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Sure, you can choose your favorite type of radio station i. You passively listen to the radio station hoping to hear songs that you like. On the other hand, measuring is like creating a custom playlist from your personal music collection for an MP3 player. If you want to adjust your playlist afterwards, you have that flexibility.

Difference between Monitor & Measurement

The songs that you listen to on the road via your MP3 player are the same versions that you listen to on your home computer. In terms of tracking and optimizing social media initiatives, I feel as though there are three main areas: monitoring , measurement , and management. All three of these areas are different but complementary. Sometimes you only have one option such as monitoring and in other cases all three options come together to complement each other.

However, as a brief overview management focuses on managing the various creative elements of your social media efforts e. Most social media tracking is currently done with monitoring solutions, which leverage the APIs of each platform Facebook, Twitter, YouTube, etc. Safety is concerned with the myriad ways in which a system can fail to function, which are necessarily vastly more numerous than the acceptable modes of functioning. Some of these failures may be familiar, even predictable, but the system may also malfunction in unpredictable ways.

Safety is partly achieved by being alert to these perturbations, responding rapidly to keep things on track. Doctors, nurses and managers do this all the time in healthcare, probably to a greater extent than in any other industry.

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But when they succeed, or the system compensates in other ways, these actions are in a sense invisible. This suggests that assessing safety will require looking beyond a set of metrics to considering how it might be possible to monitor the functioning of the wider healthcare system. This gap is costly and should be closed. We began by conducting three scoping reviews.

These reviews covered safety measurement in a range of high risk industries; conceptual approaches and models of systems safety; and the measurement of safety in healthcare. Abridged versions of these reviews became chapters in the main report. These reviews used author and keyword searches using PubMed and internet search engines together with a review of bibliographic lists to identify relevant publications. The websites of key organisations were included where appropriate, enabling us to access technical reports and guidance documents, for example those issued by national and state regulators of different industries.

The scoping reviews on high risk industries and models of safety drew out the main practical implications for healthcare. We found that the measurement and monitoring of safety in other industries has evolved to encompass both lagging and leading indicators, to examine several different facets of safety and to use a variety of different methods of assessment and measurement. The specific tools, techniques and methods of other industries may not always transfer easily to healthcare. However, the understanding and principles behind safety measurement in other industries informed our approach to healthcare.


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We conducted interviews with a range of senior staff in national organisations in the UK. For our case studies in healthcare organisations we developed a template to describe the information we required. We approached organisations in both the UK and internationally that we knew to be seriously engaged in the assessment and improvement of safety. These covered acute, community, mental health and primary care services, and specific services such as obstetrics and anaesthetics where measurement of safety is well developed.

The case studies were conducted by interviews and visits to the organisations or via email where visits were impractical. To supplement the case studies we reviewed websites and board papers relating to patient safety from a range of other NHS trusts in England. What exactly do we want to know when we ask whether a healthcare organisation is safe?

We could look for a single defining index of safety; we might think of safety in terms of a set of core standards; we might seek it in the attitudes and behaviours of staff, perhaps in terms of safety culture. One reason these discussions are so difficult is that the underlying question has a number of different facets, which are not always clearly distinguished. A further problem is that safety is sometimes equated with compliance and assurance; in contrast we consider that safety must be approached in the spirit of active inquiry. In considering the evidence from the scoping reviews and the case studies we therefore decided that organisational safety should be approached by posing five fundamental questions:.

Has patient care been safe in the past? We need to assess rates of past harm to patients, both physical and psychological. Are our clinical systems and processes reliable? This is the reliability of safety critical processes and systems but also the capacity of the staff to follow safety critical procedures. Is care safe today? This is the information and capacity to monitor safety on an hourly or daily basis. Will care be safe in the future?

This refers to the ability to anticipate, and be prepared for, problems and threats to safety. Are we responding and improving? The capacity of an organisation to detect, analyse, integrate, respond and improve from, safety information. These five core questions lead directly to the five dimensions of our framework figure 1 : i past harm; ii reliability; iii sensitivity to operations; iv anticipation and preparedness; and v integration and learning. We next summarise some of the key features of each dimension.

Most patients are vulnerable, to some degree, to infections, adverse drug events, falls, and the complications of surgery and other treatments. Patients who are older, frailer or have several co-morbidities may be affected by over-treatment, polypharmacy and other problems such as delirium, dehydration or malnutrition. In any setting patients may also suffer harm from rare and perhaps unforeseeable events stemming from new treatments or new equipment box 1.

To assess harm from healthcare, we ideally have to consider all these kinds of events. Harm that results from specific treatments or the management of a particular disease, with varying degrees of preventability. This would include adverse drug reactions, surgical complications, wrong site surgery and the adverse effects of chemotherapy. For example, polypharmacy and the consequent drug interactions are a major hazard, in that the benefits received from multiple treatments can be outweighed by the risks and adverse consequences. Hospital-acquired infections, falls, delirium and dehydration are examples of problems that can affect any patient with a serious illness.

Frailties or co-morbidities that increase vulnerability to falls, infections and so on. A cancer diagnosis may be delayed because the patient delayed contacting their doctor or because the physician failed to refer. In either case the outcome may be poorer. To the patient this is harm, although not necessarily generally considered as an aspect of patient safety.

Many patients fail to receive standard evidence-based care which may lead to harm; failure to provide rapid thrombolytic treatment for stroke provides an example. Such problems may be viewed as poor quality care, rather than safety, but for the patient may represent avoidable harm. Patients may simply feel unsafe on psychiatric in-patient units and even on general wards. Awareness of unsafe care may have consequences for the wider population if it leads to a loss of trust.

For instance, people may be unwilling to have vaccinations, give blood, donate organs or receive transfusions. Healthcare organisations have used a range of methods and data sources to assess harm. Some methods, such as record review, attempt to cover a very broad range of possible types of harm. In contrast, patient safety indicators derived from administrative data reflect highly specific events or processes.

Each of these groups of measures has strengths and limitations, and none can claim to reflect all the kinds of harm discussed above.

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The concept of reliability can be applied most meaningfully to relatively standardised aspects of healthcare 8 which include procedures that staff need to carry out reliably. This would include compliance with hand hygiene procedures, the timely administration of antibiotics before operations, the timely ordering of diagnostic tests and many other fundamental processes. It also covers clinical systems supporting the delivery of care, such as the availability of essential medical records.

Many healthcare systems have very poor reliability. In the English NHS, reliability is typically assessed through a rolling programme of clinical audits. These audits are important but, at a local level, the focus can be haphazard and insufficiently proactive. The next step for many organisations is to identify all safety critical processes within each clinical area and specify the levels of reliability expected. This seemingly simple step would be a massive transformation in healthcare, representing a move from gradual improvement towards an engineering perspective in which systems are designed to operate to certain specifications under a range of conditions.

Monitoring reliability across a system would be a major challenge but is necessary if healthcare is to take safety seriously. Problems and crises that potentially threaten safety occur on a daily or even hourly basis, such as a sudden influx of very sick patients, staff sickness or equipment breakdowns. We might have been safe yesterday but how can we know whether we are safe today?

When we drive a car, operate machinery or cross the road, we continuously monitor our own actions and attend to the environment adapting to emerging hazards. This vision can be expanded to consider how to monitor the safe running of a healthcare organisation. Such conversations are often thought of as ancillary to the real work of the organisation but are in fact critical to monitoring safety. Patient interviews and conversations are a particularly vital form of safety monitoring 14 15 and have been the most potent warning of recent tragedies.

Both the Berwick and Keogh reviews 6 9 have emphasised the need to seek out the patient voice as an essential and timely warning sign of deteriorating care. The experience of safety probably depends very much on their moment-to-moment experience of care. Safety may be conveyed more by the manner of the staff, the care they take, their concern for checking details, and their empathy and compassion.

Highlighting practical difficulties and harms experienced by patients that might not be immediately obvious to staff, such as assumptions by staff that a patient has understood the information provided at discharge, is important. In clinical work, treating complex, fluctuating conditions requires thinking ahead and being prepared to adjust treatment as the patient's condition changes. Considering the safety of an organisation requires a similar but broader vision. Clinicians and managers need to anticipate and assess potential hazards and take action to reduce the risks over time.

There is no special type of information that is suitable or unsuitable for reflecting on future hazards and potential problems. Total forest conversion to oil palm km 2 was subsequently calculated in annual time steps based on visual delineation of Landsat images.

The data and approaches used for these studies of oil palm are applicable to other plantation forests, such as eucalyptus. As more high-resolution imagery is acquired and archived, monitoring the conversion of natural forests to plantations with such imagery can become more routine, as can larger area assessments of the kind now only practical with moderate-resolution imagery. A number of countries, such as Mexico, are systematically acquiring high-resolution imagery e. Because high resolution imagery can be expensive and quickly adds up to large volumes of data, using it routinely to map large areas s of km 2 requires the availability of significant computing and processing capabilities.

With increasingly more powerful computers the former issue is generally being overcome; however, consistent data processing of large data volumes requires systems that can properly address cloud cover, calibration and many other issues that arise among adjacent scenes through time. Figure 5. High resolution image from Indonesia showing the ability to discriminate plantation from primary forest, as well as recently deforested areas.

As described above, these issues are more easily overcome with moderate resolution imagery i.

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The Measurement and Monitoring of Safety

Landsat and MODIS , but are increasingly available for application to higher resolution data sets like those being used in Mexico and elsewhere. With the advent of constellations of 'micro-satellites', including the recent acquisition of Skybox by Google www. Moreover, taking advantage of platforms like GoogleEarth allows users in many parts of the world with limited computational infrastructure to conduct their own interpretations using high resolution imagery, as long as they have reliable internet access.

Summary : using multi-sensor data sets that combine the advantages of each data source allows accurate discrimination of oil palm plantations from natural forests, and thus the ability to monitor the extent of plantations and the associated conversion of natural forests to plantations. Because many oil palm plantations are large hundreds of hectares , moderate resolution imagery like that of Landsat and even coarser resolution MODIS is often adequate for mapping and monitoring plantation forests relative to natural forests, as well as the conversion of the latter to the former.

Higher resolution imagery is becoming increasingly more common, with some countries acquiring 'wall to wall' mapping of their forested lands, and these data provide additional accuracy in mapping plantations and the conversion of natural forests. While high resolution image analysis of large areas requires greater data storage, computing and processing capacity to ensure continuity of mapping efforts, these capabilities are rapidly advancing. Also, such imagery will continue to become more readily accessible via web platforms such as GoogleEarth, and can be relatively easily interpreted visually by trained technicians, so use of this technology is accessible to countries with varied levels of technical capacity—particularly for more local mapping efforts.

These guidelines affirm the importance of spatial information, such as that conveyed by remote sensing observations, as an important component of biodiversity assessments. They also recommend a number of key elements that should be incorporated into mapping and monitoring protocols, as capacities permit, including the identification of high conservation value areas, distributions of priority and indicator species, and types of natural forests and ecosystems.

Targeting the areas with both climate change mitigation potential i. Parrotta et al There is thus an opportunity to achieve both climate change mitigation and biodiversity conservation by better preserving and protecting forest habitat. A first step in meeting the objective of climate change mitigation and biodiversity conservation co-benefits is to document the diversity of areas that are priorities for avoiding emissions i.

Being able to identify the diversity of these areas, by linking field surveys with remote sensing of habitat characteristics beyond cover and type, such as canopy vertical structure information, would augment the broad scale data sets on species richness and endemism distributed by the UNEP-WCMC, the International Union for the Conservation of Nature IUCN 15 and Birdlife International Here we briefly touch on remote sensing capabilities for mapping tree species diversity, and then the links between unique forest habitat characteristics and animal species diversity.

Characterizing the tree species diversity of forests, understood here as the number and distribution of individual species or assemblages of species, was considered beyond the capability of remote sensing technologies until relatively recently. Tropical rainforests present a particularly unique challenge given the large number of species per unit area together with high structural complexity e.

Recent advances in remote sensing of tree reflectance spectra combined with canopy structure from lidar has changed our view of what is possible in terms of mapping aspects of biodiversity e. Leutner et al As with approaches to measuring and monitoring forest cover and carbon stocks, these approaches to mapping patterns of forest traits and canopy chemistry can be divided into so-called direct approaches capable of detecting the individual tree diversity or communities themselves, and indirect approaches that depend on proxies such as environmental characteristics i.

Technologies to measure and monitor forest biodiversity in tropical ecosystems have advanced rapidly as a result of the evolution of new sensor classes including LiDAR and hyperspectral remote sensing, with the latter also known as imaging spectroscopy. Whereas LiDAR is capable of characterizing forest biodiversity based on detailed measurements of canopy three-dimensional structure Bergen et al , imaging spectroscopy sensors are capable of sensing the canopy's chemical and physiological properties or 'fingerprints' of plant spectra Asner et al Imaging spectroscopy is currently operational onboard both airborne and satellite remote sensing platforms.

However, because satellite sensors provide only moderate resolution data Hyperion: 30 m; CHRIS: 17 m and both air- and satellite acquisitions are generally only available to researchers on a geographically limited basis, the overall utility of these data for detailed species-level mapping across large areas remains limited. As a result, and because a number of studies have successfully demonstrated the potential of imaging spectroscopy in a variety of environments Schimel et al , there are ongoing efforts to advance our capability to distinguish and map forest canopy chemistry and trait diversity from a spaceborne platform at high resolution.

Animal diversity distributions have been estimated using field surveys, often rapid assessments, linked with vegetation type maps, climate and other environmental variables to extrapolate over larger areas. In addition, one of the best-known proxies for forest species diversity is canopy habitat and three-dimensional canopy structural diversity i.

Canopy structure data sets have not been widely available before but with the advent of LiDAR remote sensing, these data are now much more widely and consistently available than was possible from limited field studies. A number of studies have shown the utility of airborne LiDAR data for predicting not only bird richness Vierling et al but also individual species preferences and competition for specific habitats and nesting locations e.

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Goetz et al Thus characterizing multi-dimensional habitat heterogeneity with remote sensing, particularly LiDAR coupled with field observations, is extremely useful for identifying and mapping biodiversity patterns. Summary : the state-of-the-art in species-level mapping and monitoring of subtropical and tropical forest ecosystems combines the spectral sensitivity of imaging spectroscopy with the canopy structural sensitivity of LiDAR measurements.

This combination provides for unprecedented discriminating power in three dimensions, improving upon the information that either sensor type is capable of providing alone, and thereby also providing capability to map tree composition, animal species richness, habitat diversity and use, and changes in these ecosystem attributes through time.

Monitoring technologies can help countries reduce deforestation in a variety of ways. For example, they can help attribute deforestation to particular land uses or industries, suggesting targeted policies that can address the proximal and ultimate causes of deforestation and forest degradation.

Furthermore, high-frequency deforestation alert systems can assist law enforcement agencies EAs in enforcing forest laws. We can only briefly address these issues here and so encourage interested readers to follow up with some of the literature we cite. In order to reduce deforestation it is useful to understand who or what is responsible for deforestation. Forest monitoring technologies can assist in identifying drivers 17 of deforestation in at least four ways, briefly described below: distinguishing anthropogenic from non-anthropogenic forest loss; directly attributing forest loss to particular owners or forest users based on ownership or use maps; attributing forest loss to particular land uses or industries based on remote sensing signatures; and assessing the relative contributions of multiple causal factors using econometric techniques.

This allows agencies in forest countries to penalize or reward the emission or sequestration of carbon resulting from management activities but not those resulting from natural processes. In the tropics, most clearing is the result of human action on the landscape, but there are also natural changes resulting from wildfires, wind storms or other causes. A recent example attributing deforestation from natural versus anthropogenic causes in Peru Potapov et al nicely demonstrates the potential of distinguishing these different causes using remote sensing, as well as the value of close partnerships between the policy and research communities see also Pelletier and Goetz Natural and human causes of fire also interact, for example where degraded forests may have more fuel conducive to carrying fires initiated from a natural cause like lightning.

Proximate causes of human-induced forest loss include mechanical removal of vegetation and the intentional setting of fire, both of which are often employed in the clearing of tropical forests. Natural processes like drought also exacerbate the likelihood of fire. Thus identifying the direct cause of forest disturbance would improve carbon emissions estimations and their attribution to human or natural origins. Remote sensing of fire disturbance is well advanced and routinely used in a number of temperate countries for fire management efforts.

Justice et al , Boschetti et al , remote sensing of fire disturbance in the tropics has focused largely on direct fire event emissions into the atmosphere Giglio et al or the proximity of fire events to populated areas and roads Kumar et al Using frequent repeat remote sensing can often assist in identifying the sources of different disturbance types, given timber harvest, forest cutting for shifting agriculture, and other forest conversion and degradation processes have unique patterns on the landscape.

Monitoring and measurement

However it is usually necessary to inform remote sensing of these patterns with additional ancillary information, such as the indirect methods discussed in sections 2. To the extent it is possible to distinguish the land use to which previously forested land is converted, attribution to particular land uses, owners or industries can be made.

As noted in the previous section, it is often possible to match patterns of deforestation with obvious indication of human activity, such as extension of roads into the affected areas. When available, data on land ownership and land use rights can provide additional information on the probability of accurate attribution to human drivers as agents of change.

A nice example of this approach is Killeen et al documenting land transformations by different social and economic groups in Bolivia over multiple decades. Land use in recently deforested or disturbed areas can also be discerned by statistically relating regressing the patterns and the texture of the land surface to areas where deforestation and degradation has already been attributed to human land use e. Data from observational platforms such as Landsat are a prerequisite to these efforts.

Where multiple causal factors contribute to deforestation, statistical techniques can be used to assess their relative contributions and suggest promising interventions. These techniques, termed spatially explicit econometrics, analyze the relationship between spatial patterns of deforestation and maps of potential driver variables Ferretti-Gallon and Busch Forest policy implementation often requires law-enforcement activities.

There is a wide range of forest monitoring technologies that can assist forest law enforcement. We touch on two of them here; one from Earth observing satellites in space and one based in monitoring devices deployed in situ , in the forest environment. Sensors that can detect hotspots of forest-cover change activity on a frequent basis can be used to target law-enforcement actions.

DETER data have been used to support enforcement of land use regulations by prosecuting illegal deforestation. The system was launched in May and has been instrumental in allowing rapid reaction to signs of deforestation and thereby reducing emissions associated with such activities figure 6 Arima et al Based on this information the EAs can define priority areas for action in the field as well as refine the process of distinguishing legal and illegal deforestation.

Figure 6. Avoided deforestation in Brazil between and attributed to alert and enforcement efforts based on rapid-response satellite remote sensing of forest conversion after Arima et al This transparency also decreases the potential for corruption since available data allow investigation of illegal activity by all interested parties. This high-temporal acquisition frequency provides the most rapid update of the land surface currently available while increasing the probability of cloud-free acquisitions.

MODIS thereby provides the ability to detect deforestation associated with many human activities, such as the expansion of frontiers of deforestation, soy agriculture, and other activities. The best use of MODIS for forest monitoring is as an alarm or indicator of larger 'hotspots' of forest loss rather than to calculate specific changes in forest area. This is because the moderate spatial resolution of MODIS is somewhat limited for observations of forest cover loss because forest disturbances often occur over areas that are smaller than MODIS pixels i.

Landsat, RaDAR and other higher resolution data sources can then be used to establish specific measurements of forest loss in the affected areas. Using these systems together, along with other data sources, is a powerful means to enforce policies. Moreover, because MODIS acquires data more frequently than Landsat, it can be used to determine the timing of deforestation, which can be important in some cases for attributing change to human versus natural causes. The updated system allows detection of areas smaller than 25 ha and has improved accuracy resulting in more efficient enforcement action.

Another set of technologies that can aid detection of illegal activity and enhance enforcement activities is in situ monitoring devices. These include motion-detection cameras and audio devices that can detect the sound of chain saws from logging and gun shots from poaching. Motion activated cameras are now widely used for wildlife monitoring, but also have utility for detecting illegal logging, particularly when installed to image larger areas e.

Audio detection and alert transmission systems have been in development for a number of years and some make use of proven technology that is widely available e. Related recent efforts make use of discarded cellular telephones for detecting audio signals and then transmitting alerts to enforcement authorities or conservation groups These techniques are likely to become more readily available and so available for broader adoption. Summary : the distinction between forest change attributable to human activity versus natural processes is advanced by systematic remote sensing of forest conversion though time, and by incorporating additional data sources to make attribution more reliable.

It is often possible to infer attribution using remote sensing based on patterns across the landscape unique to timber harvest or plantations, as well as proximity to the expansion of roads and towns, coupled with local knowledge and field observations. Detection systems that make use of frequent satellite observations to identify hot-spots of change are also valuable for attribution and are used to alert EAs to deforestation and to deter illegal operations, with Brazil having the most advanced capabilities for this type of near real-time monitoring.

Related capabilities that help with enforcement include in situ motion cameras and audio sensors that are triggered by potentially illegal activities. These detection and alert systems have had a measurable impact on reducing deforestation related emissions and associated illegal land conversion in Brazil. Similar systems can be implemented in other countries by learning from and building upon these advances. In addition to the progress described thus far, there are exciting new advances in remote sensing technology and monitoring capabilities that are already underway.

The combination of these missions will be particularly useful for mapping forest biomass more accurately and also for better capturing the timing and magnitudes of change in aboveground carbon stocks.


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There are also previously mentioned micro-satellite systems Skybox, PlanetLabs that are likely to rapidly proliferate in the commercial value-added remote sensing domain. Recent developments in estimating carbon stock density with combined field and aircraft LiDAR, as well as demonstrations of satellite LiDAR, indicate it will soon be possible to routinely and remotely measure changes in forest carbon stock directly.

This will continue to require calibration and validation with field measurements, but it is significant and will be transformational because it means we can systematically monitor aboveground carbon stock changes, and their associated emissions or sequestration, even if they are not associated with changes in forest area. In theory there is no threshold cut-off and no need for definitions of forest or deforestation. What matters is the change in carbon stock, whether it results from deforestation, degradation, or regrowth. Indeed, the synergistic measurement of change in density suggests that a new approach for calculating emissions may be appropriate, i.

The approach will make use of space-based LiDAR and RaDAR missions, sensitive to changes in three-dimension canopy structure, measured at much higher sampling densities and with higher accuracy that any previous observational system. When combined with the new mission to install a LiDAR sensor onboard the in ISS, these will substantially advance our capability to map aboveground biomass and canopy structure changes from current reference baselines into the future.

The launch of the Orbiting Carbon Observatory OCO in early , which measures gas concentrations in the atmosphere and enables fluxes to be estimated, will provide an independent assessment of terrestrial fluxes of carbon to and from the atmosphere. The spatial resolution of OCO is coarse hundreds of km 2 , but the frequency of repeat measurements every 16 days may allow for seasonal estimates of carbon flux at national scales. The estimated fluxes will include those from all pools of carbon living biomass, dead biomass, aboveground, belowground, and soil. They will also include the effects of both management and natural processes.

In terms of biodiversity applications and safeguards, the technologies now available for mapping the types and trait composition of tropical forests are maturing and new spaceborne imaging spectroscopy sensors are expected to overcome or greatly reduce the limitations i. These will be complemented by the new satellite LiDAR and RaDAR missions described above, as well as others in development, which together will dramatically improve our ability to monitor not just forest composition but also the multi-dimensional aspects of forest habitat that support biodiversity across the tropics and around the globe.

Each of these emerging trends, and others that are beyond the scope of what we can cover here, will continue to not only advance technological capabilities but also the transfer of those capabilities to the operational realm. This transition includes more than just making data sets and validated measurements available more widely, even though that alone has dramatically advanced over what was possible just a decade ago see Romijn et al Technology transfer and transition to the operational realm has to occur hand-in-hand with capacity building efforts, which will take time, and will likely be incremental and geographically uneven.

Nonetheless, the transition is already proceeding apace and should be embraced as an essential component of monitoring systems required for effective climate change mitigation and safeguarding the multiple benefits of forest conservation. Satellite-based technologies to monitor forest cover and biomass density i.

Figure 7. This table summarizes our view of the current state of readiness, as assessed herein, and identifies areas where there are limitations to current capabilities. We emphasize prioritizing capabilities that can be gained by synergies between the technology sources, recognizing that capabilities will not be available everywhere and will be uneven geographically depending on internal technical capacities and available resources. Here we have presented an overview of the current state and near future potential of capabilities for measuring and monitoring deforestation, forest degradation, and associated carbon stocks and emissions.

In all cases, the overarching objective is focused on repeatable measurements that are consistent over time and transparent for the purposes of systematic monitoring, as outlined in the Warsaw agreement 19 and other UNFCCC decisions discussed herein. Remotely sensed data enable large area mapping and monitoring of forest cover and change at regular intervals, providing information on where and how changes are taking place at bi-annual or even annual time scales.

Field data provide a basis for linking to remote sensing and thereby extending measurements to much larger areas e. Land satellite Landsat data sets in particular are routinely used to measure and monitor forest changes over time, providing operational monitoring of deforestation used in a number of national programs. This requires measurement of carbon stock density, or emission factors in IPCC terminology. Because carbon density varies spatially, the quantity of carbon lost to the atmosphere due to deforestation is dependent on the specific forest areas that have undergone change.

Tropical forests have a wide range of carbon content and thus have a wide range in their potential for mitigating emissions from deforestation and degradation. Calculating emissions from degradation is more difficult than calculating emissions from deforestation because degradation happens at finer scales albeit not necessarily smaller areas , and because removal and regrowth of trees often take place at the same time. Therefore multi-scale imagery is useful to consistently and accurately map degradation, particularly over short time intervals.

Degradation can be assessed using approaches that capture change in canopy cover coupled with field measurements of carbon stocks, or more synergistically via field data calibration of remote sensing maps of stock changes through time. The measurement need for forest regrowth is similar to that for forest degradation except forest cover and carbon stocks increase through time.

Our continuing role

Landsat satellite data are capable of detecting regrowth through time, and LiDAR data are particularly useful for measuring, mapping and monitoring the carbon stocks associated with forest growth and recovery as well as forest degradation. Some of the approaches are useful to monitor other land-based mitigation activities already implemented in the Clean Development Mechanism of the Kyoto Protocol, including reforestation and afforestation. Remote sensing can and has been used to unambiguously distinguish long-lived natural forest cover from managed tree plantations, in order to monitor safeguards on conservation of natural forest and the ecosystem services they provide and the biodiversity they support.

Given rapid growth in the oil palm industry across the tropics, remote sensing has been used to monitor natural forests and to track the expansion of plantations. As more high-resolution imagery is acquired and archived, monitoring the conversion of natural to managed and plantation forests becomes more accurate, as does larger area assessments of the kind now only practical with moderate-resolution imagery e.

Biodiversity safeguards can also be informed by directly detecting and characterizing the diversity of tree species within forests, as well as indirectly assessing animal diversity by the habitat characteristics of the forest. Until recently these capabilities were considered beyond the capability of remote sensing technologies, but we discuss how they have advanced rapidly in the past decade.

Maps of local variation in patterns of species diversity provide a basis for prioritizing and managing lands to promote conservation e. The state-of-the-art in species-level mapping and monitoring of subtropical and tropical forest ecosystems combines high spectral sensitivity with high canopy structural sensitivity e. These capabilities are not currently available everywhere but will become increasingly more available with a rapid transition underway from research and development to 'off the shelf' systems and a greater number of commercial data providers competing for business.

The distinction between emissions attributable to management and emissions attributable to natural processes enables law enforcement activities. Deforestation alert systems can identify human sources and detect illegal logging activity. Brazil's DETER program is an example where regular monitoring has made it possible to identify forest conversion within the Brazilian Amazon and to enforce regulations by prosecuting illegal deforestation. In the next few years these capabilities will advance even more rapidly and be implemented even more broadly. Investments in human resources associated with use of these technology advances will ensure this broader implementation takes place as rapidly as possible, and where it is most needed.

Globally consistent maps of forest cover loss based on Landsat at 30 m resolution are freely available online. These data are being regularly updated and improved, and can be made more relevant for national reporting by combining with country-specific information e. Pan-tropical maps of aboveground forest carbon stocks at m resolution are freely available online and are rapidly being improved in spatial resolution, global scope, and time series.