Land use conversion from peat swamp forest to oil palm agriculture greatly modifies microclimate and soil conditions

Oil palm (Elaeis guineensis) agriculture is rapidly expanding and requires large areas of land in the tropics to meet the global demand for palm oil products. Land cover conversion of peat swamp forest to oil palm (large- and small-scale oil palm production) is likely to have negative impacts on microhabitat conditions. This study assessed the impact of peat swamp forest conversion to oil palm plantation on microclimate conditions and soil characteristics. The measurement of microclimate (air temperature, wind speed, light intensity and relative humidity) and soil characteristics (soil surface temperature, soil pH, soil moisture, and ground cover vegetation temperature) were compared at a peat swamp forest, smallholdings and a large-scale plantation. Results showed that the peat swamp forest was 1.5–2.3 °C cooler with significantly greater relative humidity, lower light intensities and wind speed compared to the smallholdings and large-scale plantations. Soil characteristics were also significantly different between the peat swamp forest and both types of oil palm plantations with lower soil pH, soil and ground cover vegetation surface temperatures and greater soil moisture in the peat swamp forest. These results suggest that peat swamp forests have greater ecosystem benefits compared to oil palm plantations with smallholdings agricultural approach as a promising management practice to improve microhabitat conditions. Our findings also justify the conservation of remaining peat swamp forest as it provides a refuge from harsh microclimatic conditions that characterize large plantations and smallholdings

Detecting Trends in Wetland Extent from MODIS Derived Soil Moisture Estimates

A soil wetness index for optical satellite images, the Transformed Wetness Index (TWI) is defined and evaluated against ground sampled soil moisture. Conceptually, TWI is formulated as a non-linear normalized difference index from orthogonalized vectors representing soil and water conditions, with the vegetation signal removed. Compared to 745 ground sites with in situ measured soil moisture, TWI has a globally estimated Random Mean Square Error of 14.0 (v/v expressed as percentage), which reduces to 8.5 for unbiased data. The temporal variation in soil moisture is significantly captured at 4 out of 10 stations, but also fails for 2 to 3 out of 10 stations. TWI is biased by different soil mineral compositions, dense vegetation and shadows, with the latter two most likely also causing the failure of TWI to capture soil moisture dynamics. Compared to soil moisture products from microwave brightness temperature data, TWI performs slightly worse, but has the advantages of not requiring ancillary data, higher spatial resolution and a relatively simple application. TWI has been used for wetland and peatland mapping in previously published studies but is presented in detail in this article, and then applied for detecting changes in soil moisture for selected tropical regions between 2001 and 2016. Sites with significant changes are compared to a published map of global tropical wetlands and peatlands

Nutrient removal processes in freshwater submersed macrophyte systems

A recent development for the control of eutrophication is the application of ecological engineering, involving designed wetlands for water treatment. The most undeveloped concept for designed wetlands is the use of submersed macrophytes. Apart from nutrient uptake, the macrophytes play a crucial role by creating a favourable environment for a variety of complex chemical, biological and physical processes that contribute to the removal and degradation of nutrients. In unharvested systems nitrogen is mainly removed by denitrification. If the system is harvested, nutrient assimilation is approximately of the same magnitude. Also, sedimentation of nitrogen is important, especially during colder periods when biological activity decreases. The removal of phosphorus is more dependent on biomass uptake and subsequent harvesting. Immobilisation by sorption and precipitation processes are also important removal mechanisms, especially in unharvested systems. The efficiency of the removal processes is largely determined by the Chemical and physical composition of the media. Much efficiency can be gained by tuning the composition and management of the plant according to the kind of water that is treated, thus creating favourable conditions for the different kind of removal processes

Hydrological modelling and resource management in the Okavango Delta

The Okavango Delta in Botswana is one of the world's most fascinating wetland systems. A hydrological model of the Delta is presented, which is based on a finite difference formulation of the relevant flow processes (surface water and groundwater). Spatially distributed input data include rainfall, evapotranspiration and microtopography. The model results are compared to flooding patterns derived from remote sensing. Some questions concerning the sustainable water use and management of the Delta are discussed in view of the modelling results.</p

Ecoregion classification in the Okavango Delta, Botswana from multitemporal remote sensing

The Okavango inland Delta in Botswana is characterized by a high spatial and temporal variation in vegetation patches and flooding. Predicting the effects of escalating development projects in this pristine wildlife area is hampered by a lack of accurate maps. Efforts using traditional statistical methods have been futile. The processes forming this highly dynamic environment, however, give rise to a well-documented consistency in the land cover pattern at scales ranging from single island architecture to an overall gradient in wetland, flood plain and island occurrence. We conducted a classification in a two-step process starting with statistical methods, and then refining using indices and flooding data. The indices and flooding data were created and selected to make possible the inferring of knowledge about the patterns at different scales through declarative IF ... THEN ... statements. The initial statistical classification achieved a best result of 46% accuracy for 10 classes, whereas the rule-based classification achieved an accuracy of 63%. Application of the derived classification for mapping islands and topography shows a surprisingly high accuracy.</p

Mapping of peatlands in the forested landscape of Sweden using lidar-based terrain indices

Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover change and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against lidar-based terrain indices. Machine learning methods were used to produce nationwide raster maps at 10 m spatial resolution indicating the presence or not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated an accuracy of 0.89-0.91 and Matthew's correlation coefficient of 0.79-0.81. The final maps showed a national forest peatland extent of 60 292-71 996 km(2), estimates which are in the range of previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with lidar-based terrain indices. The final peatland maps are publicly available at (Rimondini et al., 2023) and may be employed for spatial planning, estimating carbon stocks and evaluating climate change mitigation strategies.</p

Mapping of peatlands in the forested landscape of Sweden using LiDAR-based terrain indices

Abstract. Globally, northern peatlands are major carbon deposits with important implications for the climate system. It is therefore crucial to understand their spatial occurrence, especially in the context of peatland degradation by land cover and climate change. This study was aimed at mapping peatlands in the forested landscape of Sweden by modelling soil data against LiDAR-based terrain indices. Machine learning methods were used to produce nation-wide raster maps at 10 m spatial resolution indicating presence-or-not of peatlands. Four different definitions of peatlands were examined: 30, 40, 50 and 100 cm thickness of the organic horizon. Depending on peatland definition, testing with a hold-out dataset indicated Accuracy of 0.89–0.91 and Matthew’s correlation coefficient of 0.79–0.81. The final maps showed a national forest peatland extent of 60,726–72,604 km2, estimates which are in range with previous studies employing traditional soil maps. In conclusion, these results emphasize the possibilities of mapping boreal peatlands with LiDAR-based terrain indices. The final peatland maps are publicly available and may be employed for spatial planning, estimating carbon stocks and evaluate climate change mitigation strategies. </jats:p

A regional coupled surface water/groundwater model of the Okavango Delta, Botswana

In the endorheic Okavango River system in southern Africa a balance between human and environmental water demands has to be achieved. The runoff generated in the humid tropical highlands of Angola flows through arid Namibia and Botswana before forming a large inland delta and eventually being consumed by evapotranspiration. With an approximate size of about 30,000 km2, the Okavango Delta is the world's largest site protected under the convention on wetlands of international importance, signed in 1971 in Ramsar, Iran. The extended wetlands of the Okavango Delta, which sustain a rich ecology, spectacular wildlife, and a first-class tourism infrastructure, depend on the combined effect of the highly seasonal runoff in the Okavango River and variable local climate. The annual fluctuations in the inflow are transformed into vast areas of seasonally inundated floodplains. Water abstraction and reservoir building in the upstream countries are expected to reduce and/or redistribute the available flows for the Okavango Delta ecosystem. To study the impacts of upstream and local interventions, a large-scale (1 km2 grid), coupled surface water/groundwater model has been developed. It is composed of a surface water flow component based on the diffusive wave approximation of the Saint-Venant equations, a groundwater component, and a relatively simple vadose zone component for calculating the net water exchange between land and atmosphere. The numerical scheme is based on the groundwater simulation software MODFLOW-96. Since the primary model output is the spatiotemporal distribution of flooded areas and since hydrologic data on the large and inaccessible floodplains and tributaries are sparse and unreliable, the model was not calibrated with point hydrographs but with a time series of flooding patterns derived from satellite imagery (NOAA advanced very high resolution radiometer). Scenarios were designed to study major upstream and local interventions and their expected impacts in the Delta. The scenarios' results can help decision makers strike a balance between environmental and human water demands in the basin.</p