{"query_echo":{"q":"","filters":{"geography":["Santa Rosa National Park"]},"warnings":[{"code":"facet_value_not_in_vocabulary","message":"Value 'Santa Rosa National Park' for facet 'geography' is not in the controlled vocabulary."}]},"total":{"value":4,"relation":"eq"},"page":1,"page_size":20,"self":"https://climateinsightsdirectory.org/api/v1/search?geography=Santa+Rosa+National+Park","stability":"beta","ai_marking":{"digital_source_type":{"uri":"http://cv.iptc.org/newscodes/digitalsourcetype/trainedAlgorithmicMedia","name":"Created using Generative AI"},"notice":"Summary and key insights are generated using artificial intelligence."},"results":[{"id":"iai-000016","url":"https://climateinsightsdirectory.org/document/iai-000016","title":"Mapping tropical dry forest succession using multiple criteria spectral mixture analysis","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-15","source_url":"https://iai.int/es/pub_acad/mapping-tropical-dry-forest-succession-using-multiple-criteria-spectral-mixture-analysis/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"The study introduces the MCSMA method to improve the mapping of tropical dry forest succession stages by utilizing spectral and spatial information and an evaluation framework based on RMSE, spatial distance, and fraction consistency. Tested in Costa Rica's Santa Rosa National Park, the method outperformed the MESMA approach in reducing error across early, intermediate, and late successional stages.","ai_generated":true,"unit":"iai-000016#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"The proposed multiple criteria spectral mixture analysis (MCSMA) method demonstrated superior performance compared to multiple endmember spectral mixture analysis (MESMA) across early, intermediate, and late successional stages, showing lower root mean square error, mean absolute error, and systematic error.","ai_generated":true,"unit":"iai-000016#insight-1","date_modified":"2026-10-23"},{"text":"Shortwave infrared (SWIR) data is significant for distinguishing between different successional stages of tropical dry forests, which exhibit high spectral variability due to differences in biomass.","ai_generated":true,"unit":"iai-000016#insight-2","date_modified":"2026-10-23"},{"text":"The MCSMA method utilizes an evaluation framework consisting of three criteria—root mean square error (RMSE), spatial distance (SD), and fraction consistency (FC)—to select the best-fit endmember model.","ai_generated":true,"unit":"iai-000016#insight-3","date_modified":"2026-10-23"}],"climate_tags":["remote sensing","forest conservation"],"geography_discussed":["Americas"],"frame_perspective":[],"analysis_type":["methodological evaluation","remote sensing analysis"]},{"id":"iai-000050","url":"https://climateinsightsdirectory.org/document/iai-000050","title":"Modeling seasonal surface temperature variations in secondary tropical dry forests","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-13","source_url":"https://iai.int/es/pub_acad/modeling-seasonal-surface-temperature-variations-in-secondary-tropical-dry-forests/","generator":"Climate Insights Directory","generated_fields":["summary","climate_tags","analysis_type"],"summary":{"text":"The research modeled land surface temperature (LST) time series from July 2013 to July 2016 using Landsat-8 data and a radiative transfer equation algorithm. The study found that LST in the dry season was 15\u001020 K higher than in the wet season, with early successional stages being significantly warmer than intermediate and late stages during the dry season. While leaf phenology and canopy architecture explain most variations, precipitation was identified as the ultimate determinant of LST variations through biogeochemical and biogeophysical processes.","ai_generated":true,"unit":"iai-000050#summary","date_modified":"2026-10-23"},"key_insights":[],"climate_tags":["climate change","carbon sequestration","biodiversity conservation"],"geography_discussed":[],"frame_perspective":[],"analysis_type":["observational","modeling"]},{"id":"iai-000136","url":"https://climateinsightsdirectory.org/document/iai-000136","title":"Interception of rainfall in successional tropical dry forests in Brazil and Costa Rica","publisher":{"name":"Inter-American Institute for Global Change Research","organization":"inter-american-institute-for-global-change-research"},"document_type":"research paper","language":"en","date_published":"2025-01-12","source_url":"https://iai.int/es/pub_acad/interception-of-rainfall-in-successional-tropical-dry-forests-in-brazil-and-costa-rica/","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","geography_discussed","analysis_type"],"summary":{"text":"The study analyzed rainfall interception in early, intermediate, and late successional stages of tropical dry forests in Costa Rica and Brazil. In Brazil's Mata Seca State Park, net rainfall remained relatively stable between 79.3% and 85.4% across stages, influenced by high liana density. In Costa Rica's Santa Rosa National Park, net rainfall decreased significantly as succession progressed, from 87.5% in early stages to 63.4% in late stages, correlating negatively with the plant area index.","ai_generated":true,"unit":"iai-000136#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"In Mata Seca State Park (MSSP), Brazil, net rainfall ranged from 79.3% to 85.4% of gross rainfall across all plots, showing no trend related to forest succession because of the high density of lianas in intermediate and late stage plots.","ai_generated":true,"unit":"iai-000136#insight-1","date_modified":"2026-10-23"},{"text":"In Santa Rosa National Park (SRNP), Costa Rica, net rainfall showed a clear downward trend as forest succession progressed, moving from 87.5% in the early stage to 73.0% in the intermediate stage and 63.4% in the late stage.","ai_generated":true,"unit":"iai-000136#insight-2","date_modified":"2026-10-23"},{"text":"Net rainfall in Santa Rosa National Park (SRNP) was negatively correlated with the plant area index (r = -0.755, p < 0.05).","ai_generated":true,"unit":"iai-000136#insight-3","date_modified":"2026-10-23"}],"climate_tags":[],"geography_discussed":["Santa Rosa National Park","Mata Seca State Park"],"frame_perspective":[],"analysis_type":["empirical research"]},{"id":"wri-001482","url":"https://climateinsightsdirectory.org/document/wri-001482","title":"Case Example: Costa Rica","publisher":{"name":"World Resources Institute","organization":"world-resources-institute"},"document_type":"case study","language":"en","date_published":"2015","source_url":"https://wriorg.s3.amazonaws.com/s3fs-public/WRI_Restoration_Diagnostic_Case_Example_Costa_Rica.pdf","generator":"Climate Insights Directory","generated_fields":["summary","key_insights","climate_tags","geography_discussed","analysis_type"],"summary":{"text":"Between 1986 and 2005, Costa Rica increased its forest area by 394,000 hectares, primarily through passive restoration on abandoned pastures. This success is attributed to a combination of crisis-driven motivation, ecological viability, and market shifts—specifically the decline of the cattle industry and the rise of eco-tourism. While the Pago de Servicios Ambientales (PSA) system is a well-known financial incentive, the document argues that structural economic changes and restrictive forestry laws were equally or more critical to the country's reforestation outcomes.","ai_generated":true,"unit":"wri-001482#summary","date_modified":"2026-10-23"},"key_insights":[{"text":"Costa Rica experienced a significant decline in forest cover from 77 percent of land area in 1943 to 41 percent by 1986, driven by cattle grazing and crop production. Following this, the country achieved a net increase in forest area of 394,000 hectares by 2005, reaching 48 percent coverage.","ai_generated":true,"unit":"wri-001482#insight-1","date_modified":"2026-10-23"},{"text":"The decline of the cattle industry was a primary driver of restoration. A fall in international beef prices and the removal of national subsidies—part of a World Bank structural adjustment program—reduced the profitability of ranching, leading the national cattle herd to drop from 2.1 million heads in 1989 to 1.4 million by 2000.","ai_generated":true,"unit":"wri-001482#insight-2","date_modified":"2026-10-23"},{"text":"Legislative restrictions played a critical role in preventing further deforestation. Forestry Law No. 7575, passed in 1996, banned the conversion of forested land and the export of round wood and squared timber, while also removing previous taxes on 'unproductive' land that had incentivized deforestation.","ai_generated":true,"unit":"wri-001482#insight-3","date_modified":"2026-10-23"},{"text":"The Pago de Servicios Ambientales (PSA) system, established in 1996, provided direct payments for forest protection, reforestation, and agroforestry, funded by a 3.5 percent fossil fuel tax. However, the document notes that the PSA may have biased toward wealthier landowners and that some participants would have protected forests even without the program due to existing laws.","ai_generated":true,"unit":"wri-001482#insight-4","date_modified":"2026-10-23"},{"text":"The growth of the tourism industry provided a strong economic incentive for conservation. The number of international tourists visiting Costa Rica increased from 60,000 in 1986 to 1.7 million in 2005, fostering market conditions that favored restored natural forests.","ai_generated":true,"unit":"wri-001482#insight-5","date_modified":"2026-10-23"}],"climate_tags":["deforestation","agroforestry","ecotourism","land tenure","biodiversity protection","soil erosion"],"geography_discussed":["Nicaragua","Panama","San José","Santa Rosa National Park"],"frame_perspective":[],"analysis_type":["case study"]}]}