in mangrove soil, and 4) assess the soil fluxes of greenhouse gases and the potential of Ordinary Kriging for mapping the soil GHG fluxes. I used intensive field assessments, combined with laboratory analysis, remote sensing and GIS methods, to achieve the above objectives. To address the first objective, the biomass C stocks of the study land uses were quantified. Their relationships with selected canopy variables were also evaluated. Results reveal that for mangrove forests, the mean biomass was 22.4 to 178.1 Mg ha-1, which store 10 to 80 MgC ha-1 or 47.9 MgC ha-1, on average. Leaf Area Index significantly correlated with mangrove biomass C. In contrast, the biomass C stock of the land uses that replaced mangroves was, on average, 97% less than that in mangrove forests, ranging from zero in salt pond and cleared mangrove, 0.04 Mg C ha-1 in abandoned aquaculture ponds, to 5.7 Mg C ha-1 in the coconut plantation. C losses in biomass from conversion were estimated at 46.5 Mg C ha-1, on average. For the second objective, the potential of Sentinel imagery for the retrieval and predictive mapping of aboveground biomass in mangrove area was evaluated. I used both Sentinel SAR and multispectral imagery. Biomass prediction models were developed through linear regression and Machine Learning algorithms, each from SAR backscatter data, multispectral bands, vegetation indices, and canopy biophysical variables. The results show that the model based on biophysical variable Leaf Area Index (LAI) derived from Sentinel-2 was more accurate in predicting the overall aboveground biomass. However, the SAR-based model was more accurate in predicting the biomass in the usually deficient-to-low vegetation cover replacement land uses. These models had 0.82 to 0.83 correlation/agreement of observed and predicted value. Overall, Sentinel-1 SAR and Sentinel-2 multispectral imagery can provide satisfactory results in the retrieval and predictive mapping of aboveground biomass in mangrove area. In the third objective, the soil C stocks of the study land uses were quantified to estimate C losses in soil owing to conversion. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil C stock distribution in the entire study site. On average, the soil C stock of mangrove forests was 851.9 MgC ha-1 while that of their non-forest competing land uses was less than half at 365.15 MgC ha-1. Aquaculture, salt pond and cleared mangrove had comparable C stocks (453.6, 401, 413 MgC ha-1, respectively) and coconut plantation had the least (42.2 MgC ha-1). Overall, C losses in soil owing to land use conversion in mangrove ranged from 398 to 809 MgC ha-1 (mean: 486.8 MgC ha-1) or a decline of 57% in soil C stock, on average. It was possible to map the site-scale spatial distribution of soil C stock and predict their values with 85% overall certainty using Ordinary Kriging approach. To achieve the fourth objective, the soil fluxes of CO2, CH4 and N2O in the study land uses were investigated using static chamber method. I also evaluated the potential of GIS-based Ordinary Kriging for predictive mapping of the soil GHG fluxes in the entire study site. Results show that the emissions of CO2 and CH4 were higher in mangrove forests by 2.6 and 6.6 times, respectively, while N2O emissions were lower by 34 times compared to the average of non-forest land uses. CH4 and N2O emissions accounted for 0.59% and 0.04% of the total emissions in mangrove forest as compared to 0.23% and 3.07% for non-forest land uses, respectively. Site-scale soil GHG flux distribution could be mapped with 75% to 83% accuracy using Ordinary Kriging. This study has shown that C losses in biomass and soil arising from mangrove conversion are substantial (63%; 571 MgC ha-1). Moreover, mangrove conversion heavily altered the soil-atmosphere fluxes of GHG, increasing the N2O fluxes by 34 times. The use of Sentinel imagery for biomass mapping, as well as the application of Ordinary Kriging for soil mapping of C stocks and GHG fluxes, offer good potentials for mangrove area monitoring. This study advances current knowledge on the C stocks and soil GHG fluxes in mangrove area and the C emissions owing to mangrove conversion. The mapping techniques presented here contribute to advancing the knowledge for mapping the biomass and soil attributes in mangrove ecosystem

Earth Resources. A continuing bibliography with indexes, issue 34, July 1982

This bibliography lists 567 reports, articles, and other documents introduced into the NASA Scientific and Technical Information System between April 1, and June 30, 1982. Emphasis is placed on the use of remote sensing and geophysical instrumentation in spacecraft and aircraft to survey and inventory natural resources and urban areas. Subject matter is grouped according to agriculture and forestry, environmental changes and cultural resources, geodesy and cartography, geology and mineral resources, hydrology and water management, data processing and distribution systems, instrumentation and sensors, and economic analysis

Monitoring Changes and Soil Characterization in Mangrove Forests of the United Arab Emirates Using the Canonical Correlation Forest Model by Multitemporal of Landsat Data

Mangrove forests are an important indicator of blue carbon storage and biodiversity and provide several benefits to the environment. This study showed the first attempt to apply the canonical correlation forest (CCF) model to classify mangroves and monitor changes in the mangrove forests of the entire region. The CCF model obtained a satisfactory accuracy with an F1 score of more than 0.90. Compared to Sentinel-2, Landsat 8 exhibited good temporal resolution with relatively little mangrove details. The resultant mangrove maps (1990–2020) were used to monitor changes in mangrove forests by applying a threshold value ranging from +1 to −1. The results showed a significant increase in the UAE mangroves over the period from 1990 to 2020. To characterize soil in mangrove forests, a set of interpolated maps for calcium carbonate, salinity concentration, nitrogen, and organic matter content was constructed. The results showed that there is a positive relationship between mangrove distribution and the calcium carbonate, nitrogen, salinity, and organic matter concentrations in the soil of the mangrove forests. Our results are of great importance to the ecological and research community. The new maps presented in this study will be a good reference and a useful source for the coastal management organization

Soil erosion in the Alps : causes and risk assessment

The issue of soil erosion in the Alps has long been neglected due to the low economic value of the agricultural land. However, soil stability is a key parameter which affects ecosystem services like slope stability, water budgets (drinking water reservoirs as well as flood prevention), vegetation productivity, ecosystem biodiversity and nutrient production. In alpine regions, spatial estimates on soil erosion are difficult to derive because the highly heterogeneous biogeophysical structure impedes measurement of soil erosion and the applicability of soil erosion models. However, remote sensing and geographic information system (GIS) methods allow for spatial estimation of soil erosion by direct detection of erosion features and supply of input data for soil erosion models. Thus, the main objective of this work is to address the problem of soil erosion risk assessment in the Alps on catchment scale with remote sensing and GIS tools. Regarding soil erosion processes the focus is on soil erosion by water (here sheet erosion) and gravity (here landslides). For these two processes we address i) the monitoring and mapping of the erosion features and related causal factors ii) soil erosion risk assessment with special emphasis on iii) the validation of existing models for alpine areas. All investigations were accomplished in the Urseren Valley (Central Swiss Alps) where the valley slopes are dramatically affected by sheet erosion and landslides. For landslides, a natural susceptibility of the catchment has been indicated by bivariate and multivariate statistical analysis. Geology, slope and stream density are the most significant static landslide causal factors. Static factors are here defined as factors that do not change their attributes during the considered time span of the study (45 years), e.g. geology, stream network. The occurrence of landslides might be significantly increased by the combined effects of global climate and land use change. Thus, our hypothesis is that more recent changes in land use and climate affected the spatial and temporal occurrence of landslides. The increase of the landslide area of 92% within 45 years in the study site confirmed our hypothesis. In order to identify the cause for the trend in landslide occurrence time-series of landslide causal factors were analysed. The analysis revealed increasing trends in the frequency and intensity of extreme rainfall events and stocking of pasture animals. These developments presumably enhanced landslide hazard. Moreover, changes in land-cover and land use were shown to have affected landslide occurrence. For instance, abandoned areas and areas with recently emerging shrub vegetation show very low landslide densities. Detailed spatial analysis of the land use with GIS and interviews with farmers confirmed the strong influence of the land use management practises on slope stability. The definite identification and quantification of the impact of these non-stationary landslide causal factors (dynamic factors) on the landslide trend was not possible due to the simultaneous change of several factors. The consideration of dynamic factors in statistical landslide susceptibility assessments is still unsolved. The latter may lead to erroneous model predictions, especially in times of dramatic environmental change. Thus, we evaluated the effect of dynamic landslide causal factors on the validity of landslide susceptibility maps for spatial and temporal predictions. For this purpose, a logistic regression model based on data of the year 2000 was set up. The resulting landslide susceptibility map was valid for spatial predictions. However, the model failed to predict the landslides that occurred in a subsequent event. In order to handle this weakness of statistic landslide modelling a multitemporal approach was developed. It is based on establishing logistic regression models for two points in time (here 1959 and 2000). Both models could correctly classify >70% of the independent spatial validation dataset. By subtracting the 1959 susceptibility map from the 2000 susceptibility map a deviation susceptibility map was obtained. Our interpretation was that these susceptibility deviations indicate the effect of dynamic causal factors on the landslide probability. The deviation map explained 85% of new independent landslides occurring after 2000. Thus, we believe it to be a suitable tool to add a time element to a susceptibility map pointing to areas with changing susceptibility due to recently changing environmental conditions or human interactions. In contrast to landslides that are a direct threat to buildings and infrastructure, sheet erosion attracts less attention because it is often an unseen process. Nonetheless, sheet erosion may account for a major proportion of soil loss. Soil loss by sheet erosion is related to high spatial variability, however, in contrast to arable fields for alpine grasslands erosion damages are long lasting and visible over longer time periods. A crucial erosion triggering parameter that can be derived from satellite imagery is fractional vegetation cover (FVC). Measurements of the radiogenic isotope Cs-137, which is a common tracer for soil erosion, confirm the importance of FVC for soil erosion yield in alpine areas. Linear spectral unmixing (LSU), mixture tuned matched filtering (MTMF) and the spectral index NDVI are applied for estimating fractional abundance of vegetation and bare soil. To account for the small scale heterogeneity of the alpine landscape very high resolved multispectral QuickBird imagery is used. The performance of LSU and MTMF for estimating percent vegetation cover is good (r²=0.85, r²=0.71 respectively). A poorer performance is achieved for bare soil (r²=0.28, r²=0.39 respectively) because compared to vegetation, bare soil has a less characteristic spectral signature in the wavelength domain detected by the QuickBird sensor. Apart from monitoring erosion controlling factors, quantification of soil erosion by applying soil erosion risk models is done. The performance of the two established models Universal Soil Loss Equation (USLE) and Pan-European Soil Erosion Risk Assessment (PESERA) for their suitability to model erosion for mountain environments is tested. Cs-137 is used to verify the resulting erosion rates from USLE and PESERA. PESERA yields no correlation to measured Cs-137 long term erosion rates and shows lower sensitivity to FVC. Thus, USLE is used to model the entire study site. The LSU-derived FVC map is used to adapt the C factor of the USLE. Compared to the low erosion rates computed with the former available low resolution dataset (1:25000) the satellite supported USLE map shows “hotspots” of soil erosion of up to 16 t ha-1 a-1. In general, Cs-137 in combination with the USLE is a very suitable method to assess soil erosion for larger areas, as both give estimates on long-term soil erosion. Especially for inaccessible alpine areas, GIS and remote sensing proved to be powerful tools that can be used for repetitive measurements of erosion features and causal factors. In times of global change it is of crucial importance to account for temporal developments. However, the evaluation of the applied soil erosion risk models revealed that the implementation of temporal aspects, such as varying climate, land use and vegetation cover is still insufficient. Thus, the proposed validation strategies (spatial, temporal and via Cs-137) are essential. Further case studies in alpine regions are needed to test the methods elaborated for the Urseren Valley. However, the presented approaches are promising with respect to improve the monitoring and identification of soil erosion risk areas in alpine regions

Development of a methodology for monitoring changes in Ghanaian forest reserves

The Ghanaian Forests are a significant component of the country’s development. Occasioned by the rapid population growth of the country, increasing phenomena such as shifting agriculture, logging, fuelwood harvesting and fire outbreaks have claimed over 70% of the original forests. The reduction of forests has stimulated the development of management tools to control forest depletion. In order to focus the intervention of forest managers and environmental planners, the rate and impact of forest depletion must be monitored and well documented. Financial constraints and the lack of adequate maps have hindered the setting up of effective monitoring mechanisms. This study illustrated the feasibility for using Landsat data and GIS to map changes in the Ghanaian forest reserves. GIS was used to create the initial database for the study. Three image analysis change detection methods namely image algebra (image differencing), spectral temporal and spectral temporal principal component analysis were employed. The results of the analysis showed that spatial distributions of the changed areas produced by all three methods were similar, varying only in the extent. The remote sensing image analysis required the information stored in the GIS database for rectification and for the assessment of the classification procedure. A quantitative accuracy assessment was not possible for the procedures due to limited ground truthing. The use of GPS in field data collection was demonstrated by its use in delineating the boundary of a selected reserve. The GPS data was able to adequately display the reserve boundary, the spatial distribution of Taungya and farms along the boundary as well as relocated boundary pillars. All new layers of information generated from the research were displayed and stored in the GIS. Finally, the importance of the outlined procedures in the monitoring of Ghanaian forest and the limitations of the study were discussed