CLASlite Publications

Understanding the patterns of land degradation and desertification to develop mitigation strategies requires identification of methods for accurate and spatially explicit assessment and monitoring. Remote sensing data offer the possibility to develop strategies that outline degradation and desertification. The free access policy on satellite imagery enables a new pathway to measure, assess, and monitor land degradation using indicators derived from multispectral satellite data. This chapter seeks to explore a methodology for land degradation and desertification assessment and monitoring, based on freely available multispectral satellite data. The method identifies net primary productivity (NPP) and canopy cover (CC) as indicators of degradation. The trajectories of these indicators show patterns and trends over time. The methodological development presented here is intended to be a tool for regional landscape monitoring and assessment, enabling the formulation of corrective action plans. This methodology was tested in a semi-deciduous ecosystem in the southeast of Mexico.

Figure 1. Study area.

• National Institute of Forestry, Agriculture and Livestock Research, Mexico City, Mexico

Tropical forests provide a crucial carbon sink for a sizable portion of annual global CO2emissions. Policies that incentivize tropical forest conservation by monetizing forest carbon ultimately depend on accurate estimates of national carbon stocks, which are often based on field inventory sampling. As an exercise to understand the limitations of field inventory sampling, we tested whether two common field-plot sampling approaches could accurately estimate carbon stocks across approximately 76 million ha of Perúvian forests. A 1-ha resolution LiDAR-based map of carbon stocks was used as a model of the country's carbon geography.

  • Department of Global Ecology, Carnegie Institution for Science, United States

Figure1. Maps used for the analyses. a The model aboveground carbon density (ACD) map of Perú at 1-ha resolution with all non-forested areas masked out, and the strata binned and colored by quintiles for b cloudiness, c dry season length, d slope, e mean annual precipitation, f elevation and g relative elevation.

Ecology seeks general principles describing how the biota respond to multiple environmental factors, partly to build a more prognostic science in the face of global climate change. One such principle to emerge is the "leaf economics spectrum" (LES), which relates ecologically important plant nutrients to leaf construction and growth along simple relational axes. However, interrelationships between LES traits have not been tested at large geographic scales. Using airborne imaging spectroscopy and geospatial modeling, we discovered strong climatic and geophysical controls on LES traits and their interrelationships throughout Andean and western Amazonian forest canopies. This finding highlights the need for biogeographically explicit treatment of plant traits, afforded by imaging spectroscopy, in the next generation of biospheric models.

  • Department of Global Ecology, Carnegie Institution for Science, United States

Figure 2. Maps of forest canopy foliar N, P, and LMA throughout the Peruvian Andes and Amazon region. Inset graph shows the distributions of N, P, and LMA for the entire study region.

Over the past two decades, one of the research topics in which many works have been done is spatial modeling of biomass through synergies between remote sensing, forestry, and ecology. In order to identify satellite-derived indices that have correlation with forest structural parameters that are related with carbon storage inventories and forest monitoring, topics that are useful as environmental tools of public policies to focus areas with high environmental value. In this chapter, we present a review of different models of spatial distribution of biomass and resources based on remote sensing that are widely used. We present a case study that explores the capability of canopy fraction cover and digital canopy height model (DCHM) for modeling the spatial distribution of the aboveground biomass of two forests, dominated by Abies Religiosa and Pinus spp., located in Central Mexico. It also presents a comparison of different spatial models and products, in order to know the methods that achieved the highest accuracy through root-mean-square error. Lastly, this chapter provides concluding remarks on the case study and its perspectives in remote sensing-based biomass estimation.

Figure 1. Interferometric synthetic aperture radar.

  • Centro de Investigación en Geografía y Geomática “Ing. Jorge L. Tamayo”, México DF, Mexico

It is often cited that large scale oil palm plantation were responsible for forest cover changes in Sumatra and Kalimantan. Objective of the research was to identify whether oil palm concessions were the direct cause of intact forest cover changes in study area. The study areas are situated at Jambi Province, Indonesia and are experiencing rapid expansion of oil palm plantation. We used Landsat temporal images from year 1988, 1990, 2000, 2007, and 2013 to detect forest cover change. We also made use Carnegie Landsat Analysis System-Lite (CLASlite) fractional cover module to differentiate undisturbed (intact), disturbed (logged) forest and also oil palm growing stages on Landsat images. Our study showed that, there were only 8% of oil palm plantation development occurred by direct clearing of intact forest in the study area in the last 25 years. Oil palm concessions in the last 25 years were mostly developed on logged forest, agroforests, and shrub lands.

Figure 2. Oil palm plantation distribution in Bungo and Merangin Districts on; (a) Landsat 2007 image; (b) Landsat 2013 image.

  • Bogor Agriculture University, Darmaga, Bogor 16118, Indonesia
  • University of Jambi, Jambi, Indonesia
  • BPDAS Batanghari, Jambi, Indonesia

Tropical forests are vast and scientifically underexplored places. Biotic losses, gains, and reorganization within these systems go undetected due to a lack of access to technologies needed to monitor forest cover, composition, and carbon content. Provision of forest cover-monitoring tools for non-scientists has, thus, become a focus of innovation in the remote-sensing community, while advances in high-resolution forest carbon and biodiversity mapping science has progressed more slowly. This paper focuses on high-resolution remote-sensing developments to measure and monitor tropical forest canopies at the "organismic scale," which is the resolution that resolves individual canopies and species throughout the forest landscape. Emphasis is placed on how forest carbon stocks can be mapped with precision and accuracy comparable to that of field-based estimates. Biodiversity mapping poses the greatest challenge, but recent advances in three-dimensional functional and structural trait imaging can reveal variation in species richness, abundance, and functional diversity over large geographic regions. The pay-off in pursuing these studies will be a vastly improved understanding of tropical biodiversity patterns and their underlying ecological and evolutionary drivers, which will have positive cascading effects on conservation decision-making and resource policy development.

  • Department of Global Ecology, Carnegie Institution for Science, United States

Figure 2. Airborne Light Detection and Ranging (LiDAR) provides three-dimensional imaging of ecosystems by measuring precise distances between ground and vegetation elements including foliage and woody stems. LiDAR works by sweeping a high-pulse rate laser outward from the bottom of an airplane, recording the elapsed time between laser pulses and returns. The upper image of the lowland Amazonian forest in Colombia was collected by the Carnegie Airborne Observatory (CAO; ,http://cao.ciw.edu.) and is color-coded by height above ground as shown in the lower sectional view. Red indicates taller portions of the canopy; blue indicates shorter portions of the canopy.

We assessed the expected historical and current species richness of shrubs and trees in the Department of Antioquia, northwest region of Colombia. We used the Fisher's alpha value associated with the pooled dataset of identified species in 16 1-ha plots that were used to extrapolate the scaled species richness of the Antioquia Province under three different scenarios: 1) the entire region before deforestation began, assuming an original forest cover of around 92% of the entire province (excluding paramos, rivers, and lakes). 2) The forest cover in 2010. 3) The expected forest cover in 2100 assuming the observed deforestation rate between 2000 and 2010 as a constant. We found that, despite relatively low local and global losses of species, global extinctions in terms of number of species could be dramatically high due to the high endemism and deforestation rates.

Figure 3. Forest cover of the Antioquia province at 2010.

  • Universidad Nacional de Colombia - Departamento de Ciencias, Colombia
  • IDEAM, Colombia
  • Universidad de Antioquia - Instituto de Biología- Herbario Universidad de Antioquia, Colombia

A number of methods to overcome the 2003 failure of the Landsat 7 Enhanced Thematic Mapper (ETM+) scan-line corrector (SLC) are compared in this article in a forest-monitoring application in the Yucatan Peninsula, Mexico. The objective of this comparison is to determine the best approach to accomplish SLC-off image gap-filling for the particular landscape in this region, and thereby provide continuity in the Landsat data sensor archive for forest-monitoring purposes. Four methods were tested: (1) local linear histogram matching (LLHM); (2) neighbourhood similar pixel interpolator (NSPI); (3) geostatistical neighbourhood similar pixel interpolator (GNSPI); and (4) weighted linear regression (WLR). All methods generated reasonable SLC-off gap-filling data that were visually consistent and could be employed in subsequent digital image analysis. Overall accuracy, kappa coefficients (κ), and quantity and allocation disagreement indices were used to evaluate unsupervised Iterative Self-Organizing Data Analysis (ISODATA) land-cover classification maps. In addition, Pearson correlation coefficients (r) and root mean squares of the error (RMSEs) were employed for estimates agreement with fractional land cover. The best results visually (overall accuracy > 85%, κ < 9%, quantity disagreement index < 5.5%, and allocation disagreement index < 12.5%) and statistically (r > 0.84 and RMSE < 7%) were obtained from the GNSPI method.

These results suggest that the GNSPI method is suitable for routine use in reconstructing the imagery stack of Landsat ETM+ SLC-off gap-filled data for use in forest-monitoring applications in this type of heterogeneous landscape.

Figure 1. Location of the study area on the Yucatan Peninsula, Mexico.

  • Environmental and Life Science Graduate Program, Trent University, Canada
  • Environmental and Resource Science Program/Department of Geography, Trent University, Canada
  • Facultad de Ingeniería, Universidad Autónoma de San Luis Potos, Mexico

Remote sensing is gaining considerable traction in forest monitoring efforts, with the Carnegie Landsat Analysis System lite (CLASlite) software package and the Global Forest Change dataset (GFCD) being two of the most recently developed optical remote sensing-based tools for analysing forest cover and change. Due to the relatively nascent state of these technologies, their abilities to classify land cover and monitor forest dynamics have yet to be evaluated against more established approaches. Here, we compared maps of forest cover and change produced by the more traditional supervised classification approach with those produced by CLASlite and the GFCD, working with imagery collected over Sierra Leone, West Africa. CLASlite maps of forest change from 2001-2007 and 2007-2014 exhibited the highest overall accuracies (79.1% and 89.6%, respectively) and, importantly, the greatest capacity to discriminate natural from planted mature forest growth. CLASlite's comparative advantage likely derived from its more robust sub-pixel classification logic and numerous user-defined parameters, which resulted in classified products with greater site relevance than those of the two other classification approaches. In light of today's continuously growing body of analytical toolsets for remotely sensed data, our study importantly elucidates the ways in which methodological processes and limitations inherent in certain classification tools can impact the maps they are capable of producing, and demonstrates the need to understand and weigh such factors before any one tool is selected for a given application.

Figure 1. Land-cover maps of Gola and its surrounding 25 km region in early 2001 derived from three classification techniques. The top row of zoomed-in panels depicts a palm plantation; the bottom row of zoomed-in panels depicts a small town and its surrounding area. The two panels in the left-most column are Landsat reflectance images represented in R = Band 5 (1.55–1.75 μm), G = Band 4 (0.77–0.90 μm), and B = Band 3 (0.63–0.69 μm). For the Carnegie Landsat Analysis System lite (CLASlite) and Global Forest Change dataset (GFCD) maps, only the Non-vegetation and Forest categories from the figure key apply.

  • Department of Plant Sciences, University of Cambridge, Downing Street, Cambridge CB2 3EA, UK

Forest canopy structure is strongly influenced by environmental factors and disturbance, and in turn influences key ecosystem processes including productivity, evapotranspiration and habitat availability. In tropical forests increasingly modified by human activities, the interplay between environmental factors and disturbance legacies on forest canopy structure across landscapes is practically unexplored. We used airborne laser scanning (ALS) data to measure the canopy of old-growth and selectively logged peat swamp forest across a peat dome in Central Kalimantan, Indonesia, and quantified how canopy structure metrics varied with peat depth and under logging. Several million canopy gaps in different height cross-sections of the canopy were measured in 100 plots of 1 km2 spanning the peat dome, allowing us to describe canopy structure with seven metrics. Old-growth forest became shorter and had simpler vertical canopy pro- files on deeper peat, consistent with previous work linking deep peat to stunted tree growth. Gap size frequency distributions (GSFDs) indicated fewer and smaller canopy gaps on the deeper peat (i.e. the scaling exponent of Pareto functions increased from 1.76 to 3.76 with peat depth). Areas subjected to concessionary logging until 2000, and illegal logging since then, had the same canopy top height as old-growth forest, indicating the persistence of some large trees, but mean canopy height was significantly reduced. With logging, the total area of canopy gaps increased and the GSFD scaling exponent was reduced. Logging effects were most evident on the deepest peat, where nutrient depletion and waterlogged conditions restrain tree growth and recovery. A tight relationship exists between canopy structure and peat depth gradient within the old-growth tropical peat swamp forest. This relationship breaks down after selective logging, with canopy structural recovery, as observed by ALS, modulated by environmental conditions.

These findings improve our understanding of tropical peat swamp ecology and provide important insights for managers aiming to restore degraded forests. This relationship breaks down after selective logging, with canopy structural recovery, as observed by ALS, modulated by environmental conditions. These findings improve our understanding of tropical peat swamp ecology and provide important insights for managers aiming to restore degraded forests.

Figure 1. Map of old-growth (light grey), selectively logged forest (red) and non-forest (dark grey) within the 750 km2 Mawas landscape, Indonesian Borneo (location shown in inset). Full red zones indicate areas affected by selective concessionary timber extraction until 2000 and illegal selective logging thereafter as estimated from logging routes detected in historical satellite imagery.

  • Department of Plant Sciences, University of Cambridge, UK

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