Browsing by Author "Mondal, Pinki"

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    Estimating forest extent across Mexico
    (Environmental Research Letters, 2024-01-12) Braden, Dustin; Mondal, Pinki; Park, Taejin; Alanís de la Rosa, José Armando; Aldrete Leal, Metzli Ileana; Cuenca Lara, Rubi Angélica; Mayorga Saucedo, Rafael; Paz, Fernando; Salas-Aguilar, Victor Manuel; Soriano-Luna, María De Los Ángeles; Vargas, Rodrigo
    Information on forest extent and tree cover is required to evaluate the status of natural resources, conservation practices, and environmental policies. The challenge is that different forest definitions, remote sensing-based (RSB) products, and data availability can lead to discrepancies in reporting total forest area. Consequently, errors in forest extent can be propagated into forest biomass and carbon estimates. Here, we present a simple approach to compare forest extent estimates from seven regional and global land or tree cover RSB products at 30 m resolution across Mexico. We found substantial differences in forest extent estimates for Mexico, ranging from 387 607 km2 to 675 239 km2. These differences were dependent on the RSB product and forest definition used. Next, we compared these RSB products with two independent forest inventory datasets at national (n = 26 220 plots) and local scales (n = 754 plots). The greatest accuracy among RSB products and forest inventory data was within the tropical moist forest (range 82%–95%), and the smallest was within the subtropical desert (range <10%–80%) and subtropical steppe ecological zones (range <10%–60%). We developed a forest extent agreement map by combining seven RSB products and identifying a consensus in their estimates. We found a forest area of 288 749 km2 with high forest extent agreement, and 340 661 km2 with medium forest extent agreement. The high-to-medium forest extent agreement of 629 410 km2 is comparable to the official national estimate of 656 920 km2. We found a high forest extent agreement across the Yucatan Peninsula and mountain areas in the Sierra Madre Oriental and Sierra Madre Occidental. The tropical dry forest and subtropical mountain system represent the two ecological zones with the highest areas of disagreement among RSB products. These findings show discrepancies in forest extent estimates across ecological zones in Mexico, where additional ground data and research are needed. Dataset available at https://doi.org/10.3334/ORNLDAAC/2320.
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    Multiple cropping alone does not improve year-round food security among smallholders in rural India
    (Environmental Research Letters, 2021-06-17) Mondal, Pinki; DeFries, Ruth; Clark, Jessica; Flowerhill, Nicole; Arif, Md.; Harou, Aurelie; Downs, Shauna; Fanzo, Jessica
    Achieving and maintaining food and nutrition security is an important Sustainable Development Goal, especially in countries with largely vulnerable population with high occurrence of hunger and malnutrition. By studying a small-scale agricultural system in India, we aim to understand the current state of dietary diversity and food insecurity among the farmer communities. The study landscape has witnessed a steady rise in multiple cropping (i.e. harvesting more than once a year) along with irrigation over the last two decades. Whether this multiple cropping can be expected to improve year-round food security is not well understood. We specifically examine if planting multiple food crops within a year is associated with dietary diversity and food security. We collected information on demographic and economic variables, farming activities and livelihood choices, from 200 unique households for three seasons (monsoon/rainy, winter, summer) during 2016–2018 (n = 600). Based on both a 24 h and a 30 days recall, we calculated several indicators, including the household dietary diversity score, the minimum dietary diversity for women, and household food insecurity access scale. At least 43% of the sample population experiences moderate to severe food insecurity in all seasons. Cereals (mainly rice) remain the most important food item irrespective of the season, with negligible consumption of other nutrient-rich food such as tubers, fish, eggs, and meats. Around 81% of women in all seasons do not consume a minimally diverse diet. Multiple cropping is associated with higher food security only during monsoon, while selling monsoon crops is associated with winter food security. Households practicing multiple cropping consume more pulses (a plant-based protein source) compared to single-cropping or non-farming households (p < 0.05). We find that multiple cropping cannot be used as a cure-all strategy. Rather a combination of income and nutrition strategies, including more diverse home garden, diverse income portfolio, and access to clean cooking fuel, is required to achieve year-round dietary diversity or food security.
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    Radar and optical remote sensing for near real-time assessments of cyclone impacts on coastal ecosystems
    (Remote Sensing in Ecology and Conservation, 2022-02-14) Mondal, Pinki; Dutta, Trishna; Qadir, Abdul; Sharma, Sandeep
    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.
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    Sensor-based measurements of NDVI in small grain and corn fields by tractor, drone, and satellite platforms
    (Crop and Environment, 2024-02-01) Miller, Jarrod O.; Mondal, Pinki; Sarupria, Manan
    The use of sensors for variable rate nitrogen (VRN) applications is transitioning from equipment-based to drone and satellite technologies. However, regional algorithms, initially designed for proximal active sensors, require evaluation for compatibility with remotely sensed reflectance and N-rate predictions. This study observed normalized difference vegetation index (NDVI) data from six small grain and two corn fields over three years. We employed three platforms: tractor-mounted active sensors (T-NDVI), passive multispectral drone (D-NDVI), and satellite (S-NDVI) sensors. Averaged NDVI values were extracted from the as-applied equipment polygons. Correlations between NDVI values from the three platforms were positive and strong, with D-NDVI consistently recording the highest values, particularly in areas with lower plant biomass. This was attributed to D-NDVI's lower soil reflectance and its ability to measure the entire biomass within equipment polygons. For small grains, sensors spaced on equipment booms might not capture accurate biomass in poor-growing and low NDVI regions. Regarding VRN, S-NDVI and D-NDVI occasionally aligned with T-NDVI recommendations but often suggested half the active sensor rate. Final yields showed some correlation with landscape variables, irrespective of N application. This finding suggests the potential use of drone or satellite imagery to provide multiple NDVI maps before application, incorporating expected landscape responses and thereby enhancing VRN effectiveness.
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    Social media analysis reveals environmental injustices in Philadelphia urban parks
    (Scientific Reports, 2023-08-03) Walter, Matthew; Bagozzi, Benjamin E.; Ajibade, Idowu; Mondal, Pinki
    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.