Mapping of Phragmites in estuarine wetlands using high-resolution aerial imagery.

Phragmites australis is a widespread invasive plant species in the USA that greatly impacts estuarine wetlands by creating dense patches and outcompeting other plants. The invasion of Phragmites into wetland ecosystems is known to decrease biodiversity, destroy the habitat of threatened and endangered bird species, and alter biogeochemistry. While the impact of Phragmites is known, the spatial extent of this species is challenging to document due to its fragmented occurrence. Using high-resolution imagery from the National Agriculture Imagery Program (NAIP) from 2017, we evaluated a geospatial method of mapping the spatial extent of Phragmites across the state of DE. Normalized difference vegetation index (NDVI) and principal component analysis (PCA) bands are generated from the NAIP data and used as inputs in a random forest classifier to achieve a high overall accuracy for the Phragmites classification of around 95%. The classified gridded dataset has a spatial resolution of 1 m and documents the spatial distribution of Phragmites throughout the state's estuarine wetlands (around 11%). Such detailed classification could aid in monitoring the spread of this invasive species over space and time and would inform the decision-making process for landscape managers.

Improved household living standards can restore dry tropical forests.

Despite multiple approaches over the last several decades to harmonize conservation and development goals in the tropics, forest-dependent households remain the poorest in the world. Durable housing and alternatives to fuelwood for cooking are critical needs to reduce multi-dimensional poverty. These improvements also potentially reduce pressure on forests and alleviate forest degradation. We test this possibility in dry tropical forests of the Central Indian Highlands where tribal and other marginalized populations rely on forests for energy, construction materials, and other livelihood needs. Based on a remotely sensed measure of forest degradation and a 5000 household survey of forest use, we use machine learning (causal forests) and other statistical methods to quantify treatment effects of two improved living standards-alternatives to fuelwood for cooking and non-forest-based housing material-on forest degradation in 1, 2, and 5 km buffers around 500 villages. Both improved living standards had significant treatment effects (-0.030 ± 0.078, -0.030 ± 0.023, 95% CI), respectively, with negative values indicating less forest degradation, within 1 km buffers around villages. Treatment effects were lower with increasing distance from villages. Results suggest that improved living standards can both reduce forest degradation and alleviate poverty. Forest restoration efforts can target improved living standards for local communities without conflicts over land tenure or taking land out of production to plant trees.

Landsat-Derived Estimates of Mangrove Extents in the Sierra Leone Coastal Landscape Complex during 1990-2016.

This study provides the first assessment of decadal changes in mangrove extents in Sierra Leone. While significant advances have been made in mangrove mapping using remote sensing, no study has documented long-term changes in mangrove extents in Sierra Leone-one of the most vulnerable countries in West Africa. Such understanding is critical for devising regional management strategies that can support local livelihoods. We utilize multi-date Landsat data and cloud computational techniques to quantify spatiotemporal changes in land cover, with focus on mangrove ecosystems, for 1990-2016 along the coast of Sierra Leone. We specifically focus on four estuaries-Scarcies, Sierra Leone, Yawri Bay, and Sherbro. We relied on the k-means approach for an unsupervised classification, and validated the classified map from 2016 using ground truth data collected from Sentinel-2 and high-resolution images and during field research (accuracy: 95%). Our findings indicate that the Scarcies river estuary witnessed the greatest mangrove loss since 1990 (45%), while the Sierra Leone river estuary experienced mangrove gain over the last 26 years (22%). Overall, the Sierra Leone coast lost 25% of its mangroves between 1990 and 2016, with the lowest coverage in 2000, during the period of civil war (1991-2002). However, natural mangrove dynamics, as supported by field observations, indicate the potential for regeneration and sustainability under carefully constructed management strategies.

Sensitivity of crop cover to climate variability: insights from two Indian agro-ecoregions.

Crop productivity in India varies greatly with inter-annual climate variability and is highly dependent on monsoon rainfall and temperature. The sensitivity of yields to future climate variability varies with crop type, access to irrigation and other biophysical and socio-economic factors. To better understand sensitivities to future climate, this study focuses on agro-ecological subregions in Central and Western India that span a range of crops, irrigation, biophysical conditions and socioeconomic characteristics. Climate variability is derived from remotely-sensed data products, Tropical Rainfall Measuring Mission (TRMM - precipitation) and Moderate Resolution Imaging Spectroradiometer (MODIS - temperature). We examined green-leaf phenologies as proxy for crop productivity using the MODIS Enhanced Vegetation Index (EVI) from 2000 to 2012. Using both monsoon and winter growing seasons, we assessed phenological sensitivity to inter-annual variability in precipitation and temperature patterns. Inter-annual EVI phenology anomalies ranged from -25% to 25%, with some highly anomalous values up to 200%. Monsoon crop phenology in the Central India site is highly sensitive to climate, especially the timing of the start and end of the monsoon and intensity of precipitation. In the Western India site, monsoon crop phenology is less sensitive to precipitation variability, yet shows considerable fluctuations in monsoon crop productivity across the years. Temperature is critically important for winter productivity across a range of crop and management types, such that irrigation might not provide a sufficient buffer against projected temperature increases. Better access to weather information and usage of climate-resilient crop types would play pivotal role in maintaining future productivity. Effective strategies to adapt to projected climate changes in the coming decades would also need to be tailored to regional biophysical and socio-economic conditions.

Social media analysis reveals environmental injustices in Philadelphia urban parks.

The United Nations Sustainable Development Goal (SDG) target 11.7 calls for access to safe and inclusive green spaces for all communities. Yet, historical residential segregation in the USA has resulted in poor quality urban parks near neighborhoods with primarily disadvantaged socioeconomic status groups, and an extensive park system that addresses the needs of primarily White middle-class residents. Here we center the voices of historically marginalized urban residents by using Natural Language Processing and Geographic Information Science to analyze a large dataset (n = 143,913) of Google Map reviews from 2011 to 2022 across 285 parks in the City of Philadelphia, USA. We find that parks in neighborhoods with a high number of residents from historically disadvantaged demographic groups are likely to receive lower scores on Google Maps. Physical characteristics of these parks based on aerial and satellite images and ancillary data corroborate the public perception of park quality. Topic modeling of park reviews reveal that the diverse environmental justice needs of historically marginalized communities must be met to reduce the uneven park quality-a goal in line with achieving SDG 11 by 2030.

Land cover and forest health indicator datasets for central India using very-high resolution satellite data.

Satellite imagery has been used to provide global and regional estimates of forest cover. Despite increased availability and accessibility of satellite data, approaches for detecting forest degradation have been limited. We produce a very-high resolution 3-meter (m) land cover dataset and develop a normalized index, the Bare Ground Index (BGI), to detect and map exposed bare ground within forests at 90 m resolution in central India. Tree cover and bare ground was identified from Planet Labs Very High-Resolution satellite data using a Random Forest classifier, resulting in a thematic land cover map with 83.00% overall accuracy (95% confidence interval: 61.25%-90.29%). The BGI is a ratio of bare ground to tree cover and was derived by aggregating the land cover. Results from field data indicate that the BGI serves as a proxy for intensity of forest use although open areas occur naturally. The BGI is an indicator of forest health and a baseline to monitor future changes to a tropical dry forest landscape at an unprecedented spatial scale.

Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems.

Rapid impact assessment of cyclones on coastal ecosystems is critical for timely rescue and rehabilitation operations in highly human-dominated landscapes. Such assessments should also include damage assessments of vegetation for restoration planning in impacted natural landscapes. Our objective is to develop a remote sensing-based approach combining satellite data derived from optical (Sentinel-2), radar (Sentinel-1), and LiDAR (Global Ecosystem Dynamics Investigation) platforms for rapid assessment of post-cyclone inundation in non-forested areas and vegetation damage in a primarily forested ecosystem. We apply this multi-scalar approach for assessing damages caused by the cyclone Amphan that hit coastal India and Bangladesh in May 2020, severely flooding several districts in the two countries, and causing destruction to the Sundarban mangrove forests. Our analysis shows that at least 6821 sq. km. land across the 39 study districts was inundated even after 10 days after the cyclone. We further calculated the change in forest greenness as the difference in normalized difference vegetation index (NDVI) pre- and post-cyclone. Our findings indicate a <0.2 unit decline in NDVI in 3.45 sq. km. of the forest. Rapid assessment of post-cyclone damage in mangroves is challenging due to limited navigability of waterways, but critical for planning of mitigation and recovery measures. We demonstrate the utility of Otsu method, an automated statistical approach of the Google Earth Engine platform to identify inundated areas within days after a cyclone. Our radar-based inundation analysis advances current practices because it requires minimal user inputs, and is effective in the presence of high cloud cover. Such rapid assessment, when complemented with detailed information on species and vegetation composition, can inform appropriate restoration efforts in severely impacted regions and help decision makers efficiently manage resources for recovery and aid relief. We provide the datasets from this study on an open platform to aid in future research and planning endeavors.

Trends of forest dynamics in tiger landscapes across Asia.

Protected areas (PAs) are cornerstones of biodiversity conservation, but small parks alone cannot support wide-ranging species, such as the tiger. Hence, forest dynamics in the surrounding landscapes of PAs are also important to tiger conservation. Tiger landscapes often support considerable human population in proximity of the PA, sometimes within the core itself, and thus are subject to various land use activities (such as agricultural expansion and road development) driving habitat loss and fragmentation. We synthesize information from 27 journal articles in 24 tiger landscapes to assess forest-cover dynamics in tiger-range countries. Although 29% of the PAs considered in this study have negligible change in overall forest cover, approximately 71% are undergoing deforestation and fragmentation. Approximately 58% of the total case studies have human settlements within the core area. Most changes-including agricultural expansion, plantation, and farming (52%), fuelwood and fodder collection (43%), logging (38%), grazing (38%), and tourism and development (10%)-can be attributed to human impacts largely linked to the nature of the management regime. This study highlights the need for incorporating new perspectives, ideas, and lessons learned locally and across borders into management plans to ensure tiger conservation in landscapes dominated by human activities. Given the increasing isolation of most parks due to agricultural, infrastructural, and commercial developments at the periphery, it is imperative to conduct planning and evaluation at the landscape level, as well as incorporate multiple actors and institutions in planning, instead of focusing solely on conservation within the PAs as is currently the case in most tiger parks.

Groundwater depletion will reduce cropping intensity in India.

Groundwater depletion is becoming a global threat to food security, yet the ultimate impacts of depletion on agricultural production and the efficacy of available adaptation strategies remain poorly quantified. We use high-resolution satellite and census data from India, the world's largest consumer of groundwater, to quantify the impacts of groundwater depletion on cropping intensity, a crucial driver of agricultural production. Our results suggest that, given current depletion trends, cropping intensity may decrease by 20% nationwide and by 68% in groundwater-depleted regions. Even if surface irrigation delivery is increased as a supply-side adaptation strategy, which is being widely promoted by the Indian government, cropping intensity will decrease, become more vulnerable to interannual rainfall variability, and become more spatially uneven. We find that groundwater and canal irrigation are not substitutable and that additional adaptation strategies will be necessary to maintain current levels of production in the face of groundwater depletion.

Uncertainties in measuring populations potentially impacted by sea level rise and coastal flooding.

A better understanding of the impact of global climate change requires information on the locations and characteristics of populations affected. For instance, with global sea level predicted to rise and coastal flooding set to become more frequent and intense, high-resolution spatial population datasets are increasingly being used to estimate the size of vulnerable coastal populations. Many previous studies have undertaken this by quantifying the size of populations residing in low elevation coastal zones using one of two global spatial population datasets available - LandScan and the Global Rural Urban Mapping Project (GRUMP). This has been undertaken without consideration of the effects of this choice, which are a function of the quality of input datasets and differences in methods used to construct each spatial population dataset. Here we calculate estimated low elevation coastal zone resident population sizes from LandScan and GRUMP using previously adopted approaches, and quantify the absolute and relative differences achieved through switching datasets. Our findings suggest that the choice of one particular dataset over another can translate to a difference of more than 7.5 million vulnerable people for countries with extensive coastal populations, such as Indonesia and Japan. Our findings also show variations in estimates of proportions of national populations at risk range from <0.1% to 45% differences when switching between datasets, with large differences predominantly for countries where coarse and outdated input data were used in the construction of the spatial population datasets. The results highlight the need for the construction of spatial population datasets built on accurate, contemporary and detailed census data for use in climate change impact studies and the importance of acknowledging uncertainties inherent in existing spatial population datasets when estimating the demographic impacts of climate change.