Combining remote sensing with local knowledge is vital for understanding forest change in West Africa

Global, Landsat-based forest-cover change from 1990 to 2000

Time-series analysis of multi-resolution optical imagery for quantifying forest cover loss in Sumatra and Kalimantan, Indonesia

Estimates derived from ground data-rich programs such as the US Forest Service Forest Information and Analysis (FIA) program contribute valuable information to quantifying forest dynamics. The results of our study, Hansen et al. (1), comparing gross forest cover loss (GFCL) globally, are not intended to replace estimates such as the southeast US net-change estimate reported by Reams et al. (2). The result that we reported for the United States (table 3 in ref. 1) is a national estimate of GFCL, and Reams et al. (2) do not provide estimates for other US regions or a national estimate of net or gross change that can be compared with our result. Estimates of forest change vary depending on the definition of forest (including forest cover vs. forest use), the time period assessed, the primary data, the change dynamic quantified, and the region evaluated. For example, whereas Reams et al. (2) report no net change in forest area for the southeast United States for 1997–2007, Drummond and Loveland (3) report 2.1 million hectares of net forest loss (5.7% of forest cover) between 1973 and 2000 for the six southeastern US ecoregions that they evaluated and net forest loss of 3.7 million hectares (4.1% of forest cover) for eastern US forests.

Do livelihood typologies influence local perceptions of forest cover change? Evidence from a tropical forested and non-forested rural landscape in western Uganda

Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia

Abstract. Dramatic political and economic changes in Eastern European countries following the dissolution of the “Eastern Bloc” and the collapse of the Soviet Union greatly affected land-cover and land-use trends. In particular, changes in forest cover dynamics may be attributed to the collapse of the planned economy, agricultural land abandonment, economy liberalization, and market conditions. However, changes in forest cover are hard to quantify given inconsistent forest statistics collected by different countries over the last 30 years. The objective of our research was to consistently quantify forest cover change across Eastern Europe from 1985 until 2012 using the complete Landsat data archive. We developed an algorithm for processing imagery from different Landsat platforms and sensors (TM and ETM+), aggregating these images into a common set of multi-temporal metrics, and mapping annual gross forest cover loss and decadal gross forest cover gain. Our results show that forest cover area increased from 1985 to 2012 by 4.7% across the region. Average annual gross forest cover loss was 0.41% of total forest cover area, with a statistically significant increase from 1985 to 2012. Most forest disturbance recovered fast, with only 12% of the areas of forest loss prior to 1995 not being recovered by 2012. Timber harvesting was the main cause of forest loss. Logging area declined after the collapse of socialism in the late 1980s, increased in the early 2000s, and decreased in most countries after 2007 due to the global economic crisis. By 2012, Central and Baltic Eastern European countries showed higher logging rates compared to their Western neighbours. Comparing our results with official forest cover and change estimates showed agreement in total forest area for year 2010, but with substantial disagreement between Landsat-based and official net forest cover area change. Landsat-based logging areas exhibit strong relationship with reported roundwood production at national scale. Our results allow national and sub-national level analysis of forest cover extent, change, and logging intensity and are available on-line as a baseline for further analyses of forest dynamics and its drivers.

Для оцінювання втрат лісового покриву Українських Карпат на прикладі території Сколівських Бескидів використано дистанційні методи. Для території досліджень на основі аналізу цифрових моделей рельєфу виокремлено ділянки, де, відповідно до чинних інструкцій та нормативів, заборонені суцільні рубки головного користування. На таких ділянках були виявлено та проаналізовано зміни лісового покриву. Для аналізу довгострокових змін лісового покриву використано Карту глобальних змін лісу (Global Forest Change – GFC). За даними аналізу такої інформації встановлено, що у 2010 р. частка природних лісів становила 19 % від загальної площі країни, або від 60,1 млн га. За період з 2001 по 2018 рр. в Україні втрачено 958 тис. га, що відповідає 8,6 % відносно площі лісового покриву за 2000 р. Для порівняння карт змін використано знімки із супутників Sentinel2 з роздільною здатністю 10 м×pix-1 для аналізу втрат лісу за 2015-2018 рр. Розмежування вододілу проведено для досліджуваної території за допомогою інструменту SAGA "Басейни вододілу" з використанням цифрової моделі рельєфу ASTER GDEM. За допомогою інструменту QGIS розраховано стрімкість схилів на основі цифрової моделі рельєфу ASTER GDEM2. Окрім цього, обчислено середнє значення, мінімум та максимум стрімкості схилу для порівняння її із наведеними даними стрімкості в базах лісовпорядкування для кожного виділу. Для визначення площі для екорегіону Українські Карпати на території Сколівських Бескидів спочатку вирізано растрову карту змін за даними Глобальної лісової варти (Global Forest Watch – GFW) за контурами екорегіону, векторизовано растр за картою змін, а потім обчислено площі за кожною категорією змін. Розраховано площі втрат лісового покриву. Встановлено, що вища частка втрат лісового покриву припадає на 2014-2018 рр. Він істотно вищий за середній щорічна частка втрат. Також виявлено, що останніми роками втрати лісового покриву зумовлені рубками, значна частка, котрих припадає на висоту понад 1100 м н.р.м. Аналіз змін лісового покриву для території Сколівських Бескид дав змогу порівняти такі зміни в лісах різної відомчої приналежності: Національного природного парку "Сколівські Бескиди", державного підприємства "Сколівське лісове господарство" та деяких лісництв, котрі належать до юрисдикції Сколівського війського лісгоспу ДП "Івано-Франківський військовий ліспромкомбінат". Порівняння даних втрати лісового покриву показав значні обсяги втрат на території військових лісництв, які були набагато вищими, ніж на інших територіях, що свідчить про їх антропогенне походження, тобто значні обсяги рубок.

Evaluating forest policy implementation effectiveness with a cross-scale remote sensing analysis in a priority conservation area of Southwest China

Western Madagascar's dry forests have suffered greater levels of deforestation than the island's humid eastern forests, and many of the largest remaining contiguous tracts of dry forest are conserved in Kirindy Mite National Park. To assist Kirindy Mite's management plan, we assessed land cover change in and around the Park, remotely sensing forest cover within the Park and an arbitrary 5-km buffer from Landsat images taken in 1990, 2000 and 2006. We then quantified forest cover and change and compared the values between the Park and buffer, interpreting the results through expert knowledge of the area. Kirindy Mite had lower rates of deforestation, higher rates of reforestation, and less net change than the unprotected zone in both 1990–2000 and 2000–2006. Park deforestation rates were approximately one third to one fourth those of the buffer, and Park reforestation rates were approximately double those of the buffer. Net change in the Park fluctuated between the two periods, with deforestation during 2000–2006 slightly exceeding reforestation during 1990–2000. All land cover changes accelerated over the study period, and disturbances in the Park were most frequent near its boundary. To maintain the forest as differences in forest cover across the Park boundary increase, we suggest including or intensifying measures to: (1) expand the Park boundary, simplifying its shape, (2) cooperate with the local people in managing a buffer zone, and (3) increase monitoring to minimize anthropogenic disturbances crossing the Park boundary.

The temperate forest is a complex biome due to the diversity of forest types, forest cover change dynamics and forest use management practices. While temperate forests play an important role in the global carbon cycle, their net carbon exchange is uncertain. Quantifying forest cover change is an important step in documenting disturbance regimes and carbon exchange estimates. Biome-wide gross forest cover loss was estimated using a probability-based sampling approach that integrated moderate and high spatial resolution satellite data sets. Area of gross forest cover loss from 2000 to 2005 within the temperate forest biome is estimated to be 1.03% of the total biome area, or 18.41 Mha. Estimated forest cover loss represented a 3.5% reduction in year 2000 forest area. About 68% of the total forest cover loss occurred in Eastern North America and in Europe. The mid-latitude forests of the United States exhibited the highest forest cover loss rates within the biome. Biome-wide rate of gross forest cover loss gradually increased from 2001 to 2005. The lowest change was detected in 2004, followed by the year of the highest change over the 5-year period. The regional forest cover change dynamics were confirmed by official forest fire and timber production statistics. The validation of the MODIS-based product demonstrated its efficiency in forest cover mapping and monitoring. Forest cover change monitoring using the approach presented should bring greater understanding on forest cover dynamics in temperate forests and enable improved carbon accounting.

This paper overviews observations and examines modeling issues associated with the mean state, climate variability and climate change in West Africa. The Tropical Rain Measuring Mission (TRMM) satellite allows for the first time estimates of Unconditional, Convective and Stratiform rain rates in West Africa. The 1998 estimated TRMM rates are compared to long-term observed rain rates and a merged rain data set (CMAP) during 1998. Further, the TRMM estimates are compared to the simulated rain rates from the Community Climate Model Version 3.6. The TRMM Precipitation Radar rain estimates are generally lower than either the long-term observations or the CMAP rates during 1998. Moreover, the TRMM rain estimates show a significant fraction of the total rain (convective + stratiform) is characterized as stratiform rain (30–40%). The CCM3 simulates primarily convective rain and negligible amounts of non-convective rain for West Africa. Furthermore, the TRMM high-resolution rain patterns strongly imply that rain in West Africa occurs on mesoscales in association with mesoscale convective systems (squall lines, mesoscale convective complexes and non-squall tropical clusters). We demonstrate this by briefly examining two mesoscale convective systems during May 1998 with METEOSAT data. Regional climate models may offer the best solution to understanding climate change in West Africa because of their ability to capture mesoscale systems and better their representation of orographic features. Adequate boundary conditions from Global Climate Models are still necessary for regional climate model simulations to successfully reproduce mean climate conditions and provide understanding with respect to future climate change. Observations in West Africa should be maintained or increased for monitoring climate variability and possibility of climate change in West Africa, proper initialization of numerical weather prediction models and the validation of climate models.

The focus of the study was to develop a nation-wide forest cover database of Myanmar by assessing and predicting the forest cover changes in the period of 1950 to 2027. This study estimated the net changes in forests at regional level along with spatial patterns of forest fragmentation using multi-source data. The results indicate forest area representing as 77.1%, 65.3%, 54.1% and 50.6% of the total geographical area of Myanmar during 1950, 1975, 2005 and 2016 respectively. This study predicted the forest cover changes in Myanmar using Module for Land use change evaluation. The five spatial variables were used to determine the relationship between deforestation and explanatory variables. The predicted forest cover of Myanmar for 2027 shows 48.4% of total geographical area under forest. The model predicted a further decrease of 14,878 km2 of forest area in Myanmar between 2016 and 2027. The forest cover loss analysed using the classified maps of 1950 and 2016 indicated an overall loss of 34.4% of the forest cover. Ayeyarwady, Mandalay and Nayi Pyi Taw were found to be showing the highest rate of deforestation in the recent period of 2005–2016. This study has provided an insight for understanding of long-term deforestation trends of Myanmar. It offers a valuable inputs for effective management of forest resources and restoration programs as it delineates and forecast the spatial changes in forests from past to future.

We estimate changes in forest cover (deforestation and forest regrowth) in the tropics for the two last decades (1990–2000 and 2000–2010) based on a sample of 4000 units of 10 ×10 km size. Forest cover is interpreted from satellite imagery at 30 × 30 m resolution. Forest cover changes are then combined with pan-tropical biomass maps to estimate carbon losses. We show that there was a gross loss of tropical forests of 8.0 million ha yr−1 in the 1990s and 7.6 million ha yr−1 in the 2000s (0.49% annual rate), with no statistically significant difference. Humid forests account for 64% of the total forest cover in 2010 and 54% of the net forest loss during second study decade. Losses of forest cover and Other Wooded Land (OWL) cover result in estimates of carbon losses which are similar for 1990s and 2000s at 887 MtC yr−1 (range: 646–1238) and 880 MtC yr−1 (range: 602–1237) respectively, with humid regions contributing two-thirds. The estimates of forest area changes have small statistical standard errors due to large sample size. We also reduce uncertainties of previous estimates of carbon losses and removals. Our estimates of forest area change are significantly lower as compared to national survey data. We reconcile recent low estimates of carbon emissions from tropical deforestation for early 2000s and show that carbon loss rates did not change between the two last decades. Carbon losses from deforestation represent circa 10% of Carbon emissions from fossil fuel combustion and cement production during the last decade (2000–2010). Our estimates of annual removals of carbon from forest regrowth at 115 MtC yr−1 (range: 61–168) and 97 MtC yr−1 (53–141) for the 1990s and 2000s respectively are five to fifteen times lower than earlier published estimates.

Insight into the imbalance of forest cover change at county level in mainland China during 2000–2020: From the perspective of subdividing forest cover change into forest gain and loss

Local ecological knowledge (LEK) can shed light on ecosystem change, especially in under- researched areas such as South Africa's Wild Coast. However, for ecosystem planning purposes, it is necessary to assess the accuracy and validity of LEK, and determine where such knowledge is situated in a community, and how evenly it is spread. Furthermore, it is relevant to ask: does LEK add value to science, and how do science and local knowledge complement one another? We assessed change in woodland and forest cover in the Nqabara Administrative Area on South Africa's Wild Coast between 1974 and 2001. The inhabitants of Nqabara are Xhosa-speaking people who are highly dependent on natural resources for their livelihoods. More recently, however, infrastructural development has influenced traditional lifestyles at Nqabara, although poverty remains high and formal education levels low. We assessed LEK about changes in woodland and forest cover over the past 30 years by interviewing 11 local experts, who were recognized as such by the Nqabara community, and 40 senior members of randomly selected households in each village. We also analyzed land-cover change, using orthorectified aerial photos taken in 1974 and 2001. Forest and woodland cover had increased by 49% between 1974 and 2001. The 11 had a nuanced understanding of these changes and their causes. Their understanding was not only remarkably consistent with that of scientists, but it added considerable value to scientific understanding of the ultimate causes of land-cover change in the area. The experts listed combinations of several causal factors, operating at different spatial and temporal scales. The 40 randomly selected respondents also knew that forest and woodland cover had increased, but their understanding of the causes, and the role of fire in particular, was somewhat simplistic. They could identify only three causal factors and generally listed single factors rather than the combinations of factors listed by the experts. In some instances, their understanding even appeared to be seriously flawed. In contemporary Xhosa society, ecological knowledge is unevenly spread and held by individuals rather than by groups. Therefore, it is important to work with experts rather than randomly selected individuals in ecological studies that incorporate local knowledge. Expert local knowledge adds value to science by providing detailed insights into the ultimate causes of change, and by contributing a rare historical perspective. Scientists add value to local knowledge through their ability to study and predict obscure processes such as the impact of atmospheric change on vegetation. Scientists must, however, acknowledge that positivist studies that compare local knowledge to science are fraught with ethical and methodological challenges. Certain aspects of local knowledge, particularly in terms of fire, are sacred and do not have the same origins as Western science. Local knowledge and science can complement one another, but we advise against integrating them in a way that co-opts local knowledge for scientific purposes.