Agroforestry tree cover change in agricultural landscapes following farmland exclosure in Tigray, Ethiopia

Tree and impervious cover change in U.S. cities

Plant phenology provides information on the seasonal dynamics of plants, and changes herein are important for understanding the impact of climate change and human management on the biosphere. Land surface phenology is the study of plant phenology across large spatial scales estimated by satellite observations. However, satellite observations (pixels) are often composed of a mixture of vegetation types, like woody vegetation and herbaceous vegetation, having different phenological characteristics. Therefore, any changes in tree cover presumably impact land surface phenology, as trees usually have a different seasonal cycle compared to herbaceous vegetation. On the other hand, changes in land surface phenology are often interpreted as a result of climate change-induced impacts on the photosynthetic activity of vegetation. Therefore, it is important to better understand the role of changes in vegetation cover (here, the proportion between tree and short vegetation cover) in satellite-derived land surface phenology analysis. We studied the impact of changes in tree cover on satellite observed land surface phenology at a global scale over the past three decades. We found an extension of the growing season length in 36.6% of the areas where tree cover increased, whereas only 20.1% of the areas where tree cover decreased showed an increase in growing season length. Furthermore, the ratio between tree cover and short vegetation cover was found to affect changes in the length of the growing season, with the denser tree cover showing a more pronounced extension of the growing season length (especially in boreal forests). These results highlight the importance of changes in tree cover when analyzing the impact of climate change on vegetation phenology. Our study thereby addresses a critical knowledge gap for an improved understanding of changes in land surface phenology during recent decades in the context of climate and human-induced global land cover change.

Changes in tree cover and impervious surfaces have been observed across many cities in the United States over the past 70 years. Many municipalities are implementing tree planting programs in efforts to increase tree cover. A detailed understanding of historical changes in land cover can inform urban forest management. I applied a convolutional neural network image segmentation approach to historical aerial imagery to delineate changes in land cover in 1957, 1974, and 2017 in Utica, New York, a small, postindustrial city. The model predicted tree, pavement, and building land cover in each year with overall accuracies ranging from 82-87%. From 1957 to 2017, tree cover declined in many areas and impervious surface cover (buildings and pavement) increased. Tree cover gains largely occurred in uninhabited, natural areas; whereas, the greatest declines in tree coverage occurred in many residential areas following the start of the urban renewal efforts in 1957. Current tree planting efforts targeted at homeowners could drive disparities in future tree cover since several areas of Utica with low tree have a high proportion of renter occupied homes and a low median household income. Convolutional Neural Network approaches for image segmentation of aerial imagery are a helpful tool in understanding patterns in changes in tree and impervious surfaces. A better understanding of the legacies of historical policies and neighborhood-scale changes in land cover can assist in highlighting priorities for urban forest management and justice-oriented urban forestry approaches to urban tree planting.

Detecting tree cover is crucial for sustainable land management and climate mitigation. Here we develop an automatic detection algorithm using high-resolution satellite data (<5 m) to map pan-tropical tree cover (2015–2022), enabling identification and change analysis for previously undetected tree cover (PUTC). Our findings reveal that neglecting PUTC represents 17.31 ± 1.78% of the total pan-tropical tree cover. Tree cover net decreased by 61.05 ± 2.36 Mha in both forested areas (63.93%) and non-forested areas (36.07%) between 2015 and 2022. Intense changes in tree cover are primarily observed in regions with PUTC, where the World Cover dataset with a resolution of 10 m often fails to accurately detect tree cover. We also conduct a sensitivity analysis to quantify the contributions f climate factors and anthropogenic impacts (including human activities and land use cover change) to tree cover dynamics. Our findings indicate that 43.98% of tree cover gain is linked to increased precipitation, while 56.03% of tree cover loss is associated with anthropogenic impacts. These findings highlight the need to include undetected tree cover in strategies combating degradation, climate change, and promoting sustainability. Fine-scale mapping can improve biogeochemical cycles modeling and vegetation-climate interactions, improving global change understanding.

Declining urban and community tree cover in the United States

Spatio‐temporal variation in tropical savanna tree cover remains poorly understood. We aimed to quantify the drivers of tree cover in tropical mesic savannas in Kakadu National Park by relating changes in tree cover over 40 years to: mean annual rainfall, fire activity, initial tree cover and prior changes in tree cover. Aerial photography, acquired in 1964, 1984 and 2004, was obtained for fifty sites in Kakadu that spanned a rainfall gradient from approximately 1200 to 1600 mm. The remotely sensed estimates of tree cover were validated via field survey. Linear mixed effects modelling and multi‐model inference were used to assess the strength and form of the relationships between tree cover and predictor variables. Over the 40 years, tree cover across these savannas increased on average by 4.94 ± 0.88%, but was spatio‐temporally variable. Tree cover showed a positive albeit weak trend across the rainfall gradient. The strength of this positive relationship varied over the three measurement times, and this suggests that other factors are important in controlling tree cover. Tree cover was positively related to prior tree cover, and negatively correlated with fire activity. Over 20 years tree cover was more likely to increase if (i) tree cover was initially low or (ii) had decreased in the previous 20‐year interval or (iii) there had been fewer fires. Across the examined rainfall gradient, the greater variability in fire activity and inherently higher average tree cover at the wetter latitudes resulted in greater dynamism of tree cover compared with the drier latitudes. This is consistent with savanna tree cover being determined by interactions between mean annual rainfall, tree competition and frequent fire in these tropical mesic savannas.

The co-existence of trees and grasses is a defining feature of savannah ecosystems and landscapes. During recent decades, the combined effect of climate change and increased demographic pressure has led to complex vegetation changes in these ecosystems. A number of recent Earth observation (EO)-based studies reported positive changes in biological productivity in the Sahelian region in relation to increased precipitation, triggering an increased amount of herbaceous vegetation during the rainy season. However, this ‘greening of the Sahel’ may mask changes in the tree–grass composition, with a potential reduction in tree cover having important implications for the Sahelian population. Large-scale EO-based evaluation of changes in Sahelian tree cover is assessed by analysing long-term trends in dry season minimum normalized difference vegetation index (NDVImin) derived from three different satellite sensors: Système Pour l’Observation de la Terre (SPOT)-VEGETATION (VGT), Terra Moderate Resolution Imaging Spectroradiometer (MODIS), and the Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) dataset. To evaluate the reliability of using NDVImin as a proxy for tree cover in the Sahel, two factors that could potentially influence dry season NDVImin estimates were analysed: the total biomass accumulated during the preceding growing season and the percentage of burned area observed during the dry season. Time series of dry season NDVImin derived from low-resolution satellite time series were found to be uncorrelated to dry grass residues from the preceding growing season and to seasonal fire frequency and timing over most of the Sahel (88%), suggesting that NDVImin can serve as a proxy for assessing changes in tree cover. Good agreement (R2 = 0.79) between significant NDVImin trends (p < 0.05) derived from VGT and MODIS was found. Significant positive trends in NDVImin were registered by both MODIS and VGT dry season NDVImin time series over the Western Sahel, whereas trends based on GIMMS data were negative for the greater part of the Sahel. EO-based trends were generally not confirmed at the local scale based on selected study cases, partly caused by a temporal mismatch between data sets (i.e. different periods of analysis). Analysis of desert area NDVImin trends indicates less stable values for VGT and GIMMS data as compared with MODIS. This suggests that trends in dry season NDVImin derived from VGT and GIMMS should be used with caution as an indicator for changes in tree cover, whereas the MODIS data stream shows a better potential for tree-cover change analysis in the Sahel.

Agroforestry can contribute to an increase in tree cover in historically forested tropical landscapes with associated gains in biodiversity and ecosystem functioning, but only if established on open land instead of underneath a forest canopy. However, declines in yields with increasing shade are common across agroforestry crops, driving shade-tree removal in forest-derived agroforests and hindering tree regrowth in open-land-derived agroforests. To understand trajectories of change in tree cover in forest- and open-land-derived agroforests, and the impacts of tree cover on vanilla yields, we studied 209 vanilla agroforests along an 88-year chronosequence in Madagascar. Additionally, we used remotely sensed canopy cover data to investigate tree cover change in the agricultural landscape. We found yields to vary widely but independently of canopy cover and land-use history (forest- vs. open-land-derived), averaging at 154.6 kg ha−1 year−1 (SD = 186.9). Furthermore, we found that forest- and open-land-derived vanilla agroforests gained canopy cover over time, but that only open-land-derived agroforests gained canopy height. Canopy cover increased also at the landscape scale: areas in the agricultural landscape with medium initial canopy cover gained 6.4% canopy cover over 10 years, but canopy cover decreased in areas with high initial canopy cover. These opposing trends suggest tree cover rehabilitation across areas covered by vanilla agroforests, whereas remnant forest fragments in the agricultural landscape were transformed or degraded. Our results indicate that yield-neutral tree rehabilitation through open-land-derived agroforestry could, if coupled with effective forest protection, provide benefits for both ecosystem functions and agricultural production in a smallholder-dominated agricultural landscape.

After the severe droughts in the 1970s and 1980s, and subsequent debates about desertification, analyses of satellite images reveal that the West African Sahel has become greener again. In this paper we report a study on changes in tree cover and tree species composition in three village landscapes in northern Burkina Faso, based on a combination of methods: tree density change detection using aerial photos and satellite images, a tree species inventory including size class distribution analysis, and interviews with local farmers about woody vegetation changes. Our results show a decrease in tree cover in the 1970s followed by an increase since the mid-1980s, a pattern correlating with the temporal trends in rainfall as well as remotely sensed greening in the region. However, both the inventory and interview data shows that the species composition has changed substantially towards a higher dominance of drought-resistant and exotic species. This shift, occurring during a period of increasing annual precipitation, points to the complexity of current landscape changes and questions rain as the sole primary driver of the increase in tree cover. We propose that the observed changes in woody vegetation (densities, species composition and spatial distribution) are mediated by changes in land use, including intensification and promotion of drought tolerant and fast growing species. Our findings, which indicate a rather surprising trajectory of land cover change, highlight the importance of studies that integrate evidence of changes in tree density and species composition to complement our understanding of land use and vegetation change trajectories in the Sahel obtained from satellite images. We conclude that a better understanding of the social-ecological relations and emerging land use trajectories that produce new types of agroforestry parklands in the region is of crucial importance for designing suitable policies for climate change adaptation, biodiversity conservation and the sustainable delivery of ecosystem services that benefit local livelihoods in one of the world’s poorest regions.

Tree and impervious cover in the United States

Despite their significant role for biological diversity, ecosystem stability and human comfortability, tree coverage in cities is continuously destructed and degraded to cater to increasing urbanisation in developing countries, including Malaysia. Cities are more susceptible to environmental change impacts and become unlivable. Thus, monitoring and mapping tree cover is critical (i) for conserving trees and (ii) for making decisions to increase green cover in cities. Mapping and monitoring urban green cover can be done routinely using remote sensing technologies. In this study, tree cover data produced by the University of Maryland, USA using Landsat satellite imagery was used to assess the tree cover changes in four cities, namely Penang Island, Kuala Lumpur and two cities in Iskandar Malaysia in Peninsular Malaysia. The 30 m x 30 m ground resolution tree cover data were overlaid onto the city boundaries to calculate the losses and gains in tree cover relative to the tree cover in the year 2000. Results of the study show that over a period of 12 years (2000-2012), Penang, Kuala Lumpur, Johor Bahru and Pasir Gudang lost about 6.9 %, 3.5 %, 9.5 % and 17.4 % of tree cover from their total land area. Nevertheless, these areas also gained some tree cover over the same period, which resulted in a net loss of 723 ha, 494 ha, 2,366 ha and 3,678 ha. From 2012 to 2014, the total loss of tree cover in the 4 cities were 138 ha, 64 ha, 626 ha and 1,159 ha, respectively. The loss of tree cover in cities needs to be controlled and efforts should be taken to plant more trees in Malaysia. Although the importance of green space within urban areas is more noticeable recently in Malaysia, no specific policies and legislation relevant to the protection and management of urban forests are available. Clear and detailed urban forest strategies and policies must be formulated to protect, plant and manage the urban forests in Malaysia.

Variability in the effects of disturbances and extreme climate events can lead to changes in tree cover over time, including partial or complete loss, with diverse ecological consequences. It is therefore critical to identify in space and time the change processes that lead to tree cover change. Studies of change are often hampered by the lack of data capable of consistently detecting different types of change. Using the Landsat satellite record to create a long time-series of land cover and land cover change, the U.S. Geological Survey Land Change Monitoring Assessment and Projection (LCMAP) project has made an annual time series of land cover across the conterminous United States for the period 1985 to 2018. Multiple LCMAP products analyzed together with map validation reference plots provide a robust basis for understanding tree cover change. In LCMAP (Collection 1.2), annual change detection is based on harmonic model breaks calculated at each Landsat pixel from the Continuous Change Detection and Classification (CCDC) algorithm. The results showed that the majority of CCDC harmonic model breaks (signifying change) indicated partial tree cover loss (associated with management practices such as tree cover thinning) as compared to complete tree cover loss (associated with practices like clearcut harvest or fire disturbance). Substantially fewer occurrences of complete tree cover loss were associated with change in land cover state. The area of annual tree cover change increased after the late 1990s and stayed high for the rest of the study period. The reference data showed that tree harvest dominated across the conterminous United States. The majority of tree cover change occurred in evergreen forests. Large estimates of disturbance-related tree cover change indicated that tree cover loss may have previously been underreported due to omission of partial tree cover loss in prior studies. This has considerable implications for forest carbon accounting along with tracking ecosystem goods and services.

Reliable mapping of tree cover and tree-cover change at regional, continental, and global scales is critical for understanding key aspects of ecosystem structure and function. In savannas, which are characterized by a variable mixture of trees and grasses, mapping tree cover can be especially challenging due to the highly heterogeneous nature of these ecosystems. Our objective in this article was to develop improved tools for large-scale classification of savanna tree cover in grass-dominated savanna ecosystems that vary substantially in woody cover over fine spatial scales. We used multispectral, low-resolution Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery to identify the bands and metrics that are best suited to quantify woody cover in an area of the Serengeti National Park, Tanzania. We first used 1-m resolution panchromatic IKONOS data to quantify tree cover for February 2010 in an area of highly variable tree cover. We then upscaled the classification to MODIS (250 m) resolution. We used a 2 year time series (IKONOS date ± 1 year) of MODIS 16 day composites to identify suitable metrics for quantifying tree cover at low resolution, and calculated and compared the explanatory power of three different variable classes for four MODIS bands using Lasso regression: longitudinal summary statistics for individual spectral bands (e.g. mean and standard deviation), Fourier harmonics, and normalized difference vegetation index (NDVI) green-up metrics. Longitudinal summary statistics showed better explanatory power (R 2 = 73% for calibration data; R 2 = 61% for validation data) than Fourier or green-up metrics. The mid-infrared, near-infrared, and NDVI bands were all important predictors of tree cover. Mean values for the time series were more important than other metrics, suggesting that multispectral data may be more valuable than within-band seasonal variation obtained from time series data for mapping tree cover. Our best model improved substantially over the MODIS Vegetation Continuous Fields product, often used for quantifying tree cover in savanna systems. Quantifying tree cover at coarse spatial resolution using remote-sensing approaches is challenging due to the low amount and high heterogeneity of tree cover in many savanna systems, and our results suggest that products that work well at global scales may be inadequate for low-tree-cover systems such as the Serengeti. We show here that, even in situations where tree cover is low (<10%) and varies considerably across space, satisfactory predictive power is possible when broad spectral data can be obtained even at coarse spatial resolution.

Participatory Rural Appraisal was used to compare stakeholder perceptions of spatial and temporal variation in tree cover and water availability using Uganda’s Mt. Elgon Water Tower as a case study. This study was guided by the research question: How do multi-level stakeholders’ perceptions of changes in tree-cover and water availability vary across spatial and temporal scales of the water tower? Five Participatory Rural Appraisal tools were applied to understand multi-level stakeholders’ perspectives on the changes in tree-cover and water availability. Data was collected from farmers’ focus group discussions, key experts from local government offices and structured household interviews with local communities. This study focused on the upper and lower zones of the water tower, which differ in terms of proximity to Mt. Elgon National Forest Park, household water-use, elevation, and tree-cover. Results showed that there are both similarities and differences in perceptions of changes in tree cover and water availability among stakeholders. Farmers and key experts perceived a decrease in water availability and a decrease in overall tree cover for the period 1990–2020. There are differences in stakeholders’ perceptions of water availability across the zones and sub-catchments. For instance, more farmers in Sipi River Sub-catchment perceived a decrease in water availability during 2006–2020 compared to 1990–2005 period. There were notable contradictions in farmers’ and key experts’ perceptions on tree cover. Farmers’ perceptions of 1990–2020 precipitation were in agreement with trends in existing precipitation data. Declining trends of natural tree cover especially outside the Mt. Elgon national forest park pose great threat to water resources originating from forests. Changes in tree cover and water availability in the study area can be partly explained by the tree species grown. Local stakeholders’ perceptions complemented the existing data gaps in explaining the changes in tree cover and water availability. Participatory Rural Appraisal tools are helpful for documenting the perceptions of local communities. However, this cannot replace reliable data resulting from large-scale efforts for monitoring changes and variations in tree-cover and water availability. The findings of this paper can be valuable to inform strategies for community involvement in sustainable agroforested landscape management.

Background: The alpine treeline ecotone is regarded as a sensor of the effects of global change on alpine plant communities. However, little is known about how treeline dynamics influence the diversity and composition of alpine plant communities. Such information is necessary to forecast how ascending montane forests may affect the composition of alpine flora. Aims: We analysed the temporal variations in tree cover, plant diversity and composition, and the effect of tree cover dynamics on field layer vegetation over a period of 11 years, at two alpine treeline ecotones in the central Pyrenees, Spain. Methods: Tree and field layer vegetation was sampled in permanent transects in 1998 and 2009, using the point-intercept method. Temporal changes in tree cover, plant species richness and abundance were characterised along the ecotone by using a randomisation approach, rarefaction curves, and a non-parametric multivariate test, respectively. Results: Tree cover increased significantly at one of the sites, whereas plant species richness only increased at the other site where tree cover had not changed. Vegetation composition changed significantly at both sites, but it was not spatially coupled with changes in tree cover along the ecotone. Conclusions: A change of tree cover does not necessarily trigger changes in the ground flora at the treeline over relatively short periods (decade scale). The results challenge our ability to infer short-term biodiversity impacts from upslope advance of forests. Integrated tree and field layer monitoring approaches are necessary to produce a better understanding of the impact of ongoing global change on treeline ecotones.