No abstract

Globally, cities are growing rapidly in size and density and this has caused profound impacts on urban forest ecosystems. Urbanization requiring deforestation reduces ecosystem services that benefit both city dwellers and biodiversity. Understanding spatial and temporal patterns of vegetation changes associated with urbanization is thus a vital component of future sustainable urban development. We used Landsat time series data for three decades from 1988 to 2018 to characterize changes in vegetation cover and habitat connectivity in the Perth Metropolitan Area, in a rapidly urbanising Australian biodiversity hotspot, as a case study to understand the impacts of urbanization on urban forests. Moreover, as golf courses are a major component in urban areas, we assessed the role of golf courses in maintaining vegetation cover and creating habitat connectivity. To do this we employed (1) land use classification with post-classification change detection, and (2) Morphological Spatial Pattern Analysis (MSPA). Over 17,000 ha of vegetation were cleared and the area of vegetation contributing to biodiversity connectivity was reduced significantly over the three decades. The spatial patterns of vegetation loss and gain were different in each of the three decades reflecting the implementation of urban planning. Furthermore, MSPA analysis showed that the reduction in vegetation cover led to habitat fragmentation with a significant decrease in the core and bridge classes and an increase in isolated patches in the urban landscape. Golf courses played a useful role in maintaining vegetation cover and contributing to connectivity in a regional biodiversity hotspot. Our findings suggest that for future urban expansion, urban planning needs to more carefully consider the impacts of deforestation on connectivity in the landscape. Moreover, there is a need to take into consideration opportunities for off-reserve conservation in smaller habitat fragments such as in golf courses in sustainable urban management.

High revenues from rubber latex exports have led to a rapid expansion of commercial rubber cultivation and, as a consequence, the conversion of different land use types (e.g., natural forests) into rubber plantations, which may lead to a decrease in soil health. In this study in Quang Tri Province, Vietnam, we determined: (1) the variation of soil health parameters along a chronosequence of rubber tree stands and natural forests and (2) the relationships and potential feedback between vegetation types, vegetation structures and soil health. Our results revealed that: (1) soil health was higher in natural forests than in rubber plantations with a higher values in higher biomass forests; (2) soil health was lower in younger rubber plantations; (3) soil health depends on vegetation structure (with significantly positive relationships found between soil health and canopy cover, litter biomass, dry litter cover and ground vegetation cover). This study highlights the need for more rigorous land management practices and land use conversion policies in order to ensure the long-term conservation of soil health in rubber plantations.

Can Gio mangrove is the largest in Vietnam, developing on approximately 35000 hectares. This forest was partially destroyed during the Vietnamese war. A restoration program was developed between the late 70s and the early 90s, using Rhizophora apiculata Blume propagules. Currently, the Can Gio mangrove forest regenerates naturally and presents a specific species zonation along the intertidal elevation gradient. Rhizophora dominates the inner forest at the highest elevation, while at an intermediate location, Rhizophora and Avicennia cohabit with other scattered species. The lowest position is colonized by Avicennia.Within this context, the main objectives of this study were to determine the soil physicochemical characteristics, as well as the quality (C/N ratios and δ 13 C) and the quantity (carbon content and stocks) of the organic matter stored beneath each mangrove stand. In addition, we were interested in determining the above-ground biomass and the total carbon stocks of the ecosystem (without considering the below-ground biomass). Carbon stocks of the Can Gio mangrove forest ranged from 150 to 479 Mg C ha -1 , with up to 86 % of the C stored in the upper meter of the soil. The inner forest has the highest stock, followed by the transitional forest, and the fringe forest. The depth extension of the root system of the current forest was estimated, and its contribution to the soil carbon stock was calculated, using the adjacent mudflat as a proxy for the antecedent stocks. Our results show that, for the last 40 years, the current mature planted Rhizophora forest stored 25.26 Mg C ha -1 . Consequently, mangrove plantation and restoration after the war was a success in terms of carbon storing.We suggest that the destruction of the Can Gio mangrove forests for urban development would induce the loss of an efficient CO2 sink.

Agricultural land fires have been linked to various and adverse impacts on ecosystems, food security and the agriculture sector. Understanding the patterns and drivers of agricultural land fires is essential for effective agricultural land fire management. The key objectives of this study were to (1) analyze the temporal and spatial patterns of agricultural land fires using satellite remote sensed data, (2) assess a range of environmental conditions that could drive the occurrence of agricultural land fires, (3) determine the best model for predicting agricultural land fires and (4) determine the relative contribution of each environmental condition variable on the best predictive model. We used both univariate and multivariate regressions for the fire prediction capability of four independent environmental conditions (fuel, weather, topographic and anthropogenic). Analysis of historical satellite data revealed that agricultural land fires were more frequent than forested land fires. Our analyses also revealed that fuel condition was the most important variable for predicting agricultural land fires followed by weather, topographic and anthropogenic conditions. This study provides a novel multivariate model for predicting agricultural land fires that harbors the potential to improve agricultural land fire management and reduce fire risk within the agricultural sector.