Urban forest loss using a GIS-based approach and instruments for integrated urban planning:A case study of Jo(a)o Pessoa,Brazil

Urban forests play an important role in the thermal comfort and overall life of local populations in large-and medium-sized cities.This study analyzes urban forest loss and maps land use and land cover (LULC) changes between 1991 and 2018 by evaluating the use of urban planning instruments for the mitigation of urban forest loss in Jo～o Pessoa,Brazil.For this purpose,satellite-derived LULC images from 1991,2006,2010 and 2018 and data on urban forest loss areas obtained using the Google Earth Engine were used.In addi-tion,this paper also discusses the instruments used for integrated urban planning,which are(a) the legal sector,responsibility and nature;(b) the urban expansion process;and (c) the elements of urban infrastructure.The results show a clear shift in land use in the study area.The major changes in LULC classes occurred in urban areas and herbaceous vegetation,while the greatest loss was in arboreal/shrub vegetation.Thus,an increase in the pressure to occupy zones intended for environmental preservation could be estimated.Our results showed similar accuracies with other studies and more spatial details.The characteristics of the patterns,traces,and hotspots of urban expansion and forest cover loss were explored.We highlighted the potential use of this proposed framework to be applied and validated in other parts of the world to help better understand and quantify various aspects of ur-ban-related problems such as urban forest loss mapping using instruments for integrated urban planning and low-cost approaches.     

Growth, ionic and osmotic relations of an Allenrolfea occidentalis population in an inland salt playa of the Great Basin Desert

Variation in growth, physiology and ionic relations patterns of Allenrolfea occidentalis, a perennial halophyte of dry habitats, was studied under field conditions from May 1996 to November 1997. An A. occidentalis community has a characteristic soil pH of 7·3–8·3. During the two years, the population was exposed to great variations in soil salinity, from 29 to 146 dS m⁻¹, and soil moisture, ranging from drought (9·2%) to wet (19%). The salt concentrations were significantly higher in the surface soil layers than in the subsurface layers. Seasonal changes in dry weight are directly related to soil salinity stress. Allenrolfea occidentalis had greater growth and biomass production under saline conditions. Na⁺and Cl⁻ions were accumulated in plant tissues in much greater amounts than K⁺, Ca²⁺, and Mg²⁺. Soil salinities were significantly reduced at the end of the growing season. Water potentials of the shoots decreased significantly with increasing salinity. The plant (F_v/F_mratio) was more affected by salinity and irradiation levels during the summer period.     

RHESSI Microflares: II. Implications for Loop Structure and Evolution

We present simple analytic models which predict the peak X-ray emission measure and temperature attained in flares in which the chromospheric evaporation process takes place either in a single ‘monolithic’ loop or in a loop consisting of several filaments that are created successively as the energy release process proceeds in time. As possible mechanisms driving chromospheric evaporation we consider both classical heat conduction from the loop top and non-thermal electron beams. The model predictions are tested for a set of 18 well studied RHESSI microflares. The results suggest beam driven evaporation in filamented loops as being capable of accounting for the observed emission measures and temperatures though there are issues with the very high beam densities needed. On the other hand, estimates of the emission measures achieved by conductive evaporation which are derived by using the Rosner – Tucker – Vaiana (RTV) scaling law are much larger than the observed ones. Possible reasons for this discrepancy are discussed.     

Validation of an annual cycle simulation with a T42-L20 GCM

An annual cycle of an atmospheric general circulation model (AGCM) is presented. The winter and summer zonal averages of the atmospheric fields are compared with an observed climatology. The main features of the observed seasonal means are well reproduced by the model. One of the main discrepancies is that the simulated atmosphere is too cold, particularly in its upper part. Some other discrepancies might be explained by the interannual variability. The AGCM surface fluxes are directly compared to climatological estimates. On the other hand, the calculation of meridional heat transport by the ocean, inferred from the simulated energy budget, can be compared to transport induced from climatologies. The main result of this double comparison is that AGCM fluxes generally are within the range of climatological estimates. The main deficiency of the model is poor partitioning between solar and non-solar heat fluxes in the tropical belt. The meridional heat transport also reveals a significant energy-loss by the Northern Hemisphere ocean north of 45° N. The possible implications of model surface flux deficiencies on coupling with an oceanic model are discussed.This paper was presented at the International Conference on Modelling of Global Climate Change and Variability, held in Hamburg 11–15 September 1989 under the auspices of the Meteorological Institute of the University of Hamburg and the Max Planck Institute for Meteorology. Guest Editor for these papers is Dr. L. Dümenil     

Land cover classification by an artificial neural network with ancillary information

Abstract

Remote sensing is an important source of land cover data required by many GIS users. Land cover data are typically derived from remotely–sensed data through the application of a conventional statistical classification. Such classification techniques are not, however, always appropriate, particularly as they may make untenable assumptions about the data and their output is hard, comprising only the code of the most likely class of membership. Whilst some deviation from the assumptions may be tolerated and a fuzzy output may be derived, making more information on class membership properties available, alternative classification procedures are sometimes required. Artificial neural networks are an attractive alternative to the statistical classifiers and here one is used to derive a fuzzy classification output from a remotely–sensed data set that may be post–processed with ancillary data available in a GIS to increase the accuracy with which land cover may be mapped. With the aid ancillary information on soil type and prior knowledge of class occurrence the accuracy of an artificial neural network classification was increased by 29–93 to 77–37 per cent. An artificial neural network can therefore be used generate a fuzzy classification output that may be used with other data sets in a GIS, which may not have been available to the producer of the classification, to increase the accuracy with which land cover may be classified.     

Spatiotemporal variability in channel deformations on rivers of Russia

We assess the spatiotemporal changes in channel processes on rivers of Russia, determine the causes for vertical (incision or directional sediment accumulation) and horizontal (displacement of channel forms) deformations and show the distribution of stream channel of different morphodynamical types and with a different reconfiguration rate. The conditions are revealed, under which the channel types change over time. Particular emphasis is placed on the analysis of spatiotemporal changes in channels caused by anthropogenic disturbances and by direct technogenic interferences in the life of rivers (hydroelectric schemes, quarries in the river channels, and waterway dredging).