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1. Rules of thumb for predicting tropical forest recovery

   https://doi.org/10.1111/avsc.12394  

   Holl, Karen D. ; Reid, John Leighton ; Oviedo‐Brenes, Federico ; Kulikowski, Andy J. ; Zahawi, Rakan A. ; Morgan, ed., John ( August 2018 , Applied Vegetation Science)

   Natural regeneration is increasingly recognized as a potentially cost‐effective strategy to reach ambitious forest landscape restoration targets, but rates of recovery are notoriously variable. We asked how well initial habitat conditions after cessation of agriculture predict forest recovery after nearly a decade. We aimed to provide land managers with general rules of thumb to assess when it is necessary to invest resources in active restoration, such as tree planting, to accelerate forest recovery.

   Coto Brus County, Puntarenas, Costa Rica.

   We compiled data on initial vegetation structure, soil nutrients, prior land‐use history and surrounding forest cover at 13 sites. After 8.5 years, we measured vegetation indicators commonly used to assess forest recovery, namely amount of canopy closure and number and diversity of woody recruits.

   Two variables, grass cover and canopy closure, measured 1.5 years after site abandonment, explained 47–87% of five of the six response variables after 8.5 years; recovery was faster in sites with lower grass cover and higher canopy closure initially. Waiting an additional year to measure initial vegetation variables did not improve model fit. Time since the original forest was cleared explained 62% of change in canopy cover, whereas percentage of surrounding forest cover, length of pasture use and soil variables explained minimal additional variation.

   Our results suggest that two easily measurable vegetation variables can provide guidance to land managers and policy makers about where to invest scarce restoration resources to facilitate forest recovery.

    

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2. A Land-use/Land Cover Classification of Baltimore City in 1927

   https://doi.org/10.6073/pasta/28793d6417db548f26679ab0e5197fb8  

   Lagrosa IV, John J ; Sonti, Nancy ; Grove, J Morgan ( January 2021 , Environmental Data Initiative)

   Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 93 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 585 km2 (226 mi2) including all of Baltimore City, and an area immediately adjacent to the city known at the time as the Metropolitan District of Baltimore County. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures a moment of dynamic expansion in the city, just prior to the Great Depression and can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region. 

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3. A Land-use/Land Cover Classification of Baltimore City in 1953

   https://doi.org/10.6073/pasta/84b59c8e375e3915cc62dea2cf73627a  

   Lagrosa IV, John J ; Sonti, Nancy ; Grove, J Morgan ( January 2022 , Environmental Data Initiative)

   Land-use and land cover classifications are typically created using automated methods to analyze modern, spatially explicit color aerial imagery. However, creating classifications from black and white historical aerial imagery presents a number of challenges that require a combination of more traditional, manual techniques and approaches. A georectified mosaic of 113 aerial images was digitized in ArcGIS to create a land-use/land cover classification. The analyzed area covered 700 km2 (270 mi2) including all of Baltimore City, and a portion of Baltimore County immediately surrounding the city. A combination of 8 land-use and land cover classes were used: Agriculture, Barren, Built (Other), Forest, Grass/Shrubland, Industrial, Residential, and Water. This geospatial data set captures an ecologically and socially important moment in the post-war history of the city. It can be used to examine relationships between property ownership and forest patch dynamics across time. These insights may help inform future environmental planning, conservation, management, and stewardship goals for Baltimore City forest patches, and other cities throughout the region. 

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4. Monitoring tropical forest succession at landscape scales despite uncertainty in Landsat time series

   https://doi.org/10.1002/eap.2208  

   Caughlin, T. Trevor ; Barber, Cristina ; Asner, Gregory P. ; Glenn, Nancy F. ; Bohlman, Stephanie A. ; Wilson, Chris H. ( October 2020 , Ecological Applications)

   Forecasting rates of forest succession at landscape scales will aid global efforts to restore tree cover to millions of hectares of degraded land. While optical satellite remote sensing can detect regional land cover change, quantifying forest structural change is challenging. We developed a state‐space modeling framework that applies Landsat satellite data to estimate variability in rates of natural regeneration between sites in a tropical landscape. Our models work by disentangling measurement error in Landsat‐derived spectral reflectance from process error related to successional variability. We applied our modeling framework to rank rates of forest succession between 10 naturally regenerating sites in Southwestern Panama from about 2001 to 2015 and tested how different models for measurement error impacted forecast accuracy, ecological inference, and rankings of successional rates between sites. We achieved the greatest increase in forecasting accuracy by adding intra‐annual phenological variation to a model based on Landsat‐derived normalized difference vegetation index (NDVI). The best‐performing model accounted for inter‐ and intra‐annual noise in spectral reflectance and translated NDVI to canopy height via Landsat–lidar fusion. Modeling forest succession as a function of canopy height rather than NDVI also resulted in more realistic estimates of forest state during early succession, including greater confidence in rank order of successional rates between sites. These results establish the viability of state‐space models to quantify ecological dynamics from time series of space‐borne imagery. State‐space models also provide a statistical approach well‐suited to fusing high‐resolution data, such as airborne lidar, with lower‐resolution data that provides better temporal and spatial coverage, such as the Landsat satellite record. Monitoring forest succession using satellite imagery could play a key role in achieving global restoration targets, including identifying sites that will regain tree cover with minimal intervention.

    

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5. Spatial configuration and time of day impact the magnitude of urban tree canopy cooling

   https://doi.org/10.1088/1748-9326/ac12f2  

   Alonzo, Michael ; Baker, Matthew E ; Gao, Yuemeng ; Shandas, Vivek ( August 2021 , Environmental Research Letters)

   Abstract Tree cover is generally associated with cooler air temperatures in urban environments but the roles of canopy configuration, spatial context, and time of day are not well understood. The ability to examine spatiotemporal relationships between trees and urban climate has been hindered by lack of appropriate air temperature data and, perhaps, by overreliance on a single ‘tree canopy’ class, obscuring the mechanisms by which canopy cools. Here, we use >70 000 air temperature measurements collected by car throughout Washington, DC, USA in predawn (pd), afternoon (aft), and evening (eve) campaigns on a hot summer day. We subdivided tree canopy into ‘soft’ (over unpaved surfaces) and ‘hard’ (over paved surfaces) canopy classes and further partitioned soft canopy into distributed (narrow edges) and clumped patches (edges with interior cores). At each level of subdivision, we predicted air temperature anomalies using generalized additive models for each time of day. We found that the all-inclusive ‘tree canopy’ class cooled linearly at every time (pd = 0.5 °C ± 0.3 °C, aft = 1.8 °C ± 0.6 °C, eve = 1.7 °C ± 0.4 °C), but could be explained in the afternoon by aggregate effects of predominant hard and soft canopy cooling at low and high canopy cover, respectively. Soft canopy cooled nonlinearly in the afternoon with minimal effect until ∼40% cover but strongly (and linearly) across all cover fractions in the evening (pd = 0.7 °C ± 1.1 °C, aft = 2.0 °C ± 0.7 °C, eve = 2.9 °C ± 0.6 °C). Patches cooled at all times of day despite uneven allocation throughout the city, whereas more distributed canopy cooled in predawn and evening due to increased shading. This later finding is important for urban heat island mitigation planning since it is easier to find planting spaces for distributed trees rather than forest patches. 

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