The conservation and development of forests are vital to the welfare of human beings. Forests management is essential to maintain social, economic and ecological services. Forrest monitoring allows to track their state of health and productivity, in order to conduct proper management, according to the state of resources, to enhance their functionality and promote conservation. Remote sensors, optical and radar, offer the possibility of locating changes in forest areas using various analysis techniques, ranging from the purely visual interpretation to the implementation of a fully automated algorithm. This report is a review of the literature on the techniques used to observe changes in forest cover and monitoring through remote sensing.
Figure 1. World map of forest distribution.
The Bolsa Floresta Programme (BFP) is an incentive-based forest conservation initiative of the State of Amazonas (Brazil). Launched in 2007, the programme was among the first initiatives in Brazil that relied on direct and conservation-conditional incentives to protect forests at a large scale. One of the 15 sustainable development reserves (SDR) enrolled in the programme, the SDR Juma, became Brazil’s first certified REDD project, and also the first worldwide to receive the “gold” status of the Climate, Community & Biodiversity Alliance (CCBA). This study characterizes the BFP intervention context and documents preliminary impacts, with the objective to identify lessons learned for this and other conservation initiatives in the Amazon, and beyond. It relies on household survey data collected in two BFP reserves, the SDR Juma and Uatumã, as well as some remote sensing-based analyses that cover the programme’s total intervention area. Here we summarize key findings on (1) the predominant livelihood strategies of programme participants and non-participants, respectively, inside and outside the SDRs Juma and Uatumã, (2) recent trends in land cover change in and around the two reserves, (3) preliminary evidence on BFP impact, and (4) the main lessons from our study.
Figure 2. Protected areas with and without Bolsa Floresta Programme in the state of Amazonas. Black circles indicate study areas for field data collection.
The Amazon basin sustains more than half of the world’s remaining tropical rain-forest and plays a vital role in maintaining biodiversity, climate and terrestrial carbon storage. The Amazon has the world’s highest absolute rates of deforestation. Land use/cover change (LUCC) practices in the Brazilian Amazon, such as cattle ranching, logging, agriculture, mining, and urbanization are the major contributors to deforestation and have major impacts on ecosystems and environmental processes at local, regional and global scales. Such impacts include land fragmentation and degradation, biodiversity loss, alteration in atmospheric composition and climate change. Understanding the determinants of LUCC is vital for developing sustainable environmental management policies and forest protection. Modelling provides insights on land use dynamics and the driving factors of change and allows to quantitatively predicting where future change might occur. A simulation of future landscape in 2020 in the Kayabi Indigenous Territory in the Brazilian Amazon was carried out using Geographic Information Systems (GIS), Remote Sensing and the IDRISI’s Land Change Modeler following five sequential steps: (1) Creation of forest land cover maps from 2000, 2006 and 2009 derived from CLASlite’s fractional cover image; (2) Land-change cover analysis by cross-tabulating forest land cover maps; (3) Calculation of transition potentials from forest to anthropogenic disturbance using a MLP neural network methodology. Afterwards, a prediction of future landscape was simulated using a Markovian process; (4) Assessment of the model performance by predicting a 2009 land cover and comparing it with an actual 2009 land cover map and (5) Predicting a 2020 land cover map.
The model was able to successfully simulate deforestation expansion in the region and identify the main landscape attributes driving anthropogenic disturbance expansion in the studied area. Distance from roads and distance from existing disturbance were found as the key factors driving deforestation in the Kayabi area
Figure 1. ‘Arc of deforestation’ in Legal Amazon.
Agricultural expansion is the predominant mode of tropical land cover change, leading to profound alterations in vegetation, carbon stocks, and freshwater systems. The dynamics of these ecosystem changes depend on land cover trajectories preceding agricultural conversion. Assessing ecological outcomes from major land cover transitions is therefore critical for reducing uncertainties about how food production affects the human-natural system. This dissertation examines the influence of oil palm plantation expansion on land cover and ecosystem processes at nested regional (Kalimantan, 538,346 km2 ) and local (Ketapang, 12,038 km2 ) scales. Major regional land covers were classified from a timeseries of Landsat satellite images. Using a spatially-explicit model of oil palm expansion, future land cover change was assessed under various scenarios. Carbon emissions from plantations were estimated with a carbon bookkeeping model of above- and below-ground carbon flux from deforestation, forest degradation, vegetation regrowth, and peatland soil burning and draining. To discern the effects of plantation development on freshwater ecosystems, streams draining watersheds dominated by forests, agroforests, and oil palm were monitored from 2008-2012. From 1990-2010 across Kalimantan, -70% of oil palm expansion cleared intact and logged forests. In Ketapang, plantation land sources exhibited distinctive temporal dynamics, composing mostly forests on mineral soils from 1994-2001, shifting to peatlands from 2008-2011. If all government-allocated plantation leases are developed, oil palm will occupy 34% of Kalimantan lowlands (< 300m) outside of protected areas. Such rapid plantation expansion affects ecological processes at multiple scales. Locally, results indicate that plantation land use significantly alters stream metabolism, temperature, and sediment loads; moreover, such changes persist as oil palm matures. Regionally, Kalimantan oil palm plantations are projected to contribute 18-22% (0.12- 0.15 GtC y ‘) of Indonesia’s 2020 CO2 equivalent emissions. Analysis of Ketapang scenario model outcomes suggests that emissions mitigation will require protection of existing carbon stocks.
Figure 1. Study area: red box Afobaka subset, green box Atjoni.
In many tropical countries forest are destroyed to expand timber, mining and agricultural industries and are affected by infrastructure investments such roads and dams. Deforestation rates in Suriname have been historically low due to the low population pressure and relative remoteness. Suriname’s status as High Forest Low Deforestation (HFLD) country is set to change if planned infrastructure investments (a hydrodam, a road to Brazil and agriculture extension with prospects for biofuels) through the heart of the country realize, moreover, if low institutional capacity and environmental regulations continue inhibiting the capacity response of governments to control the destruction of tropical forest overlapping greenstone deposits. Analytical and empirical studies have shown that an important determinant of deforestation is the improved access to previously inaccessible forested areas alongside low governance gradients with high socio-economic value. Timely information about the underlying and proximate drivers of actual and future deforestation and on the location and extent of expected deforestation is one condition to properly manage this process of forest cover destruction. Therefore, this study uses spatial deforestation models to assess the influence of environmental drivers on forest cover change and to project future deforestation trends. During the first stage of this project, forest cover maps were developed for 2005 and 2009 based on Landsat 5TM images. The resulting forest cover maps were used in a spatial explicit model which calculates forest change rates and simulates deforestation between 2009 and 2020 based on the spatial distribution of spatial variables and a historical deforestation scenario assuming that deforestation trajectories into the future will continue under the historical trend found between the period analyzed. The model demonstrates how land use, infrastructure, socio-economic aspects and biophysical features drive forest loss in Suriname. With the outcomes of this research the researchers expect to be able to demonstrate the potential of this type of studies to visualize the effects of land use decisions on forest conservation along future infrastructure developments in the country, and to inform these decisions so that they minimize undue negative impacts on forest-dependent people and forest.
Mapping the presence of trees is an important tool for assessing tree-covered habitats, their changes, and calculating variables, like forest area and fragmentation. Despite the popularity of automated pattern recognition to make tree cover maps, their accuracy and precision are rarely tested or compared to more modest methods, like human-based pattern recognition to identify tree cover. Here, we test the performance of two computer-generated tree mapping products, the Global Change Forest database and the Carnegie Landsat Analysis System, against ground surveys and a human-made tree cover map created using Google Earth to hand digitize the presence and absence of trees in a diversified agricultural region in Costa Rica (934 km2). The human-made tree cover map properly classified 100% ground survey sites and explained 81% of the variance in percent of canopy cover values from the field. The Global Change Forest database misclassified 18 of 23 ground survey sites in deforested locations and explained 6% of the variance in percent of canopy cover values from ground surveys. The Carnegie Landsat Analysis System misclassified 9 of 23 ground survey sites in deforested locations and explained 38% of the variance in percent of canopy cover values from the field. Our results suggest that the Global Change Forest database overestimated tree cover by of 20% and the Carnegie Landsat Analysis System by 1%. We caution landscape ecologists working at fine spatial scales against using computer-generated tree cover, especially in the partially forested lands that increasingly cover the planet.
En este estudio, examinamos la distribución geográfica de la castaña y el impacto de la deforestación ocurrida entre los años 2010 y 2015. Específicamente: a) modelamos la distribución de la especie usando el algoritmo Maxent para confirmar si su distribución se encuentra restringida a los bosques tropicales húmedos del norte de Bolivia, b) usando información generada por OTCA-MMAyA (2016) sobre la deforestación ocurrida entre los años 2010-2015, estimamos la pérdida de bosque con castaña en este periodo y c) utilizando información sobre censos completos realizados en la TCO Tacana II (norte de La Paz) y la RNVSA Manuripi (suroeste de Pando), calculamos el número de árboles de castaña que podrían haberse perdido por tal deforestación. Los resultados muestran que la castaña en Bolivia ocupa cerca de 84 mil km2 (16% menos que la estimación histórica), distribuida en dos áreas, una en la región Heath-Alto Madeira (subcuencas de los ríos Tahumanu, Yata, Abuná, Acre, Manurimi y Madre de Dios) y la otra en la región de Iténez (en la subcuenca del mismo nombre). Cerca del 0.76% (639 km2) del bosque con castaña fue deforestado en el periodo 2010-2015, sugiriendo la pérdida de 27 mil árboles de castaña (0.15% del total histórico estimado). Futuras investigaciones que evalúen las variaciones geográficas de la fenología reproductiva y su relación con el clima o la diversidad genética de las poblaciones de castaña que crecen en Bolivia serán importantes para determinar el estado actual de la especie y proponer pautas de manejo.
Iberia is one of the districts of the province of Tahuamanu that in recent years has been a growing deforestation produced by anthropic activities such as agricultural expansion, livestock, felling and burning of trees, construction of roads and roads, among other aspects collateral to the activity, with what arises the need to provide the authorities with inputs on prospective change of land use for the development of land management plans. In this context, prospective models of land use change are a tool to know the dynamics that help to establish patterns of change in land use, and to explore possible scenarios. The objective of this paper is to analyze and model the change of coverage (forest to deforestation) to determine deforested areas for the periods 2004, 2011, 2016 and 2030. The images involved in this investigation were acquired from the Landsat 5 TM sensor (2004 and 2011) and Landsat 8 OLI (2016). The calculations report for the period 2004, 2011 and 2016, 4 824.09 ha, 12 260.08 ha, 17 063.72 ha of deforested areas respectively. The detection of areas of change through differentiation images, highlight areas of subtraction with increase in gradual change for periods of 2004-2011 (7 years) with 7 767.78 ha, and 2011-2016 (5 years) with 5 123.71 ha.