Bare Land Cover Research Articles

Temporal Dynamics of Global Barren Areas between 2001 and 2022 Derived from MODIS Land Cover Products

Long-term monitoring studies on the transition of different land cover units to barren areas are crucial to gain a better understanding of the potential challenges and threats that land surface ecosystems face. This study utilized the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover products (MCD12C1) to conduct geospatial analysis based on the maximum extent (MaxE) concept, to assess the spatiotemporal changes in barren areas from 2001 to 2022, at global and continental scales. The MaxE area includes all the pixels across the entire period of observations where the barren land cover class was at least once present. The relative expansion or reduction of the barren areas can be directly assessed with MaxE, as any annual change observed in the barren distribution is comparable over the entire dataset. The global barren areas without any land change (UA) during this period were equivalent to 12.8% (18,875,284 km2) of the global land surface area. Interannual land cover changes to barren areas occurred in an additional area of 3,438,959 km2 (2.3% of the global area). Globally, barren areas show a gradual reduction from 2001 (91.1% of MaxE) to 2012 (86.8%), followed by annual fluctuations until 2022 (88.1%). These areas were mainly interchanging between open shrublands and grasslands. A relatively high transition between barren areas and permanent snow and ice is found in Europe and North America. The results show a 3.7% decrease in global barren areas from 2001 to 2022. Areas that are predominantly not barren account for 30.6% of the transitional areas (TAs), meaning that these areas experienced short-term or very recent transitions from other land cover classes to barren. Emerging barren areas hotspots were mainly found in the Mangystau region (Kazakhstan), Tibetan plateau, northern Greenland, and the Atlas Mountains (Morocco, Tunisia).

Land use land cover change intensity analysis for sustainable natural resources management: The case of northwestern highlands of Ethiopia

Various studies conducted in the northwestern highlands of Ethiopia from 2001 to 2020 indicated two contrasting directions of change in land use land cover. One direction is the expansion of farmlands at the expense of the natural vegetation, and the other direction is expansion of plantation forest on farm and grazing lands. But, the net effect of land use land cover change is not as such clear. A clear understudying of such dynamics in land use land cover and the net effect of the change helps design effective land use planning policy, which enhances sustainable natural resources management. Accordingly, the study analyzed intensity of land use land cover change for three-time periods (1990–2000; 2000–2010 and 2010–2020). The findings of this study can be applicable to different parts of the globe where two contrasting directions of change in land use land cover exist. The study employed both qualitative and quantitative methods, which were specifically applied through carrying out step-by-step: satellite image acquisition, preprocessing, classification, accuracy assessment, and intensity analysis. Result of the analysis indicated that land use land cover change over the three intervals showed considerable variation where intensity was consistently increasing from the first (1990–2000) interval to the subsequent (2000–2010) interval, and it was fast (8.24%) during the third interval (2010–2020). The analysis also indicated considerably high transition from bare land to grazing land and bush land during the third interval, especially on areas where the community practiced controlled management of grazing lands (zero grazing) and protection of bush lands. From the total bare land cover in 2010, 21% converted to grazing land and 17% converted to bush land. LULC transition from bush land to cultivated land was rising due to encroachment of bush lands by farming. Transition from bush land to cultivated land increased by 4%, 16% and 50% in the period 1990–2000, 2000–2010, and 2010–2020, respectively. On the other hand, transition from cultivated land to forest land was considerably high during the third interval. From the total cultivated land cover in 2010, 25% converted to forest land, where the change is mainly associated to the introduction of woodlot plantation on individual farmland holdings. In general, the finding suggests that controlled management of grazing lands; comparing environmental and economic benefits of quarry/stone extraction sites; and plantation of economically viable and environmentally sound tree species are key policy issues which needs due attention in land use planning and management practices.

SPATIO-TEMPORAL DYNAMICS OF URBAN THERMAL ENVIRONMENT IN UDAIPUR CITY, RAJASTHAN, INDIA

Abstract. The Udaipur urban agglomeration was selected to analyse the urban heat island (UHI) effect in the area from 2005 to 2017. Landsat 7 ETM+ sensor data was used to derive land surface temperatures (LST) and map landscape characteristics. The agglomeration was classified into seven land use land cover classes including agriculture cropped, agriculture fallow, barren, built up, scrub, vegetation and water using unsupervised classification method. The LST results were obtained using NDVI method. Urban heat island intensities were mapped using z-score method. Regions with UHI values of above 2 standard deviations were considered to reflect UHI effect. Results show an overall decrease of 10.2 percent in agricultural cropped and fallow lands. The scrub class also shows a moderate decline of 2.3 percent. Increase in area were observed for built up, barren and vegetation classes by 8.8, 2.1 and 1.9 percent respectively. The dominant LULC change was the transformation of agricultural lands to built-up class. The class-wise mean LST observations increase in order of water, vegetation, cropped, fallow, built up, scrub and barren land. The mean LST records an increase of 1.7° C from 30.1° C to 31.8° C over the study period. Results suggest that UHI effects are more prominent in urban fringe area corresponding with the built up and barren land cover classes contrary to its core area. Built up areas surrounded by barren lands left vacant for city’s sprawl in the future exhibit highest UHI intensities.

Arctic tundra shrubification can obscure increasing levels of soil erosion in NDVI assessments of land cover derived from satellite imagery

Monitoring soil erosion in the Arctic tundra is complicated by the highly fragmentated nature of the landscape and the limited spatial resolution of even high-resolution satellite data. The expansion of shrubs across the Arctic has led to substantial changes in vegetation composition that alter the spectral reflectance and directly affect vegetation indices such as the normalized difference vegetation index (NDVI), which is widely applied for environmental monitoring. This change can mask soil erosion if datasets with too coarse spatial resolutions are used, as increases in NDVI driven by shrub expansion can obscure concurrent increases in barren land cover. Here we created land cover maps from a multispectral uncrewed aerial vehicle (UAV) and land cover survey and assessed satellite imagery from PlanetScope, Sentinel-2 and Landsat-8 for several areas in north-eastern Iceland. Additionally, we used a novel application of the Shannon evenness index (SHEI) to evaluate levels of pixel mixing. Our results show that shrub expansion can lead to spectral confusion, which can obscure soil erosion processes and emphasize the importance of considering spatial resolution when monitoring highly fragmented landscapes. We demonstrate that remote sensing data with a resolution < 3 m greatly improves the amount of information captured in an Icelandic tundra environment. The spatial resolution of Landsat data (30 m) is inadequate for environmental monitoring in our study area. We found that the best platform for monitoring tundra land cover is Sentinel-2 when used in combination with multispectral UAV acquisitions for validation. Our study has the potential to improve environmental monitoring capabilities by introducing the use of SHEI to assess pixel mixing and determine optimal spatial resolutions. This approach combined with comparing remote sensing imagery of different spatial and time scales significantly advances our comprehension of land cover changes, including greening and soil degradation, in the Arctic tundra.

Land Use or Land Cover Change and its Driving Forces in Mago National Park, Southern Ethiopia

Land use/land cover change analysis is one of the most particular techniques to understand how land was used in the past, what types of changes are to be expected in the future, as well as the forces and processes behind the changes. Thus, the objective of this study was to investigate the land use land cover changes and its driving forces in Mago National Park, southern Ethiopia. Satellite image of Landsat5 TM (1988, 1998 and 2008) and Landsat8 OLI/TIRS (2018) years were employed. In addition, social survey was conducted to study the drivers of land use/land cover changes. QGIS 3.2 and SPSS software’s were used for satellite image processing, accuracy assessment, map preparation and descriptively analyze the driving forces of LULCC respectively. Supervised classification with maximum likelihood algorithm was conducted for satellite image analysis and generation of information using Quantum GIS 3.2. In the first period (1988-1998), woodland, riverine forest, water body and bare land decreased by 6.76%, 37.98%, 22.37% and 70.14% respectively, while grass land, and degraded land increased by 16.11% and 85.67% respectively. In the second period, (1998 2008), woodland, riverine forest and degraded land were decreased by 5.44%, 4.61% and 80.74% respectively, while grass land, water body and bare land is increased by 14.74%, 3.76% and 52.58% respectively. From 2008-2018 riverine forest, grassland, water body and bare land decreased by 1.33%, 15.16% and 4.82% and 25.02% respectively, while woodland increased by 11.84%, and degraded land increased by 85.49% respectively. Riverine forest, water body, grass land and bare land showed decrement and that of woodland, degraded land indicated increment during study period. From 1988-2018, woodland, riverine forest, water body and bare land indicated decrement and the remaining grass land and bare land cover types indicated increment during study period. The result of social survey indicated that expansion of agriculture, human induced fire, overgrazing and hunting are proximate driving forces of the change in Mago National Park. Population pressure, poverty, decreased farmlands productivity, education, weak law enforcement and cultural factors are the major underlying causes of the observed changes. Therefore, proper land use planning, legal support, and strong law enforcement are the key recommendations to sustain natural resources of the study area.

Land cover classification and urbanization monitoring using Landsat data: A case study in Changsha city, Hunan province, China

The United Nations predicts that by 2050, 64.1% of the developing world and 85.9% of the developed world will be urbanized. This has resulted in a rapid change in land use and land cover types in the areas surrounding cities in all countries, particularly in China, which determines the relevance of this article. The aim of the study was to evaluate the dynamics of land cover change in Changsha City, Hunan Province, China, between 2005 and 2020, using Landsat time series satellite images and the Random Forest classification algorithm. The data acquisition, pre-processing, and analysis were conducted on the Google Earth Engine (GEE) publicly available online platform. Land cover thematic continuous raster maps were produced using ESRI ArcGIS 10.5.1 software. The overall classification accuracy was obtained by more than 83% for every produced map and the Kappa coefficient was 0.84 and higher, which approves the reliable classification results that are close to similar recent studies in terms of obtained accuracy. The study shows that from 2005 to 2020, the area of settlement in Changsha City, China, increased significantly, with an exponential increase in urban area from 3.23% to 15.95%. The proportion of forest cover gradually decreased from 2005 to 2015 but increased from 2015 to 2020. Cropland was the second most dominant land cover type, with a peak of almost 50% in 2010. Water bodies remained stable at around 3%. The proportion of open soil and bare land cover fluctuated between 180 and 400 km2 (1.5-3%). The study suggests that the offered monitoring approach provides reliable results, and the research findings can be used for sustainable urban planning and management, as well as conservation and development initiatives. The remote sensing data and advanced GIS technologies can provide decision-makers with the accurate data to ensure sustainable development in this area

Rapid spatial growth of cities and its planning implications for developing countries: a case study of Abuja, Nigeria.

Accelerated spatial growth of urban areas is a key driver to land use/land cover change with its concomitant effect on environmental sustainability. The dearth of data on the rate of urban expansion, especially in many developing countries, including Nigeria has continued to hinder effective land use planning and sustainable development. The study aims to identify and analyze the settlement patterns and trends in urban growth at ten years intervals and their planning implications in Abuja, Nigeria. It relied on data generated via remote sensing and Geographic Information Systems to create the map and examine the land cover change in the study area. Classification of land cover using LANDSAT data and land cover transitions for 29 years (1990 to 2019) were mapped and the net land cover change was computed. The results showed the settlement pattern and an increase in the urban built-up area ranging from 1.8% in 1990 to 19.3% in 2019. The dispersion pattern revealed a large concentration of the built-up spaces to be in the eastern region and that the expansion continued from east to south and south-west. The bare land cover types were found to have increased while vegetation land cover decreased rapidly by 30.4% from 1990-2019. The study recommends the need for city planners to decentralize urban planning and development control with adequate provision of affordable urban facilities at the peripheries of cities in Nigeria. Furthermore, massive integration of green infrastructure in built-up areas is required to mitigate the effects of vegetation loss in cities.

A Model between Near-Surface Air Temperature Change and Dynamic Influencing Factors in the Eastern Tibetan Plateau, China

Climate change, characterized by global warming, is profoundly affecting the global environment, politics, economy, and social security. Finding the main causes of climate change and determining their quantitative contributions are key points to making climate decisions on responses to climate change. The Tibetan Plateau (TP) is sensitive to global climate change. Taking the 100 km buffer zones of 45 meteorological stations in the eastern TP as research objects, we conducted an experimental study on temperature change and its influencing factors. Using the least squares multivariate statistical analysis method, a model between the annual and seasonal standardized temperature change and its dynamic influencing factors in the past 20 years was established. The results showed that, in the eastern TP, temperature change was affected by different factors in different periods. Vegetation cover and snow cover were the most correlated factors to temperature change. The influence of carbon dioxide, vegetation cover, and water cover was subject to seasonal changes. Urban cover and bare land cover did not pass the t-test. This research not only provides a theoretical basis for the analysis of temperature change over the TP, but also points out the direction for the analysis of temperature change causes in three polar regions.

Detecting Arsenic Contamination Using Satellite Imagery and Machine Learning.

Arsenic, a potent carcinogen and neurotoxin, affects over 200 million people globally. Current detection methods are laborious, expensive, and unscalable, being difficult to implement in developing regions and during crises such as COVID-19. This study attempts to determine if a relationship exists between soil’s hyperspectral data and arsenic concentration using NASA’s Hyperion satellite. It is the first arsenic study to use satellite-based hyperspectral data and apply a classification approach. Four regression machine learning models are tested to determine this correlation in soil with bare land cover. Raw data are converted to reflectance, problematic atmospheric influences are removed, characteristic wavelengths are selected, and four noise reduction algorithms are tested. The combination of data augmentation, Genetic Algorithm, Second Derivative Transformation, and Random Forest regression ( and normalized root mean squared error (re-scaled to [0,1]) = ) shows strong correlation, performing better than past models despite using noisier satellite data (versus lab-processed samples). Three binary classification machine learning models are then applied to identify high-risk shrub-covered regions in ten U.S. states, achieving strong accuracy (=0.693) and F1-score (=0.728). Overall, these results suggest that such a methodology is practical and can provide a sustainable alternative to arsenic contamination detection.

Land surface thermal alteration and pattern simulation based on influencing factors of rural landscape

Is it true that rural areas, like urban areas, experience temperature changes over time? The aim of this mission was to find the season-wise trend associated with land surface thermal alteration based on satellite imagery from the last 30 years using the least square regression method, as well as to determine the stimulus of LST for the years 2027 and 2037 using Artificial Neural Network and Support Vector Machine techniques. The average temperature in the winter, summer, and Monsoon months has risen by 0.11 °C/year, 0.19 °C/year, and 0.07 °C/year, respectively, according to the analysis. Additionally, the simulated models reveal which extreme finish temperature group (>37.13 °C) may include more areas than the current one. For example, in 2017, a total area of 28.95 km2 was above the 37.13 °C temperature class, but this could increase to 37.91 km2 in 2027 and 42.67 km2 in 2037. Fragmentation analysis of the extreme temperature patches shows that the location of the high-temperature core steadily increases over time. The simulated water body, vegetation, built-up land, and bare land cover area show a decreasing trend in the first two parameters and an increasing trend in the last two, all of which influence temperature increase incident.

Climate response to introduction of the ESA CCI land cover data to the NCAR CESM

The European Space Agency Climate Change Initiative Land Cover data (ESA CCI-LC, from 1992 to 2015) is introduced to the National Center for Atmospheric Research Community Earth System Model version 1.2.1 (NCAR CESM1.2.1). In comparison with the original land surface data in the Community Land Model version 4 (ORG), the new data features notable land use and land cover change (LULCC) with increased forests over northeastern Asia and Alaska by decreasing shrublands and grasslands. Overestimated bare land cover over the Tibetan Plateau (TP) and the Rocky Mountains in the ORG are corrected with the replacements by grasslands and shrublands respectively in the new data. The model simulation results show that with the introduction of the ESA CCI-LC, the simulated surface albedo, surface net radiation flux, sensible and latent heat fluxes are relatively improved over the regions where significant LULCC exists, such as northeastern Asia, Alaska, the TP, and Australia. Surface air temperature, precipitation, and atmospheric circulation are improved in boreal winter but degraded in summer. The winter warming over northeastern Asia results from increased longwave downwelling flux and adiabatic heating while the notable winter cooling over Alaska is attributed to strong cold advection followed by reduced longwave downwelling flux. LULCC alters precipitation by influencing water vapor content. In winter, LULCC affects atmospheric circulation via modulating baroclinicity while in summer, it influences land-sea thermal contrast, thus affecting the intensity of East Asian summer monsoon. LULCC also alters the simulated dust burden.

A GIS and remote sensing aided assessment of land use/cover changes in resettlement areas; a case of ward 32 of Mazowe district, Zimbabwe

The research assessed land use/cover changes and their socio-economic impacts on local resident farmers in ward 32 of Mazowe District. A mixed method research design that combines both qualitative and quantitative approaches was adopted. Questionnaires, interviews and observations were employed to gather data from the field. GIS and Remote sensing techniques were used for land use/cover change detection and calculations. Data collected from the field was analyzed using Statistical Package for Social Sciences (SPSS) version 25.0 and Microsoft excel. Arc map 10.5 was used to analyze Remote sensing data. Four major land use/covers namely forest, grassland, water and cultivated were identified as baseline land covers of Mazowe ward 32 in 2000. The research revealed that there has been significant loss in forest, grassland and wetland/water cover and gain in cultivated and bare land cover between 2000 and 2018 in ward 32 of Mazowe District. Scarcity of high energy tree species, fruit trees, construction poles and extinction of wild animals are some of the observed impacts of forest cover loss. The study recommends that Mazowe EMA, RDC and Forestry Commission should cooperate in monitoring natural resources in Mazowe District to ensure sustainable utilization.

Effect of Land Use and Land Cover Change on Soil Erosion in Erer Sub-Basin, Northeast Wabi Shebelle Basin, Ethiopia

Land use and land cover change (LULCC) is a critical factor for enhancing the soil erosion risk and land degradation process in the Wabi Shebelle Basin. Up-to-date spatial and statistical data on basin-wide erosion rates can provide an important basis for planning and conservation of soil and water ecosystems. The objectives of this study were to examine the magnitude of LULCC and consequent changes in the spatial extent of soil erosion risk, and identify priority areas for Soil and Water Conservation (SWC) in the Erer Sub-Basin, Wabi Shebelle Basin, Ethiopia. The soil loss rates were estimated using an empirical prediction model of the Revised Universal Soil Loss Equation (RUSLE) outlined in the ArcGIS environment. The estimated total annual actual soil loss at the sub-basin level was 1.01 million tons in 2000 and 1.52 million tons in 2018 with a mean erosion rate of 75.85 t ha−1 y−1 and 107.07 t ha−1 y−1, respectively. The most extensive soil loss rates were estimated in croplands and bare land cover, with a mean soil loss rate of 37.60 t ha−1 y−1 and 15.78 t ha−1 y−1, respectively. The soil erosion risk has increased by 18.28% of the total area, and decreased by 15.93%, showing that the overall soil erosion situation is worsening in the study area. We determined SWC priority areas using a Multi Criteria Decision Rule (MCDR) approach, indicating that the top three levels identified for intense SWC account for about 2.50%, 2.38%, and 2.14%, respectively. These priority levels are typically situated along the steep slopes in Babile, Fedis, Fik, Gursum, Gola Oda, Haramaya, Jarso, and Kombolcha districts that need emergency SWC measures.

Assessing the impacts of land-use/land-cover change on the development of urban heat island effects

Urban heat island (UHI) has been a detrimental problem over the last decades. Also, climate change has obtained more attention due to increased urbanization. Turkey is one of the countries experiencing a rapid urbanization which has resulted in significant UHI impacts on the regional climate and environment. This study was conducted in Osmaniye city, Turkey, where there is a need to determine the effects of land-use/land-cover changes (LUCC) on the UHI using Landsat images. The geospatial analyses were undertaken using USGS/NASA Enhanced Thematic Mapper Plus geospatial data from 2000 to 2018. The results showed that percent cover of urban areas significantly increased from 6.13 to 17.76%, broad-leaved forest cover decreased from 14.2 to 11.6%, road and rail network cover increased from 5.23 to 10.85%, and coniferous forest cover decreased from 15.85 to 9.02% between 2000 and 2018. During the process of urbanization from 2000 to 2018, most of the bare land cover was converted to agricultural lands and urbanized areas due to vegetative degradation, and industrial units cover increased from 7.76 to 9.53%. The land surface temperature was 35.57 °C for approximately 25% of the study area in 2000, while the same temperature was calculated for 76% of the study area in 2018. The results suggest that LUCC from vegetation to urbanized areas has significantly increased the land surface temperature. This implies that the application of remote sensing technique could help to design methods to mitigate UHI effects by estimating future land-use/land-cover change in urban areas.

AMSR2 snow depth downscaling algorithm based on a multifactor approach over the Tibetan Plateau, China

Accurate dynamic measurements of snow depth (SD) on the Tibetan Plateau (TP) are of great importance for understanding local climate systems and surface hydrological cycles. Currently, passive microwave remote sensing is the most effective approach used to monitor the spatiotemporal variations in SD. However, the large uncertainties and low spatial resolution of existing SD products lead to less satisfactory passive microwave remote sensing in regions with complex terrain conditions, strong seasonal transitions and great spatiotemporal heterogeneity, such as the TP. In this study, geographic location, terrain, snow cover days and brightness temperature were evaluated. Then, critical factors were used to develop an Advanced Microwave Scanning Radiometer 2 (AMSR2) SD downscaling model. The results indicated the following: (1) SD is greatly influenced by geographic location, snow covered days, terrain parameters and brightness temperature difference. The surface roughness exhibited the best correlations with ground observations, which reflects 44% of the variation in SD; (2) the novel multifactor power SD downscaling model, which combines AMSR2 brightness temperature data and other auxiliary data, showed higher accuracy and stability and was closely correlated with the SD ground observations, and it reflected 80% of the SD variation; (3) compared with the AMSR2 ascending and descending orbital SD products released by the Japan Aerospace Exploration Agency (JAXA) and the SD datasets in China from the Environmental and Ecological Science Data Center for West China (WESTDC), the proposed downscaled SD datasets for the TP were greatly improved, and the root-mean-square error (RMSE) and average absolute error (MAE) were greatly reduced (2.00 cm and 0.25 cm, respectively); (4) the downscaled SD datasets for the TP showed good accuracy (RMSE = 0.58 cm) in areas with a SD of <3 cm; and (5) the worst monitoring accuracy of the downscaled SD product was for grasslands, with a RMSE of 2.07 cm. The best accuracy of the downscaling SD product was found for bare land cover conditions, with an RMSE of only 0.41 cm.

Dynamics of land use land cover and resulting surface runoff management for environmental flood hazard mitigation: The case of Dire Daw city, Ethiopia

Study regionThis study was conducted in Dire Dawa city, Ethiopia. Study focusThis study aims to explore the hydrological impacts of land use land cover changes using rational method. As to perform land use analysis, four decade based satellite imagery were analyzed using ERDAS imagine. The hydrological effect of land cover change and respective surface runoff was calculated using rational method. New hydrological insights for the regionThe trend of land use land cover analysis shows that the city has been experiencing significant decrease in forest land with continuous concomitant increases in bare land and settlements through the study period. In response of land use changes, the trend of surface runoff showed continuous increasing trend. The result of focus group discussion confirms that land use change has significantly affected the trend of surface runoff generation. The study also found out that the amount of surface runoff was positively sensitive to forest land and negatively to bare land and settlement classes. To demonstrate the future effect of surface runoff, the study developed worst and best case scenarios. The worst case showed that if the current land use continuous in the coming years, the watershed could receive more surface runoff that threaten the existence of the city. The best case scenario suggested rehabilitation of bare land cover and rooftop rainwater harvesting as promising condition to drop the amount of surface runoff. The output of the study provides environmental friendly surface runoff mitigation strategies that can be adopted at household and community levels.

Land Cover Change in the Abuja City-Region, Nigeria: Integrating GIS and Remotely Sensed Data to Support Land Use Planning

Rapid urban expansion is a significant contributor to land cover change and poses a challenge to environmental sustainability, particularly in less developed countries. Insufficient data about urban expansion hinders effective land use planning. Therefore, a high need to collect, process, and disseminate land cover data exists. This study focuses on urban land cover change detection using Geographic Information Systems and remote sensing methods to produce baseline information in support for land use planning. We applied a supervised classification of land cover of LANDSAT data from 1987, 2002, and 2017. We mapped land cover transitions from 1987 to 2017 and computed the net land cover change during this time. Finally, we analyzed the mismatches between the past and current urban land cover and land use plans and quantified the non-urban development area lost to urban/built-up. Our results indicated an increase in urban/built-up and bare land cover types, while vegetation land cover decreased. We observed mismatches between past/current land cover and the existing land use plan. By providing detailed insights into mismatches between the regional land use plan and unregulated urban expansion, this study provides important information for a critical debate on the role and effectiveness of land use planning for environmental sustainability and sustainable urban development, particularly in less developed countries.

Agroecological Pest Management in the City: Experiences from California and Chiapas

Urban gardens are a prominent part of agricultural systems, providing food security and access within cities; however, we still lack sufficient knowledge and general principles about how to manage pests in urban agroecosystems in distinct regions. We surveyed natural enemies (ladybeetles and parasitoids) and conducted sentinel pest removal experiments to explore local management factors and landscape characteristics that influence the provisioning of pest control services in California, USA, and Chiapas, Mexico. We worked in 29 gardens across the two locations. In each location, we collected data on garden vegetation, floral availability, ground cover management, and the percentage of natural, urban, and agricultural land cover in the surrounding landscape. We sampled ladybeetles, Chalcidoidea, and Ichneumonoidea parasitoids with sticky traps, and monitored the removal of three different pest species. Ladybeetle abundance did not differ between locations; abundance decreased with garden size and with tree cover and increased with herbaceous richness, floral abundance, and barren land cover. Chalcicoidea and Ichneumonoidea parasitoids were more abundant in Chiapas. Chalcicoidea abundance decreased with herbaceous richness and with urban cover. Ichneumonoidea abundance increased with mulch and bare ground cover, garden size, garden age, and with agriculture land cover but decreased with tree richness and urban cover. Predators removed between 15–100% of sentinel prey within 24 h but prey removal was greater in California. Generally, prey removal increased with vegetation diversity, floral abundance, mulch cover, and urban land cover, but declined with vegetation cover and bare ground. Although some factors had consistent effects on natural enemies and pest control in the two locations, many did not; thus, we still need more comparative work to further develop our understanding of general principles governing conservation biological control in urban settings.

Assessing seasonal land cover dynamics in the tropical Kilombero floodplain of East Africa

Increasing demand for food in East Africa has created a shift to utilize wetlands for food production. Lack of spatial information hampers sustainable use of the Kilombero Valley floodplain. We take advantage of multispectral data from RapidEye, Landsat-8, and Sentinel-2 to derive high temporal resolution maps along three hydrological zones of the Kilombero Valley, assess seasonal land cover dynamics, and relate these dynamics to groundwater measurements. The depth of groundwater increases from June and declines from December, consistent with the end and the beginning of the rainy season, respectively. Bare land cover over the study area was 45% to 57% and increases to 62% to 69% as the season shifts from rainy to dry seasons while vegetation coverage, which was 34% to 47%, decreased to 25% to 27%. During the dry season, 68% to 81% of the total vegetation is within the riparian zone indicating the hydrological conditions favor plant growth. Vegetation growth in the fringe and middle zones mainly relies on precipitation whereas that in the riparian zone relies on saturation from the river. Our findings exemplify the relationship between seasonal land cover change and hydrological conditions and contribute to improved understanding of the spatial–temporal land cover dynamic in the Kilombero floodplain, required for planning sustained use of the wetland.

Tracers Reveal Recharge Elevations, Groundwater Flow Paths and Travel Times on Mount Shasta, California

Mount Shasta (4322 m) is famous for its spring water. Water for municipal, domestic and industrial use is obtained from local springs and wells, fed by annual snow melt and sustained perennially by the groundwater flow system. We examined geochemical and isotopic tracers in samples from wells and springs on Mount Shasta, at the headwaters of the Sacramento River, in order to better understand the hydrologic system. The topographic relief in the study area imparts robust signatures of recharge elevation to both stable isotopes of the water molecule (δ18O and δD) and to dissolved noble gases, offering tools to identify recharge areas and delineate groundwater flow paths. Recharge elevations determined using stable isotopes and noble gas recharge temperatures are in close agreement and indicate that most snowmelt infiltrates at elevations between 2000 m and 2900 m, which coincides with areas of thin soils and barren land cover. Large springs in Mt Shasta City discharge at an elevation more than 1600 m lower. High elevation springs (&gt;2000 m) yield very young water (&lt;2 years) while lower elevation wells (1000–1500 m) produce water with a residence time ranging from 6 years to over 60 years, based on observed tritium activities. Upslope movement of the tree line in the identified recharge elevation range due to a warming climate is likely to decrease infiltration and recharge, which will decrease spring discharge and production at wells, albeit with a time lag dependent upon the length of groundwater flow paths.