Monitoring of Compliance in Western Australian Conservation Contracts

Contracting with private landholders for labor towards production of environmental services (payment for actions) or the environmental services themselves (payment for outcomes) is reliant on the environmental organization’s ability to monitor and assess the environmental outcomes provided. Inaccurate and costly assessment reduces the cost effectiveness of the contract. Different assessment technologies will have different impacts on the cost effectiveness and optimal contracting choice of the environmental organization. The paper compares the influence of field assessment by a local expert, and remote assessment via satellite imagery, on the optimal contracting decision for the Western Australian wheat belt.conservation, environmental, compliance, monitoring, enforcement, environmental regulation, Crop Production/Industries, Environmental Economics and Policy,

Thirty years of land cover and fraction cover changes over the Sudano-Sahel using landsat time series

Historical land cover maps are of high importance for scientists and policy makers studying the dynamic character of land cover change in the Sudano-Sahel, including anthropogenic and climatological drivers. Despite its relevance, an accurate high resolution record of historical land cover maps is currently lacking over the Sudano-Sahel. In this study, 30 m resolution historically consistent land cover and cover fraction maps are provided over the Sudano-Sahel for the period 1986–2015. These land cover/cover fraction maps are achieved based on the Landsat archive preprocessed on Google Earth Engine and a random forest classification/regression model, while historical consistency is achieved using the hidden Markov model. Using these historical maps, a multitude of variability in the dynamic Sudano-Sahel region over the past 30 years is revealed. On the one hand, Sahel-wide cropland expansion and the re-greening of the Sahel is observed in the discrete land cover classification. On the other hand, subtle changes such as forest degradation are detected based on the cover fraction maps. Additionally, exploiting the 30 m spatial resolution, fine-scale changes, such as smallholder or subsistence farming, can be detected. The historical land cover/cover fraction maps presented in this study are made available via an open-access platform

Hyper-Spectral Image Analysis with Partially-Latent Regression and Spatial Markov Dependencies

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.Comment: 12 pages, 4 figures, 3 table

Urban land cover change detection analysis and modeling spatio-temporal Growth dynamics using Remote Sensing and GIS Techniques: A case study of Dhaka, Bangladesh

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Dhaka, the capital of Bangladesh, has undergone radical changes in its physical form, not only in its vast territorial expansion, but also through internal physical transformations over the last decades. In the process of urbanization, the physical characteristic of Dhaka is gradually changing as open spaces have been transformed into building areas, low land and water bodies into reclaimed builtup lands etc. This new urban fabric should be analyzed to understand the changes that have led to its creation. The primary objective of this research is to predict and analyze the future urban growth of Dhaka City. Another objective is to quantify and investigate the characteristics of urban land cover changes (1989-2009) using the Landsat satellite images of 1989, 1999 and 2009. Dhaka City Corporation (DCC) and its surrounding impact areas have been selected as the study area. A fisher supervised classification method has been applied to prepare the base maps with five land cover classes. To observe the change detection, different spatial metrics have been used for quantitative analysis. Moreover, some postclassification change detection techniques have also been implemented. Then it is found that the ‘builtup area’ land cover type is increasing in high rate over the years. The major contributors to this change are ‘fallow land’ and ‘water body’ land cover types. In the next stage, three different models have been implemented to simulate the land cover map of Dhaka city of 2009. These are named as ‘Stochastic Markov (St_Markov)’ Model, ‘Cellular Automata Markov (CA_Markov)’ Model and ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model. Then the best-fitted model has been selected based on various Kappa statistics values and also by implementing other model validation techniques. This is how the ‘Multi Layer Perceptron Markov (MLP_Markov)’ Model has been qualified as the most suitable model for this research. Later, using the MLP_Markov model, the land cover map of 2019 has been predicted. The MLP_Markov model shows that 58% of the total study area will be converted into builtup area cover type in 2019. The interpretation of depicting the future scenario in quantitative accounts, as demonstrated in this research, will be of great value to the urban planners and decision makers, for the future planning of modern Dhaka City