Operationalizing a land systems classification for Laos

Abstract

Land cover data is widely used for the design and monitoring of land use policies despite the incapability of this type of data to represent multiple land uses and land management activities within the same landscape. In this study, we operationalized the concept of land systems for the case of the Lao PDR (Laos). Distinct land systems like shifting cultivation and plantations (land concessions) cannot be fully captured by land cover inventories alone, in spite of their relevance for land use policies. Using a decision tree and a matrix approach, we integrated several datasets for the period 2010/11, including land cover, an agricultural census and a land concession inventory. We selected thresholds for distinguishing land systems based on an expert survey. The resulting 17 land systems cover the whole territory of the Lao PDR and represents landscapes of 2x2 km pixel size. The largest area is occupied by smallholder agriculture land systems intertwined with forests. Only 27% of the territory are agriculturally undisturbed, dense forest systems. The assessment can serve as a basis to identify areas that could change shortly and locates the full range of land systems, from land concessions to smallholder systems, in one, integrated spatial assessment. The land system representation can help policy makers to link land systems to the diversity of different stakeholders and their backgrounds and support discussions about ecologic and socioeconomic consequences of different land uses within a landscape.

A Scenario-Based Simulation of Land System Changes on Dietary Changes: A Case Study in China

“…The series of land systems was categorized based on land system classification designed by three main classification factors: (1) land cover, (2) livestock density, and (3) agricultural intensity [23,24]. Land cover is represented by a series of variables, such as forest cover (%), grassland cover (%), bare cover (%), cropland cover (%), and built-up area cover (%).…”

“…Specifically, land systems cannot be defined as mutually exclusive classes according to their land-use or land-cover but can be regarded as a series of entities with the same land cover but different land use intensity in application. In addition, land systems can represent multifunctional land, such as rural villages combining residential utilization with crop production and livestock production [23,24].Simulating the trajectories and patterns of land change by a series of scenarios is an accepted way to anticipate land change [25][26][27][28][29]. Comparing different outcomes under different scenarios is conducive to illustrating the adaptation of land systems to macroscopic environments and assisting in the decision-making of sustainable socioeconomic development and ecosystem conservation.…”

“…Specifically, land systems cannot be defined as mutually exclusive classes according to their land-use or land-cover but can be regarded as a series of entities with the same land cover but different land use intensity in application. In addition, land systems can represent multifunctional land, such as rural villages combining residential utilization with crop production and livestock production [23,24].…”

The Impact of Land Use and Land Cover Changes on the Nkula Dam in the Middle Shire River Catchment, Malawi

“…When identifying the training sites, the spectral signatures separability of all the eight land use classes presented in Table 1 were verified including control fields in situ that were also set for validation of each classified image [38]. Land use types were classified by supervised classification maximum likelihood method since it's among the broadly used methods in the scientific literature in addition to it being the fastest and easy to use and giving a perfect interpretation of the outcomes [38][39][40][41][42][43][44]. In addition, the method is able to accommodate covarying data which is common with satellite image data [41,45].…”

“…Land use types at the sampling sites were evaluated according to field surveys (photographs + GPS) where photographs were taken using a camera and coordinates of the spot were taken using GPS. Accuracy of the supervised classification methods was checked by a confusion matrix of accuracy (Table 2) [38,44,51] to ensure that various measures, such as error-rate, accuracy, specificity, sensitivity, and precision, were checked.…”